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Comparing Homeland
Security Risks Using a
Deliberative Risk Ranking
Methodology
Russell Lundberg
PARDEE RAND GRADUATE SCHOOL
Comparing Homeland
Security Risks Using a
Deliberative Risk Ranking
Methodology
Russell Lundberg
This document was submitted as a dissertation in September 2013 in partial
fulfillment of the requirements of the doctoral degree in public policy analysis
at the Pardee RAND Graduate School. The faculty committee that supervised
and approved the dissertation consisted of Henry Willis (Chair), Brian Jackson,
and Lisa Jaycox.
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Abstract
Managing homeland security risks involves balancing concerns about numerous types of accidents, disasters, and terrorist attacks. These risks can vary greatly in kind and consequence, and as a result are perceived differently. How people perceive the risks around them influences the choices they make about activities to pursue, opportunities to take, and situations to avoid. Reliably capturing these choices in risk management is a challenging example of comparative risk assessment. The National Academy of Sciences review of Department of Homeland Security (DHS) risk analysis identifies developing methods of comparative risk assessment as an analytic priority for homeland security planning and analysis. The Deliberative Method for Ranking Risks incorporates recommendations from the empirical literature on risk perceptions into both the description of the risks and the process of eliciting preferences from individuals and groups. It has been empirically validated with the participation of hundreds of citizens, risk managers, and policy makers in the context of managing risks to health, safety, and the environment. However, these methods have not as of yet been used in addressing the challenge of managing natural disaster and terrorism hazards. Steps in this effort include first identifying the set of attributes that must be covered when describing terrorism and disaster hazards in a comprehensive manner, then developing concise summaries of existing knowledge of how the hazards in a unique comparative dataset of a broad set of homeland security risks. Using these materials, the study elicits relative concerns about the hazards that are being managed. The relative concerns about hazards provide a starting point for prioritizing solutions for reducing risks to homeland security. This research presents individuals' relative concerns about homeland security hazards and the attributes which influence those concerns. The consistency and agreement of the rankings, as well as iii the individual satisfaction with the process and results, suggest that the deliberative method for ranking risks can be appropriately applied in the homeland security domain. iv Summary
Managing priorities in homeland security requires understanding the risks due to a range of disasters, terrorist events, and accidents. These risks vary greatly in their causes and the kinds and extent of consequences associated with them. This makes comparative risk assessments of homeland security risks a challenging enterprise. A National Academies report on the risk analysis activities of the Department of Homeland Security identified the need to improve comparative risk assessments, particularly suggesting the consideration of non‐quantitative comparative methodologies. This dissertation adopts one such comparative risk assessment methodology, the Deliberative Method for Ranking Risks, and applies it to the homeland security domain. The Deliberative Method for Ranking Risks was developed in the 1980’s and 1990’s to compare environmental risks that included multiple attributes of concern. The method has been validated in a range of studies of addressing the environmental and health & safety, ranking the concerns of risk experts, government officials, and the lay public. This is the first known attempt to apply this method to homeland security concerns. The Deliberative Method for Ranking Risks contains five steps. The first two steps, described in the Chapter 3, involve conceptualizing the risk, including classifying the risks to compare and the attributes by which they will be compared. This not only involved selecting attributes that were available but also developing new attributes, including a comparative measure of mental health consequences described in a technical appendix. The third step is to assess the risks individually by describing the risks using the categories and attributes developed in Chapter 3. The result of this assessment— a unique dataset describing a broad set of homeland security risks in comparable and transparent terms— is described in the Chapter 4 and documented in detail in additional technical appendices. The final steps involved conducting risk ranking sessions, where individuals consider the v risks in a structured process designed to encourage analytical thinking, and analyzing the data from those sessions. SummaryofFindings
PublicConcernoverHazardsSuggestsBalancingNaturalandTerroristPriorities
The participants ranked the set of risks from the hazards about which they were most concerned to those about which they were least concerned. While the nature of the convenience sample of risk ranking session participants limits my ability to make claims as to its representativeness, there is some evidence that people are able to set aside their personal biases and focus on the expected consequences of the risks. The hazards about which there is the least consensus are terrorist nuclear detonations and toxic industrial chemical accidents, reflecting high consequence, uncertain likelihood events with the greatest uncertainty in the expected consequences. The rank order of hazards, from most concerning to least concerning, is: 1. Pandemic Influenza 6. Terrorist Explosive Bombings 2. Hurricanes 7. Toxic Industrial Chemical Accidents 3. Earthquakes 8. Oil Spills 4. Tornadoes 9. Anthrax Attacks 5. Terrorist Nuclear Detonations 10. Cyber‐attacks Natural disasters are generally of greater concern than those of human‐induced events. While there is a wide‐ranging literature suggesting that all things being equal, people care more about terrorist events than natural disasters, all things are not equal. The natural risks in this set are generally associated with greater consequences, something that is reflected in the rankings. These results should be interpreted with caution, as the sample involved in this study is not representative of the nation as a whole. These results should be confirmed with a broader set of rankings designed to examine vi hypothesized differences in populations. Still, this finding suggests that DHS should examine the balance between natural disasters and terrorist risks. IndividualsAreConcernedaboutMultipleAspectsofRisk
Individuals were concerned about a large number of attributes of homeland security risks. While these include first and foremost aspects of physical health and economic damage, it also included psychological harms, societal and government disruption, environmental damage, and non‐consequence aspects of the risk including aspects of how the risk is perceived. Comparative risk assessments in the homeland security domain should be compared using multiple attributes to describe this entire range of concerns. In particular, aspects of psychological consequences, environmental damage, and societal disruption, which are not commonly included in risk assessments, should be integrated in a comparable fashion. This may indicate a need for greater surveillance of psychological damage associated with homeland security events. TheDeliberativeMethodforRankingRisksisEffectiveforComparativeRiskAssessmentsin
theHomelandSecurityDomain
The Deliberative Method for Ranking Risks was appropriate to elicit a useful ranking of homeland security risks from the hazard of greatest concern to the hazard of least concern. The objective of these workshops was to elicit informed, reliable judgments of the participants’ concerns. Whether this objective was met was examined in two ways: by asking the participants directly and by revealing the participants’ considerations indirectly through an analysis of their rankings. Participants reported learning information from all stages of the process, learning from the risk summary sheets, the calculated ranking exercise, and the group discussion. Additionally, participants reported that their current knowledge of homeland security risks was based more on what they learned in the risk ranking workshop than what they had known prior to the exercise. In addition to the vii reported learning, an analysis of the rankings provides additional support that the rankings were based on informed judgments. The rankings were consistent within and across ranking sessions. The degree of consensus grew throughout the process as people learned about the risks. There is evidence that this was not forced consensus, as individuals reported finding the workshop to be open, engaging, and encouraging of different points of view. Instead, the data suggests that the individuals’ rankings were taking the attributes of risk into account, with the individuals’ rankings becoming more like the rankings based on the attributes of risk. This provided a measure of convergent validity. These findings of informed, reliable judgments are comparable to those found in previous studies using the Deliberative Method for Ranking Risk applied in other domains. Participants were also satisfied with both the process and the results. Satisfaction with the results represents a measure of face validity. Additionally, risk rankings where participants are satisfied with the results are more useful to policymakers, and as a result are more likely to be adopted and used. Individuals reported being satisfied with the group’s ranking and would approve the rankings being used for decisions in a real organization. While most individuals saw the group rankings as representative of their own concerns, even those who did not see the rankings as representative of their own concerns were supportive of the rankings. Based on the results of this study, the use of the Deliberative Method for Ranking Risks should be expanded for use in homeland security in several ways. First, the method should be extended to include other DHS risks. While the set of hazards was selected to cover an interesting and useful set, it was not comprehensive and would be strengthened by including a wider set of hazards. At a minimum, this would include expanding the set of hazards of the types of punctuated events identified here, adding hazards such as floods, wildfires, tsunamis, chemical viii weapons attacks, and others. However, the method could be expanded to include more chronic concerns, such as drought, or non‐hazards concerns, such as illegal immigration. Second, the method should be extended through a purposeful examination of additional participants. The initial workshop participants were a non‐representative sample of the general public, and additional studies should be selected to examine hypothesized concerns of bias. Additional studies of rural areas and populations with different risks or different experiences with these hazards can be useful to identify possible biases. The concerns of other stakeholders, particularly policymakers, should also be identified. Finally, the risk assessment priorities are only a starting point for strategic planning. Priorities should be based not on which risks are of greatest concern, but on which policies can reduce the risk to the greatest extent for a given cost. These rankings do not prioritize risk reducing policies or activities but can be used to inform existing processes to select risk reduction methods. The Deliberative Method for Ranking Risks could be useful to generate informed rankings representative of the DHS concerns by focusing on selected groups of risk experts, including potentially the Risk Steering Committee, to inform the selection of risk reduction methods in the Quadrennial Homeland Security Review. Additional potential applications can be made on the national or sub‐national levels, both within the U.S. and foreign countries. ix Contents
Abstract ........................................................................................................................................... iii Summary ............................................................................................................................................ v Summary of Findings ............................................................................................................................. vi Public Concern over Hazards Suggests Balancing Natural and Terrorist Priorities ........................ vi Individuals Are Concerned about Multiple Aspects of Risk ........................................................... vii The Deliberative Method for Ranking Risks is Effective for Comparative Risk Assessments in the Homeland Security Domain ................................................................................................................ vii Contents ........................................................................................................................................... xi List of Figures ......................................................................................................................................... xvii List of Tables .......................................................................................................................................... xxi Acknowledgements ................................................................................................................................... xxiii Chapter 1. Introduction ........................................................................................................................ 1 Comparative Risk Analyses and Deliberative Risk Ranking .................................................................... 2 Risk Ranking in Homeland Security .................................................................................................. 2 Deliberative Method for Ranking Risks............................................................................................ 5 Organization of this Dissertation ............................................................................................................ 7 References .............................................................................................................................................. 9 Chapter 2. Applying the Deliberative Method for Ranking Risks to the Homeland Security Domain 11 Previously Published Studies Using the Deliberative Method for Ranking Risks ................................. 12 xi Applying the Deliberative Method for Ranking Risks to Homeland Security ....................................... 13 Categorizing Risks of Disasters ....................................................................................................... 14 Categorizing Attributes of Disasters .............................................................................................. 14 Describing Accidents, Disasters and Terrorist Attacks ................................................................... 15 Eliciting Concerns about Hazards through Risk Ranking Sessions ................................................. 15 Analyzing Data from the Risk Ranking Sessions ............................................................................. 16 References ............................................................................................................................................ 17 Chapter 3. Conceptualizing Homeland Security Risk .......................................................................... 19 Categorizing Hazards and Selecting a Hazard Set ................................................................................ 19 Identifying Appropriate Attributes of Hazards ..................................................................................... 24 Criteria for a Good Attribute Set .................................................................................................... 25 Methods and Data ......................................................................................................................... 28 Results ............................................................................................................................................ 31 References ............................................................................................................................................ 44 Chapter 4. Describing a Comparable Set of Homeland Security Risks ............................................... 53 The Risk Assessment Process ............................................................................................................... 54 Deriving Estimates for Quantitative Attributes of Risk .................................................................. 56 Describing Attributes of Risk in Qualitative Terms ........................................................................ 65 What These Data Tell Us about Homeland Security Risks ................................................................... 71 A Range of Attributes is Needed to Describe the Diversity of Homeland Security Risks .............. 75 xii The Estimates Provide Some Information to Distinguish between Risks, but Only Some ............ 80 Conclusions ........................................................................................................................................... 84 References ............................................................................................................................................ 86 Chapter 5. Identifying Homeland Security Concerns from Risk Ranking Sessions ............................. 88 The Risk Ranking Workshops ................................................................................................................ 88 Participants in the Risk Ranking Sessions ............................................................................................. 90 Analysis of Data from the Risk Ranking Sessions ................................................................................. 92 Analysis of Risk Ranking Results .................................................................................................... 92 Assessing the Quality and Level of Support for the Ranking Results ........................................... 109 Discussion ........................................................................................................................................... 117 References .......................................................................................................................................... 119 Chapter 6. Conclusions and Recommendations ............................................................................... 120 The Deliberative Method for Ranking Risks Can Be Effective at Comparing Homeland Security Risks
.............................................................................................................................................................. 120 Lessons from the Ranking Sessions .................................................................................................... 124 Participants Were More Concerned about Natural Disasters than Terrorism ............................ 124 Homeland Security Risks Should Be Described Using a Range of Attributes that Address More than Just Health and Economic Damage .......................................................................................... 127 References .......................................................................................................................................... 130 Appendix A. Comparing Mental Health Consequences of Homeland Security Risk ........................... 132 Background ......................................................................................................................................... 132 xiii Approach and Data ............................................................................................................................. 137 Applying a Stressor‐based Approach to the Homeland Security Risk ................................................ 139 Selecting a Representative Set of Disorders: PTSD and Depression ............................................ 139 Identifying Attributes to Describe the Annual Risk of PTSD ........................................................ 140 Identifying Attributes to Describe the Annual Risk of Depression .............................................. 141 Applying Stressors to Estimate Overall Psychological Consequences ................................................ 142 References .......................................................................................................................................... 149 Appendix B. Deriving Comparable Quantitative Attributes of Homeland Security Hazards from Available Open‐source Data ..................................................................................................................... 157 Considerations in Estimating Quantitative Attributes of Risk for Homeland Security Hazards ......... 158 Earthquakes ................................................................................................................................. 164 Hurricanes .................................................................................................................................... 171 Tornadoes .................................................................................................................................... 175 Pandemic Influenza ...................................................................................................................... 179 Anthrax Attacks ............................................................................................................................ 186 Terrorist Nuclear Detonation ....................................................................................................... 193 Terrorist Explosive Bombings ....................................................................................................... 202 Cyber‐attacks ............................................................................................................................... 207 Toxic Industrial Chemical Accident .............................................................................................. 213 Oil Spills ........................................................................................................................................ 219 References .......................................................................................................................................... 224 xiv Appendix C. Supporting Documents to Guide a Deliberative Ranking of Risks in the Homeland Security Domain — “Notes on the Risk Calculations” .............................................................................. 232 Notes on the Risk Calculations ........................................................................................................... 232 Definitions of Risk Attributes ....................................................................................................... 232 Appendix D. Risk Summary Sheets Describing a Set of Homeland Security Hazards .......................... 239 Earthquake ......................................................................................................................................... 240 Hurricanes .......................................................................................................................................... 250 Tornadoes ........................................................................................................................................... 261 Pandemic Influenza ............................................................................................................................ 271 Anthrax Release .................................................................................................................................. 281 Terrorist Nuclear Detonation ............................................................................................................. 289 Terrorist Explosive Bombings ............................................................................................................. 298 Cyber‐Attacks ..................................................................................................................................... 304 Toxic Industrial Chemical Accidents ................................................................................................... 312 Oil Spills .............................................................................................................................................. 322 xv ListofFigures
Figure 1‐ Steps to the Deliberative Risk Ranking Process (from Florig et al. 2001) .................................... 14 Figure 2‐ Steps in the Process for Eliciting Concerns about Hazards in the Homeland Security Domain (Adapted from Willis et al., 2010) ............................................................................................................... 16 Figure 3‐ Example Risk Summary Sheet ...................................................................................................... 55 Figure 4‐ Estimates of Average Levels of PTSD per Year for Selected Homeland Security Hazards ........... 69 Figure 5‐ Estimates of Average Levels of Depression per Year for Selected Homeland Security Hazards . 70 Figure 6‐ Combined Uncertainty, Calculated Quantities on a Log Scale with Identified Cut Points ........... 71 Figure 7‐ A Comparison of Quality of Scientific Understanding and Combined Uncertainty ..................... 76 Figure 8‐ Normalized Attributes of Selected Homeland Security Hazards ................................................. 77 Figure 9‐ A Comparison of Average Annual Lives Lost and Greatest Lives Lost in a Single Event .............. 81 Figure 10‐ A Comparison of Average Economic Damage and Greatest Economic Damage in a Single Event
.................................................................................................................................................................... 82 Figure 11‐ Overview of the Process Used During Risk‐Ranking Workshops (Adapted from Willis et al. 2010) ........................................................................................................................................................... 90 Figure 12‐ Characteristics of Participants Cover a Range of Experiences ................................................... 92 Figure 13‐ Participants' Final Rankings of Homeland Security Risks in the U.S. ......................................... 93 Figure 14‐ Percentages of Respondents by the Number of Attributes the Respondent Reported as Important .................................................................................................................................................... 96 Figure 15‐ Average Individual Ranking of Attributes of Importance .......................................................... 98 Figure 16‐ Distribution of Rankings for Natural Disasters (with Smoothing, Span=3) .............................. 105 Figure 17‐ Distribution of Rankings for Terrorist Events (with Smoothing, Span =3) ............................... 105 Figure 18‐ Distribution of Rankings for Major Accidents (with Smoothing, Span =3) .............................. 105 Figure 19‐ Distribution of Rankings for Natural Disasters (with Smoothing, Span =2) ............................. 105 xvii Figure 20‐ Distribution of Rankings for Terrorist Events (with Smoothing, Span =2) ............................... 105 Figure 21‐ Distribution of Rankings for Major Accidents (with Smoothing, Span =2) .............................. 105 Figure 22‐ Standard Deviation of Rankings at Three Stages of the Process ............................................. 108 Figure 23‐ Sources of Knowledge that Informed Participant Rankings .................................................... 110 Figure 24‐ Individual Perceptions of the Contributions to Their Final Ranking ........................................ 112 Figure 25‐ Influence of First and Group Rankings on Final Rankings Using a Pooled Regression ............ 114 Figure 26‐ Workshop Participation Encouraged Different Points of View ............................................... 115 Figure 27‐ Participants’ Support for Using the Rankings to Develop Risk Management Policies ............ 116 Figure 28‐ Estimates of Average Levels of PTSD per Year for Selected Homeland Security Hazards ....... 144 Figure 29‐ Estimates of Average Levels of Depression per Year for Selected Homeland Security Hazards
.................................................................................................................................................................. 144 Figure 30‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Earthquakes ........................................................................................................................ 167 Figure 31‐ Scenarios of Lives Lost in Major Earthquakes .......................................................................... 168 Figure 32‐ Estimates of Average Economic Damage per Year from Earthquakes .................................... 169 Figure 33‐ Deaths from Hurricanes in the U.S. 1940‐2009 ....................................................................... 172 Figure 34‐ Average Deaths per Year from Tornadoes (Plotted by the Last Year of the Averaged Period)
.................................................................................................................................................................. 176 Figure 35‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Pandemic Influenza ............................................................................................................ 182 Figure 36‐ Identified Estimates of Lives Lost in a Flu Pandemic ............................................................... 183 Figure 37‐ Estimates of Expected Lives Lost per Year on Average Include in the Range of Selected Estimates for Anthrax Attacks .................................................................................................................. 189 Figure 38‐ Estimates of Likelihood of a Terrorist Nuclear Detonation in the U.S. in a Given Year ........... 195 xviii Figure 39‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Terrorist Nuclear Detonations ............................................................................................ 197 Figure 40‐ Estimates of Lives Lost from a Terrorist Nuclear Detonation Should One Occur .................... 198 Figure 41‐ Estimates of Expected Economic Damage per Year on Average Included in the Range of Selected Estimates for Terrorist Nuclear Detonations ............................................................................. 199 Figure 42‐ Estimates of Expected Average Individuals Displaced per Year Included in the Range of Selected Estimates for Terrorist Nuclear Detonations ............................................................................. 201 Figure 43‐ Estimates of Lives Lost per Year on Average for Terrorist Explosive Bombings ...................... 204 Figure 44‐ Estimates of Economic Damage from a Cyber‐attack ............................................................. 212 Figure 45‐ Estimates of Lives Lost from Toxic Industrial Chemical Accidents .......................................... 215 Figure 46‐ Estimates of Average Economic Damages per Year ................................................................ 223 xix ListofTables
Table I‐ Hazards selected for risk ranking ................................................................................................... 22 Table II‐ Database Search for Disasters or Terrorism ................................................................................. 29 Table III‐ Homeland Security Attributes Used to Describe Consequences of Importance Identified from the Literature .............................................................................................................................................. 39 Table IV‐ Approaches used to estimate homeland security risks for selected hazards and attributes ...... 62 Table V‐ Best Estimates and Range of Estimates for Each Attribute of Risk by Hazard (Rounded to One Significant Digit) .......................................................................................................................................... 72 Table VI‐ Pairwise Correlations between Best Estimates of Attributes of Risk .......................................... 79 Table VII‐ Precision of Estimates by Hazard and Consequence as Measured by Orders of Magnitude between Lower Bound and Upper Bound .................................................................................................. 83 Table VIII‐ Summary Statistics of Workshop Participants ........................................................................... 91 Table IX‐ Spearman Correlation of Average Final Individual Ranking with Rankings by Attribute ............. 95 Table X‐ Percent of Participants Considering a Given Risk as Important.................................................... 97 Table XI‐ Group Differences in Perception of Earthquake Risk ................................................................ 100 Table XII‐ Group Differences in Perception of Hurricane Risk .................................................................. 101 Table XIII‐ Group Differences in Perception of Cyber‐attack Risk ............................................................ 101 Table XIV‐ Group Differences in Perception of Toxic Industrial Chemical Accident Risk ......................... 102 Table XV‐ Agreement and Disagreement in Group Rankings of Risk ........................................................ 104 Table XVI‐ Agreement among Individuals’ First and Final Rankings as Measured through Mean Pairwise Correlations of Results .............................................................................................................................. 108 Table XVII‐ Average of Pairwise Correlations with Ranking Based on Attributes Increases from the Initial Ranking to the Final Ranking ..................................................................................................................... 109 xxi Table XVIII‐ Participant's Responses Regarding Sources of Current Information, on a Scale from 0 (lowest) to 6 (highest) ............................................................................................................................... 111 Table XIX‐ Participants’ Perceptions of the Influence of Intermediate Steps on Their Final Ranking, on a Scale from 0 (lowest) to 6 (highest) .......................................................................................................... 113 Table XX‐ Participant's Perceptions of the Risk Ranking Workshops, on a Scale from 0 (lowest) to 6 (highest) .................................................................................................................................................... 116 Table XXI‐ Attributes Available to Describe Aspects of Psychological Consequences .............................. 138 Table XXII‐ Categories for Translating Quantitative Estimates to Qualitative Levels ............................... 143 Table XXIII‐ Combined Annual Risk of Psychological Damage by Hazard ................................................. 145 Table XXIV‐ Sources and Methods for Estimates of Risk by Hazard and Attribute................................... 163 Table XXV‐ Sources and Methods for Estimates of Earthquake Risk ........................................................ 164 Table XXVI‐ Sources and Methods for Estimates of Hurricane Risk ......................................................... 171 Table XXVII‐ Sources and Methods for Estimates of Tornado Risk ........................................................... 175 Table XXVIII‐ Sources and Methods for Estimates of Pandemic Flu Risk .................................................. 179 Table XXIX‐ Sources and Methods for Estimates of Anthrax Attack Risk ................................................. 186 Table XXX‐ Sources and Methods for Estimates of Terrorist Nuclear Detonation Risk ............................ 193 Table XXXI‐ Sources and Methods for Estimates of Terrorist Explosive Bombing Risk ............................ 202 Table XXXII‐ Sources and Methods for Estimates of Cyber‐attack Risk .................................................... 207 Table XXXIII‐ Sources and Methods for Estimates of Toxic Industrial Chemical Risk ............................... 213 Table XXXIV‐ Sources and Methods for Estimates of Oil Spill Risk ........................................................... 219 Table XXXV‐ Categorization Used to Describe Nonfatal Risks .................................................................. 233 Table XXXVI‐ Disruption of Government Operations ................................................................................ 236 xxii Acknowledgements
I would first like to thank my committee chair, Henry Willis. His mentorship and guidance were invaluable to me throughout the dissertation process, and I will be forever grateful both for his scholastic and moral support. I would also like to thank the other members of my committee— Brian Jackson and Lisa Jaycox— and my outside reader, Richard John of the University of Southern California. Their subject‐matter expertise and methodological perspectives were extremely valuable in strengthening this dissertation. This research was supported by the United States Department of Homeland Security (DHS) through the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California (USC) under award number 2010‐ST‐061‐RE0001. However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the United States Department of Homeland Security, or the University of Southern California, or CREATE. This research also received support from the RAND National Defense Research Institute’s Homeland Security and Defense Center. I also would like to thank those who provided funding for my education generally: RAND’s Center for Global Risk and Security and their Harold Brown Fellowship, and PRGS and their generous benefactors that provided a Newton Minow Fellowship. Additionally, I would like to thank members of the PRGS community. Several current and prior administration members have provided important guidance and support through the program. In particular, I would like to thank the deans— Susan Marquis, Rachel Swanger, and Gery Ryan, as well as Jeffery Wasserman— for their concern and support through professional and personal challenges, as well as the dedicated and friendly PRGS staff. xxiii Interaction with RAND researchers is a unique and appreciated part of the PRGS curriculum: the PRGS professor who takes extra time to engage a student; the RAND researcher who asks tough questions starting on the title slide of a presentation; the program director who takes time to talk with the lowest student on the totem pole. The RAND community has been a challenging but supportive home for the last several years. I would like to spotlight a few special researchers who have taken a particular interest in my development beyond those on my committee: Rosalie Pacula and Beau Kilmer at RAND’s Drug Policy Research Center; Lois Davis; Jessica Saunders; and Paul Heaton. They not only provided useful guidance on particular projects, methods, and subject‐matter, but have been fantastic mentors to the world of research and post‐research life. Finally, I would like to thank my family: Larry, Barbara, Steve, and all of the Stephens clan. Most importantly, I want to thank my wife, Luci Stephens. I never would have made it through the process without her support, and I never would have started without her belief. She lifted me up and encouraged me in hard times and celebrated with me in good ones. She has done so many things—
from serving as a need sounding‐board and editor to assisting in focus groups to providing insightful perspective on the process and results. Luci, you believed in me before I ever believed in myself. I am forever thankful for your love and support. xxiv Chapter1. Introduction
The Department of Homeland Security is responsible for protecting the nation from countless types of accidents, disasters, and malicious attacks motivated by terrorism. The damage caused by these events can be high‐ at the most severe end of the spectrum, an influenza outbreak could cause millions of deaths while a terrorist nuclear attack could cause trillions of dollars in damages. These are worst case scenarios, but the U.S. can still expect to face tens of billions of dollars and hundreds to thousands of deaths per year on average due to these types of events. Managing the risks from these hazards is similarly expensive, involving making choices among alternatives to protect facilities, strengthen infrastructure resilience, and enhance communities’ emergency preparedness. The Department of Homeland Security (DHS) had a budget of $60 billion in fiscal year 2012, with a substantial portion of its budget going to managing natural disasters and terrorism. It is difficult to compare the current expenditures for managing natural disasters and terrorism today with those in 2002, due to the reorganization of the agencies that were combined into DHS and the multiple priorities of DHS components beyond natural disasters and terrorism, but the amount spent greatly exceeds what was spent 10 years ago (DHS 2013). Because DHS is a relatively new department, there are significant opportunities to improve management, reducing fragmentation, overlap, and duplication of activities and purchases (GAO 2013). Managing the risks more efficiently could save billions of dollars for the same level of protection. However, doing this requires setting priorities about how to manage risks and therefore a way to compare risks that are different in kind and consequence. The way people perceive the risks around them influences the choices they make about activities to pursue, opportunities to take, and situations to avoid. Reliably capturing these choices in risk management is a challenging example of comparative risk assessment. A 2010 National Academy of Sciences review of the Department of Homeland Security’s (DHS) risk analyses identifies developing methods of comparative risk assessment as an 1 analytic priority for homeland security planning and analysis (Committee to Review the DHS's Approach to Risk Analysis 2010). This dissertation describes such an approach to compare the homeland security risks using comparative risk ranking. ComparativeRiskAnalysesandDeliberativeRiskRanking
RiskRankinginHomelandSecurity
The National Academy of Science review of DHS risk analyses describes a variety of risk analyses within DHS (Committee to Review the DHS's Approach to Risk Analysis 2010). The number and variety of risk analyses within DHS preclude a single approach to risk analysis. Among the risk models and processes used by DHS, which were identified in the National Academies report, were risk analyses for natural disasters, analyses in support of the protection of critical infrastructure, risk‐informed grants programs, risk analysis in Terrorism Risk Assessment and Management (TRAM), Biological Threat Risk Assessment, and the Integrated Risk Management Framework. These models include the Strategic Homeland Infrastructure Risk Assessment (SHIRA), the Homeland Security Threat Assessment (HSTA), the Risk Management Analysis Process/Tool (RMAP/RMAT), Hazards U.S.‐ Multi‐hazard (HAZUS‐MH), and the National Maritime Strategic Risk Assessment (NMSRA) (Committee to Review the DHS's Approach to Risk Analysis 2010, Figure 2‐1). Similar risk assessments are being done internationally. A recent study by Vlek (2013) identifies 10 nations that are currently undertaking national risk assessments in one form or another, plus additional assessments by the OECD, the European Commission, and the World Economic Forum. DHS analysis of risk informs a range of decisions in different sectors, considered in different ways, for different purposes. Some decisions support specific agency operations (such as deciding which cargo containers should be subjected to additional screening for Customs and Border Protection) and need to be made and updated daily. Risk‐informed decisions can also be useful at a program level; DHS 2 uses risk information to inform within‐directorate decisions, critical infrastructure prioritizations, and response exercises. Finally, risk is used to inform more strategic decisions including determining national priorities, developing program plans, and balancing mission objectives. These decisions with longer time horizons are typically (although not always) less certain, more qualitative, and based on less firm data (Committee to Review the DHS's Approach to Risk Analysis 2010, p. 23). Within DHS is a need to understand in a comparable fashion the risks to the nation as a whole to support the nation’s priorities. While the Homeland Security National Risk Assessment details the risks to the nation, and the Quadrennial Homeland Security Review details the priorities for implementing risk management solutions, there is still a need for a deliberative comparative risk analysis to bridge the gap between them and detail the most pressing problems caused by the identified risks. A first limitation of homeland security risk analyses is the extent to which the risk is integrated across the homeland security domain. Many homeland security risk analyses are ad hoc, relating to a single threat or target (e.g. ranking the threats to a nuclear power plant). Others are comparative within a field or a sector (e.g. ranking the riskiest bridges within the transportation sector). This is appropriate for operational purposes— for example, knowing the relative risk of specific travelers can be useful for deciding which passengers to screen in greater detail. However, ad hoc analyses are less useful at a policy level. This is particularly true for terrorism risks involving an adaptive adversary, as the apparent benefits of one layer of security may be attenuated by risk‐shifting. If one target is heavily protected, terrorists will seek another target which is less protected. It is appropriate to compare risks at a high level when seeking to prioritize policies across entire risk space. A second set of limitations in homeland security risk analyses is methodological. Comparative risk assessments in homeland security often rely on quantitative models. In these models, DHS commonly defines risk as a function of threat, vulnerability, and consequence. Threat relates to the 3 likelihood that an event will be attempted (DHS‐RSC 2008). Vulnerability relates to the likelihood that an attack will be successful if it is attempted (DHS‐RSC 2008). Consequence relates to the effects if an event were to occur (Willis 2005; DHS‐RSC 2008). Threat and vulnerability combine to describe the likelihood that a consequence will be realized. Each of these components can present methodological challenges. Homeland security threats often reflect rare events, which can make the likelihood of an attack difficult to estimate. The National Academies report notes that because estimating vulnerability can be difficult and expensive it is sometimes ignored, equating threat to consequence (Committee to Review the DHS's Approach to Risk Analysis 2010, p. 72). Assessing the threat component can be challenging, not only in practice but also for inherent theoretical reasons. While natural disasters are probabilistic events with a historical record that contributes to informed risk analyses, terrorism is more difficult to model; historical data may not be available to describe a threat, terrorists hide their intent and capabilities, new threats can emerge for which the nation is not prepared, and attempts to reduce one risk may be less effective when terrorists shift the risk to other vulnerabilities. Consequences can also be difficult to model. Homeland security threats often have multiple dimensions of consequence that are important but difficult to quantify. Comparative assessments that use only a single aspect of consequence (such as lives lost) ignore other aspects that can be important. Combining multiple aspects of consequence into a single measure has its own challenges. Translating non‐economic consequences into an economic value requires estimated values such as the statistical value of a life, quality‐adjusted life years, disability‐adjusted life years, and/or contingent valuations of lives, injuries, and other non‐economic factors. While determining the value of non‐economic damages can be done, combining multiple aspects of consequence into a single unit requires subjective judgments as to which consequences to consider and how to include them. 4 Finally, the relationship between threat, vulnerability, and consequence is not immediately obvious. The relationship between the terms is often simplified to threat times vulnerability times consequence, which can be misleading in cases where threat, vulnerability, and consequence are not independent (Committee to Review the DHS's Approach to Risk Analysis 2010). This is not a trivial consideration, as terrorist attacks are inherently non‐probabilistic events reflecting terrorist intent to evade defenders. Incorporating multiple aspects of consequence and the conditional probabilities associated with intelligent adversaries encourages quantitative models that are complicated. As a result, the National Academies study found that the reductionist approach fails to describe risks accurately and comprehensively. Resulting quantitative models often have limited transparency, preventing validation and verification and limiting the utilization of the models by policy‐makers and key stakeholders (Committee to Review the DHS's Approach to Risk Analysis 2010; Morral et al. 2012). But while DHS focuses on quantitative models, they are not the only approaches for risk analysis. While quantitative analyses may be useful for specific hazards or isolated components of a comparative analysis, the challenges of uncertainty and threat‐shifting limit the usefulness of quantitative methods for an overarching comparative analysis. Other options are available for risks with less than maximum data. The National Academies report recommends qualitative rather than quantitative approaches for comparing risks in the homeland security domain, including the use of relative risk ranking approaches (Committee to Review the DHS's Approach to Risk Analysis 2010). One specific relative risk ranking methodology, the Deliberative Method for Ranking Risks, is the focus of this research. DeliberativeMethodforRankingRisks
The Deliberative Method for Ranking Risks (also known as the Carnegie Mellon Risk Ranking Method) was developed by researchers at Carnegie Mellon University to address issues of concern in 5 environmental policy. The problems the problems of risk assessment in the homeland security domain today were much like the initial comparative risk assessments in environmental policy in the 1970’s and 1980’s. Since the 1970’s, the Environmental Protection Agency (EPA) has had to manage a portfolio of environmental risks that vary greatly in kind and consequence, from acute chemical spills to chronic air pollution and ground contamination at Superfund sites. After a 1987 EPA report entitled Unfinished Business: A Comparative Approach of Environmental Priorities concluded that environmental risks were too often considered in isolation and in reaction to political perceptions rather than actual risk (EPA 1987), steps were taken for more systematic approaches to analyzing the risk (DeKay et al. 2001). One particular approach developed to compare environmental risks is the Deliberative Method for Ranking Risks. The Deliberative Method for Ranking Risks was initially tested in a ranking of the health and safety risks at a school and was validated for both lay‐people and risk experts (Morgan et al. 2001). Later papers expanded the set of risks to compare risks that affect both human‐related and ecological environmental concerns (Willis et al. 2004) and food‐related concerns (Webster et al. 2010), applied the risk ranking method in high‐level governmental decision‐making contexts (Willis et al. 2010), and examined the utility of the method in different contexts of the United Arab Emirates (Willis et al. 2010) and China (Xu et al. 2011). This dissertation builds on this literature as the first known approach to apply the Deliberative Method for Ranking Risks to homeland security, providing proof of concept for the utility of the approach in the homeland security domain. Additionally, the method provides an initial understanding of the relative concerns with regards to natural disasters, terrorism, and major accidents, as well as the attributes that describe homeland security threats. 6 OrganizationofthisDissertation
This dissertation examines the use of the Deliberative Method for Ranking Risks in the homeland security domain. This first chapter lays out the challenges of homeland security comparative risk ranking and introduces the method being used. Subsequent chapters will examine the steps within this method and the questions being addressed in each step. The Deliberative Method for Ranking Risks presents a clear framework for conducting a comparative risk assessment. Risk rankings begin with categorizing the risks to be ranked into comparable concerns and identifying the attributes that should be considered to describe these concerns. The hazards are then described using these attributes in clear and concise summary sheets. Selected participants then rank these risks based on the summary sheets in a multi‐stage process designed to reduce the biases that often accompany risk perception. Finally, the data from the ranking sessions are analyzed, not only ranking the results but identifying issues and attributes of concern identified in the ranking process. Chapter 2 describes this method as it will be applied for homeland security in greater detail. Chapter 3 addresses the first two steps of the Deliberative Method for Ranking Risks, defining the risks to be ranked and identifying attributes to describe those risks. These two steps are taken concurrently and address how homeland security risks should be conceptualized. Conceptualizing the risks involves two questions: 
What are the appropriate hazards to compare for homeland security risks? 
What are the appropriate attributes to describe homeland security risks? Chapter 4 describes the process of summarizing what is known about each of the risks using the framework identified in chapter two. There is substantial variation in the kinds and quality of data 7 available to describe homeland security risks, and estimating the risks in a comparative fashion requires substantial judgment. I examine the open‐source data available for describing the risks in an attempt to ascertain whether the data are sufficient to describe a set of homeland security risks. Summarizing the risks involves three questions: 
Is there sufficient open‐source information to describe a set of homeland security risks in a way that can support a deliberative comparative risk ranking? 
What types of variation exist among risks in the U.S. from natural disasters, terrorism, and large‐scale accidents? 
Does that variation warrant a holistic assessment of these risks when developing strategies for domestic safety and security? Chapter 5 examines the use of these materials in the risk ranking workshops. After describing the workshop method, the results of the workshop are examined. An analysis of the workshops can provide perspective on both the process and the outcomes of the ranking sessions. The risk ranking workshop informs two sets of questions, on the process and on the outcomes: 
What do the risk rankings suggest about which homeland security risks are of greatest concern? o
Which homeland security risks are ranked as greater concerns relative to others? o
Which homeland security risks have the most agreement as to their amount of concern? o
What factors contribute to people ranking some homeland security risks as higher than others? 8 
Is the deliberative method for ranking risks effective for comparative risk assessments in the homeland security domain? A summary of the findings appear in the final chapter, along with implications for policy and opportunities for future research. Appendices include the support materials used to inform the risk rankings (e.g. risk summary sheets) and a description of a method to describe a new characteristic (viz. psychological damage). References
Committee to Review the DHS's Approach to Risk Analysis (2010). Review of the Department of Homeland Security's Approach to Risk Analysis. National Research Council of the National Acadamies. Washington, D.C., National Academies Press: 148. DeKay, M. L., H. K. Florig, P. Fischbeck, M. G. Morgan, K. M. Morgan, B. Fischhoff and K. Jenni (2001). The Use of Public Risk Ranking in Regulatory Development. Improving regulation: Cases in environment, health, and safety. P. Fischbeck and R. S. Farrow. Washington, DC, Resources for the Future: 208‐230. DHS‐RSC (2008). DHS Risk Lexicon. U.S. Department of Homeland Security ‐ Risk Steering Committee. Washington, DC. DHS (2013). DHS Budget‐in‐Brief FY 2013. US Department of Homeland Security. Washington, DC. EPA (1987). Unfinished Business: A Comparative Assessment of Environmental Problems. U.S. Environmental Protection Agency (EPA). Alexandria, VA, National Technical Information Service Report. 9 GAO (2013). 2013 Annual Report: Actions Needed to Reduce Fragmentation, Overlap, and Duplication and Achieve Other Financial Benefits Goverment Accountability Office. Washington, D.C. Morgan, K. M., M. L. DeKay, P. S. Fischbeck, M. G. Morgan, B. Fischhoff and H. K. Florig (2001). "A deliberative method for ranking risks (II): Evaluation of validity and agreement among risk managers." Risk Analysis 21(5): 923‐923. Morral, A. R., C. C. Price, D. S. Ortiz, B. Wilson, T. LaTourrette, B. W. Mobley, S. McKay and H. H. Willis (2012). Modeling Terrorism Risk to the Air Transportation System, Rand Corporation. Vlek, C. (2013). "How Solid Is the Dutch (and the British) National Risk Assessment? Overview and Decision‐Theoretic Evaluation." Risk Analysis. Webster, K., C. Jardine, S. B. Cash and L. M. McMullen (2010). "Risk ranking: investigating expert and public differences in evaluating food safety hazards." Journal of Food Protection® 73(10): 1875‐
1885. Willis, H. H. (2005). Estimating terrorism risk, Rand Corporation. Willis, H. H., M. L. DeKay, M. G. Morgan, H. K. Florig and P. S. Fischbeck (2004). "Ecological risk ranking: Development and evaluation of a method for improving public participation in environmental decision making." Risk Analysis 24(2): 363‐378. Willis, H. H., J. MacDonald Gibson, R. A. Shih, S. Geschwind, S. Olmstead, J. Hu, A. E. Curtright, G. Cecchine and M. Moore (2010). "Prioritizing Environmental Health Risks in the UAE." Risk Analysis 30(12): 1842‐1856. Xu, J., H. K. Florig and M. L. DeKay (2011). "Evaluating an analytic–deliberative risk‐ranking process in a Chinese context." Journal of Risk Research 14(7): 899‐918. 10 Chapter2. ApplyingtheDeliberativeMethodforRankingRiskstothe
HomelandSecurityDomain
The Deliberative Method for Ranking Risks was initially developed in the 1990’s to compare dissimilar environmental risks in a comparative fashion. At the time, risk assessments of environmental concerns were too often ad hoc and were not useful to create a standardized, comparative assessments of risk (see DeKay et al. for an overview of studies examining earlier CRAs) (DeKay et al. 2001). To clarify and formalize comparative risk assessments, Morgan et al. proposed a framework for a risk‐ranking method that could engage a wide range of stakeholder participation in a systematic process that used multiple quantitative and qualitative estimates of consequence (Morgan et al. 1996). Later papers refined the framework into a systematic process called the Deliberative Method for Ranking Risks (Jenni 1997; Morgan et al. 2000; Florig et al. 2001; Morgan et al. 2001). The normative goals of the Deliberative Method for Ranking Risks were that (1) priorities should be informed by the best available physical and natural sciences describing hazards and that (2) the concerns about the hazards should be captured using empirically tested methods from the social sciences. As described in Florig et al. (2001), the Carnegie Mellon researchers who developed the method were guided by five concerns: In our view, a good ranking method should (a) make use of available theory and empirical knowledge in behavioral science, decision theory, and risk analysis; (b) encourage those doing the ranking to systematically consider all relevant information; (c) assist individual participants in expressing (or constructing) internally consistent rankings; (d) ensure that participants understand the procedures and feel satisfied with both the projects and products; and (e) describe the level of agreement and the sources of disagreement. 11 PreviouslyPublishedStudiesUsingtheDeliberativeMethodforRankingRisks
The first published article to use the Deliberative Method for Ranking Risks was Morgan et al. (2001). The study involved a set of health and safety risks for students at a fictitious middle school. These risks were ranked by 218 risk managers in five sessions at the Harvard School of Public Health. Each group ranked approximately 10 of the 21 risks in order to completely cover the set while not being overwhelmed with options at any particular session. The results were internally consistent with the rankings, externally consistent with attributes of harm, and produced participant satisfaction of both method and results. This study served as an initial proof‐of‐concept for the Deliberative Method for Ranking Risks. Willis et al. (2004) extended the method to incorporate a set of ecological risks and their attributes. While the public health and safety attributes in Morgan et al. (2001) were concrete and countable, the Willis et al. (2004) study’s examination of ecological risks required attributes to handle more amorphous consequences such as habitat affected and effects on variety of native species. As before, the results suggested the applicability of the Deliberative Method for Ranking Risks; not only was the method able to handle these more abstract descriptors, they were viewed as important to the consideration of environmental risks. Webster et al. (2010) applied a modified version of the method with regards to food safety issues for both public and expert participants. Additionally, the paper paired the risk ranking workshops with a survey of 1,207 participants randomly selected under a stratified sample design. Five questions were asked, soliciting a ranking of the risks and some reasons (not open‐ended) why they were or were not of concern. As the survey provided little information to the respondents about the risks, it was more useful as a comparison between informed and uninformed public opinion rather than as a validation of the informed consideration of the risks as generated in the risk workshops. 12 Willis et al. (2010) extended the literature in two ways. First, it illustrated the applicability of the method in a different cultural context (i.e. the Muslim world in general and the United Arab Emirates in specific). Second, it was used to inform actual strategic planning on government operations rather than serving just as proof‐of‐concept. The participation of high‐level stakeholders could have presented a challenge to the method, as participants may have been biased to their official interests; instead, both the results and the respondents’ perceptions reflected strong consensus, suggesting that the method was useful in attenuating personal or professional bias that may occur in participants’ rankings. Xu et al. (2011) also extended the Deliberative Method for Ranking Risks to another context, examining environmental hazards in a Chinese context. This analysis solicited rankings from the general public with Chinese participants (both in the U.S. and in China) for a set of Chinese risks. As compared to Willis et al. (2010), Xu et al. examined the cultural differences in a more explicit fashion and found few differences in the applicability of the method in different cultural contexts. ApplyingtheDeliberativeMethodforRankingRiskstoHomelandSecurity
Florig et al. (2001) describe the framework for conducting comparative risk assessments using the Deliberative Method for Ranking Risk. Step A involves dividing the risk space into multiple comparable risks in a logical framework, while Step B involves identifying the attributes that are needed to describe that risk. These two steps are undertaken in parallel and inform each other. These are utilized in Step C, where the categorized risks are defined in terms of the attributes. These summary sheets are used to inform participants in the risk ranking sessions conducted in Step D. Finally, the data from the ranking sessions are analyzed and described in Step E. Each of these steps will be described in greater detail in the subsequent section. 13 Figure 1‐ Steps to the Deliberative Risk Ranking Process (from Florig et al. 2001) CategorizingRisksofDisasters
The first step of the method is to develop a set of clear, mutually exclusive, discrete risks to compare. There are many ways to categorize hazards. For example, Willis et al. (2004) examined the environmental literature and found hazards categorized variously by “specific agent or stressor (e.g., lead), the activity giving rise to the hazard (e.g., mining), the location (e.g., specific streams and rivers), the physical medium (land, water, or air), the endpoint of concern (e.g., biological diversity)….” With this in mind, a useful way to divide the risks must be identified and a set of risks, either a comprehensive set or a set with an interesting range of risks, is selected. CategorizingAttributesofDisasters
In addition identifying risks to compare, it is necessary to identify a set of attributes by which to compare them. The attributes used to describe the risks must cover all aspects of the risks that influence how one risk is perceived compared to others in that domain. While the number of ways that hazards could be described can be quite large, the high correlation among many attributes (for example, between population affected and economic losses) allows us to describe them in a more parsimonious 14 set of attributes. These attributes should reflect the important varying consequences of the risk as well as non‐consequence aspects of risk that affect how people perceive the risk. DescribingAccidents,DisastersandTerroristAttacks
Once the attributes are selected, the hazards are described in terms of these attributes in a way that can inform risk ranking participants. As an initial step, the attributes of risk need to be estimated in a comparable fashion for each of the hazards. These estimates are then used to describe the risk in standardized summary sheets. These summary sheets are designed to facilitate participation in the risk ranking process by comprehensively describing the hazards in a way that is clear, concise, and consistent with modern risk communications (Florig et al. 2001). Summary sheets are typically no more than four pages, with a summary table at the beginning that describes the attributes. The subsequent pages of the summary sheet describe what is known about how the hazard is harmful, the extent of the exposure to the hazard, and the steps that have already been taken to manage the risk of the hazard. ElicitingConcernsaboutHazardsthroughRiskRankingSessions
The process for ranking risks to homeland security builds on validated decision‐sciences methods of eliciting preferences from individuals and groups (see Figure 2). The first step in this process is to allow people to rank the hazards after reviewing the risk summary sheets. These risk summary sheets provide a basis for consistently informing judgments of concern about hazards in the homeland security domain. Next, because ranking these risks is a complex cognitive task, participants are guided through use of a multi‐attribute approach to calculating a ranking of the hazards based on their perceptions of the risk attributes. Third, because people can learn about hazards from each other, participants are led through a facilitated group process to produce a consensus ranking of the hazards. Finally, because group processes could lead to forced consensus, participants are provided a final 15 opportunity to express their concerns, to dissent from the group and incorporate new insights into their original rankings. Figure 2‐ Steps in the Process for Eliciting Concerns about Hazards in the Homeland Security Domain (Adapted from Willis et al., 2010) AnalyzingDatafromtheRiskRankingSessions
The final step in the risk ranking process is to analyze the data gathered in the risk ranking workshops. The risk ranking workshops provide two kinds of data on participant perceptions on risk. First there are the rankings themselves. The primary outcome of the ranking sessions is a rank ordering of the risks for each of the individuals at each of the steps in the process. Multiple rankings are recorded, reflecting the initial, multi‐attribute, group, and final rankings; these rankings can be compared to each other or to the estimates of risk generated for the risk summary sheets. Additionally, participants are asked to rank the attributes that describe the risks based on their order of concern. The ranking data can be used directly, to understand the risks of concern, and indirectly, to understand the process. Second, the participants are asked for their perceptions of the process and its outcomes in a 16 final survey. This data can also be used to understand the process, whether it does a good job of describing true concerns about the risk and whether participants support its use. References
DeKay, M. L., H. K. Florig, P. Fischbeck, M. G. Morgan, K. M. Morgan, B. Fischhoff and K. Jenni (2001). The Use of Public Risk Ranking in Regulatory Development. Improving regulation: Cases in environment, health, and safety. P. Fischbeck and R. S. Farrow. Washington, DC, Resources for the Future: 208‐230. Florig, H. K., M. G. Morgan, K. M. Morgan, K. E. Jenni, B. Fischhoff, P. S. Fischbeck and M. L. DeKay (2001). "A deliberative method for ranking risks (I): Overview and test bed development." Risk Analysis 21(5): 913‐913. Jenni, K. E. (1997). Attributes for Risk Evaluation. Pittsburgh, PA, Carnegie Mellon University. Morgan, K. M., M. L. DeKay, P. S. Fischbeck, M. G. Morgan, B. Fischhoff and H. K. Florig (2001). "A deliberative method for ranking risks (II): Evaluation of validity and agreement among risk managers." Risk Analysis 21(5): 923‐923. Morgan, M. G., B. Fischhoff, L. Lave and P. Fischbeck (1996). "A proposal for ranking risk within federal agencies." Comparing environmental risks: Tools for setting government priorities: 111‐147. Morgan, M. G., H. K. Florig, M. L. DeKay and P. Fischbeck (2000). "Categorizing risks for risk ranking." Risk Analysis 20(1): 49‐58. 17 Webster, K., C. Jardine, S. B. Cash and L. M. McMullen (2010). "Risk ranking: investigating expert and public differences in evaluating food safety hazards." Journal of Food Protection® 73(10): 1875‐
1885. Willis, H. H., M. L. DeKay, M. G. Morgan, H. K. Florig and P. S. Fischbeck (2004). "Ecological risk ranking: Development and evaluation of a method for improving public participation in environmental decision making." Risk Analysis 24(2): 363‐378. Willis, H. H., J. MacDonald Gibson, R. A. Shih, S. Geschwind, S. Olmstead, J. Hu, A. E. Curtright, G. Cecchine and M. Moore (2010). "Prioritizing Environmental Health Risks in the UAE." Risk Analysis 30(12): 1842‐1856. Xu, J., H. K. Florig and M. L. DeKay (2011). "Evaluating an analytic–deliberative risk‐ranking process in a Chinese context." Journal of Risk Research 14(7): 899‐918. 18 Chapter3. ConceptualizingHomelandSecurityRisk
This chapter describes the creation of a framework to describe risks in the homeland security domain in support of the deliberative risk ranking method. The first steps in a comparative risk assessment involve identifying a framework for how the risks are going to be compared. To compare risks, there must be discrete risks to compare and they must be described in roughly comparable ways. This consists of two activities— defining and categorizing the risks to be ranked, and identifying the attributes to describe those risks. These two steps are performed concurrently, with one step informed by and contingent upon the other. Framing the risks in the homeland security context requires addressing two questions: 
What are the appropriate hazards to compare for homeland security risks? 
What are the appropriate attributes to describe homeland security risks? The outputs of this chapter are one set of hazards and one set of attributes selected to describe the risks. The specific definitions of hazard and attribute are available in technical documents in the appendices, with specific definitions of hazards contained in the risk summary sheets and specific definitions of attributes contained in the “Notes on the Risk Calculations.” CategorizingHazardsandSelectingaHazardSet
The first step in the deliberative risk ranking method involves deciding what is to be compared. This involves first determining how the risks should be categorized and then selecting a useful set of risks using those categories. Comparing risks requires that there be discrete risks to compare, but how those risks are divided is not initially obvious. There are many ways to categorize risks. Similar to the Willis et al. (2004), homeland security risks can be categorized in several ways such as by event (e.g., hurricane), area (e.g., 19 Washington, D.C.), sector (e.g., power plants), general or specific type of assets (e.g. bridges, or the bridge at 29th Street), or cause of risk (e.g., terrorist groups). Any of these categorizations could be used for comparative risk analyses. When considering the alternative ways to categorizing risks, no single approach is universally correct. Instead, the categorizations should be matched to the purpose for which they are used and the structure of the organization using them (Morgan et al. 2000). In particular, this should include consideration of the risk‐management interventions that can be applied to policy action. For the purposes of this study, this means aligning homeland security risks with the organizational structures and interventions of DHS. DHS intervenes in homeland security risks in several ways. For example, DHS’s approach to critical infrastructure identifies specific potential targets (e.g. the 9th street bridge) while grants for the Urban Areas Security Initiative on the other hand focus on particular cities (e.g. Las Vegas, Nevada). Other grants and training supports specific activities such as biohazard decontamination or responder communication. At the strategic level documents and planning often take a scenario‐based approach, looking at risks at the hazard level (e.g. earthquakes). For the purposes of this study, I developed a set of risks based on this high level conceptualization of risks as hazards. This choice of risks as hazards does not represent an objectively preferred perspective, only one perspective that can be useful to address risks at the strategic level. Addressing the risk at the hazard level defines risk in terms of “an incident, event, or occurrence” (DHS‐
Risk Steering Committee 2008). Within DHS, risks are framed in terms of hazards in their mission statement, in strategic documents (including the National Infrastructure Protection Plan, the Quadrennial Homeland Security Review, and the National Response Framework) and utilized in risk assessments (including DHS’s National Planning Scenario and the Bioterrorism Risk Assessment) 20 (HSC/DHS 2005; DHS 2006; DHS 2008; DHS 2009; DHS 2010). Components within DHS specifically tasked with disaster response may also use this framing— for example, FEMA seeks to inform the public on how to prepare for risks by hazard, such as earthquakes or tornadoes (FEMA 2012a; FEMA 2012b). Categorization by hazard can also be reflected in the structure of the organization— for example, FEMA’s National Flood Insurance Program and National Earthquake Hazards Reduction Program distinguish between risks at a hazard level. Dividing risks into hazards is most useful in describing strategic priorities but can be less useful in identifying more concrete program‐specific decisions. Some risk‐reduction actions may be more specifically described in terms of targets or locations (e.g. the Golden Gate Bridge) than in terms of the hazard. Other risk‐reduction actions can relate to multiple hazards‐ building codes can be useful for mitigating against multiple kinds of hazards while strengthening the public health system can improve the response for a range of priorities. Describing risks as hazards is useful in setting priorities, but it is only a first step in moving from concerns to policy. After identifying a frame for identifying risks, specific risks were selected. A relevant set of risks should be logically consistent, administratively comparable, equitable and comparable with cognitive constrains and biases (Morgan et al. 2000). Important considerations included that the hazards be clearly defined, comprehensible, and distinct from one another. For reasons of comparability, I sought hazards which reflected the types of incidents described by DHS in their mission statement, specifically “…a terrorist attack, natural disaster, or other large‐scale emergency” (DHS 2013). This focus did not address other aspects of the DHS mission such as securing the borders or managing immigration. The decision to exclude chronic societal concerns is consistent with DHS risk analyses, notably the Strategic National Risk Assessment (DHS 2011). Additionally, this set of risks did not include other risks such as crime or clear acts of war that would fall under the jurisdiction of other governmental departments. 21 Having described the risks in these terms is not only compatible with existing mandates and inevitable framing biases but also allowed a set of homogenous risks that could be described using the same set of attributes. However, this set of risk will only be useful for comparing the risks associated with homeland security hazards relative to each other and not to other aspects of the DHS mission or interests of the nation as a whole. A list of hazards was identified from DHS documents, including the National Planning Scenarios, National Infrastructure Protection Plan, the Quadrennial Homeland Security Review, and the National Response Framework (formerly the National Response Plan) (HSC/DHS 2005; DHS 2008; DHS 2009; DHS 2010). Rather than using this exhaustive list of risks, a subset of the hazards was selected for this exercise to be more manageable in a workshop setting consistent with other studies of the Deliberate Method for Ranking Risks (see Table I). Table I‐ Hazards selected for risk ranking Natural Terrorist
Earthquakes Hurricanes Tornadoes Pandemic influenza Accidental Nuclear detonation Explosive bombings Anthrax attacks Cyber‐attacks Toxic industrial chemicals Oil spills This set of hazards was selected to include risks that vary in interesting ways, involving different causal agents and consequences. A first distinction captured in the hazard set is the cause of the event, including who caused it and why. I identified three kinds of events— natural disasters, terrorist events, and major accidents. The distinction of hazards into natural disasters, terrorist attacks, and major accidents reflects several differences. First, the underlying processes that generate natural disasters, accidents, and terrorist attacks are completely different. The generative process for natural disasters can be described probabilistically while terrorist attacks are inherently not probabilistic events but rather intentional events undertaken by an adaptive adversary; while it may be useful in some cases to 22 describe terrorist events in terms of probability, it can also be misleading (For more detail on the debate over using probabilities to describe terrorist actions, see the special issue of Risk Analysis relating to advances in terrorism risk analysis, http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291539‐
6924/homepage/custom_copy.htm). These differences in cause are also reflected in policy. While DHS prepares for all hazards, it was created in response to a terrorist event and its structure reflects that generating event. In its mission, ensuring resilience to disaster (including both natural and human‐
induced) is listed separately from preventing terrorism and enhancing security. This distinction carries over to functions within DHS, as the Federal Emergency Management Agency prepares to respond to natural and accidental events while programs to monitor and prevent terrorism occur primarily in other components. Incorporating the distinction between natural and human‐induced events in this framework matches the structures and processes within DHS. Within each of these kinds of hazards, I sought to include hazards that covered a range of consequences, including low and high consequence events across multiple dimensions of consequence. For example, a terrorist attack using a nuclear weapon would have much larger consequences than one using conventional explosives and should therefore be considered differently. Not only does the size of the consequences matter but also the kinds of consequences. For this reason, I included hazards with high numbers of casualties but little direct economic damage (e.g., pandemic influenza), low casualties but high economic damage (e.g., hurricanes), as well as hazards that were high in both (e.g., terrorist nuclear detonations) or low in both (e.g., oil spills). Consideration was also given to whether the event was common (e.g., tornadoes, terrorist explosive bombings), rare (e.g., pandemic influenza), or completely novel (e.g. terrorist nuclear detonations) and whether the event was associated with significant contamination (e.g. oil spills, terrorist nuclear detonations). 23 This set of hazards covers a large number of the hazards of interest to DHS, including six of the seven hazard categories in the National Response Framework incident annexes, five of the six threat types identified in the Quadrennial Homeland Security Review, and six of the seven hazard types described in the National Planning Scenarios (HSC/DHS 2005; DHS 2010). The hazard types that were identified in DHS documents but not selected (food and agriculture and radiological hazards) can be represented by hazards with similar consequences that were included. For example, the kinds of consequences represented by a radiological attack are represented by other scenarios—size of the physical damages is represented in explosive bombings, contamination is represented in anthrax attacks, oil spills, and toxic industrial chemical accidents, and intent is represented in several scenarios. Similarly, food and agricultural incidents include consequences of contamination (as above) and disruption of a network rather than nodes as represented in cyber‐attacks. While the selected hazards do not include every hazard of concern to DHS, they do present an interesting set of hazards that can be useful to inform DHS concerns. More importantly, the use of a broad set of hazards can demonstrate the feasibility and applicability of using this approach within the context of a homeland security strategic planning process. IdentifyingAppropriateAttributesofHazards
Comparing risks also requires having a set of attributes to describe the hazards. This set of hazards should be representative of the aspects of risk about which people and policy makers are concerned. This step occurs at the same time as the conceptualization of the risks, as understanding the important attributes of the risks allows us to prepare a set of hazards that is representative of the larger universe of risks and vice versa. Selecting a set of attributes that comprehensively yet parsimoniously describes the aspects of risk that people are concerned about requires significant judgment in applying the scientific literature (Keeney 1992). This section details how the set of attributes to describe the risks in comparative terms was selected. 24 CriteriaforaGoodAttributeSet
Risk, both in the risk‐perception literature generally and in the homeland security domain specifically, is a multiattribute concept. People care about a large number of consequences of disasters, including damage to life and health, property, and society, and all of these concerns should be included in the societal utility function. DHS risk analyses typically neglect measures of consequence beyond health and physical damage, which may present a biased perception of one risk compared to another (Committee to Review the DHS's Approach to Risk Analysis 2010). For example, a risk‐based methodology that considers only economic consequences and effects on life and health would misrepresent risks such as radiological threats as compared to methodologies that consider a larger range of psychological and societal harms (National Research Council 2007). A range of consequences is required to describe risks completely and with less distortion. The focus on health effects and physical damage has ramifications for policy beyond just mischaracterization of risk. There is a common adage, ‘that which we measure, improves.’ This is true in homeland security, as focusing on concrete consequences and omitting societal ones has been associated with the prioritization of policies that “harden” physical infrastructure rather than “preparing society on a broader basis to withstand the effects of disaster” (Committee to Review the DHS's Approach to Risk Analysis 2010). It is also important to describe a range of non‐consequence attributes. While objective measures are favored by technical experts (Slovic et al. 1985; Slovic 1992; Keeney 1995; Tengs et al. 1995), factors of perceived risk are still important. The recognition by Starr in 1969 that voluntary acceptance of risk was an important factor in how risks were perceived led to a wider examination of non‐consequence attributes important to risks (Starr 1969). The line of inquiry that followed is commonly called the psychometric paradigm. These studies found that in addition to the consequences 25 of the risk, people are concerned about non‐consequence aspects of risk characterized by the knowledge of the risk, the dread of the risk, and the societal and personal exposure to the risk (Slovic et al. 1985). The importance of these non‐consequence attributes is distinct from issues leading to bias. While some ways of looking at a risk may bias perception of that risk, including anchoring or the use of very small probabilities (Lichtenstein et al. 1978), those aspects associated with the psychometric paradigm are viewed to be important in addition to perceptions of expected death or damages for several reasons. First, they reflect the actual concerns of lay people and stakeholders. There is a substantial literature exploring the use of models and heuristics as they are used by nonscientists (see Morgan 2002), and including such non‐consequence factors is important to develop consensus on risk rankings (Committee on Risk Characterization 1996). But while these factors are viewed as important, their influence is not always clear. Reasonable people can disagree about how these factors influence judgments of risk. For example, one person may be more concerned about the risk of dying now while another is more concerned about the risk of dying in 30 years from an exposure now. While non‐
consequence attributes of risk are important, they are also more complex than aspects of consequence. Second, other impacts can arise from these perceptions following an event, including psychological trauma and secondary societal consequences where the perception of the event is a mediating cause in the social amplification of risk (Kasperson et al. 1988). An event may have secondary economic consequences manifesting in altered consumption or savings patterns for individuals and businesses, or it may result in new and different policies or regulations. These secondary effects are not necessarily intrinsic to the event but may be more damaging than the initial primary effects. As Slovic notes, the Three Mile Island nuclear reactor accident in 1979 resulted in significant societal impacts 26 despite the fact that no lives were lost (Slovic 1987). A more recent example is the terrorist attacks of September 11, 2001. While thousands were killed and billions of dollars were lost in directly as a consequence of the terrorist attacks that day, the secondary effects were even stronger, with behavioral changes, a restructuring of the executive branch, and two wars that were indirect results of the attacks (Gigerenzer 2004; Jenkins and Godges 2011; Mueller and Stewart 2012). The approach to identify attributes that can be used to describe risk in the deliberative risk ranking method was initially performed by Jenni (1997). Jenni’s approach set three criteria for an appropriate attribute: they should be justifiable, clearly defined, and measurable (Jenni 1997; Florig et al. 2001). Justifiable attributes are ones where an argument for differentiating between risks based on that attribute can be made. Different attributes may be important in different situations (Fischhoff et al. 1984; Jenni and Loewenstein 1997). Attributes can be identified from “formal analysis, professional judgment, political process, or revealed preferences” (Jenni 1997). While expected value is often preferred by risk professionals, analyses using attributes generated solely by risk experts may face public opposition (Slovic 1987) so the attributes selected should consider multiple perspectives and include the considerations of policy makers, risk professionals, and the lay public (Jenni 1997). Justifiability is also related to how the attributes are described. For example, lives lost is one of the most widely used attributes to describe disasters, making it easily justifiable. However, it can be described as an attribute in many ways— expected lives lost per year, chance of death in a million for those exposed, chance of death for those at highest risk, catastrophic potential number of people killed in a single event – and it may be useful to justify the specific format that is used. Comparable attributes must also be clearly defined. These definitions should reflect the normative justifications in enough detail that people are able to understand the attribute without 27 relying on a description of the hazard for context. Attributes must also be defined in sufficient detail that the hazards can be measured and differentiated based on that attribute. Finally, an attribute must be measurable to make comparisons possible. For an attribute to be measurable it does not need to be quantitative or have any obvious physical components; qualitative descriptions can be measured on an ordinal scale. All that is required for a hazard to be measurable is for it to be reliably and consistently evaluable for the hazard set, and those evaluations must be able to support distinguishing between one hazards and another based on the attributes. In addition to the criteria for attributes individually, the attributes need to work together as a set. First, the set of attributes should describe the risk comprehensively, covering the range of outcomes about which people are concerned (Committee to Review the DHS's Approach to Risk Analysis 2010) as well as perceptual attributes (Florig et al. 2001). At the same time, the set of attributes should be parsimonious, as numerous studies have found that too many attributes can be confusing when making decisions (see Eppler and Mengis 2004 for a review of the concept of information overload). Additionally, the set of attributes should avoid double counting; while a single attribute can be presented in multiple, formally equivalent ways to minimize framing effects, no attributes should be counted more than once (for example, presenting both lives lost and casualties, of which lives lost is a part). I attempted to identify a set of between 12 and 20 attributes, consistent with previous studies using the Deliberative Method for Ranking Risks, which can cover the a range of consequence and non‐
consequence aspects of disasters. MethodsandData
The set of consequences in this study was drawn from the literature on homeland security and emergency management. A review of the literature on homeland security and disasters identified the number of articles related to disasters or terrorism that were associated with specific consequence 28 terms. A search of the literature on homeland security and disasters performed using EBSCO databases (specifically Academic Search Complete, Business Source Complete, CINAHL, EconLit, GreenFile, MEDLINE, PsycARTICLES, PsycINFO, Social Science Abstracts, Criminal Justice Abstracts, and National Criminal Justice Reference Service Abstracts) of peer reviewed articles between 1970 and June 25, 2012 identified over 100,000 articles. While health concerns and economic or physical damage were the most commonly studied, there were also substantial literatures that reflected on psychological, environmental, and societal consequences (see Table II for specific search terms). Table II‐ Database Search for Disasters or Terrorism All articles with keyword of disaster or terroris* Subset of articles with additional keyword of consequence Deaths, lives, casualties Injur*, illness, QALY, DALY Damage, destruction, collapse Economic, economy Psychological, mental health, PTSD, depression Societ* Homeless*, displace* Unemploy*, jobless* Environment* and species or aesthetic or contamination 109,871 13,437 9,893 7,557 13,804 11,211 7,932 1,639 328 1,403 Given the size of this literature, I could not review every article that examined disasters or terrorism in terms of one consequence or another in isolation but instead focused on articles that reviewed the overall literature. This search included articles that reviewed the literature or explicitly or implicitly described a comprehensive framework of consequences as well as government documents that described consequence attributes actually in use. Seven recent papers describing an overarching framework for consequences were used: the National Academies reports from the Committee on Assessing the Costs of Natural Disasters (1999), the Committee on Disaster Research in the Social Sciences (2006): Future Challenges and Opportunities (2006), and the Committee to Review DHS’s Approach to Risk Analysis (2010); DHS’s Risk Lexicon; and academic articles that review the emergency 29 management or homeland security literature with regards to aspects of consequence, notably Mileti (1999), Lindell and Prater (2003), and Keeney and von Winterfeldt (2011). Additionally, I examined DHS papers or processes that utilized a framework in an attempt to comprehensively describe risks, including Homeland Security Presidential Directive 7 (HSPD‐7), the National Planning Scenarios, the National Infrastructure Protection Plan (NIPP), the Quadrennial Homeland Security Review (QHSR), and multiple methodologies as identified in the Committee to Review DHS’s Approach to Risk Analysis (2010) (White House 2003; HSC/DHS 2005; DHS 2009; Committee to Review the DHS's Approach to Risk Analysis 2010; DHS 2010). Other papers were included only insofar as they identified alternative specifications or subsets of known hazards (such as identifying Post‐Traumatic Stress Disorder as a component of psychological consequences) but are not directly used in identifying overarching attributes. In selecting attributes, I focused on consequences that are directly linked to the hazard but did not include those that are not directly linked (e.g., I include homelessness as it is directly related to physical destruction and contamination but I do not include violent crime which is indirectly related to disruption). If I were to include these aspects, for example, I may include the costs of the wars in Iraq and Afghanistan, increased government surveillance, and the creation of DHS as consequences of the attacks on September 11, 2001. However, I could also hypothesize that a different administration would make different policy decisions with different consequences as a result. As the purpose of this ranking is to inform policy, I did not presume the policy choices following an event as part of the intrinsic consequences of the hazard. While I do include attributes of policy perception that may influence how the public and policy‐makers respond after an event, attributes that directly reflect those public and policy choices were not included. Once important attributes were identified, I created specific measures to describe them. First, a determination was made as to whether the attribute would be described in quantitative or qualitative 30 terms. Attributes that could be described in comparable, discrete units are presented quantitatively while those that represent a more abstract concept or a wider range of consequence that were not readily comparable are presented qualitatively. Then an appropriate format for the attribute is selected. Specific attributes drawn from other peer‐reviewed studies were used when possible for reasons of justifiability and consistency. The risk attributes related to non‐consequence attributes of importance were adapted from Jenni (1997), which serves as the standard for studies using the Deliberative Method for Ranking Risks (Jenni 1997; Florig et al. 2001). Results
The focused literature review identifies a range of consequences from disasters and terrorist events about which people are concerned, including not only lives lost and economic damages, but also social, psychological, environmental, and political concerns. Lives lost and economic damages are the consequences of greatest concern. They are discussed frequently in both the literature and DHS risk analyses, often with a mention of their prominence. This reflects not only the importance of the loss of life and physical damage but also the relative ease of using discrete quantitative estimates over more nebulous and complex concerns. Papers that describe a comprehensive framework for the consequences of disasters (either natural or human‐induced) identified additional relevant consequences, including secondary economic effects and job loss, environmental damage and contamination, psychological damage, societal disruptions, and symbolic damage (Committee on Assessing the Costs of Natural Disasters 1999; Mileti 1999; Lindell and Prater 2003; Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; Committee to Review the DHS's Approach to Risk Analysis 2010; Keeney and von Winterfeldt 2011). References to other aspects of consequence are more commonly present in applications of consequence by DHS. DHS largely utilizes quantitative measures of consequence, notably lives lost, injuries, illnesses, and economic damages (often reflecting physical damages and first‐order economic effects) (Committee to Review the 31 DHS's Approach to Risk Analysis 2010). FEMA’s HAZUS model, for example, models deaths, injuries, numbers of buildings damaged, and costs of repairs (FEMA Mitigation Directorate. et al. 2001). Some DHS documents encourage the consideration of other consequences— the National Infrastructure Protection Plan also discusses indirect economic losses, psychological damage and mission impact, while the National Planning Scenarios also include in their summary tables the area of infrastructure damage, recovery timeline, displaced people, and contamination and regularly discuss environmental damage and international dimensions (HSC/DHS 2005; DHS 2009). Still, to a large extent, decisions at DHS are informed by a smaller set of consequences, which are more measurable and deemed more important, rather than a more comprehensive set of consequences (Committee to Review the DHS's Approach to Risk Analysis 2010). The remainder of this section will describe the specific attributes selected to describe the risk and the reasons for their selection. Based on this review, it is important to describe the consequences of homeland security hazards using multiple attributes. The attributes of foremost concern are related to health and economic damages, and multiple measures were used to describe each of these. Additional attributes were also selected that reflect environmental damage, societal disruption, and governmental disruption. Finally, a set of perceptual attributes of risk was selected. This set of selected measures covers the range of identified attributes either directly or indirectly. Table III details how the attributes constructed to describe the risk match to the attributes of consequence identified in the focused literature review. The rest of this section details these constructed attributes in detail. Deaths were described using two attributes, average number of deaths per year and greatest number of deaths in a single episodes. Expected value calculations are the preferred measures of exposure preferred by experts and the numbers to describe them are easily understood by the general public. Greatest number of deaths in a single episode is a measure of catastrophic potential that serves 32 several purposes: it supports a different perspective on decision‐making under uncertainty (such as minimax decision rules); it provides insight into the skewness of the distribution of annual damages; and it serves as an aspect of the psychometric attribute of dread. These attributes were selected to describe the expected risk of death in terms of counts for several reasons. First, these specific attributes were selected because they were used successfully in previous studies using the Deliberative Method for Ranking Risk (Florig et al. 2001; Willis et al. 2004; Willis et al. 2010). Additionally, the specific format of counts of deaths is consistent with the format used in documents within DHS (HSC/DHS 2005), is easily understood without needing to understand fractions or decimals, and is readily available in the open‐
source literature. Attributes can be described in formats other than counts, such as rates per thousand, average time between deaths, etc., but while there is significant evidence that the format of an estimate can lead to bias in perceiving a risk generally (Slovic 1992), an examination of effect of different attribute formats on relative risk rankings conducted in a deliberative fashion showed little sensitivity to the specific format used (Johnson 2004). Non‐fatal injuries and illnesses represent a continuum of harms rather than a binary state. As with lives lost, these particular attributes were selected because they were used in other studies using the Deliberative Method for Ranking Risks. As in those studies, a distinction was made between more serious injuries or illnesses per year (which I defined as those requiring hospitalization) and less serious injuries or illnesses per year (defined as potentially requiring medical attention but without resulting in admission to a hospital). However, unlike previous studies using the Deliberative Method for Ranking Risks for environmental or public risks, data on injuries from homeland security events were not sufficient to identify whether injuries were short or long term. For this reason, the attributes of more severe and less severe injuries or illnesses used here did not include a distinction between short‐term and long‐term injuries or illnesses. 33 Additionally, a new measure was created to describe homeland security events in terms of psychological harms. Psychological harms can result from the immediate exposure to the event, from dealing with loss from the event, or from the societal disruption following the event. As psychological harms are numerous, complex, and difficult to count, quantitative measures are less appropriate. Instead, a new attribute of psychological harms per year on average was developed as a qualitative measure presented in levels of low, moderate, and high. This attribute was generated based on the stressors that contribute to two significant components of disaster‐related mental health (Post‐
Traumatic Stress Disorder and depression). For more information on the derivation of the attribute, see section Describing a Comparable Set of Homeland Security Risks in Chapter 4 and Appendix A on Comparing Mental Health Consequences of Disasters. Economic damage directly comprises physical damage from the event itself and immediate business interruption in the aftermath. Additionally, there are many secondary effects due to changes in consumption and investment patterns. The attributes selected to describe economic damage focus solely on the direct effects for two reasons. First, estimates of indirect damages vary significantly, not only due to what considerations are included and what are not, but also under the assumptions underlying a competitive market economy. Second, indirect damages are not inherent to the event but instead reflect the individual and policy choices following an event. As described above, I do not present measures of the secondary effects directly but do include perceptual attributes that may influence those secondary effects. Four attributes were selected to describe the diversity of economic harms. Using a range of estimates has two purposes. First, multiple attributes were needed to describe the diversity of homeland security risks, reflecting short‐term and long‐term risks and expected value and greatest value in a single event perspectives. Second, using a set of economic attributes roughly as large as the set of 34 health attributes and the societal issues could reduce biases that arise from one type of attribute was mentioned more often than others. The inherent physical damage and business interruption of the event are described using average economic damage per year and greatest economic damage in a single episode. These attributes were selected because of their consistency with attributes describing lives lost. Two additional attributes are also applied— the duration of economic damages and the size of area affected by economic damages. These two additional attributes, while specifically describing the direct economic harm, provide context for participants to consider indirect economic harm. The scale and persistence of an event provide additional information not only on direct damages but also indirectly inform consideration of secondary economic impact and societal disruption through prolonged unemployment and societal cohesion (Marshall et al. 2003; Picou et al. 2003). Damage to the environment is another physical consequence that is recognized generally by DHS. The varied nature of the attribute limits the use of any single quantitative estimate (such as the acres of trees destroyed). Instead, a qualitative attribute was created to describe average environmental damage per year in levels of low, moderate, and high. Based on the work of Willis (2004), environmental damage represents impact to species and aesthetic impacts (Willis et al. 2004). Other aspects of environmental damage, such as impacts on humans or controllability, are accounted for in other attributes. Non‐economic societal harms are also of widespread importance. The National Academies report includes societal harms as part of their recommended full range of consequences for homeland security risk analysis (Committee to Review the DHS's Approach to Risk Analysis 2010). Societal impacts are a particular concern for terrorist threats— while terrorist events do intentionally cause casualties, one purpose of terrorism is to produce a sense of terror in the population and disrupt societal or effect policy change. 35 Societal harms are complex and represent a bundle of related concerns. Several other attributes can inform societal harms indirectly, including those related to the scale of the disaster (duration of economic damages and size of area affected by economic damages) and important perceptual attributes that will be discussed in the forthcoming pages. However, it is also important to present a description of societal harms directly. I adopted two attributes to directly reflect non‐
economic social harms, one reflecting social harms and one reflecting political and strategic harms. By incorporating these two aspects, the attribute set had direct measures for each of the aspects recommended by the National Academies report on DHS risk analyses (Committee to Review the DHS's Approach to Risk Analysis 2010). I selected displacement from one’s home to represent societal harms, due to its relative importance. As Lindell and Prater note in an overview of the community impacts of disasters, “[p]erhaps the most significant sociodemographic impact of a disaster on a stricken community is the destruction of household dwellings” (Lindell and Prater 2003). Displacement from one’s home is not only a measurable loss in itself but can also contribute to other harms of losing one’s job and support network and can contribute to drug use, violence, or other social ills (Rubin et al. 1985; Berke et al. 1993; Lindell and Prater 2003). As a direct measure of societal harms I use a quantitative estimate of average individuals displaced per year. This attribute of displacement was characterized in terms of an average count of individuals per year to be consistent with other aspects of consequence such as average lives lost and average injuries or illnesses per year. Disruption of government operations was selected to directly describe the political and strategic consequences, completing the range of categories of consequences recommended by the National Academies report (Committee to Review the DHS's Approach to Risk Analysis 2010). As described earlier, I avoided anticipating actions taken in response to a disaster, particularly those that are sensitive 36 to a small number of decision‐makers rather than reactions on a population‐level— for example, one could consider the Iraq war among the consequences of the terrorist attacks on Sept. 11, 2001, but it is plausible that the U.S. would not have engaged in that war if not for the particular administration in place at the time. For this reason, I did not try to describe the political and strategic choices directly, although aspects of the risk that could indirectly influence those choices were included in attributes of risk perceptions that will be described subsequently. Additionally, to describe political and strategic consequences directly, an attribute of governmental capacity rather than governmental choices was used. This attribute, disruption of government events, was selected because it directly reflects the objectives of DHS as the government component responsible for managing these risks. By including service disruption, I have now also included all of the dimensions of consequence chosen by DHS to describe disasters in the National Planning Scenarios (HSC/DHS 2005) and all categories of loss incorporated in a National Academies report on the impacts of natural disasters (notably, losses to businesses, individuals, and government) (Committee on Assessing the Costs of Natural Disasters 1999). Quarantelli describes disruption of government operations as being associated with scale in natural disasters, describing the difference between a mere disaster and a catastrophe in terms of whether the government capacity to respond to the event is overwhelmed (Quarantelli 2005). For terrorist events, it is not merely the scale of the disaster but also how it is targeted, as intentional attacks may select their targets to increase disruption. To represent these concerns, disruption of government operations is presented as the consequences if an event were to occur rather than the average disruption per year, using levels of low, moderate, and high. In addition to the societal exposure to the risks, risk perception literature identifies two primary factors that describe risks in addition to the personal or societal consequences: dread and the unknown. To describe these perceptual factors, I adopted the attributes standardly used in studies using the Deliberative Method for Ranking Risk as identified by Jenni (1997). For dread, previous studies 37 described dread using the greatest number killed in a single event and the ability of an individual to control their exposure. The ability of an individual to control their exposure relates to involuntariness and the extent to which the risk can be reduced (presented in levels of low, moderate, and high). For aspects of the unknown, I adopted time between exposure and health effect (presented in terms of days, weeks, months, etc.) and quality of scientific understanding (presented in levels of low, moderate, and high). The definitions associated with each of these levels, within the attributes, such as the conditions that lead to ability to control exposure being classified as low for a given risk, are described in the next chapter. Finally, natural/human‐induced was added as a perceptual attribute to describe the accountability for the action. This attribute is related to dread and the unknown but was less applicable to studies using the Deliberative Method for Ranking Risks in domains such as school safety or the environment rather than in homeland security. The extent to which the difference between natural and human‐induced events has ramifications on the social consequences is debatable (Quarantelli 1989), but it is certainly an important concern in how people perceive risk and an important factor in how people and policy‐makers respond to events (Freudenburg 1997). Because of the importance of this distinction in emergency management literature, I include a distinction between risks being caused by natural forces and those intentionally or accidentally caused by humans. 38 Consequences of Disaster Physical Health Deaths QALY/DALYs Injuries Illnesses Severity of injury Length of injury average number of deaths per year greatest number of deaths in a single episode more severe injuries or illnesses per year on average less severe injuries or illnesses per year on average psychological damages per year on average average economic damages per year greatest economic damage in a single episode duration of economic damages size of area affected by economic damages average environmental damage per year average displaced households per year disruption of government operations natural/human‐induced ability of individual to control own exposure time between exposure and health effects quality of scientific understanding combined uncertainty Table III‐ Homeland Security Attributes Used to Describe Consequences of Importance Identified from the Literature Source (Committee on Assessing the Costs of d d
Natural Disasters 1999, many others; Mileti 1999; Lindell and Prater 2003; Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; DHS‐RSC 2008; Committee to Review the DHS's Approach to Risk Analysis 2010; Keeney and von Winterfeldt 2011) World Bank, others d d d d
(Committee on Assessing the Costs of d d
Natural Disasters 1999, many others; Mileti 1999; Lindell and Prater 2003; Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; DHS‐RSC 2008; Committee to Review the DHS's Approach to Risk Analysis 2010; Keeney and von Winterfeldt 2011) (DHS‐RSC 2008; Committee to Review d d
the DHS's Approach to Risk Analysis 2010; Keeney and von Winterfeldt 2011) component of QALY/DALYs
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39 average number of deaths per year greatest number of deaths in a single episode more severe injuries or illnesses per year on average less severe injuries or illnesses per year on average psychological damages per year on average average economic damages per year greatest economic damage in a single episode duration of economic damages size of area affected by economic damages average environmental damage per year average displaced households per year disruption of government operations natural/human‐induced ability of individual to control own exposure time between exposure and health effects quality of scientific understanding combined uncertainty Consequences of Disaster Psychological Health Psychological‐ general Source (Lindell and Prater 2003; Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; DHS‐RSC 2008; Committee to Review the DHS's Approach to Risk Analysis 2010) PTSD (Norwood et al. 2000, many others)
ASD, depression (Norwood et al. 2000, many others)
Other stress related (Norwood et al. 2000, many others)
psychological harms Economic Economic damage‐ (Committee on Assessing the Costs of property damage Natural Disasters 1999; Mileti 1999; Lindell and Prater 2003; Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; DHS‐RSC 2008; Committee to Review the DHS's Approach to Risk Analysis 2010; Keeney and von Winterfeldt 2011) Houses destroyed (Committee on Disaster Research in the Social Sciences: Future Challenges and Opportunities 2006; Rose et al. 2007) Business disruption (Committee on Assessing the Costs of Natural Disasters 1999; Rose et al. 2007; Committee to Review the DHS's Approach to Risk Analysis 2010) ,HAZUS model, many others 40 i
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i average number of deaths per year greatest number of deaths in a single episode more severe injuries or illnesses per year on average less severe injuries or illnesses per year on average psychological damages per year on average average economic damages per year greatest economic damage in a single episode duration of economic damages size of area affected by economic damages average environmental damage per year average displaced households per year disruption of government operations natural/human‐induced ability of individual to control own exposure time between exposure and health effects quality of scientific understanding combined uncertainty Consequences of Disaster Economic (cont.) Damage to critical infrastructures (e.g. electrical, transportation) Source (Tierney 1997; Committee on Assessing the Costs of Natural Disasters 1999; Mileti 1999; Rose and Lim 2002; Haimes et al. 2005; Rose and Liao 2005; Committee to Review the DHS's Approach to Risk Analysis 2010) Indirect economic (Committee on Assessing the Costs of damage Natural Disasters 1999; Lindell and Prater 2003; White House 2003; Rose et al. 2007; DHS 2009; Rose 2009; Keeney and von Winterfeldt 2011) Regional economic (Rose et al. 1997; Frey et al. 2007; impact Greenberg et al. 2007; Rose 2009) Sector economic (Enders et al. 1992; Ito and Lee 2005; impact (e.g. airlines, Fleischer and Buccola 2006; Gordon et tourism) al. 2007, many others; Greenberg et al. 2007) National economic (Abadie and Gardeazabal 2003; impact Blomberg et al. 2004; Nitsch and Schumacher 2004; Frey et al. 2007, more; Greenberg et al. 2007; Blomberg and Hess 2009; Rose 2009) Stock/capital (Collier 1999; Abadie and Gardeazabal markets 2003; Fielding 2003; Chen and Siems 2004; Frey et al. 2007, others; Rose 2009) Savings switching to (Fielding 2003; Eckstein and Tsiddon consumption 2004) Non‐market (Rose et al. 2007; Committee to damage (e.g. Review the DHS's Approach to Risk historic sites) Analysis 2010) 41 d i
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i i average number of deaths per year greatest number of deaths in a single episode more severe injuries or illnesses per year on average less severe injuries or illnesses per year on average psychological damages per year on average average economic damages per year greatest economic damage in a single episode duration of economic damages size of area affected by economic damages average environmental damage per year average displaced households per year disruption of government operations natural/human‐induced ability of individual to control own exposure time between exposure and health effects quality of scientific understanding combined uncertainty Consequences of Disaster Environmental Environmental damage Source (Committee on Assessing the Costs of Natural Disasters 1999; Lindell and Prater 2003; DHS‐Risk Steering Committee 2008; Keeney and von Winterfeldt 2011) Damage to ecology (Willis et al. 2004; Rose et al. 2007)
Human impacts‐ (Committee on Assessing the Costs of i
including agriculture Natural Disasters 1999; Mileti 1999; Willis et al. 2004) Aesthetic impacts (Willis et al. 2004) Socioeconomic Homelessness (Mileti 1999; Lindell and Prater 2003; HSC/DHS 2005; Committee on Assessing Vulnerabilities Related to the Nation's Chemical Infrastructure 2006) Demographic (Committee on Assessing the Costs of changes Natural Disasters 1999; HSC/DHS 2005) Crime (Keeney and von Winterfeldt 2011)
Disruption of (Committee on Risk Characterization society 1996) Degradation of (Committee on Risk Characterization lifestyle 1996; Keeney and von Winterfeldt 2011) Unemployment (Mileti 1999; Keeney and von Winterfeldt 2011) Community (Freudenburg 1997, many others; cohesion Picou et al. 2003) 42 i
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average number of deaths per year greatest number of deaths in a single episode more severe injuries or illnesses per year on average less severe injuries or illnesses per year on average psychological damages per year on average average economic damages per year greatest economic damage in a single episode duration of economic damages size of area affected by economic damages average environmental damage per year average displaced households per year disruption of government operations natural/human‐induced ability of individual to control own exposure time between exposure and health effects quality of scientific understanding combined uncertainty Consequences of Disaster Source Governmental Mission disruption (White House 2003; DHS‐Risk d Steering Committee 2008) Disruption of (White House 2003; HSC/DHS 2005; d provision of DHS 2009) government services Loss of public order (White House 2003; DHS 2009)
i i Government (White House 2003; DHS 2009)
d continuity Reduced emergency (Rose et al. 2007) d response Behavioral/Policy Restrictions on (DHS 2009; Keeney and von i freedoms/rights Winterfeldt 2011) Loss of public (Committee on Risk Characterization morale/confidence 1996; White House 2003; DHS 2009) International (Travalio 2000; HSC/DHS 2005; i relations Treverton et al. 2008) Increased (Committee on Assessing the Costs of i government Natural Disasters 1999; Treverton et expenditures al. 2008; Keeney and von Winterfeldt 2011) d= attribute directly represents consequence, i= attribute indirectly represents consequence framework documents in bold, other documents in normal, subcategories of consequence in italics 43 i i
i
i
i
i i
i
i
i
i i
i
i
i
References
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For individuals to a provide informed ranking of risks they must have an understanding of what those risks are; in the Deliberative Method for Ranking Risk, participants are informed as to the risks using a set of risk summary sheets. These summary sheets describe the risks concisely and clearly using nontechnical terms in a standardized fashion to facilitate comparisons. At the heart of these summary sheets is an accurate assessment of the risk. Describing the risks in this fashion can be a challenging exercise of risk assessment. The National Academies’ review of DHS’s risk analyses describes what is needed to adequately document a risk analysis: (1) how they construct risk assessment models, (2) what assumptions are made to characterize relationships between variables and parameters and the justifications of each, (3) the mathematical foundations for the analysis, (4) the source of values assigned to the parameters for which there are available data, and (5) the anticipated impact of uncertainty for assumptions and parameters. (Brisson and Edmunds 2006; as cited in Committee on Methodological Improvements to the Department of Homeland Security's Biological Agent Risk Analysis 2008; as cited in Committee to Review the DHS's Approach to Risk Analysis 2010) This chapter discusses the process of estimating the risks associated with the selected homeland security hazards generally, including describing the types of data that are available to estimate the attributes and important considerations to take into account when making those estimates. Quantitative estimates are more involved and are discussed first, and then qualitative estimates are discussed. This chapter also examines the implications of the risk estimates that develop from this process, including consideration of the risks across multiple attributes and of the anticipated impact of uncertainty in the estimates. 53 The decisions involved in estimating the data are described in detail in technical documents provided in appendices. A first appendix documents the summary sheets that were prepared as an output of this process. These risk summary sheets include documentation for both the descriptions of the risk and the source of values assigned to the hazard (although, for reasons of clarity, this documentation was not included in the versions provided to participants in the risk ranking sessions). A second appendix documents the entire process of estimating the risks, including the range of variables identified and the assumptions and calculations that led to a parameter being selected. For specific documentation of the hazards, including the sources that were used to estimate risks and the justifications for those sources, please see these technical appendices. In this chapter, I describe the process of using open source information to estimate the risks associated with the previously identified set of 10 selected homeland security hazards. Three questions are addressed: 
Is there sufficient open‐source information to describe a set of homeland security risks in a way that can support a deliberative comparative risk ranking? 
What types of variation exist among risks in the U.S. from natural disasters, terrorism, and large‐scale accidents? 
Does that variation warrant a holistic assessment of these risks when developing strategies for domestic safety and security? TheRiskAssessmentProcess
In the Deliberative Method for Ranking Risks, the third step involves preparing a set of risk summary sheets to inform the risk ranking participants of what the risks actually are (Florig et al. 2001). These summary sheets draw on modern risk communications to encourage deliberative consideration of 54 the risks, reduce cognitive biases, and increase understanding and perspective of the risks being considered. In this study, risk summary sheets were developed for each of the ten hazards identified in the previous chapter. The design of the risk summary sheets used in the method calls for them to be four pages in length and describe the risk in a standardized fashion for ready comparison (Florig et al. 2001). The first page contains a one‐page summary of the risk followed by a summary table with values and uncertainty intervals for the identified set of attributes of concern (Figure 3). The subsequent pages of the summary sheet follow with a description of the risk generally and how it causes harm, the exposure to the risk, and what has already been done to address the risk. This structure is designed to place the risk in context, clarifying that the risk being examined is the residual risk as it currently exists, and focus the rankings on the seriousness of the risks Figure 3‐ Example Risk Summary Sheet themselves rather than any consideration of risk reduction efforts (Florig et al. 2001). Estimating the attributes of concern and describing the risk in context involves HURRICANES
Hurricanes are large storms that form over large, warm bodies of water like the Western Atlantic
or Gulf of Mexico. When hurricanes reach land, they bring high winds, tornadoes, and flooding
from both rain and storm surge. Hurricanes regularly strike the southeastern U.S. along the
coasts of the Atlantic Ocean and Gulf of Mexico. A major storm can affect a significant portion
of one or more states, and several major storms make landfall each year. Hurricanes are
typically associated with some casualties and major economic damage. While preventing
hurricanes is impossible, weather forecasting provides opportunities to reduce the consequences
of hurricanes by implementing evacuation plans and taking other preparedness measures days in
advance. Furthermore, because the effects of hurricanes occur predictably, communities can
reduce their risks by implementing building codes and zoning plans to make buildings more
resilient and limit the exposure of buildings in floodplains.
Risk Characteristics of Hurricanes
significant judgment in determining which sources of data to use and how to use them. For each of the hazards, the attributes of concern were estimated to describe the current risk to the nation. These attributes could be described in quantitative or qualitative terms, depending on whether or not the risk could be described in clear, Public Health and Safety
Average number of deaths per year
Greatest number of casualties in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damage per year
Greatest economic damage in a single episode
Duration of economic damage
Size of area affected by economic damage
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Best
High
10
40
2,000-4,000
600
1,000
High
60
200
400
$2B
$10B
$60-200B
Months to years
Counties to states
High
10,000-100,000
Moderate to High
1,000
2,000
$20B
Natural
High
Immediate up to years
Moderate to high
Low
WHAT IS KNOWN ABOUT RISK OF HURRICANES?
Hurricanes are natural, highly destructive cyclonic weather systems that primarily affect coastal
areas. Hurricanes form over warm water, and are predominantly a risk to the Atlantic and Gulf
Coast regions of the United States, spanning the coast from Texas to Maine and including Puerto
Rico. While hurricanes can form in the Eastern Pacific, they rarely do. Hurricanes may be 200600 miles across. As a result, they create damage across a wide area in
55 Low
discrete terms. The attributes of concern are defined in a technical document, “Notes on the Risk Calculations,” which is attached as an appendix. These attributes can describe consequences in two ways— in terms of the average annual consequences and in terms of the greatest consequences if an event were to occur. While a few of the attributes describe the risk in terms of what would happen if an event were to occur, most of the attributes describe the risk in terms of average consequences expected per year. This does not necessarily mean a typical year; many homeland security hazards occur rarely which means that the averages include the rare years in which a severe event occurs and the larger number of years in which nothing occurs. Attributes that describe an average consequence per year can be estimated by averaging the consequences across many years or by multiplying the consequences of an event scenario times the likelihood of an event occurring. DerivingEstimatesforQuantitativeAttributesofRisk
Estimates of consequence were drawn from open‐source literature for all of the hazards. Open‐
source data were used because it was available and because it could be openly shared with risk ranking participants. I identified specific data sources through a familiarization with the literature for each risk, starting with government records, and then expanding the search to relevant references and additional sources identified through keyword searches. Because these searches were wide‐ranging and not limited to peer‐reviewed information, the searches were conducted using Google (for historical and raw data), Google Uncle Sam (a specialized Google search page for searching U.S. federal, state, and local government information, discontinued in 2011), and Google Scholar (for peer‐reviewed articles). Searches were conducted throughout 2010 and early 2011. A small number of additional sources were suggested by subject‐matter experts involved in reviewing the estimates. 56 This process identified estimates in 137 documents including datasets, government documents, peer‐reviewed articles, NGO publications, published books, and news articles. Many of these documents provided multiple estimates. For example, estimates of consequence were used from nine of the National Planning Scenarios, and in many cases both low and high estimates were identified (HSC/DHS 2005). Datasets were particularly useful in creating multiple estimates including estimates for multiple hazards, for different periods of time, or for different places. For example, the RAND Database of Worldwide Terrorist Incidents provided a number of estimates for consequences over different time periods and for different countries (RAND 2012). Estimates of consequence came in two forms, those reflecting combined data and those reflecting discrete events. Estimates of combined data consider all events over a period of time, and can be in the form of yearly averages, counts per month, counts per year, etc. Examples include the counts of deaths per year from tornadoes as recorded over the past 70 years from the National Oceanic and Atmospheric Administration (NOAA 2011) or the number of deaths from terrorist bombings in the U.S. over the past 15 years from the RAND Database of Worldwide Terrorist Incidents (RAND 2012). Discrete estimates consider the consequences of distinct events individually; these can take the form of a single event scenario or case study, a database that records the count of deaths by event, or a list of the top events. Examples include the estimate of deaths that occurred in the Deepwater Horizon oil spill (a single event), records of the number killed in U.S. industrial disasters in EM‐DAT (a database record), and the list of top ten deadliest tornadoes reported by NOAA (a list of top events). These two kinds of estimates can be used in different ways. Combined estimates translate readily into average annual estimates, often directly. Combined estimates can also be used to inform estimates of average consequences where an expected value is calculated, multiplying the probability of an event in a given year by the consequences of an event should one occur. In this case, combined 57 estimates of consequences by event can be used to identify the consequence of an event should one occur. Discrete events also lend themselves to estimates of averages using expected value calculations, although judgment plays a part in deciding which of the events should be representative of the consequence estimates. Using discrete events as an estimate of greatest consequence for a single event is more straightforward, as identifying an event with the greatest consequences relies less on judgment than identifying an event with consequences representative of the average. Discrete events can also be used to compare one aspect of consequence to another, which can be useful for identifying averages for attributes where data are not available by comparing it to another attribute where the data are available. For example, if an estimate of the average number of injuries per year cannot be found but an estimate of the ratio of injuries to lives lost is identified, then that ratio can be applied to the known average lives lost per year to calculate an estimate of average injuries per year. Estimates were drawn from the data in six general ways— historical data, analogous data, modeled data, expert opinion, data on proportionality, and bounding estimates. 
Projections from historical data— estimates based on the record of what has actually happened in the United States for the disaster type. This may be a simple average or a more sophisticated estimate taking into account trends in the data. 
Projections from analogous data— estimates based on the record of what has actually happened for an event similar to the disaster type. There are two different kinds of analogues: o
events that reflect the same hazard but in a different yet similar time (such as a hurricane that occurred in the U.S. but in 1926 ) or place (such as an oil well explosion that occurred in the last 20 years but in the United Kingdom), and 58 o
Different yet similar events within the same time and place (such as a regional power outage as an analog for a cyber‐attack). 
Modeled estimates— estimates generated from models (from simple to intricate) informed by historical data but applying assumptions as to their relationship to each other. 
Expert opinion— estimates based on the perceptions of those with expertise in the subject matter. 
Proportional multipliers— estimates that apply a relationship between the attribute to be estimated and an attribute that is already known. 
Bounding estimates— not an estimate in themselves, but rather an acceptable limit for which the actual value is lower than (for upper bound) or higher than (for a lower bound). These categories are presented in an order of increased abstraction, not necessarily in order of preference. The preferred approach depends not only on how directly it describes the actual consequences but in how precisely and accurately it describes them. Data on past experiences, for example, may be less appropriate for describing an emerging threat or one that has not occurred recently enough to reflect contemporary conditions. Additionally, it may not be certain as to whether a threat is changing. For example, while the average lives lost to terrorist bombing may be best described by the historical information on terrorist bombings in the U.S., the experiences of other Western countries may be better at describing the high estimates of expected lives lost per year in the face of a potential changing threat. The sufficiency of a kind of data varies by hazard, by attribute, and by whether it is being used to inform the low, best, or high estimate. Determining the appropriate approach to describe a hazard requires several considerations. One consideration is novelty— is the historical threat reflective of the contemporary threat? The non‐
existent record of nuclear attacks does not reflect the possibility of the threat occurring today. 59 Another consideration is the frequency of the event— do the events occur frequently enough that the available data can be expected to be representative of the actual risk over that period? While tornadoes occur every year, earthquakes are less common, and high casualty events (such as the San Francisco earthquake of 1906 or the New Madrid earthquake of 1812) are unlikely to occur in any 30 year (or even 50 year) period. A related consideration is transience— the extent to which the risk is changing over time. For events where the threat, consequences, or vulnerability to the hazard are changing, older data will be less useful than more recent data for describing the risk. The greater the change, the less useful older data will be. For example, the economic damages of hurricanes have been increasing as greater amounts of wealth accumulate in proximity to the coast, and older estimates of economic damage are less useful for informing the present risk. However, there is little evidence that the size of hurricanes is changing, so historical estimates related to the size and power of a hurricane are still useful. When integrating these sources of information there are several ways in which an estimate could be chosen. In this study, if there was a sufficient amount of historical precedence, I calculated averages directly. However, in cases where the estimates were not considered similar enough to average (for example, expert assessments as to the likelihood of a terrorist nuclear detonation) I chose a best estimate based on the strength of the type of data and expert perceptions of the estimates within the literature. In addition to the best estimate, I included the alternatives in setting the range of low and high estimates for the attribute. The rationale for choosing a specific estimate for each attribute and each hazard are summarized in a technical appendix. Determining whether one kind of approach provides a better estimate than another involves an inherent subjective judgment. It is not appropriate to simply rely on the precision of the estimates, as it is not clear that more precision is always better. This is particularly true for terrorist events where the 60 likelihood of an event is not actually probabilistic but the result of the choices of an intelligent adversary. In these cases, the uncertainty is inherent to the hazard and reducing that uncertainty can lead to false precision. In some cases, the optimal data type is obvious, largely because estimates in other data types are not readily available. If a data source is clearly inferior then it will generally not be found in the literature. However, for some attributes and hazards there is no clearly superior approach— for example, estimates for the likelihood of a terrorist nuclear detonation comprise expert opinions and modeled estimates. While I preferred to create the low, best, and high estimates using the same datasets or methods, the low or high estimates could be created using sources or methods different from the best estimate when I thought that the standard approach did not capture the entire range of possible risk for that attribute. Table IV summarizes the sources of data and the general approach used for each hazard/attribute pair. Rare and/or novel events were more typically relied on combining estimates of likelihood and consequence, as direct data on the events were not sufficient to support an annualized estimate of consequence. Additionally, terrorist events (with the exception of terrorist explosive bombings, of which there is historical evidence) typically relied on expert opinion for estimates of likelihood. For more detail on the data and the specific approaches used, see Appendix B. 61 Toxic industrial chemical accidents
Oil spills
1,3 4,3 4,3
1
4,1
1
1
Best
3
1
1
1,3 4,3 4,3
1
4,1
1
1
High
1,3
1
1
1,3 4,3 4,3
2
4,2 6,2 1,2
Greatest number of deaths in a single episode
Low
3
1
1
3
3
2
1
1
1
1
High
3
1
1
3
3
3
2
2
2
2
Low
1,3
5
5
1,3 4,3 4,3 1,5
1
1,3
1
Average more severe injuries/illnesses per year
Best
3
5
5
1,3 4,3 4,3 1,5
1
2,3
1
High
1,3
5
5
1,3 4,3 4,3 2,5
6
2,3
1
Average less severe injuries/illnesses per year
Low
1,3
5
5
1,3 4,3 4,3 1,5
1
1,3
1
Best
3
5
5
1,3 4,3 4,3 1,5
1
2,3
1
High
1,3
5
5
1,3 4,3 4,3 2,5
6
2,3
1
Low
1
1
1
1,3 4,3 4,3
5
4,3
5
1
Average economic damages per year
Best
3
1
1
1,3 4,3 4,3
5
4,3 6,5
1
High
3
1
1
1,3 4,3 4,3
5
4,3 6,5
1
Low
1
1
1
3
3
3
1
3
1
Greatest economic damages in a single episode
1
High
3
1
1
3
3
4
1
2
5
1
Average individuals displaced per year
Low
1
1
1
1
4,3 4,3
5
1
5
1
High
1,3
1
1
1
4,3 4,3
5
1
5
1
1
2
Historical data
3
Modeled data
4
Expert opinion
5
Proportional multiplier
6
Bounding estimates
Terrorist explosive bombings
1
Terrorist nuclear detonations
1
Anthrax attacks
Tornados
1,3
Pandemic influenza
Hurricanes
Low
Earthquakes
Average number of deaths per year
Cyber‐attack on critical infrastructure
Table IV‐ Approaches used to estimate homeland security risks for selected hazards and attributes Analogous data
Estimates where likelihood and
consequence are determined separately
62 ExaminingtheQuantitativeApproachestoEstimateHomelandSecurityConsequencesby
Hazard
Earthquakes, hurricanes, and tornadoes reflect natural hazards that have the best possible data and models. A National Academies report on risk analysis in DHS describes DHS’s models of natural disasters as “near the state of the art,” and the use of their estimates in this study was justified based on that claim. (Committee to Review the DHS's Approach to Risk Analysis 2010) Injuries and illnesses are less well modeled because few estimates make the same distinctions between more and less severe injuries and illnesses. Still, there are some studies that examine these numbers so injuries or illnesses have reasonable estimates. Estimates for explosive bombings and oil spills are similar to those of most natural hazards in that there is a sufficiently large amount of data to apply statistical analyses to it. The estimates are not as good as natural hazards but still present good estimates of real events. One concern with terrorist explosive bombings is that underlying cause is not probabilistic but the result of terrorist intent, and the intensity of that intent can change. For that reason, I considered analogues where terrorist intent is higher to describe the upper bound, using the same datasets for the upper bounds but drawing the data from different countries during campaigns of sustained terrorist bombings. When doing this, it was important to examine the consistency of estimates from various sources and the appropriateness of the analogue. Pandemic influenza and nuclear attacks have relatively good models of consequence but depend on estimates of likelihood that are not as good. Estimates for the likelihood of a terrorist nuclear attack are quite poor, with expert estimates over the next decade ranging from nearly no chance to near certainty. It is important to document whether the best estimate represents reasonable assumptions and identify whether the range of possible values is covered. 63 Anthrax attacks and toxic industrial chemical accidents represent the scenarios that have the least data. While there are difficulties in estimating likelihood for both anthrax attacks and terrorist nuclear detonations, the consequences of an anthrax attack are also unclear, as it can represent a range of possible scenarios from mail dispersion to fixed point dispersion to mobile dispersion. Similarly, while there have been toxic industrial chemical accidents in the U.S., the effect of disproportionately large events such as Bhopal cannot be ascertained. For these categories, there are questions as to both the likelihood and the consequence of the events. It can be useful to examine the reasonableness of these estimates with both of these in mind. ExaminingtheQuantitativeApproachestoEstimateHomelandSecurityConsequencesby
Attribute
Estimates of lives lost had what I considered the best estimates in the literature. Life is a binary state, a person is either alive or is not, which makes it easy to count. It is not as conceptually clear whether an event ultimately caused a death (for example, if a post‐disaster power outage leads to the death of patient on a ventilator), but questions about causality are shared with other aspects of consequence. Deaths are also relatively well reported to authorities, making data relatively well available. Estimates of lives lost often have historical data or well‐modeled estimates, both for expected averages per year and for greatest numbers in a single event. Estimates of displaced individuals and greatest economic damages in a single event actually used historical data to a larger extent than did estimates of lives lost, but this does not necessarily mean that the literature provided better estimates. To some extent, the use of historical data for displaced individuals reflects hazards for which there is no mechanism for displacement, notably pandemic influenza and cyber‐attacks. The remaining estimates where displacement does occur are more likely to involve rougher estimates associated with reporting issues. For estimates of greatest economic 64 damage, historical records are not a straightforward tally as with lives lost but require certain assumptions as for how the estimate is made. For example, I would classify estimates of economic damage from the Oklahoma City bombing as historical estimates in that they reflect something that actually occurred and not just a theoretical modeled scenario, but those estimates of damage may involve certain assumptions to estimate the amount of physical damage or business interruption. Average economic damages share these concerns with regards to the consequences of an event, but also reflect the likelihood of an event occurring and the uncertainty in those estimates of likelihood. The quantified attributes with the weakest data are the estimates of more severe and less severe injuries and illnesses. Injuries and illnesses are not reported or recorded as consistently as lives lost, and historical data are less available than in any other attribute. Moreover, the distinction that I use between more and less severe is rarely reported. Even in cases where counts of injuries or illnesses are available, a multiplier needs to be adopted to calculate the share that are less severe and that are more severe. These multipliers often come from a small number of cases and reflect additional assumptions in the data. DescribingAttributesofRiskinQualitativeTerms
In addition to the quantitative attributes, another set of attributes describe the risk in qualitative terms. These qualitative attributes include both consequences and non‐consequence aspects of the risk about which people are concerned. Two approaches were used to describe qualitative attributes. Some attributes were estimated based on descriptions of the hazard while others were derived from a combination of known quantitative estimates. 65 QualitativeEstimatesDerivedfromDescriptionsoftheHazard
A first set of attributes reflects aspects of consequence: duration of economic damages, size of area affected by economic damage, average environmental damage per year, and disruption of government operations. These attributes are described in qualitative terms for various reasons. Environmental damage and disruption of government operations are described in qualitative terms largely for conceptual reasons, as the attributes do not readily lend themselves to measureable quantities. Duration of and size of area affected by economic damage can be measured in quantitative ways, in seconds, miles, etc., but they are not typically measured in this fashion. A second set of attributes reflects aspects of the risk that do not involve consequence: whether an event is natural or human‐induced, the ability of an individual to control their exposure, the quality of scientific understanding, and the time between exposure and health effects. The estimates of these qualitative aspects of risk involved significant judgment in the interpretation of the general literature using the standardized definitions documented in the appendix on “Notes on the Risk Calculations.” The categories to distinguish between one level of consequence and another are derived from the data. Duration of economic damage, size of area affected by economic damage, natural/human‐induced, and time between exposure and health effects were be described in natural categories such as days, weeks, months, etc. The categories for the other attributes did not present natural categories but are described in levels of low, moderate, or high. The attribute describing disruption of economic damage was based on the severity of the disruption, with consideration of both the severity, including the scale of the disruption and whether the disruption affected emergency services or only non‐emergency services, and the duration of the disruption. Average environmental damage per year and disruption of government operations did not contain natural categories and were instead described in terms of low, moderate, or high risk relative to the 66 other hazards. Environmental risks were based on descriptions of aesthetic damage and damage to species combined with the likelihood that that damage will be realized. The levels to describe the ability of an individual to control their exposure were based on the kinds of actions that could be taken to control exposure: hazards associated with advance warning were considered high; hazards with little warning but where specific actions can reduce exposure or mitigate damage were considered moderate; and hazards where only significant lifestyle changes such as moving to another region or avoiding cities were considered low. The quality of scientific understanding describes how well science knows how a hazard harms people, not the understanding to the exposure to that harm. For most of the hazards, the connection between the hazard and a particular harm was well understood, as they reflected relatively short, kinetic events. Other events where the harms are known but where it is unknown how these harms will manifest in an event (such as contaminating chemicals or disrupted infrastructure) were considered moderate, while events where neither the mechanisms for harm nor how the harms will manifest in an event are known were considered low understanding. QualitativeEstimatesDerivedfromQuantitativeEstimates
Two final attributes reflected important aspects of the risk but could not be estimated from the literature directly but had to be estimated from other attributes. The first of these, psychological consequences, is an important consequence that is often overlooked. The second of these, combined uncertainty in lives lost, injuries, and economic damages, is a meta‐attribute, describing what is known about other estimated attributes. Each of these was described qualitatively in terms of low, moderate, or high risk compared to the other homeland security hazards. Psychological consequences of homeland security hazards are not easily quantifiable. While PTSD and depression are two of the most severe and most studied aspects of post‐event psychological harm, they are only two of a large range of syndromes which can be caused by disasters. Additionally, 67 estimating counts of psychological harm from homeland security events is challenging, even for these clearly identified syndromes. There are a large number of studies that estimate the number of cases of PTSD or depression following specific disasters (Norris et al. 2002; Norris et al. 2002), but differences in how the prevalence and exposed populations were examined limits the ability to make a comparable annual estimate. One challenge is in identifying the prevalence among those exposed; counts of PTSD and depression can depend on the tool used to evaluate patients in a mass fashion, and often the background rates of these disorders in the general population are not taken into account. A second challenge is identifying the number of people who are exposed, as it is not only those who were directly exposed to an event that can be harmed but also those with friends or family who were exposed or who viewed the event on television or experienced other mediated exposure. Thus, instead of quantifying psychological harm associated with each risk, in this study I based the description on known stressors associated with the syndromes. The stressors that were selected to describe the psychological consequences were drawn from the literature on PTSD and depression, both with regards to natural disasters and terrorist events and to the syndromes generally. Stressors that reflected objective external harms at the hazard‐level (e.g. seeing deaths) were selected while subjective, internal, personal stressors (e.g. feeling trapped) were not. This process involved significant judgment and the specific considerations in the development of this framework are documented in Appendix A‐Comparing Mental Health Consequences of . For PTSD, two relevant stressors are being at risk of death/injury and seeing a death/injury. First, I developed logical thresholds of low, moderate, and high levels of death and of injury based on estimates of those attributes in the hazard set. Then I combined those qualitative levels for the stressors in a risk matrix to get overall levels of PTSD (Figure 4). These overall levels of PTSD were described qualitatively in levels of low (represented by green), moderate (yellow), and high (red). For 68 depression, stressors include bereavement of friends or loved ones and personal hardship including loss of job or physical impairment. Using estimates of lives lost and injuries as well as average economic damage and duration of economic damage, I similarly developed an estimate for average depression per year at levels of low, moderate, and high (Figure 5). For example, the psychological level associated with lives lost for PTSD was low if there were less than 10 lives lost per year on average, high if there were more than 100 lives lost per year on average, and moderate if there were between 10 and 100 lives lost per year on average. I then combined these levels of psychological consequence for PTSD and depression to obtain an estimate of overall psychological damage. Expected deaths per year on
average
Figure 4‐ Estimates of Average Levels of PTSD per Year for Selected Homeland Security Hazards pandemic,
nuclear,
earthquake
high
terrorist nuclear
detonation,
earthquake
pandemic flu
tornadoes,
hurricanes
moderate
tornado, anthrax
attacks
hurricanes
oil spills, TIC,
cyber, explosives
low
cyber-attacks,
explosives, oil
spills
toxic industrial
chemical
accidents
low
moderate
terrorist
anthrax attacks,
explosive
terrorist nuclear
bombings,
detonation,
cyber-attacks,
earthquakes,
oil spills
tornadoes, TIC
green=low
yellow=moderate
red=high
high
pandemic flu,
hurricanes
Expected more severe injuries or illnesses per year
on average
69 Combined Losses Per Year On
Average
Figure 5‐ Estimates of Average Levels of Depression per Year for Selected Homeland Security Hazards earthquake,
pandemic flu,
hurricanes
high
anthrax, nuclear
detonation,
tornadoes
moderate
cyber-attacks,
explosives, oil
spills, TIC
low
pandemic flu,
hurricanes
earthquakes
tornadoes,
terrorist nuclear
anthrax attacks
detonation
cyber-attacks,
terrorist
explosive
bombing
toxic industrial
chemical
accidents
oil spills
low
moderate
high
Cyber,
explosives
TIC, pandemic
flu, anthrax
attacks,
hurricanes,
tornadoes
oil spills,
terrorist nuclear
detonation,
earthquakes
green=low
yellow=moderate
red=high
Duration of Economic Damages
A similar approach was used to describe combined uncertainty. First, the ratio of the high estimate and the low estimate was calculated for each of the attributes of average lives lost, more severe injuries, less severe injuries, and economic damage per year. These ratios were combined additively to generate a quantitative estimate of uncertainty. Then these quantitative estimates were placed in bins of low, moderate and high, with the range of moderate estimates ranging across one order of magnitude and using easily comprehensible round numbers as the lower and upper bounds (Figure 6). This approach is consistent both with the approach used for psychological consequences and previous studies using the Deliberative Method for Ranking Risk. 70 Figure 6‐ Combined Uncertainty, Calculated Quantities on a Log Scale with Identified Cut Points Combined Uncertainty
Low Moderate High 100,000.00
10,000.00
1,000.00
100.00
10.00
1.00
WhatTheseDataTellUsaboutHomelandSecurityRisks
This method created a set of consequences for homeland security risks that describe hazards in a multiattribute fashion. This risk assessment can be useful in and of itself as a description of the risks but can also tell us things about risks in the homeland security domain generally. This section identifies some implications regarding homeland security risks that are identified using the comparative dataset described in this section.
71 Table V‐ Best Estimates and Range of Estimates for Each Attribute of Risk by Hazard (Rounded to One Significant Digit) Risk Risk Characteristic Public Health and Safety Pandemic Influenza Earthquakes Hurricanes Tornadoes Terrorist Nuclear Detonation
Anthrax Release Terrorist Explosive Bombings Toxic Industrial Chemical Accidents Cyber‐
attacks Oil Spills Average number of deaths per year 100 (2‐300) 40 (10‐60) 40 (10‐100) 4,000 (2,000‐
10,000) 20 (2‐700) 200 (20‐50,000)
10 (1‐40) 0 (0‐1) 8 (5‐200) 1 (0‐4) Greatest number of deaths in a single episode 5,000‐
20,000 2,000‐4,000
300‐700 300,000‐
2,000,000 3,000‐
20,000 100,000‐
800,000 200‐2,000 0‐10 3,000‐
20,000 200 Average more severe injuries/illnesses per year 70 (10‐900) 600 (200‐1,000)
200 (200‐600) 20,000 (9,000‐
50,000) 60 (6‐2,000) 200 (20‐50,000)
30 (1‐70) 0 (0‐5) 50 (30‐200) 5 (3‐8) 2,000,000 300 100 (1M‐7M) (30‐10,000) (10‐40,000)
60 (1‐100) 0 (0‐5) 500 (300‐5,000)
60 (30‐90) Low Low Low Moderate Average less severe 3,000 1,000 700 injuries/illnesses per year (500‐9,000) (400‐2,000) (600‐2,000)
Psychological damage per year on average High High Moderate High Moderate 72 High Anthrax Release Terrorist Nuclear Detonation
Terrorist Explosive Bombings Cyber‐
attacks Toxic Industrial Chemical Accidents Oil Spills $4B ($2B‐$10B)
$7M ($800k‐
$100B) $3B ($300M‐
$900B) $100M ($10M‐
$400M) $50M ($10M‐$1B)
$300M ($200M‐
$7B) $1B ($1B‐$4B) $60B‐$200B $900M‐ $3B $70B‐$200B
$300M‐ $100B $1T‐$10T $1B‐$40B $100M‐
$10B $2B‐$700B
$4B‐$40B Weeks to months Days to weeks Days to years Months to decades Societal and Economic Pandemic Earthquakes Hurricanes Tornadoes Influenza Damage $10B ($60B‐
$200B) $1B ($900M‐
$2B) Average economic damages per year $5B ($1B‐$9B) Greatest economic damages in a single event $60B‐$1T Duration of economic damages Months to decades Months to years Weeks to years Months to years Months Years Size of area affected by economic damages County to state Counties to states Blocks to counties Nation/ world Neighborho
od to city Nation/ world Average environmental damages per year Moderate High Low Low Low Moderate Low Low Moderate to high High Average individuals displaced per year 700‐20,000 10,000‐
100,000 30,000‐
200,000 0 20‐6,000 100‐
300,000 3‐100 0 5,000‐
200,000 5 Disruption of government operations Moderate Moderate to high Moderate Moderate to high Moderate High Low to moderate Moderate to high Low Low 73 Less than a Company to Blocks to block to city
nation counties Counties to states Other Characteristics Earthquakes Hurricanes Tornadoes Pandemic Influenza Anthrax Release Terrorist Nuclear Detonation
Terrorist Explosive Bombings Cyber‐
attacks Toxic Industrial Chemical Accidents Oil Spills Natural/human‐
induced Natural Natural Natural Natural Human‐
induced Human‐
induced Human‐
induced Human‐
induced Human‐
induced Human‐
induced Ability of individual to control their exposure Low to moderate High Moderate Low Low Low Low Low to moderate Low to moderate Moderate Time between exposure and health effects Immediate Immediate Immediate
up to years
Days Days to weeks Immediate
Immediate Immediate to decades
to years Quality of scientific understanding High Moderate to high High High High High High Low to moderate Low to moderate Low Combined uncertainty Moderate Low Low Low High High Moderate Moderate Low to moderate Low 74 Immediate Immediate
to decades
ARangeofAttributesisNeededtoDescribetheDiversityofHomelandSecurityRisks
One important aspect of the risk is the extent to which multiple attributes are needed to describe the hazards. While multiple dimensions of consequences are considered important in the literature, in theory if the variation in those risks can be captured using only a small number of factors, then those multiple dimensions add little to the rankings. However, in practice, the different attributes estimated in this dataset do vary in interesting and non‐reducible ways. One example is that of quality of scientific understanding and combined uncertainty. These two attributes both describe the uncertainty of the hazard, with regards to what is known about the mechanisms of the risk in the case of the former and with regards to the precision of the consequences of the risk in the case of the latter. We may be concerned that these two attributes are simply double counting the same phenomenon in slightly different guises. However, as an empirical matter, this is not the case. As Figure 7 illustrates, the two attributes do represent different things. There are hazards (e.g. oil spills, toxic industrial chemical accidents) where there is little scientific understanding as to the mechanisms of the risk but the combined uncertainty of the consequences is known with precision. Conversely, there are hazards (e.g. terrorist nuclear detonations, anthrax attacks) where the mechanisms by which the hazard harms people are well known but where the uncertainty in the likelihood of the event leads to a lack of precision in the estimates of the consequences. These two aspects of uncertainty are describing different things. 75 Figure 7‐ A Comparison of Quality of Scientific Understanding and Combined Uncertainty Quality of Scientific Understanding
high
high
Terrorist Nuclear
Detonations,
Anthrax Attacks
moderate
moderate
Cyber-attacks
Earthquakes,
Terrorist Explosive
Bombings
low
Combined Uncertainty
low
Toxic Industrial
Chemical
Accidents
Oil spills
Hurricanes
Pandemic Influenza,
Tornadoes
The variation in disasters can also be seen when looking at the range of attributes holistically. Figure 8 shows a representation of the risk associated with each hazard along all of the estimated dimensions using radar graphs. Each spoke in the graph represents the best estimate for one of the attributes of that hazard. The value on that axis has been estimated on a log scale normalized relative to the largest and smallest value for that attribute. For example, the estimated value of average expected lives lost per year is represented as 1 for pandemic influenza (as it is the hazard with the largest average lives lost per year) and 0 for cyber‐attacks (as it is the hazard with the smallest average lives lost per year), with all intermediate values placed in‐between. The attributes on the graphs are arranged with the health consequences in the upper right quadrant, the economic effects in the lower right quadrant, the societal/environmental effects in the lower left quadrant, and the non‐consequence attributes in the upper left quadrant. 76 Figure 8‐ Normalized Attributes of Selected Homeland Security Hazards Earthquakes
Tornadoes
mental health
combined uncertainty
1
mental health
less severe injuries
0.8
scientific…
combined uncertainty
more severe injuries
0.4
lives average
lives greatest
environmental
controlability
economic greatest
government disruption
environmental
economic greatest
economic average
displaced individuals
duration economic
area economic
Pandemic Influenza
mental health
mental health
less severe injuries
0.8
scientific…
combined uncertainty
more severe injuries
lives average
lives greatest
environmental
controlability
economic greatest
government disruption
Terrorist Explosive Bombings
mental health
combined uncertainty 0.5
less severe injuries
less severe injuries
more severe injuries
0.4
delay of health effects
0.4
scientific…
lives average
delay of health effects
lives greatest
0
environmental
controlability
economic greatest
government disruption
economic greatest
economic average
displaced individuals
Cyber‐attacks
mental health
mental health
scientific…
delay of health effects
controlability
1
0.8
0.6
0.4
0.2
0
environmental
less severe injuries
more severe injuries
0.6
controlability
lives greatest
0.4
0
environmental
Oil Spills
delay of health effects
controlability
0.6
0.4
0.2
0
environmental
combined uncertainty
less severe injuries
scientific…
more severe injuries
delay of health effects
lives average
controlability
lives greatest
0.4
0
environmental
more severe injuries
lives average
economic average
displaced individuals
77 lives greatest
economic greatest
government disruption
duration economic
area economic
less severe injuries
0.2
economic average
displaced individuals
1
0.8
0.6
economic greatest
government disruption
lives greatest
area economic
mental health
1
0.8
lives average
duration economic
mental health
scientific…
more severe injuries
economic average
displaced individuals
Toxic Industrial Chemical Accidents
combined uncertainty
less severe injuries
economic greatest
government disruption
duration economic
area economic
0.8
scientific…
0.2
economic average
displaced individuals
combined uncertainty
delay of health effects
lives average
economic greatest
government disruption
duration economic
area economic
Terrorist Nuclear Detonations
combined uncertainty
lives greatest
0
government disruption
duration economic
area economic
lives average
environmental
economic average
displaced individuals
more severe injuries
0.3
0.2
0.1
0.2
controlability
duration economic
area economic
mental health
0.6
economic average
displaced individuals
Anthrax Attacks
scientific…
economic greatest
government disruption
economic average
area economic
lives greatest
0
environmental
duration economic
0.8
lives average
0.2
0
combined uncertainty
more severe injuries
0.4
delay of health effects
0.2
displaced individuals
less severe injuries
0.6
0.4
controlability
1
0.8
scientific…
0.6
delay of health effects
duration economic
area economic
Hurricanes
combined uncertainty
lives greatest
0
government disruption
economic average
1
lives average
0.2
0
displaced individuals
more severe injuries
0.4
delay of health effects
0.2
controlability
less severe injuries
0.6
0.6
delay of health effects
1
0.8
scientific…
duration economic
area economic
The risks vary and not only in size— larger disasters were not all larger in the same way. It was possible to distinguish the large health consequences associated with pandemic flu (upper right quadrant) from the social effects of hurricanes (lower left quadrant) and the economic effects of oil spills (lower right quadrant). Even smaller events present different patterns, with high values on one attribute or another in various ways. The risk associated with each hazard presents in a different way, suggesting that a range of attributes are needed to fully describe the diversity of the risks. The extent to which the estimates in this dataset are describing the same thing can be analyzed quantitatively. For these data, a pairwise correlation of the best estimates for these risk attributes finds only limited statistically significant correlation in the attributes (Table VI). There are two areas where the correlation between attributes is statistically significant. First, health‐related consequences are highly correlated, suggesting that for homeland security disasters loss of life and injuries are related. Second, the correlation between the greatest number killed in a single event and the greatest economic damages in a single event is also statistically significant, identifying some distinction between relatively high consequence rare events and relatively low consequence but more common events. However, most of the remaining attributes are not statistically correlated with each other. This suggests that while large disasters are worse than small disasters generally, they are not necessarily worse in the same ways and that the different attributes do reflect different things. The empirical review of the data provides some evidence that multiple attributes are appropriate to describe homeland security risks. The multiple attributes selected represent different things, and the diversity of homeland security risks cannot be captured using only attributes of lives lost and economic damage. 78 Average lives lost per year 1.0000 Greatest number killed in a single event 0.8978* 1.0000 Average more severe injuries per year 0.9392* 0.8204* 1.0000 Average less severe injuries per year 0.9050* 0.7817* 0.9386* 1.0000
Average mental health damage per year 0.7390* 0.619 Average economic damages per year 0.5342 0.3857 0.5365 0.5149 0.7384* 1.0000 combined uncertainty quality of scientific understanding delay between exposure and health effects ability to control exposure government disruption displaced households environmental damages size of area affected duration of economic damages greatest economic damages average economic damages mental health less severe injuries severe injuries greatest number killed lives lost (best) Table VI‐ Pairwise Correlations between Best Estimates of Attributes of Risk 0.666* 0.6308 1.0000 Greatest economic damages in an event 0.4843 0.7502* 0.3443 0.3135 0.4913 0.3736 1.0000
Duration of economic damages 0.2774 0.4008 0.1882 0.2657 0.5942 0.6979* 0.7014 1.0000 Size of area affected by economic damage 0.3992 0.4175 0.2787 0.3062 0.544 0.5580 0.3622 0.4663 1.0000 Environmental damages per year ‐0.122 Displaced households per year 0.2631 0.2855 0.2959 0.1083 0.2448 0.2303 0.3904 0.1511 ‐0.336 0.3129 1.0000
‐6E‐04 0.0095 0.0128 0.2588 0.5332 0.3930 0.7025* 0.0397 1.0000 Government disruption 0.4050 0.3294 0.2572 0.0929 0.5710 0.2643 0.2436 0.0000 0.6124 ‐0.291 0.0656 1.0000
Ability to control exposure 0.2145 ‐0.406 ‐0.002 ‐0.066 0.1874 0.4592 ‐0.283 0.1085 ‐0.106 0.6291 0.2676 ‐0.065 1.0000
Delay between exposure and health effects 0.0292 0.2805 0.1057 0.0330 0.1539 0.2049 0.5336 0.5458 0.1126 0.7498* 0.3094 ‐0.184 0.2000 1.0000
Quality of scientific understanding ‐0.7443* ‐0.571 ‐0.625 ‐0.497 ‐0.485 ‐0.1250 ‐0.199 0.1671 0.0000 0.5074 ‐0.336 ‐0.522 0.3194 0.4302 1.0000
Combined uncertainty 0.0555 0.3258 ‐0.165 ‐0.283 0.0322 ‐0.335 0.5870 0.0849 0.0066 ‐0.224 0.2626 0.3823 ‐0.625 0.1253 ‐0.341 1.0000
* significant at the 95% confidence level 79 TheEstimatesProvideSomeInformationtoDistinguishbetweenRisks,butOnlySome
Another important issue with regards to how the estimates can be used involves the uncertainty in the data. The estimates provided by this method have varying degrees of precision. While some of the uncertainty relates to how well future events can be predicted from past events, some of the uncertainty relates to how well the likelihood of the non‐probabilistic choices of human actions can be known. The estimates produced by this method are possible to distinguish on a gross scale but not in detail. Figure 9 presents an example of the estimates related to deaths (both in terms of average deaths per year and greatest disasters per event) from disasters on a log scale. It is possible to distinguish between the hazard with the lowest average lives lost and highest average lives lost (for example), but most hazards are in the middle where the overlap between the lower and upper bounds makes it difficult to differentiate between the hazards. Estimates for other attributes follow a similar pattern, with the estimates providing information on a gross scale but without the greater precision to distinguish between hazards based on a single quantitative estimate of consequence. The uncertainty in these estimates shows the limitations of the data for quantitative purposes. The upper and lower bounds from one hazard often overlap with those of other hazards— for example, the interval of the low to high estimated average lives lost per year for terrorist nuclear detonations overlaps with the intervals for 7 of the other 9 hazards. If a ranking was done solely using this estimate in a quantitative fashion, the outcome would be sensitive to the actual values selected, and these uncertainties would be compounded as more attributes are brought into the ranking. 80 Figure 9‐ A Comparison of Average Annual Lives Lost and Greatest Lives Lost in a Single Event 10000000
1000000
100000
Deaths
10000
1000
100
10
1
Average lives lost per year
Greatest lives lost in a single event
Figure 10 provides a similar comparison of estimates of economic damage across the hazards, both in terms of average economic damage per year and greatest economic damages in a single event. As with lives lost, it is possible to distinguish between hazards associated with little damage (such as terrorist explosive bombings and anthrax attacks) and those associated with significant damage (such as hurricanes and earthquakes). However, there are also many hazards where the bounds on economic damage overlap and distinguishing between the risks based on a quantitative estimate of the economic damages can be challenging. 81 Figure 10‐ A Comparison of Average Economic Damage and Greatest Economic Damage in a Single Event Economic Damages ($)
$100,000,000,000,000.00
$1,000,000,000,000.00
$10,000,000,000.00
$100,000,000.00
$1,000,000.00
Average Economic Damages Per Year
Greatest Economic Damages from a Single Event
This inability to distinguish between the risks based solely on the quantitative estimates supports the finding of the National Academies that quantitative rankings of homeland security risks have to explicitly address the uncertainty in the values or they will be left with misleading results (Committee to Review the DHS's Approach to Risk Analysis 2010). However, this is not as critical a concern for the Deliberative Method for Ranking Risk, which incorporates the uncertainty directly by informing individuals as to the best value and the range of values and allows individuals to place them into context. The Deliberative Method for Ranking Risks does specifically address uncertainties in the estimates, avoiding the limitations of using a quantitative approach to compare the risks. The precision of the estimates in this dataset varies by hazard and attribute. The average difference between the low estimate and the high estimate for any given attribute is between 0.9 and 1.6 orders of magnitude depending on the attribute (Table VII). To avoid false precision, I matched the precision of the estimates and presented the estimates at only one significant digit. There are some 82 exceptions—terrorist nuclear detonations have the greatest uncertainty in expected lives lost, where the difference between the low estimate and the high estimate is over three orders of magnitude— but events with more than one and a half orders of magnitude between the low and high estimate are rare. The precision of consequence estimates identified here is consistent with estimates applied successfully in comparative risk assessments using the Deliberative Method for Ranking Risk in other domains (Willis et al. 2010). Oil spills
Toxic industrial chemical accidents
Cyber‐attack on critical infrastructure
Terrorist explosive bombings
Terrorist nuclear detonations
Anthrax attacks
Pandemic influenza
Tornados
Hurricanes
Earthquakes
Table VII‐ Precision of Estimates by Hazard and Consequence as Measured by Orders of Magnitude between Lower Bound and Upper Bound Public Health and Safety
Average number of deaths per year
0.9 0.6 2.1 0.8 2.6 3.4 1.6 1.0 1.6 1.6
Greatest number of deaths in a single episode
0.4 0.3 0.6 0.8 0.8 0.9 1.1 2.0 0.8 n/a
Average more severe injuries/illnesses per year
0.4 0.6 2.0 0.8 2.6 3.4 1.8 1.7 0.7 0.5
Average less severe injuries/illnesses per year
0.4 0.6 1.2 0.8 2.6 3.4 2.1 1.7 1.2 0.5
Other Damage
Average economic damages per year
0.3 1.0 0.9 0.8 2.6 3.4 1.6 2.0 1.6 0.6
Greatest economic damages in a single episode
0.5 0.4 1.2 0.5 2.5 1.0 1.6 2.0 2.8
Average individuals displaced per year
0.9 1.0 1.5 0.3 2.6 3.4 1.6 0.3 1.6 0.5
1
Less than 0.5
mean
1.5
2
0.5 to 0.99
median
1.2
3
1.0 to 1.49
4
1.5 to 1.99
5
2.0 to 2.49
83 1
Improving the precision of the estimates may be possible but is not without tradeoffs. If the bounds of uncertainty were to be narrowed for some of the hazards, it could be possible to better distinguish between different hazards. But the benefits of narrowing the bounds of uncertainty on likelihood estimates should be balanced with the benefits of accepting the greater uncertainty. While expressing likelihood as a probability can be useful in some circumstances, it can be misleading in others. Whether or not a terrorist event occurs is not in actuality a probabilistic event. While aspects of the success of the attack may be probabilistic, the choice of whether to attack is not. Deriving expected values from a non‐probabilistic event can be misleading. This is particularly true for several of the low likelihood high consequence events examined here in that there will never be a year with consequences like the average consequences. Instead, any given year will either have no damages or catastrophic damages, but never average damages. In addition, estimating the consequences of a chemical, biological, or nuclear event is difficult, particularly the secondary economic, societal, and political consequences that result from reacting to an attack. Keeping the original bounds prevents overstating how well this difficult to predict risk can be described. Conclusions
This chapter presents estimates of the risks of a set of homeland security hazards in a transparent and comparable fashion. It is possible to describe this diverse set of homeland security risks using open source data. While the set of hazards involved in homeland security planning involves a large number of rare or completely novel events, there is a sufficient literature to describe risks across a range of attributes. Estimating the risks requires many types of data and analytic methods, and choosing which data and methods to use can involve significant judgment. Different attributes need to be described with a range of approaches, and the quality of estimates for any given estimate varies by the hazard and attribute it is describing. This dissertation summarizes one instance of estimating the risk, describing the specific choices that went into those estimates. These estimates can be useful not 84 only to support the qualitative risk ranking performed in this dissertation, but also for additional risk analyses across the homeland security domain. The findings here support the use of a range of attributes, not just lives lost and economic damage, to describe homeland security hazards in the homeland security domain. First, different attributes do appear to describe different things. While the estimates selected here do show some correlation between lives lost and injuries, other dimensions of consequence are largely independent of each other. Second, taken collectively, the risks also vary. It is more than just a difference in the scale of the event; different hazards do manifest in different ways. This finding mirrors the National Academies recommendation that DHS should consider a full range of consequence measures, including “public health, social, psychological, economic, political, and strategic outcomes” when assessing risk (Committee to Review the DHS's Approach to Risk Analysis 2010). It is also important to recognize that uncertainty matters. The National Academies report on risk analyses within DHS finds the ability of quantitative analyses to deal with uncertainties to be seriously deficient and suggests that qualitative approaches may at times be preferable (Committee to Review the DHS's Approach to Risk Analysis 2010). The estimates created in this study reflect the same concerns. There is substantial variation between the low, best, and high estimates for each approach. This variation can limit the ability of quantitative analyses to differentiate between hazards, especially when the uncertainties are compounded with each other. While some of this uncertainty may be reduced with better intelligence or more research, some of the uncertainty is inherent to the hazards. Approaches that explicitly address uncertainty, either using quantitative or qualitative methodologies, are required. One such approach is the Deliberative Method for Ranking Risk, and there is evidence that the method of generating data from open sources is appropriate to support a deliberative ranking of risks in 85 the homeland security domain. The National Academies report recommends qualitative comparisons of risks when conducting all‐hazards risk assessments at DHS, as established methodologies (such as the Deliberative Method for Ranking Risk) can support participants in the comparison of risks that are very different in kind and consequence (Committee to Review the DHS's Approach to Risk Analysis 2010). The open‐source risk analysis described in this section supports this recommendation generally and the use of the Deliberative Method for Ranking Risk in specific. The data created using this approach have a degree of precision similar to that of risks in other domains where the Deliberative Method for Risk Ranking has been successfully used in the past, supporting the use of the estimates to inform a deliberative risk ranking in the homeland security domain. References
Brisson, M. and W. Edmunds (2006). "Impact of model, methodological, and parameter uncertainty in the economic analysis of vaccination programs." Medical decision making 26(5): 434‐446. Committee on Methodological Improvements to the Department of Homeland Security's Biological Agent Risk Analysis (2008). Department of Homeland Security Bioterrorism Risk Assesment: A Call for Change. N. R. Council. Washington, DC, National Academies Press. Committee to Review the DHS's Approach to Risk Analysis (2010). Review of the Department of Homeland Security's Approach to Risk Analysis. National Research Council of the National Acadamies. Washington, D.C., National Academies Press: 148. Florig, H. K., M. G. Morgan, K. M. Morgan, K. E. Jenni, B. Fischhoff, P. S. Fischbeck and M. L. DeKay (2001). "A deliberative method for ranking risks (I): Overview and test bed development." Risk Analysis 21(5): 913‐913. 86 HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs. Norris, F. H., M. J. Friedman and P. J. Watson (2002). "60,000 disaster victims speak: Part II. Summary and implications of the disaster mental health research." Psychiatry: Interpersonal & Biological Processes 65(3): 240‐260. Norris, F. H., M. J. Friedman, P. J. Watson, C. M. Byrne, E. Diaz and K. Kaniasty (2002). "60,000 disaster victims speak: Part I. An empirical review of the empirical literature, 1981–2001." Psychiatry: Interpersonal & Biological Processes 65(3): 207‐239. RAND (2012). RAND Database of Worldwide Terrorist Incidents. RAND Corporation. Santa Monica, CA. Willis, H. H., J. MacDonald Gibson, R. A. Shih, S. Geschwind, S. Olmstead, J. Hu, A. E. Curtright, G. Cecchine and M. Moore (2010). "Prioritizing Environmental Health Risks in the UAE." Risk Analysis 30(12): 1842‐1856. 87 Chapter5. IdentifyingHomelandSecurityConcernsfromRiskRanking
Sessions
The final steps of the Deliberative Method for Ranking Risk are to hold risk ranking sessions and analyze the resulting data. The risk ranking sessions build on the information developed in the previous sessions, including the four‐page risk summary sheets. The process and analysis of the results that I used followed a format consistent with other studies using the Deliberative Method for Ranking Risks. The pages that follow describe the risk ranking process and participants, and then describe the analysis of the data that came from those sessions. These analyses provide insights into the hazards and the attributes that people are concerned about, and into the suitability of the Deliberative Method for Ranking Risks in the homeland security domain. TheRiskRankingWorkshops
Risk ranking workshops following the Deliberative Method for Ranking Risk follow multiple stages to create informed rankings of risk. The multiple stages encourage deliberative consideration of the risks based on their actual attributes by engaging with the attributes of risk in multiple ways over a 5 to 6 hour period. I used a modified approach similar to Willis et al. (2010) (see Figure 11). Four stages were included in this ranking: the initial individual ranking; an examination of a multi‐attribute ranking reflecting the participants’ individual concerns about specific attributes; a group discussion; and a final individual ranking. Each stage will be discussed in sequence. The initial stage of the ranking session in the Deliberative Method for Ranking Risk is to rank the hazards based purely on the information in the risk summary sheets. In this study, participants were afforded an opportunity to familiarize themselves with the risks before undertaking any rankings. Participants were given the set of four page risk summary sheets, as well as a technical document called “Notes on the Risk Calculations” that defined all of the attributes used in the summary sheets (see 88 appendix Notes on the Risk Calculations). Furthermore, the definitions for each of the attributes were reiterated and explained at the beginning of each ranking session through a scripted introduction. Both the “Notes on the Risk Calculations” and the script were used to make sure that all participants had a consistent understanding of what was and what was not included in the attributes as I defined them. Based on the information in the risk summary sheets and their own personal judgment and experiences, participants were asked to provide an initial ranking of the hazards. After this initial ranking, participants were asked to perform a second ranking based on the attributes of the risks. First, participants were asked to rank the attributes of the risks in their order of concern. Using the participants’ level of concern for each attribute and the estimated value of those attributes, a quantitative measure of concern was calculated for each hazard using swing weights. The hazards were ranked based on these calculated levels of concern. While creating quantitative measures of concern in this fashion can be problematic for the purpose of developing a ranking of risks— the uncertainty in the estimates and the difficulty of specifying a functional form to combine the estimates makes the utility of quantitative rankings questionable— the purpose of these multiattribute rankings is not to rank the risks themselves but focus participants concerns on the attributes of the risks. Participants were encouraged to consider the hazards for which their initial ranking and the multi‐
attribute ranking gave markedly dissimilar results and to consider the reasons why those specific differences occurred. Having considered the differences between the initial ranking and the multi‐
attribute ranking, as well as the reasons for those differences, participants were asked to perform a second revised ranking. Participants were next asked to rank the risks as a group. Participants were led through a structured discussion on the hazards and the reasons for their concerns. Participants were asked to explain their concerns for each of the risks, and alternative points of view were encouraged. Discussing 89 the risks gave each participant the opportunity to learn from others. Through this discussion, participants developed a ranking of the risks as a group. After the group session, participants were given a final opportunity to rank the risks based on all they learned throughout the process. While the group rankings allow the participants to learn from each other, the participants will not necessarily agree with the group ranking. The final rankings gave the participants an opportunity to dissent from the group ranking and present their own point of view, combining their personal judgment with all they have learned in the group and calculated rankings. Figure 11‐ Overview of the Process Used During Risk‐Ranking Workshops (Adapted from Willis et al. 2010) ParticipantsintheRiskRankingSessions
Twenty‐six individuals participated in three risk ranking workshops conducted in the fall of 2012. The first workshop was conducted in Pittsburgh, Pennsylvania, and the subsequent two workshops were conducted in Santa Monica, California. Most of the participants were recruited through online ads, although some were solicited through school parent/teacher organizations. All participants were members of the general public and were not selected because of any special expertise in homeland security specifically or risk generally. This participant selection process provided a convenience sample, 90 limiting the ability to extend the results of these ranking sessions to the concerns of U.S. residents generally. While the risk ranking groups were not necessarily representative of their nation or city as a whole, they were selected to cover an interesting range of characteristics. Participants were selected to create a group that was diverse in terms of gender, education, age, and race. Table VIII shows the demographics of the participants, while Figure 12 shows how the participants cover a range of experiences. No individuals over 40 with only a high school education applied, so none were selected. Similarly no younger individuals (in their 20s) with a post‐graduate degree applied, so none were selected. All participants were U.S. citizens and could speak and write English. Table VIII‐ Summary Statistics of Workshop Participants Number Participant Characteristics (total=26) Percent Race White 14 54% Black 7 27% Other 5 19% Age 20s 4 15% 30s 6 23% 40s 6 23% 50s 7 27% 60s or more 3 12% Education* High school diploma 4 15% Some college 5 19% Bachelors 10 38% Post‐graduate degree 7 27% Gender Male 14 54% Female 12 46% * percent of education groups does not add to 100% due to rounding 91 Figure 12‐ Characteristics of Participants Cover a Range of Experiences   20’s 30’s 40’s 50’s 60’s+ 
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




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High School Some College Bachelor’s Degree Graduate Degree Color represents participant’s race‐ white, black, and other (i.e. Asian or Hispanic) participants AnalysisofDatafromtheRiskRankingSessions
The analysis of the data can cover two separate areas: an analysis of the outcomes of the ranking, with regards to the homeland security risks of concern; and the analysis of the process, with regards to the appropriateness of the Deliberative Method for Ranking Risks to examine the homeland security domain. Each of these areas will be covered individually. AnalysisofRiskRankingResults
The primary outcome of the deliberative risk ranking process is a final ranking from the hazard of most concern (number 1) to the hazard of least concern (number 10). Figure 13 shows the final individual rankings in this study, with the average of individuals’ final rankings within the bounds of the 25th and 75th percentiles of these rankings. This figure reveals three tiers of risk: a high‐risk tier with earthquakes, hurricanes, and pandemic flu; anthrax attacks and cyber‐attacks in the low risk tier; and the rest of the hazards in a moderate‐risk tier. The use of the 25th and 75th percentile describes the variation in any individuals’ ranking rather than bounding the variation in the average of those rankings. Assuming a normal distribution for statistical purposes to determine bounds for the average ranking, I can differentiate between the risks to some extent, but there is some overlap in the estimates of the average ranks for the closest of hazards. Notably, the bounds for the average rankings overlap when 92 comparing hurricanes and earthquakes, toxic industrial chemical accidents and oil spills, anthrax and cyber‐attacks, and tornadoes, terrorist nuclear detonations, and terrorist explosive bombings. Figure 13‐ Participants' Final Rankings of Homeland Security Risks in the U.S. One interesting finding is that natural disasters were generally reported as being the greatest concerns on average. The hazard of greatest concern is pandemic influenza, followed by earthquakes and hurricanes, while anthrax attacks and cyber‐attacks were viewed as least concerning. The concern over the lowest of the natural disasters (tornadoes) was equal to that of the highest of the terrorist events (nuclear detonations). It is not necessarily the case that people were more concerned about the natural disasters because they were caused by nature; this would run counter to the established literature on risk perception which finds that people are more concerned about terrorism than natural disasters when all other things are equal. However, in this study, not all things are equal. The natural disasters used in this study had greater consequences than the human‐induced events. It may be that the people in this study are more concerned about natural disasters because the participants were 93 making their rankings based on these greater consequences. Getting participants to focus on the attributes of the risks themselves in a deliberative fashion rather than making snap judgments about the risks is a primary goal of the Deliberative Method for Ranking Risks. This provides some evidence that the Deliberative Method for Risk Ranking is useful at reducing the bias towards human‐induced events and focusing individuals’ attention on risk in an analytical fashion. There is some evidence that people were basing their rankings on the attributes of those risks. The hazards can be ranked based on each attribute individually— for example, based solely on the best estimates of average lives lost per year, pandemic influenza was the greatest risk while cyber‐attacks was the lowest risk. The participants’ solicited rankings of risk can be compared to these ranks based on each of the attributes. It would be preferable to analyze these rankings in a multi‐attribute fashion, but given the limited power of the dataset, such multivariate analyses cannot be done. Still, univariate Spearman correlations of the average individual solicited rankings and the rankings based on the attributes identified correlation for all of the attributes of consequence (Table IX). The correlations based on non‐consequence attributes were negatively correlated, which is also expected— for example, less control was correlated with higher risk. These correlations provide some evidence that people were basing their rankings on the attributes of the risk. 94 Table IX‐ Spearman Correlation of Average Final Individual Ranking with Rankings by Attribute Ranking by Attribute
Hazard Average Ranking by Individuals Average Deaths Greatest Deaths More Severe Injuries Less Severe Injuries Psycho‐
logical Average Economic Damage Greatest Economic Damage Duration of Economic Damage Size of Area Affected Pandemic Influenza 1 1 1 1 1 2.5 3 4 4.5 1.5 Hurricanes 2 4.5 6 2 3 2.5 1 5 4.5 5 Earthquakes 3 5 2 2.5 2 2 1.5 5 3 3 Tornadoes 4 4.5 8 3.5 4 6 5.5 10 6 7.5 Terrorist Nuclear Detonations 5 2 2 3.5 7 2.5 4 1 3 1.5 Terrorist Explosive Bombing 6 7 7 8 8.5 9 8 8 9 10 Toxic Industrial Chemical Accident 7 8 4.5 7 5 9 7 3 7 7.5 Oil Spill 8 9 9 9 8.5 6 5.5 7 1.5 5 Anthrax Attack 9 6 4.5 6 6 6 10 6 8 9 Cyber‐attack 10 10 10 10 10 9 9 9 10 3 0.83 0.58 0.88 0.85 0.75 0.87 0.38 0.51 0.31 Spearman Correlation with Final Ranking Hazard Average Ranking by Individuals Environ‐
mental Individuals Displaced Government Disruption Natural/ Human Ability to Control Exposure Time to Health Effects Scientific Under‐
standing Combined Uncertainty Pandemic Influenza 1 8 9.5 3 8.5 2.5 6 7.5 8.5 Hurricanes 2 1.5 4 3 8.5 10 3.5 4 8.5 Earthquakes 3 4.5 5 6 8.5 6 8.5 7.5 4 Tornadoes 4 8 2 6 8.5 8.5 8.5 7.5 8.5 Terrorist Nuclear Detonations 5 4.5 1 1 2.5 2.5 1.5 7.5 1.5 Terrorist Explosive Bombing 6 8 7 8 2.5 2.5 8.5 7.5 4 Toxic Industrial Chemical Accident 7 3 3 9.5 5.5 6 1.5 2.5 6 Oil Spill 8 1.5 8 9.5 5.5 8.5 3.5 1 8.5 Anthrax Attack 9 8 6 6 2.5 2.5 5 7.5 1.5 Cyber‐attack 10 8 9.5 3 2.5 6 8.5 2.5 4 0.12 0.23 0.35 ‐0.78 ‐0.10 ‐0.04 ‐0.47 ‐0.46 Spearman Correlation with Final Ranking 95 People used more than one attribute in making their rankings. In the multi‐attribute phase of the ranking sessions, participants were asked to identify the attributes that they considered when ranking the hazards. All respondents reported considering multiple attributes (Figure 14), providing additional support for a multidimensional approach to comparing risks. The median number of attributes considered relevant was 15. Only 19% of the participants considered all 17 of the attributes important, while the fewest that an individual reported using was 10. Participants were most likely to report concern over attributes reflecting the consequences of the event. Figure 14‐ Percentages of Respondents by the Number of Attributes the Respondent Reported as Important Percentage of Respondents
25%
19%
20%
19%
15%
15%
12%
12% 12%
10%
8%
4%
5%
0% 0% 0% 0% 0% 0% 0% 0% 0%
0%
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17
Number of Attributes Reported as Important
Any given attribute of consequence was reported as being considered by anywhere from 85% to 100% of the participants (see Table X). Non‐consequence attributes related to the psychometric paradigm were less likely to be reported as important but still played a role in the participant’s perceptions; these risks were reported as being considered for between 39% (for time between 96 exposure and health effects) and 69% (for natural/human‐induced and ability of individuals to control their exposure) of the participants depending on the attribute. Table X‐ Percent of Participants Considering a Given Risk as Important Percent of Participants Considering the Risk Important Attribute Average number of deaths per year Greatest number of deaths in a single event Average more severe injuries/illnesses per year Average less severe injuries/illnesses per year Psychological damage per year on average Average economic damages per year Greatest economic damages in a single event Duration of economic damages Size of area affected by economic damages Average environmental damage per year Average individuals displaced per year Disruption of government operations Natural/human‐induced Ability of individual to control their exposure Time between exposure and health effect Quality of scientific understanding Combined uncertainty 96.2% 92.3% 100.0% 92.3% 84.6% 84.6% 96.2% 96.2% 100.0% 96.2% 92.3% 92.3% 69.2% 69.2% 38.5% 53.8% 61.5% In addition to the binary classification of important or not, respondents ranked the attributes from the most important to the least important. The average rankings of the attributes are presented in Figure 15. This figure presents the average of the individual rankings of concern for each attribute, along with the 25th and 75th percentiles of the individual rankings. The participants identified three attributes that they considered most concerning (greatest number killed in a single event, average number killed per year, and average more severe injuries or illnesses per year) and three non‐
consequence attributes that they considered least concerning (time between exposure and health effects, quality of scientific understanding, and combined uncertainty), with the majority of the risks being of some concern. These attributes of moderate concern were to some extent indistinguishable; 97 while some people reported more concern for one attribute over another, on average these relative concerns balanced out, leaving all moderate risks similarly concerning on average. Figure 15‐ Average Individual Ranking of Attributes of Importance Two general trends emerge with regards to which attributes were of greatest concern. First, aspects of life and health were considered most important, followed by economic and socio‐economic consequences, with non‐consequence aspects of risk considered the least important. Second, aspects of greatest consequence representing the worst‐case for risk were ranked of greater concern than average consequences per year representing expected damage. This was true for both lives lost and economic damages. However, it is interesting to note that people reported being more concerned about aspects of greatest consequence than expected value (e.g., being more concerned about greatest lives lost in a 98 single event than the average number killed per year). This stated preference is the reverse of the resulting rankings, as the correlation between the final rankings and the rankings based on aspects of expected value was higher than the correlation between the final rankings and the rankings based on aspects of greatest consequence (e.g., the correlation of final rankings and rankings based on average lives lost per year was higher than the correlation of final rankings and rankings based on greatest lives lost in a single event). While individuals may have a stated concern for the worst‐case consequences, their resulting rankings suggest their rankings were made based on expected value considerations. TestingforPotentialBiases
One question regarding the use of the Deliberative Method for Ranking Risks revolves around whether possible biases are identified in the data. The Deliberative Method for Ranking Risks is designed to focus people on deliberating on the risks to the nation as a whole, reducing biases by using an analytical framework rather than an experiential framework for considering risks. But it is also possible that people are biased in their estimation of risk, ranking some risks higher than they should because it is fresh in their memory or because they have personal experience with it. Some potential biases were identified and tested. One particular question was the extent to which people are biased because of their own personal exposure to the risk. One possibility is that people who live in a higher risk area will be biased in favor of their own interests, rating risks which are important to them compared to respondents in other parts of the nation. Another possibility is that people who live in higher risk areas are more comfortable with the risk that they have accepted, rating risks which were higher for them as lower compared to respondents in other parts of the nation. The final, null hypothesis is that areas of higher risk will have no effect on how respondents rank the risks. If personal risk biases the rankings, this 99 undercuts the deliberative risk ranking process and the ability of participants to rank the hazards as they relate to the nation as a whole. I examined this question through how people responded to earthquakes. The ranking sessions were conducted in areas of low earthquake risk (Pittsburgh) and high earthquake risk (Santa Monica). Respondents in low‐risk sites ranked earthquakes as a greater concern than those in high‐risk sites on average but these differences were small. Comparing the high‐risk and low‐risk populations using a t‐
test with equal variances, I cannot reject the null hypothesis that there is no difference between the populations (p=0.49). This may be because the differences between sites were small and the variation in ranks for individuals is large, or it may reflect an actual lack of a difference. Table XI‐ Group Differences in Perception of Earthquake Risk Mean Group 1 Group 2 Group 3 P‐value Initial 3.25 4.00 3.78 0.57 Revised 3.13 2.67 4.00 0.29 Final 2.63 2.78 3.89 0.77 (alternative hypothesis was that ranking by low risk group 1 were different from high risk groups 2 and 3) In addition to the historical risks, there were three events that occurred proximate to the sessions that may have impacted opinion through the availability heuristic. These events relate to the hazards of hurricanes, cyber‐attacks, and toxic industrial chemical accidents. None of these events directly impacted the people who were ranked, and none caused significant casualties that participants could have become aware of through the media. These unintended differences provide a test as to whether recent events affect the respondents’ perceptions of the risks. As the next three paragraphs detail, there is little evidence that this was the case. The first event was the most damaging of the three events that may have impacted the results. In between the first session and the second session, the East Coast of the U.S. was struck by Hurricane Sandy, a storm which caused heavy damage, especially economic damage. Although the hurricane did 100 not affect respondents on the west coast, the event did get significant media coverage. It is possible that with the images of damage (albeit few causalities) fresh in their minds, respondents in the second and third session ranked hurricane risk higher than respondents in the first session. This was not found to be the case, as the null hypothesis that the rankings of the first group were the same as the second and third group could not be rejected. Table XII‐ Group Differences in Perception of Hurricane Risk Mean Group 1 Group 2 Group 3 P‐value Initial 4.25 4.00 4.11 0.24 Revised 3.13 3.67 3.78 0.61 Final 3.13 3.00 2.56 0.51 (alternative hypothesis was that ranking by post‐Hurricane Sandy groups 2 and 3 were different from pre‐Tropical Storm Sandy group 1) The second event that was analyzed to examine any availability bias was a cyber‐event. In the week before the first session, a series of Denial of Service attacks were conducted against U.S. banks. While this event does not rise to the level of a cyber‐attack as described in the scenario (and the participants reported knowing this in the focus group sessions), they did reference the events in the group discussion as a harbinger of what was to come. However, this did not translate into a perception of greater concern. The alternative hypothesis was that the awareness of the cyber‐event would translate into a higher concern for that risk, but the null hypothesis that the first session and the second and third sessions ranked the risk similarly could not be rejected. Table XIII‐ Group Differences in Perception of Cyber‐attack Risk Mean Group 1 Group 2 Group 3 P‐value Initial 8.00 8.44 8.00 0.14 Revised 9.00 8.89 8.22 0.30 Final 9.50 8.67 8.33 0.84 (alternative hypothesis was that ranking by group 1 aware of cyber‐events in the week of the session was different from groups 2 and 3) 101 The third event that was analyzed to examine any availability bias was a train derailed spilling toxic chemicals into a creek in New Jersey. This occurred on November 30th, after the first focus group and on the same day as the second focus group. The second focus group (which occurred on November 30th) was not aware of it, but the third group (which occurred on December 1st) was. The December 1st focus group did rank toxic industrial chemical accidents differently, but as a risk of less concern than the other two groups. The alternative hypothesis was that the third session that was aware of the event would view it as a greater concern, and this was found to be true in the case of the final rankings. However, there are reasons to believe that this was not related to the chemical spill. The difference in rankings is largely due to the difference between the third session and the first session but not with the second session, which also did not know about the spill. Additionally, while the spill was known to the third group there was very little discussion about it in the group session. Table XIV‐ Group Differences in Perception of Toxic Industrial Chemical Accident Risk Mean Group 1 Group 2 Group 3 P‐value Initial 6.13 6.56 7.56 0.71 Revised 5.88 6.11 7.00 0.84 Final 5.25 6.89 7.33 0.96 (alternative hypothesis was that ranking by group 3 aware of toxic chemical spill was different from unaware groups 1 and 2) Individual‐level differences in how the risks were perceived were observed in the group discussions, but these rarely involved risk perception biases. To some extent, these differences were desired; individuals brought their experiences in the military, in information technology, in the sciences, and in events that affected their family to their consideration of the rankings, bringing a welcome diversity of perspective. Unwelcome biases were rarely noted; only one participant continued framing the risks in terms of his personal exposure even after being instructed to consider the risks in terms of the nation’s exposure. However, that one individual’s rankings were nearly identical to the average of the individual rankings, providing no evidence of a bias based on personal exposure. 102 Taken together, these tests provide some limited evidence that the method was successful at eliminating biases of concern. The tests examine potential biases of concern associated with the availability heuristic and with personal exposure to risk rather than the national exposure to risk, and they identify no evidence that these biases are present. However, we should not over‐interpret this finding; absence of evidence does not prove evidence of absence. In this case, there are several reasons why we might expect to see no evidence of bias even if bias existed, as these examinations involve small consequence events and small numbers of participants. It is possible that there may be bias associated with larger disasters such as the terrorist attacks of Sept. 11, 2001 or Hurricane Katrina in 2005. It is also possible that the biases are present but the power to identify them is limited because of the small number of people examined. Additional risk rankings should be performed that specifically examine the hypothesis of bias to more conclusively address this issue. AgreementandDisagreementaboutRisks
The degree to which individuals agreed on the rankings also varied from hazard to hazard. As discussed above, the 25th and 75th percentile ranges in Figure 13 show the variation in how individuals ranked risks. Some hazards— notably terrorist nuclear detonations and toxic industrial chemical accidents— had noticeably less agreement over the rank of that hazard on an individual level. This disagreement over the ranking of the risk also extended to the group level. The group rankings should be different from one session to another, as the small number of participants would not be large enough to wash out individuals’ differences in background and perspective. As with the individual rankings, the hazards with the greatest disagreement over their risk were terrorist nuclear detonations and toxic industrial chemical accidents (Table XV). 103 Table XV‐ Agreement and Disagreement in Group Rankings of Risk Pandemic Influenza Earthquakes Terrorist Nuclear Detonations Hurricanes Toxic Industrial Chemical Accidents Tornadoes Terrorist Explosive Bombings Oil Spills Anthrax Attacks Cyber‐attacks Group 1 1 2 3 4 5 6 7 8 9 10 Group 2 2 4 1 3 7 6 5 8 10 9 Group 3 1 3 6 2 8 4 5 7 10 9 Standard Deviation 0.6 1.0 2.5 1.0 1.5 1.2 1.2 0.6 0.6 0.6 Figure 16 through Figure 21 present the distribution of individuals’ final rankings as they varied across the range. A rolling average was used to smooth the noise of the individual rankings; both a rolling average with a span of two and a span of three were examined. The individual rankings were generally distributed around a single value in a unimodal distribution. The one clear exception is the scenario for terrorist nuclear detonations, which has a much more uniform distribution and no clear peak. This reflects the lack of consensus regarding nuclear terrorism. Oil spills may also be an exception, with a bimodal distribution in both the rolling averages with a span of two and a span of three
104 Figure 16‐ Distribution of Rankings for Natural Disasters (with Smoothing, Span=3) 50%
Figure 19‐ Distribution of Rankings for Natural Disasters (with Smoothing, Span =2) 50%
Pandemic
Flu
40%
40%
Hurricane
30%
30%
Pandemic
Flu
Hurricane
20%
Tornado
20%
Tornado
10%
Earthquake
10%
Earthquake
0%
Figure 17‐ Distribution of Rankings for Terrorist Events (with Smoothing, Span =3) 50%
0%
Figure 20‐ Distribution of Rankings for Terrorist Events (with Smoothing, Span =2) 50%
40%
Nuclear
Detonation
40%
Nuclear
Detonation
30%
Cyber
Attack
30%
Cyber
Attack
20%
Explosive
Bombing
20%
Explosive
Bombing
10%
Anthrax
Attack
10%
Anthrax
Attack
0%
0%
Figure 18‐ Distribution of Rankings for Major Accidents Figure 21‐ Distribution of Rankings for Major Accidents (with Smoothing, Span =3) (with Smoothing, Span =2) 50%
50%
30%
Toxic
Industrial
Chemical
Accident
20%
Oil Spill
40%
30%
Toxic
Industrial
Chemical
Accident
20%
Oil Spill
40%
10%
10%
0%
105 0%
There are several reasons why individuals might not agree on the degree of concern for one risk compared to another. First, the individuals may care about different attributes of the risk or care about the same attributes but to different extents. For example, one person may care more about lives lost while a second may care more about economic damages. Second, respondents may have different beliefs as to what the risks of any particular hazard are. For example, while one person may think that the average number of lives lost per year from a terrorist nuclear attack is from the high end of the range, a second person may think it is better described by the low end of the range. These differences may be due to the different perspectives and life‐experiences that the participants bring to the ranking sessions and are an important part of using individuals in soliciting risk ranking. Other reasons why people may not agree are less constructive— people may not understand the attributes of concern or may not have a consistent way of thinking about the risks, and they may initially rely on misperceptions that create bias in their answers. A goal of the Deliberative Method for Ranking Risk is to reduce these biases in considering the risk by bringing people’s focus to the risks in an analytical framework rather than relying on their individual preconceptions. The disagreement identified in the final risk ranking largely reflects disagreement over perceptions of the value of the attributes. The risks about which there was the least consensus on their ranks were also those risks about which there is the least consensus on their consequences, notably those rare, high consequence events for which the risk cannot be well estimated. The greatest amount of disagreement occurred over the consideration of terrorist nuclear detonations. As part of the risk ranking process, participants were asked to discuss for each hazard reasons why the hazard would be low or high risk. This group discussion revealed that individuals who were not concerned about this risk thought that the likelihood of an attack is low, noting the history of nuclear weapons in World War II, particularly that they have only been used to inflict harm in one set of incidents; that the incidents occurred a long time ago; that they were used only by a nation‐state in war and not an individual or 106 group in a terrorist event; and that the U.S. was the user of the weapons and not the target. Individuals who were very concerned about the risk thought that both the likelihood and the consequences of an attack would be higher. With regards to likelihood, they noted that humans have always attacked each other and that as technology advances the weapons advance as well making their eventual use inevitable. With regards to consequences, they stated a concern that the secondary consequences, which were not included in our definition of attributes but which were included in the discussion of the risk in the summary sheets, would be very severe, particularly with regards to the disruption to the world economy and the societal and political ramifications including war and restrictions on civil rights. The data also indicate that agreement increased throughout the sessions to an extent similar to other studies of the Deliberative Method for Ranking Risk. As discussed above, there are multiple reasons why people may disagree as to the risk, including constructive disagreement based on different concerns over which attributes are important or over the true value of those attributes, and less constructive disagreement reflecting misconceptions over the attributes or uncertainty about the scope of the risks being compared. The process is designed to reduce the disagreement based on misconceptions or lack of clarity of scope while preserving the disagreement based on different individual perspectives. I examined whether it met this goal in two ways. Figure 22 shows the standard deviation of the individual rankings by hazard at the initial, revised, and final individual ranking, and shows the general decrease in deviation. I also tested the differences statistically in the fashion consistent with other studies of the Deliberative Method for Ranking Risk. The pairwise correlation was calculated between every individual and every other individual both for the initial rankings and the final rankings, and these correlations were averaged to calculate a mean pairwise correlation (Table XVI). There was more agreement in the final rankings than in the initial rankings to a statistically significant extent for two of 107 the three groups. The third group that did not show a statistically significant increase in agreement was anomalous in that there was a final level of agreement consistent with the other groups, but also had a high level of initial agreement; their agreement could not increase because it was already high. These increases in agreement were consistent with the increases in agreement identified in other studies using the Deliberative Method for Ranking Risk. Figure 22‐ Standard Deviation of Rankings at Three Stages of the Process 4
Earthquake
3.5
Hurricane
Tornado
3
Pandemic Flu
2.5
Anthrax Attack
2
Nuclear Detonation
1.5
Explosive Bombing
1
Cyber Attack
0.5
Toxic Industrial
Chemical Accident
Oil Spill
0
First
Revised
Final
Table XVI‐ Agreement among Individuals’ First and Final Rankings as Measured through Mean Pairwise Correlations of Results Willis et Willis et Morgan al. al. et al. (2010) (2004) (2001) 0.17‐
0.147 0.39 0.59 0.52 Group Group Group 1 2 3 Xu et. al. (2011) Agreement among initial rankings 0.294 0.442 Agreement among final rankings 0.771 0.541 0.538 0.45‐
0.93 0.87 0.86 0.70 p‐values for test that mean correlation among final rankings is larger than among first <0.00
1 0.113 <0.00
1 <0.001‐
0.22 0.01 <0.001 0.007 108 0.29 Additionally, there is reason to believe that the increase in consensus is due to alleviating misconceptions as to the risks and clarifying the scope rather than from forced consensus. The final rankings were more highly correlated with the estimates of risk than the initial rankings for each of the attributes of consequence (Table XVI). For example, the average of the pairwise correlations of the rankings based on expected lives lost and the individual rankings increases from 0.53 in the initial rankings to 0.61 in the final rankings, a statistically significant difference at a one percent confidence level. Similar increases exist for correlations of rankings based on other attributes. Table XVII‐ Average of Pairwise Correlations with Ranking Based on Attributes Increases from the Initial Ranking to the Final Ranking Attribute Average number of deaths per year Greatest number of deaths in a single event Average more severe injuries or illnesses per year Average less severe injuries or illnesses per year Psychological damage per year on average Average economic damages per year Greatest economic damages in a single event Duration of economic damages Size of area affected by economic damages Average environmental damage per year Average individuals displaced per year Disruption of government operations Natural/human‐induced Ability of individual to control their exposure Time between exposure and health effect Quality of scientific understanding Combined uncertainty Correlation of rank based on attribute and initial rank 0.53 0.39 Correlation of rank based on attribute and final rank 0.61 0.44 0.50 0.46 0.45 0.48 0.26 0.30 0.20 0.02 0.15 0.24 ‐0.40 0.04 ‐0.04 ‐0.35 ‐0.15 0.62 0.62 0.57 0.69 0.33 0.43 0.26 0.15 0.15 0.24 ‐0.58 ‐0.08 ‐0.02 ‐0.31 ‐0.34 AssessingtheQualityandLevelofSupportfortheRankingResults
In addition to their rankings, participants were asked to evaluate the risk ranking session itself. This evaluation included both participant perceptions of the aspects of the rankings that were important to the outcome as well as the participant perceptions of the process and outcomes directly. 109 Participants were asked where their knowledge of risk on the hazards was obtained, either prior to the session or at specific stages of the session. They were asked to report on a 7 point scale ranging from 0 (strongly disagree) to 6 (strongly agree). People did bring some prior knowledge of the risks to the session— the mean for the responses was 3.3, only slightly higher than the mid‐point of 3 representing neither agree nor disagree. Prior knowledge of risk was reported as less of a factor than any of the information provided in any aspect of the session. Participants reported an increasing amount of knowledge as the process went on, agreeing that initial materials (mean=4.2), examination of the attributes of the risk (mean=4.5), and group discussion (mean=4.7) contributed more to their knowledge of the risks at each step. See Figure 23 for details. Figure 23‐ Sources of Knowledge that Informed Participant Rankings 110 Compared to the environmental risk ranking studies, individuals had less initial knowledge of the risks (mean 3.3, c.f. 4.03 in Willis et al. 2010) and relied more upon information developed through the process, including the initial ranking (mean 4.2, c.f. 3.35 in Willis et al. 2004., 3.92 in Willis et al. 2010), the examination of the attributes (mean 4.5, c.f. 3.78 in Willis et al. 2004., 4.14 in Willis et al. 2010), and the group discussion (mean 4.7, c.f. 4.82 in Willis et al. 2004., 4.59 in Willis et al. 2010). The difference between the knowledge gained in each of the three states and the prior knowledge was statistically significant (t‐tests of the differences of the mean scores provided p‐values of 0.0001, less than 0.00001, and less than 0.00001, respectively). Table XVIII‐ Participant's Responses Regarding Sources of Current Information, on a Scale from 0 (lowest) to 6 (highest) How Much Is Your Current Knowledge of Homeland Security Risks in the U.S. Based On… Your Prior What You Learned What You Learned What You Learned Knowledge of the by Completing by Ranking the from the Group Risks? Your First Risk Risk Attributes in Discussion and Ranking Terms of Their Ranking of Risks? Importance? 3.3 4.2 4.5 4.7 Session combined Session 1 3.4 3.9 4.6 5.0 Session 2 3.4 4.2 4.4 4.1 Session 3 3.1 4.3 4.6 4.9 Morgan et al. (2001) ‐ ‐ ‐ ‐ Willis et al. (2004) ‐ 3.35 3.78 4.82 Willis et al. (2010) 4.03 3.92 4.14 4.59 Xu et al. (2012) ‐ ‐ ‐ ‐ The differences between knowledge sources in this study were similar compared to results in other domains, including expert (Willis et al., 2010) and non‐expert (Willis et al., 2004) groups. People in this study reported having less initial information to a statistically significant degree (p‐value of 0.00066), but the differences in knowledge from later sources were not statistically significant. The consensus that emerges through the process may provide evidence that the process is guiding people to a more deliberative informed judgment. However, as previously discussed, there are 111 two ways that the variance in responses could be reduced, both in positive ways (through reducing misconceptions and improving the clarity of the scope through the process) and in negative ways (forcing consensus through peer effects). The question of forced consensus was examined in this study in several ways. First, individuals were asked what influenced their final rankings. Individuals reported that their final ranking was influenced by the supporting materials in the first ranking, the multiattribute approach in the calculated ranking, and the group discussion overall. The responses for all three of these stages exceed the midpoint of 3, suggesting that most participants believed that each of the three steps did contribute some information (see Figure 24). The calculated ranking was described as having the largest effect (mean=4.2), followed by the group ranking (mean=3.9) and the initial ranking (mean=3.5). This does suggest that the process was useful for guiding a deliberative consideration of risks. Figure 24‐ Individual Perceptions of the Contributions to Their Final Ranking 112 Again, the respondents reported their final rankings were influenced by previous stages in an amount comparable to the environmental rankings (Table XIX). While the calculated multi‐attribute ranking as having the largest influence on their final ranking (the difference between the mean of 4.2 and the mid‐point of 3 was statistically significant with a p‐value of 0.00008), they also reported learning from the group discussion (mean 3.9, a difference associate with a p‐value of 0.02). The participants’ perception of the influence of the initial ranking was not statistically different from the mid‐point of 3 at a 10 percent confidence level (mean=3.5, associated with a p‐value of 0.11). These numbers are comparable to those from previous studies using the Deliberative Method for Ranking Risk in other domains, with the exception of the influence of the calculated ranking— participants reported the calculated ranking having greater influence than studies of environmental risk (mean=4.2, compared to 3.55 in Willis et al., 2004, a statistically significant difference associated with a p‐value of 0.02). It is also worth noting that the perceptions of the groups were not uniform from one session to another— the first session reported less influence from the initial ranking while the second session reported less influence from the group ranking. Table XIX‐ Participants’ Perceptions of the Influence of Intermediate Steps on Their Final Ranking, on a Scale from 0 (lowest) to 6 (highest) Respondents' agreement that their final rankings were based on… initial ranking calculated group ranking ranking Sessions Combined 3.5 4.2 3.9 Session 1 2.6 4.3 4.6 Session 2 4.1 4.3 2.4 Session 3 3.8 4.0 4.7 Morgan et al. (2001) ‐ ‐ ‐ Willis et al. (2004) 3.44 3.55 4.21 Willis et al. (2010) 3.80 ‐ 4.07 Xu et al. (2012) ‐ ‐ ‐ 113 In addition to the reported influence of intermediate steps on the final rankings, the influence of the intermediate steps based on participants’ rankings across stages also suggested that participants learned from all stages. To identify the influence of the initial ranking and the group discussion on the final ranking, I performed a pooled regression of the individuals’ rankings using the initial and group rankings as predictors in an approach consistent with other studies using the Deliberative Method for Ranking Risks. Both the group ranking and the initial ranking influenced the final ranking of risks (Figure 25), providing some evidence that while the process built consensus it did not eliminate individual differences in perspective. Figure 25‐ Influence of First and Group Rankings on Final Rankings Using a Pooled Regression Finally, I addressed the running concern regarding forced consensus directly, by asking participants the extent to which different points of view were discussed and individual opinions were encouraged. This question was included in the final questionnaire. Participants reported that the sessions were very inclusive of individual opinions (Figure 26). Participants were also asked to reflect on the process and its results. Measures of satisfaction in risk rankings serve two purposes. First, participants’ satisfaction over the ranking process and results serves as a measure of face validity (Morgan et al. 2001; Willis et al. 2004; Willis et al. 2010; Xu et al. 2011). Second, risk rankings where the participants are satisfied with the results provide a more useful 114 input to public risk‐management decision making (Morgan et al. 2001; Willis et al. 2004; National Research Council 2008). Participants approved of the use of the Deliberative Method for Ranking Risks to address homeland security issues in multiple ways (Figure 27). Participants reported satisfaction with the groups ranking (average score = 4.8), agreed that the group rankings were representative of their concerns (average score = 4.3), and approved of submitting rankings to DHS for use in making decisions (average score 4.5). Each of these values was statistically different from the mid‐point of 3 at a very high confidence level (p‐values of 1e‐10, 1.7e‐6, and 7.4 e‐9, respectively). These results were comparable to earlier studies using the Deliberative Method for Ranking Risks with regards to inclusiveness, satisfaction, representativeness, and utility of the rankings (Table XX). Figure 26‐ Workshop Participation Encouraged Different Points of View 115 Figure 27‐ Participants’ Support for Using the Rankings to Develop Risk Management Policies Table XX‐ Participant's Perceptions of the Risk Ranking Workshops, on a Scale from 0 (lowest) to 6 (highest) Sessions Combined Session 1 Session 2 Session 3 Morgan et al. (2001) Willis et al. (2004) Willis et al. (2010) Xu et al. (2011) To what extent did the group consider and discuss different points of view and encourage each member to express his or her opinion? 5.2 5.0 5.0 5.6 5.12 4.96 4.46 5.21 How satisfied or dissatisfied are you with your group’s risk ranking? 4.8 5.0 4.3 5.2 To what extent is your group’s risk ranking representative of your concerns about these risks? 4.3 4.5 3.9 4.6 How strongly would you approve of these rankings being used in making decisions in a real organization like DHS? 4.5 4.3 4.4 4.9 4.76 n/a 4.21 5.01 4.80 4.57 4.00 n/a 5.10, 4.90 4.36 5.23 116 Discussion
The results from this analysis provided evidence that the Deliberative Method for Ranking Risks as developed by researchers at Carnegie Mellon University can be useful in the homeland security domain. This analysis supported several conclusions that will be discussed below. It is possible to elicit informed judgments of risk priorities from interested, non‐expert individuals. The participants provided clear rankings, and reported satisfaction with both the process and the results of the sessions. Comparing participants’ rankings to the rankings based on the attributes of the risk provides some evidence that the rankings represented informed judgment. At the same time, the examinations of identifiable biases find no evidence of sources of bias associated with experiential thought processes. There is also little evidence for forced consensus; individuals reported feeling free to dissent from the group and provide their own point of view and there is evidence that both their individual rankings and group rankings contributed to their final rankings. The multiple stages all provided information, supporting the idea that the deliberative method guides participants to a more considered ranking based on the actual nature of the risks. These results were comparable to validated studies using the Deliberative Method for Ranking Risk in other domains. Comparative risk assessment should account for a broad set of attributes spanning health, economic, and social concerns. The results of the study support the need for a multi‐attribute approach. The elicited rankings were related to several aspects of consequence but were dictated by no single aspect of consequence. While the rankings were correlated with health related consequences, they were related to economic and societal consequences as well. Participants placed more weight on health consequences than economic consequences, societal consequences, or non‐consequence attributes but generally took a range of attributes into consideration. 117 Natural hazards may be of greater concern than terrorism. The rankings indicated greater concern for natural risks than terrorist risks. This finding would suggest different priorities within DHS than their current emphasis on terrorist risk as expressed in the budget, organizational structures, and in the department priorities. While risk literature suggests that all things being equal people are more concerned about intentional risks than natural ones, all things were not equal, and people may be more concerned about high consequence natural risks than lower consequence intentional risks. The correlations of individual rankings and rankings based on attributes of interest provide some evidence to support the hypothesis that the high ranking of natural disasters reflects an appreciation for the higher expected consequences of natural disasters. I caution against accepting this finding too broadly, as the rankings reflect a convenience sample and may not be reflective of the risk as viewed by the nation as a whole. While there is some evidence that the use of the Deliberative Method for Ranking Risks attenuates some of the biases associated with personal experience, more research is needed to examine whether these results are representative nationally. The largest source of disagreement about priorities comes from ambiguity on risk assessments. The degree about which participants agreed about their concern varied from hazard to hazard. While most participants saw pandemic influenza as the greatest or second greatest concern, there was less agreement as to the rank of other hazards. The hazards about which there was least consensus, terrorist nuclear detonations and toxic industrial chemical accidents, reflected low likelihood, high consequence events for which the likelihood is unclear. In the case of terrorist events, this uncertainty is not just a lack of precision in the estimates but reflects inherent uncertainty in the hazards themselves— terrorism is not a probabilistic event, although it can be useful at times to describe it as such. Considerations of uncertainty should be included in any comparative risk assessment. 118 References
Morgan, K. M., M. L. DeKay, P. S. Fischbeck, M. G. Morgan, B. Fischhoff and H. K. Florig (2001). "A deliberative method for ranking risks (II): Evaluation of validity and agreement among risk managers." Risk Analysis 21(5): 923‐923. National Research Council (2008). Public participation in environmental assessment and decision making, National Academies Press. Willis, H. H., M. L. DeKay, M. G. Morgan, H. K. Florig and P. S. Fischbeck (2004). "Ecological risk ranking: Development and evaluation of a method for improving public participation in environmental decision making." Risk Analysis 24(2): 363‐378. Willis, H. H., J. MacDonald Gibson, R. A. Shih, S. Geschwind, S. Olmstead, J. Hu, A. E. Curtright, G. Cecchine and M. Moore (2010). "Prioritizing Environmental Health Risks in the UAE." Risk Analysis 30(12): 1842‐1856. Xu, J., H. K. Florig and M. L. DeKay (2011). "Evaluating an analytic–deliberative risk‐ranking process in a Chinese context." Journal of Risk Research 14(7): 899‐918. 119 Chapter6. ConclusionsandRecommendations
The expansion of the Deliberative Method for Ranking Risks to the homeland security domain required addressing multiple questions regarding conceptualizing the risk, describing the risks, and identifying how the risks are perceived. These components— including a conceptualization of homeland security risk, a unique comparative dataset describing a broad range of risks, and the results of a risk ranking exercise— each make their own contributions, but they were also interconnected, either setting up a subsequent stage or providing support for the processes of a prior stage. In this chapter I summarize the evidence across the multiple chapters as to the application of the Deliberative Method for Ranking Risks to homeland security risks. A first section will discuss evidence of the efficacy of using the Deliberative Method for Ranking Risks in the homeland security domain, then lessons from the ranking sessions, their potential ramifications to DHS, and avenues for future research will be discussed. TheDeliberativeMethodforRankingRisksCanBeEffectiveatComparing
HomelandSecurityRisks
The Deliberative Method for Ranking Risks, developed as a process to prioritize concerns over risks in a variety of fields faced with multi‐attribute risks, can provide useful results when extended to the homeland security domain. The application of the method illustrates that it is possible to create a set of discrete, comparable homeland security risks as hazards at the strategic level, and that it is possible to create a comprehensive set of attributes to describe those risks. Open‐source data was available to create quantitative estimates for discrete, countable quantities, either using data on averages or combining estimates of likelihood and consequence, and to describe more complex concepts in terms of qualitative levels. The precision of the estimates— with a median range of 1.2 orders of magnitude between the lower and upper bounds reaching to over 3 orders of magnitude for some hazards— was sufficient to serve as the foundation for a qualitative risk ranking but would present 120 challenges for a quantitative comparative risk assessment. The estimates in this dataset are comparable to (in terms of orders of magnitude between lower and upper bounds) other studies which have used the Deliberative Method for Ranking Risk (Webster et al. 2010; Willis et al. 2010). Additionally, the results of the risk rankings sessions are useful. First, the risk rankings are informative. While there is some disagreement as to how an individual will rank a given hazard, the average ranks of the hazards could be distinguished from one another and were stable from session to session. These rankings represented informed, reliable judgments of the participants’ concern. Evidence for the effectiveness of the Deliberative Method for Ranking Risks in the homeland security domain comes from the analysis of two sources of data: directly reported participant perceptions and evidence from the rankings themselves. Consensus grew throughout the process, but both regression‐based analyses and participant reports of the extent to which different opinions were encouraged in the group discussion suggested that this was not simply forced consensus. Instead, there is evidence that this increased agreement represented informed judgment based on the consequences in a deliberative fashion. The individuals’ rankings became more correlated with rankings based on consequences for each of the attributes of consequence. Participants reported that their knowledge of the risks was based more on information gained through the ranking process than prior knowledge. This included learning at each stage of the sessions, including initial materials, rankings based on the attributes, and group discussions; both participant reports and regression‐based analyses found that each stage of the session influenced their final ranking. The importance of actual consequences in individual rankings was also reflected in the rankings themselves. The participant rankings correlated with the rankings based on attributes of the hazards, 121 both individually for each attribute and with the multiattribute ranking weighted by individuals’ personal concern. Additionally, no evidence of obvious biases based on personal exposure to the risk or availability bias was uncovered, although this may have been a limitation of the sample size and should be confirmed through analyses of larger samples. Finally, participants’ satisfaction with both the process and the results provides a degree of face validity. Individuals reported being satisfied with the group’s ranking, saw the rankings as representative of their own concerns, and would approve the rankings being used for decisions in a real organization. DHS should consider using the DMRR in risk‐related decision making at the strategic level. This initial test suggests that the Deliberative Method for Ranking Risks may be a useful method for identifying relative concerns over the disparate risks that are faced in homeland security. These ranked risks are just an initial step in informing risk reduction activities. The worst risks do not necessarily indicate the initial choices for risk reduction, as the concern over the risk needs to be considered along with the options to reduce those risks in a cost effective manner. Dealing with adaptive adversaries can make assessing the benefits of risk reduction efforts in homeland security particularly challenging. Still, knowing how concerned people are about various risks and why can be useful for informing risk reduction policies. Potential applications of the Deliberative Method for Ranking Risks exist for a variety of homeland security concerns. As used in this exercise the method can be integrated into national planning and prioritization activities such as the Quadrennial Homeland Security Review, grant allocations within DHS, or prioritization within FEMA. Additionally, multiple hazards can be compared at other geopolitical levels, from FEMA regions to individual cities. More broadly, this research indicates 122 that people are capable of performing deliberative comparisons within the homeland security domain, opening the door to comparisons of risk for different areas, sectors, or targets Comparative ranking methods using public participation can provide legitimacy to the ranking process. Comparative risk analyses without a public mandate lack legitimacy and are often ignored (Andrews, 1998, as cited in Florig et al., 2001). There have already been occasions in which DHS methodologies have been criticized as being disconnected from actual risks. A well‐publicized example occurred in 2004, when Congress debated funding allocations by state, with states such as Wyoming receiving more money per capita than states perceived as higher risk such as New York or California (Masse et al. 2007). Without a defendable methodology for comparing risks across the homeland security domain, expenditures can be subject to individual perceptions and political considerations (O'Sullivan 2008). Applications of the Deliberative Method for Ranking Risks to risks in the homeland security domain should include a more comprehensive set of hazards. While the hazards in this research were selected to cover an interesting and useful set of hazards, there were known omissions. The hazard set does not include extremely unlikely globally catastrophic risks (e.g. large meteorite impact), chronic rather than acute risks (e.g. drought), or non‐hazard risks that are part of the DHS mission (e.g. illegal immigration). While risks of these sorts can plausibly be included, it may require added attributes to describe them. Applications of the Deliberative Method for Ranking Risks to homeland security concerns should also build on the work in this research to develop more representative results. One mechanism explore the representativeness of the results is by applying survey methods in a targeted fashion. Replicating the multi‐hour process in a survey is infeasible; instead, specific questions should be targeted to explore specific hypotheses generated in the risk rankings. These hypotheses can be explored in additional risk 123 ranking sessions, such as examining the ranking of homeland security risks in rural areas as compared to the urban areas in this study. Additional avenues for exploration include the effect of income, education, personal exposure, personal experience, and availability bias in the familiarity with the risk. Alternatively, it may be useful to integrate the Deliberative Method for Ranking Risk using groups that are representative of the concerns of DHS. One representative group to which the method could feasibly be applied is DHS’s Risk Steering Committee. Alternative groups that are representative of concerns within DHS should also be explored. LessonsfromtheRankingSessions
The risk ranking sessions provided information on how concerned members of the public were about some homeland security risks relative to others. These risk ranking sessions were limited in that participants were selected using purposeful sampling rather than a representative sampling technique, which limits the ability to extend these results as representative of the cities from which they are drawn or the nation as a whole. With this caveat in mind, the risk ranking session did identify several findings which should be tested in other populations. ParticipantsWereMoreConcernedaboutNaturalDisastersthanTerrorism
Individuals clearly identified some hazards as greater concerns than others. These rankings were by no means unanimous, but a consensus can be identified for many of the risks by examining the 25th and 75th percentile ranges for the ranks. The risks of greatest concern were pandemic influenza, hurricanes, and earthquakes. Terrorist nuclear detonations (the terrorist risk reported as the greatest concern) was ranked on average at an equivalent level of concern to tornadoes (the natural disaster of least concern). The risks of least concern overall were anthrax attacks and cyber‐attacks. While the literature suggests that while all things being equal there is a heightened concern for terrorist risks, all things are not equal. The selected natural disasters had greater expected 124 consequences than the selected terrorist events, and the individuals’ rankings did correlate with rankings based on established attributes of consequence, particularly those aspects of life/health and economic damage. This provides some evidence that the rankings reflected informed judgment. However, it is also possible that this relative concern over natural disasters may indicate a bias based on the familiarity with the risk, undervaluing things that have not yet occurred because respondents have not experienced them. While other examinations provide no evidence for such perceptual biases in these rankings, additional research should be done to identify the true cause of the higher rankings of natural disasters. While some risks were clearly more concerning than others, the degree of consensus on that concern varied from risk to risk. The two hazards with the least consensus as to their level of concern were nuclear terrorist detonations and toxic industrial chemical accidents. These two events are characterized by being human‐induced, rare and/or novel in the U.S., and high consequence/low likelihood. By contrast, high consequence natural events had greater consensus even if they were rare, and low consequence events had greater consensus even if they were rare and/or novel in the U.S. and human‐induced. It is notable that the lack of consensus in the rankings for these attributes is not only a result of individuals doing the rankings but is also reflected in the degree of uncertainty in the bounds for the estimates of risk. Terrorist nuclear events have the least consensus in the rankings but also the greatest differences between the low and high estimates of actual expected consequences. Similarly, toxic industrial chemical accidents also have large uncertainty in their actual estimates, with high estimates of greatest economic damages nearly three orders of magnitude higher than the low estimates. Additionally, group discussions in the risk ranking sessions exploring these hazards (terrorist nuclear detonations and toxic industrial chemical accidents) did center on the different perceptions as to how 125 likely the event was to occur; some people thought that a terrorist nuclear detonation in the U.S. is inevitable while others saw it as nearly unthinkable. This provides some evidence that the individual rankings not only reflect the best estimates of risk but also reflect some of the uncertainty of the estimates as well. While the lack of a representative sample limits the ability to extrapolate this finding to the nation as a whole, this finding could be particularly relevant to DHS prioritization. While DHS is an all‐
hazards and risk‐based entity, there remains an emphasis on terrorist events over natural disasters (O'Sullivan 2008; Committee to Review the DHS's Approach to Risk Analysis 2010). The finding that informed individuals are more concerned about natural disasters than terrorist risks in the current U.S. context may suggest that DHS take steps to balance their priorities to match concerns over risk. The findings from these ranking sessions should be tested with additional research. The non‐
representative sample should be tested against specific hypothesized biases. Additional research should be done to examine if populations with different experiences with homeland security risk or different perceptions about the role of government present different rankings. One particular method of sampling that may be useful is theory‐based sampling, i.e. examining specific cases that address particular hypotheses. Particular hypotheses that this research may want to examine is whether people in rural areas rank risks similarly to those in the tested urban areas, if people are biased by their own personal or regional risks when ranking these risks, and if people rank the hazards differently after large events. Additional risk ranking sessions should be pursued with these populations that test whether the identified responses are susceptible to these identified theoretical differences. Research should also examine whether the concerns identified in these ranking sessions are representative of public concerns nationally. While survey techniques can generate representative samples, the Deliberative Method for Ranking Risk is a lengthy process and replicating it via a 126 widespread survey is likely to be infeasible. Instead, specific hypotheses drawn from the concerns identified in this research should be tested in a more focused way. HomelandSecurityRisksShouldBeDescribedUsingaRangeofAttributesthatAddressMore
thanJustHealthandEconomicDamage
This dissertation uses a broad set of attributes to describe homeland security risks, incorporating consequences of life and health; economic, environmental, societal, and governmental concerns; and non‐consequence attributes representing dread and the unknown. To support a broad set of risks in risk ranking, the risks should reflect meaningful differences about the risks that the participants in the risk ranking are concerned about. The research in this dissertation identifies that: 1) members of the public engaged in risk ranking find a wide range of attributes important to their considerations of risk; and 2) there is variation in the risks themselves with regards to these attributes. Participants engaged in the risk ranking were concerned about the range of risks, not just lives lost and economic damage. Participants reported using a large number of the attributes in their consideration of homeland security risks— the lowest number of attributes that a participant reported as relevant was 10, and the median number of attributes reported as relevant was 15. For any given attribute of consequence, over 90% of the participants reported considering that attribute to at least some respect when making their rankings. Participants’ relative rankings of concern provide additional evidence that the range of attributes was important. In addition to being asked if they considered an attribute at all when considering the risks, participants were asked to consider how concerning it was and ranked the attributes from the most concerning to the least concerning. Participants reported lives lost, more severe injuries, and economic damages reported as their greatest concerns and non‐consequence attributes reported as their least concerns. This relative importance of life/health and economic 127 damage was also reflected in the literature as the most commonly studied aspect of consequence. It was also reflected in the individuals’ rankings of the hazards— the individual’s rankings of the hazards had the greatest correlation with rankings based on consequences of life/health and economic damage and the least correlation with rankings based on non‐consequence attributes. This was also reflected in their rankings— individuals’ rankings were more correlated with rankings based on lives lost and more severe injuries or illnesses than with rankings based on other attributes of the risk. However, with the exception of lives lost and severe injuries, participants were concerned about each of the attributes to a similar degree on average. Participants reported being more concern for lives lost (both average lives lost per year and greatest lives lost in a single event) and average severe injuries or illness than other attributes of concern. However, beyond these three attributes, the level of concern about the risks was relatively equal and none of the attributes was considered of lesser concern. In addition to revealing which attributes people found as important, the rankings also identified which approaches to describe the attributes people found as important. Attributes were described in two ways: in expected value (i.e. average damage per year, taking into account both likelihood and consequence) and in worst case (i.e. greatest damage in a single event, taking into account only consequence but not likelihood). While risk experts prefer to consider risks in terms of expected value (Slovic et al. 1985; Slovic 1992), participants were concerned about both expected value and worst case scenarios. When asked to rank the attributes from the attribute of most concern to the attribute of least concern, individuals reported being more concerned about worst case than expected value formulations (reporting more concern for the greatest number of people killed in a single event than the number of people killed per year on average and more concern for greatest economic damages than average economic damages). However, the opposite was true when considering the results of their rankings, as the final rankings correlated more with rankings based on expected values of attributes than rankings based on greatest damage in a single event. The recognition that individuals are 128 concerned with other approaches to describe risk than just expected damage per year on average may suggest different decision‐making criteria at work. If individuals are actually more concerned about these other aspects of consequence, then policy makers should consider these other aspects in generating risk reduction measures. While the government should act as a rational actor and not incorporate misconceptions of risk in their policy decisions, the participant perceptions in this study do not represent uninformed or biased perceptions of the risk but rather informed concern over different aspects of the risk. The analysis of the risks also provides evidence a large number of attributes must be used to capture the variation in the hazard set. While there is some correlation between lives lost and injuries, most of the other aspects of consequence are independent of each other and describe different things. When looking at a hazard as a whole, it is not just the case that there are large hazards and small hazards but that the risks vary in different ways. The set of attributes identified in this dissertation matched the concerned identified in the literature and included aspects of risk that are not commonly included in DHS risk analyses. These additional attributes— including psychological consequences, environmental, societal, and governmental damage, and non‐consequence aspects of risk— should be included in risk analyses in the homeland security domain. Many of these attributes are currently available in some fashion or another. Aspects of life, health, and economic damage are typically available, either from ample historical data (for regularly occurring natural disasters and terrorist explosive bombings) or from modeled estimates (for rare or novel hazards). At the other extreme, aspects of environmental damage and government disruption are conceptually more complex, and do not warrant developing quantitative constructs. However, two of the selected attributes were countable but had little data available: number of people displaced from 129 their homes and psychological consequences of disasters. The data on displaced individuals is moderate, sufficient to establish quantitative bounds but not best estimates, but the data psychological consequences of disasters are significantly limited, limiting the ability to develop quantitative estimates of psychological damage in a comparable fashion. While the stressor‐based approach provided some ability to compare between hazards based on psychological damage, a more direct approach may be preferred. Given the importance of psychological consequences of disasters both in the literature and respondent perceptions, the government should consider comprehensive surveillance of psychological damage of homeland security events. References
Committee to Review the DHS's Approach to Risk Analysis (2010). Review of the Department of Homeland Security's Approach to Risk Analysis. National Research Council of the National Acadamies. Washington, D.C., National Academies Press: 148. Masse, T., S. O'Neil and J. Rollins (2007). The Department of Homeland Security's risk assessment methodology: Evolution, issues, and options for Congress. Congressional Research Service and the Library of Congress. Washington, DC. O'Sullivan, T. (2008). Comparative Risk Analysis: Biological Terrorism, Pandemics, and Other" Forgotten" Catastrophic Disaster Threats. Terrorism and Homeland Security. P. R. Viotti, M. A. Opheim and N. Bowen. Boca Raton, Fla., CRC Press: 147‐169. Slovic, P. (1992). Perceptions of risk: Reflections on the psychometric paradigm. Social Theories of Risk. S. Krimsky and D. Golding. New York, Praeger: 117‐152. 130 Slovic, P., B. Fischhoff and S. Lichtenstein (1985). Characterizing perceived risk. Perilous progress: Managing the hazards of technology. R. W. Kates, S. Hohenemser and J. X. Kasperson. Boulder, CO, Westview: 91–125. Webster, K., C. Jardine, S. B. Cash and L. M. McMullen (2010). "Risk ranking: investigating expert and public differences in evaluating food safety hazards." Journal of Food Protection® 73(10): 1875‐
1885. Willis, H. H., J. MacDonald Gibson, R. A. Shih, S. Geschwind, S. Olmstead, J. Hu, A. E. Curtright, G. Cecchine and M. Moore (2010). "Prioritizing Environmental Health Risks in the UAE." Risk Analysis 30(12): 1842‐1856.
131 AppendixA. ComparingMentalHealthConsequencesofHomeland
SecurityRisk
The mental health consequences of homeland security risk have often been overlooked by risk analysts and emergency managers (Silove et al. 2006). While a robust literature recognizes the importance of mental health following disasters, risk analyses have typically focused on physical consequences and avoid discussion of other societal consequences such as mental health (Committee to Review the DHS's Approach to Risk Analysis 2010). This dissertation seeks to examine the psychological consequences of disasters— specifically Post‐traumatic Stress Disorder (PTSD) and depression— to identify the annualized average mental health risks of one hazard relative to another on a qualitative scale. Levels of risk are estimated for a representative set of natural, intentional, and accidental risks representing the range of risks about which the Department of Homeland Security is concerned. Background
Mental health consequences of disasters are less concrete than physical damage or lives lost but are an important aspect of homeland security risk. Most of the psychological harms caused by disasters are mild and last only a short period of time (Gerrity and Flynn 1997), but severe and/or lasting psychological damage is possible in a substantial number of cases. Psychological harms of disasters can include PTSD and other acute stress‐related syndromes, severe depression, substance abuse, sleep and appetite disruption, and the exacerbation of pre‐existing mental health conditions. Disasters can be a cause of mental health consequences via several mechanisms. First, the disaster itself can produce intense fear, horror, or helplessness at the time of its occurrence, and these types of ‘traumatic’ events can lead to the development of PTSD and depression in a subset of individuals affected. For terrorism, the emotional impact is a defining characteristic (the “terror” of terrorism), but risk of direct or mediated harm has the potential for psychological damage in many 132 natural and accidental events as well. Second, living with the consequences of a disaster after the event can be harmful. This would include both health‐related consequences (e.g. the victim of a terrorist bombing dealing with the disability of losing a limb, or a family member dealing with the death of a loved one after a tornado) and economic consequences (e.g. a fisherman dealing with long‐term unemployment following an oil spill). Disasters can also make people more vulnerable to pre‐existing psychological issues. One mechanism is through the disruption of social support networks (Picou et al. 2003). When people’s support networks are disrupted through death or displacement, their susceptibility to existing stressors increases. Another mechanism is through the disruption of treatment of well‐managed conditions (Tierney 2000). For example, a hurricane may disrupt the access of a person with schizophrenia to the medicines and treatments that keep their condition manageable; while in this case the disaster did not cause the condition, it allowed the condition to reemerge. The importance of considering psychological consequences as an aspect of disasters is well recognized. Research into the psychological consequences of disasters began in the 1950s, with formative research by Tyhurst (1950) and Quarantelli (1954) (Tyhurst 1950; Quarantelli 1954). Research on the psychological consequences of specific disasters developed with natural disasters in the 1970’s and 1980’s, and more recently expanded to include terrorist events as well (Raphael and Maguire 2009). This literature is robust, with over 10,000 peer‐reviewed articles published between 1970 and June 25, 2013 examining disasters or terrorism and psychological, mental health, PTSD, or depression (see previous chapter on Conceptualizing Homeland Security Risk). Psychological consequences can be an important aspect of disasters compared to other attributes of harm. The number of people who experience psychological consequences of a disaster can be much larger than the number of people who are injured physically. The Oklahoma City bombing of 133 1995 presents an example. While 167 people were killed and nearly 700 injured in the blast, many more people were exposed to the trauma that could lead to psychological consequences (Office for Victims of Crime 2000). 6,000 people were directly exposed through working in a building that was damaged by the bomb, with an additional 10,000 close by in the downtown area that day. Another 360,000 people were exposed to stressors for depression indirectly with friends or relatives who were injured or killed. Nearly 10,000 people in the Oklahoma City area received psychological treatment for symptoms related to the bombings through the Center for Mental Health Services, although how many more people experienced symptoms but did not receive services is unclear (Pfefferbaum et al. 2002). Additionally, it is not necessary to have been in the Oklahoma City area to have experienced trauma. Exposure to the broadcast images of the event received national coverage can trigger symptoms of psychological distress although at a lower rate (Pfefferbaum et al. 2001); if only one tenth of one percent of Americans experienced psychological distress following the event, the number affected by psychological harms would be hundreds of times as large as the number injured or killed. However, data on psychological consequences are not integrated into risk analyses in any comparative fashion (Norwood et al. 2000; Tierney 2000; Norris et al. 2002; Norris et al. 2002; Norris 2006; Silove et al. 2006; Johnson and Galea 2009; Committee to Review the DHS's Approach to Risk Analysis 2010). The closest the National Planning Scenarios come to considering mental health is through a discussion of the “worried well” (HSC/DHS 2005). The Bioterrorism Risk Assessment lacks measures of psychological distress, an omission that a National Academies Report recommends changing (Committee on Methodological Improvements to the Department of Homeland Security's Biological Agent Risk Analysis 2008). Across DHS, risk analyses have tended to ignore non‐quantifiable risks including psychological consequences (Committee to Review the DHS's Approach to Risk Analysis 2010). More recent DHS analyses have tried to integrate psychological consequences as recommended in the Strategic National Risk Assessment; however, DHS notes that more work needs to be done, as 134 “[e]xperts consulted about psychological consequences emphasized caution in the application of the SNRA’s measure of psychological distress and stressed the need for additional research” (DHS 2011). Even on the level of individual disasters, data on psychological consequence are limited; while many disaster‐types have been examined, some have not. Reviews of PTSD after disasters by Galea et al. (2005) and Norris et al. (2002) find a number of estimates for event‐types where there is recent experience (e.g. hurricanes, tornadoes, explosive bombings) (Norris et al. 2002; Galea et al. 2005), but these represent only a small number of the event of each event‐type occurring each year. Additionally, some disasters are novel (e.g. nuclear detonations, cyber‐attacks) and could not yet have been studied. This limitation can plausibly be addressed by using analogues for the events (e.g. an ice storm for a cyber‐attack, SARS for pandemic flu), but each abstraction limits the ability to support the estimates. In cases where psychological consequences have been examined, there is no standard approach for estimating the numbers of individuals with psychological problems, as individual studies measure the harms in different ways using different instruments, over different timeframes, with different populations (Bromet and Dew 1995; Norris et al. 2002; Norris et al. 2002; Adams and Boscarino 2006; Neria et al. 2009). The effect of these varied methodologies contributes to widely varying estimates— McFarlane et al. (2009) note estimates of PTSD following disasters ranging from 10% to 70% — which limits the ability to compare prevalence from one disaster to another (North and Pfefferbaum 2002; North et al. 2005; McFarlane et al. 2009). Additionally, estimates of psychological harm examined in studies often do not reflect the harm attributable to the disaster. The harm attributable to the disaster should reflect the difference between the prevalence in the exposed population and the prevalence that would have been had the population not been exposed. There are underlying cases of PTSD and depression in the population that have nothing to do with the disaster. Before and after prevalence rates are rarely examined in a specific 135 study of a specific population, and baseline estimates of prevalence for the population at large are rarely known with enough precision. This lack of precision can be a limiting factor— if the baseline prevalence is subtracted from the prevalence specific to a disaster, the resulting estimates are often indistinguishable from zero. This is not necessarily a true zero but may be a reflection of the imprecision of the available estimates for calculation. Even if reliable estimates of prevalence among those exposed could be obtained, there are challenges in estimating the number of people who are exposed to various disasters each year. Studies examining the psychological consequences of disasters can define the exposed population in a variety of ways. For example, the people exposed to a bombing can include those who were initially caught in the blast, those who heard or saw the blast, those who were near the blast area and saw its aftermath, those who had friends or family in the blast area, and those who saw the blast or its aftermath on television. While each of these populations would be exposed to trauma in a disaster or terrorist event and can be expected some of their population to develop psychological symptoms, they are very different in the amount of exposure. Directly exposed populations will have higher prevalence rates but fewer people exposed for many hazards, while indirectly exposed populations will have lower prevalence rates but more people exposed. Because of the heterogeneity in these populations, no single measure of prevalence or exposure is appropriate to cover the entire population of people who may experience psychological syndromes. But even if estimates of prevalence existed for each of these populations (and they do not) there would still be the challenge of estimating the average number of people exposed each year for each of these populations. As estimating both the prevalence rate and the population exposed to disasters in the homeland domain is challenging, quantitative estimates of consequence based on multiplying the prevalence rate by the exposed population are too imprecise to be useful in comparing one homeland security risk to 136 another. Another approach is required to estimate psychological consequences in a comparative fashion for the homeland security domain. ApproachandData
The objective of this research is to identify an estimate of psychological consequences that can be used in a comparative risk ranking of homeland security risks. A measure was desired that could describe the average annual risk to the nation of one type of hazard compared to another. Due to the previously discussed challenges in estimating quantitative counts of psychological harms, I chose to describe the psychological consequences in qualitative terms. While the current precision of quantitative estimates of psychological harm limits their utility to comparative risk ranking, qualitative estimates of harm can provide some information about the risk without overstating the precision of what is known. As an alternative to prevalence‐based approaches, I described levels of psychological harms by examining the causes of psychological harm that exist in disasters and applying the data on those causal aspects in a structured fashion. Using the established literature, several steps were used to identify the qualitative aspects of psychological damage to be examined. First, I identified which diagnoses or syndromes are relevant to disasters and are appropriate to describe the concept of disaster‐related psychological damage (e.g. PTSD). Then I identified stressors that contribute to those syndromes (e.g. exposure to the dead or dying). This was accomplished through a review of the literature on disasters and mental health. Because of the large number of studies on psychological harms following disasters, I focused on systematic reviews and meta‐analyses summarizing the literature. Specific articles included reviews of the relationship between mental health consequences and either disasters (Bromet and Dew 1995; Ursano et al. 1995; Gerrity and Flynn 1997; Norwood et al. 2000; Tierney 2000; Ducrocq et al. 2001; Katz et al. 2002; Norris et al. 2002; Norris et al. 137 2002; Weiss et al. 2003; North et al. 2004; Galea et al. 2005; Adams and Boscarino 2006; Neria et al. 2008; Neria et al. 2009) or terrorism (North and Pfefferbaum 2002; Schlenger et al. 2002; Fullerton et al. 2003; Hall et al. 2003; Marshall et al. 2003; Ursano et al. 2003). Following this, stressors associated with psychological harms were matched to known attributes of the hazards. For example, average number of lives lost per year can be matched to stressors such as having a loved one die, being at personal risk of death, exposure to the dead, and other stressors. Some stressors indicate the particular circumstances of an individual, such as an individual feeling panicked at the time of the event; while these individual‐level stressors are important to explain the onset of mental health impairments at an individual‐level (Adams and Boscarino 2006), they are less useful for predicting the consequences of a hazard on the population. Only stressors which could be generalized to the population level were used. The stressors were matched to the set of defined attributes comprising aspects of health, economic damage, other damage including societal and environmental damage, and psychometric attributes of risk identified in an earlier chapter of this volume (see Table XXI). Table XXI‐ Attributes Available to Describe Aspects of Psychological Consequences Health Socioeconomic
Other attributes Average number of deaths per year Greatest number of deaths in a single event More severe injuries or illnesses per year on average Less severe injuries or illnesses per year on average Average economic damages per year Greatest economic damages in a single event Duration of economic damages Ability of individuals to control their exposure Time between exposure and health effects Size of area affected by Quality of scientific economic damages understanding Average environmental damages Combined Uncertainty per year Average displaced households per year Disruption of government services 138 Natural/human‐induced These identified attributes were then used to describe the psychological risk for each hazard. This was a multi‐step process. First, logical cut points were identified from the data to distinguish between low, moderate, and high levels of each attribute. Using these thresholds, each attribute was qualified as low, moderate, or high for each hazard. These low, moderate, and high levels for the attributes were then crossed in risk matrices to generate levels of psychological consequences for each hazard— hazards that were low on one dimension and moderate on the other dimension or low on both dimensions were considered to have low psychological consequences, while hazards that were high on one dimension and moderate on the other or were high on both dimensions were considered to have high psychological consequences. ApplyingaStressor‐basedApproachtotheHomelandSecurityRisk
SelectingaRepresentativeSetofDisorders:PTSDandDepression
Disasters have been associated with a wide range of disorders, including post‐traumatic stress disorder (PTSD), depression, general anxiety disorder, panic disorder, obsessive‐compulsive disorder, social phobia, specific phobia, and substance abuse disorders. As both the severity and the prevalence of these disorders following disasters vary substantially, I will limit consideration to two disorders consistently linked to disasters: PTSD and depression. Post‐Traumatic Stress Disorder (PTSD) is the mental health disorder most associated with disasters (McFarlane et al. 2009). Other acute stress disorders may be more widespread, but the severity of PTSD combined with the regularity of its occurrence make PTSD an important disorder for study. While the DSM (the standard for mental health diagnoses) has recently been updated, this dissertation was conducted using the DSM‐IV, which was the standard at the time. Using the DSM‐IV criteria, PTSD involves symptoms of re‐experiencing the traumatic event, persistent avoidance of 139 reminders of the traumatic event, numbing of general responsiveness, and persistent hyper‐vigilance, causing significant distress or impairment over a period of more than one month (American Psychiatric Association 1994; Lavery and Kelly 2002; Butler et al. 2003). This follows either a directly or indirectly experienced traumatic event that causes intense fear, helplessness, or horror in the person exposed. Depression is also strongly associated with disasters (Person et al. 2006; Maguen et al. 2009) and represents an archetype of severe disaster‐related mental illness distinct from stress‐related syndromes. Major depression is characterized by a depressed mood or diminished interest or pleasure, with additional possible symptoms including weight loss or gain, chronic insomnia or hypersomnia, observable agitation or psychomotor retardation, fatigue, feelings of worthlessness or guilt, diminished concentration, and recurrent thoughts of death or suicide ideation (American Psychiatric Association 1994). In major depression, these symptoms significantly impair important life functions. To some extent, PTSD is related to acute stressors while depression is related to on‐going disaster‐related problems, but this distinction is not so simple (Weiss et al. 2003). Depression can be triggered by acute stressors and is often comorbid with PTSD, while long‐term negative changes in life circumstances (such as job loss or death of a spouse) may increase stress or exacerbate existing disorders (Palinkas et al. 1993; Kessler et al. 1995; Zisook et al. 1998; Marshall et al. 2001; Norris et al. 2002; Kessler et al. 2003; Kessler et al. 2005; Adams and Boscarino 2006; Tanielian and Jaycox 2008). However, regardless of whether PTSD wholly represents acute stressors and depression wholly represents chronic stressors, or if both syndromes can result from both kinds of stressors, PTSD and depression describe are both relevant and well researched and were therefore used in this exercise. IdentifyingAttributestoDescribetheAnnualRiskofPTSD
Understandings regarding post‐traumatic stress disorder arose in the study of the mental trauma of warfare (Horowitz 1976; Horowitz 1979), and stressors contributing to PTSD have been 140 examined in contexts ranging from individual accidents to war (Baum 1993; Holeva et al. 2002; Tanielian and Jaycox 2008). Research on disasters finds that life‐threat and injury are consistent predictors of PTSD (Norwood et al. 2000; Norris et al. 2002; Fullerton et al. 2003; Galea et al. 2005; Neria et al. 2008; Neria et al. 2009). A study by Briere and Elliot (2000) found the attributes of exposure— particularly fear of death or physical injury— more important than the type of disaster with regards to reporting symptoms of mental trauma associated with PTSD (Briere and Elliott 2000). Two of the identified attributes are particularly useful for describing life‐threat and injury on an annual basis— average lives lost per year and average severe injury per year. Average lives lost per year may be useful in describing life risk in two ways. First, estimates of lives lost can describe the deaths which are directly witnessed. Second, actual lives lost can describe the number of lives nearly lost, so a higher estimate of lives lost also indicates a higher number of people whose lives were directly at risk but survived. Severe injuries may also be useful in describing the number of near deaths, plus can serve as a direct measure of physical injury as an important measure of exposure. These two attributes of average lives lost per year and average severe injuries per year were then crossed in a risk matrix. The decision was made that hazards that are either low on one factor and moderate on the other or low on both are considered to have low levels of PTSD, while those that are high on one and moderate on the other or high on both are considered to have high levels of PTSD. IdentifyingAttributestoDescribetheAnnualRiskofDepression
Depression can be triggered by a range of stressors immediately at the event itself or the consequences following the event. Given the range of impacts to which this aspect relates, more than one attribute will be used to describe the stressors. One of the most important aspects of post‐disaster depression is bereavement: dealing with the loss of a friend or loved one has been identified as a factor of depression in a large number of papers, both generally and in regards to disasters in specific (Gleser 141 et al. 1981; Green et al. 1990; Ducrocq et al. 2001; Livanou et al. 2002; Person et al. 2006). I decided to explain bereavement in terms of the number of lives lost, for which I have a direct estimate in average lives lost per year. Other important aspects of life loss relate to the loss of resources in a general sense— these include not only economic resources (such as property damage, homelessness, or joblessness) (Norris and Uhl 1993; McDonnell et al. 1995; David et al. 1996) but also physical disabilities and the drain on mental resources (such as a perceived loss of safety or a loss of free time) (Norris and Wind 2009). In this study average economic damage per year and duration of economic damages were used to describe physical damages and average severe injuries or illnesses per year was used to proxy disability. These four attributes were then combined to form an index of combined stressors for depression in a way that is conceptually identical to the process for PTSD. These two aspects of PTSD and depression were combined into a single measure of psychological consequences per year on average. The combined psychological consequence was defined as the greater of the two aspects. For example, if PTSD is high and depression is moderate, the combined psychological damage would be high. I defined combined psychological damage using the maximum because both depression and PTSD are important and because lower levels of one do not mitigate higher levels of the other. ApplyingStressorstoEstimateOverallPsychologicalConsequences
Using this framework, values describing the identified attributes were applied to estimate the average psychological damages per year. The best estimates of lives lost, more serious injuries or illnesses, economic damage, and duration of economic damage from other sections of this volume were used. These qualitative levels are separated by cut‐points. A decision was made to make the range of moderate estimates one order of magnitude wide and to use as the thresholds round numbers that can 142 be easily understood and communicated. Using these guidelines, cut points were selected that logically matched the data, described in Table XXII. Table XXII‐ Categories for Translating Quantitative Estimates to Qualitative Levels Attribute Average lives lost per year Average severe injuries per year Average economic damages per year Duration of economic damages Low level 0‐10 0‐50 $0‐$500M Days, days to weeks, weeks, weeks to months Moderate level 10‐100 50‐500 $500M‐$5B Days to years, weeks to years, months to years, months High level 100+ 500+ $5B+ Years, months to decades, years to decades, decades These levels were specifically designed for the set of hazards used here, but as the hazards used to create the scales include both high and low consequence hazards the cut points may also be useful to describe a larger set of hazards across the homeland security domain. However, these levels only describe relative risk within the homeland security domain and cannot be compared to psychological damage in other domains— while PTSD is classified as high for hurricanes, for example, this is only true relative to other homeland security hazards and not PTSD from car accidents or soldiers returning from war. The qualitative levels of the identified attributes of lives lost, injuries, etc., were then applied in the risk matrices. As discussed, the risk matrix for PTSD crosses average lives lost per year by average severe injuries per year (Figure 28). To visually represent the four attributes describing depression, I am presenting the combination of lives lost, injuries, and economic damages crossed by the duration of economic damages (Figure 29). These two aspects of psychological harm are very similar; at the level of precision of low, moderate, and high, the scale of the disaster seems to be a defining factor in identifying the level of psychological harm more than the kinds of damages the hazard creates. 143 Expected deaths per year on
average
Figure 28‐ Estimates of Average Levels of PTSD per Year for Selected Homeland Security Hazards pandemic,
nuclear,
earthquake
high
terrorist nuclear
detonation,
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pandemic flu
tornadoes,
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terrorist nuclear
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oil spills
green=low
yellow=moderate
red=high
high
pandemic flu,
hurricanes
Expected more severe injuries or illnesses per year
on average
Combined Losses Per Year On
Average
Figure 29‐ Estimates of Average Levels of Depression per Year for Selected Homeland Security Hazards earthquake,
pandemic flu,
hurricanes
high
anthrax, nuclear
detonation,
tornadoes
moderate
cyber-attacks,
explosives, oil
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low
pandemic flu,
hurricanes
earthquakes
tornadoes,
terrorist nuclear
anthrax attacks
detonation
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Cyber,
explosives
TIC, pandemic
flu, anthrax
attacks,
hurricanes,
tornadoes
oil spills,
terrorist nuclear
detonation,
earthquakes
Duration of Economic Damages
144 These two aspects of psychological harm (PTSD and depression) were then combined to create a single estimate of psychological damage per year (Table XXIII). The estimate of psychological harm was based on the greater of the two aspects of harm. In most cases, the expected psychological consequences of the hazard‐type were the same for both PTSD and depression. The one exception was the risk from oil spills where the risk of depression was considerably higher than the risk of PTSD. As oil spills contain significant long‐term economic damage through contamination, but little injury or loss of life, this corresponds with expectations. Table XXIII‐ Combined Annual Risk of Psychological Damage by Hazard Hazard
Oil spills
Toxic industrial chemical accidents
Cyber-attacks
Explosive bombings
Terrorist nuclear detonations
Anthrax attacks
Pandemic flu
Tornadoes
Hurricanes
Earthquakes
PTSD
low
low
low
low
high
moderate
high
moderate
high
high
Depression
moderate
low
low
low
high
moderate
high
moderate
high
high
Combined
moderate
low
low
low
high
moderate
high
moderate
high
high
Several important findings emerge from this framework and the assumptions spelled out here. First, it is possible to distinguish between hazards based on their psychological consequences, at least on a gross scale of low, moderate, and high. Hurricanes, earthquakes, pandemic flu, and WMD‐related terrorist attacks have distinctly higher psychological consequences per year on average than toxic industrial chemical accidents, cyber‐attacks, and terrorist explosive bombings. This reflects differences in both the likelihood and consequence of the events. Second, there is more variation in the combined psychological consequences associated with different types of terrorist attacks than different types of natural disasters. Terrorist events are defined by their cause, with events that are localized as well as events with broad damages, and the psychological consequences cover the range from low to high as a result. Natural disasters, on the other 145 hand, are distinguished from non‐disastrous thunderstorms due to their scope, making it reasonable that there are no low events included in natural disasters. Third, the levels identified for PTSD and those identified for depression are nearly identical. This suggests that at the level of precision used for this approach the scale of the disaster is the dominant force in estimating the psychological consequences, rather than specifics which focus on one aspect of psychological trauma over another. To some extent this is a reflection of the approach used, as many of the stressors that contribute to depression also contribute to PTSD. But it is also a reflection of the comorbidity of PTSD and depression, with a large percentage of people with PTSD also experiencing depression (Brady 1997; Kilpatrick et al. 2003; North et al. 2004; Kessler et al. 2005; Adams and Boscarino 2006; Felker et al. 2007). While the approach utilized in this study can usefully distinguish between the expected psychological consequences per year on a gross scale, there are several limitations. First, the qualitative levels produced here are relative measures. They will not be useful for comparing homeland security risk to other risks, such as accidents, war, or crime. Additionally, the set of hazards selected is not necessarily representative of all homeland security risks. While the set of hazards was selected to include both low and high average risk and both common and rare events in an effort to cover the range of disasters, it is possible that additional homeland security risks that were not selected may not map onto the scale in a useful way. Another limitation is that the estimated annual values that are used in the framework (e.g. expected lives lost per year on average) involve uncertainty, which in some cases can be quite large. While this approach uses a set of defensible estimates, it is possible that they are incorrect and a different estimate within the identified range is more accurate. Significant judgment is involved in estimating the risks for any given hazard and attribute. I attempted to address this limitation by 146 balancing the precision of the scale with the uncertainty of the estimates, but it is possible that misestimating the annual values will lead to misestimating the psychological risks of individual hazards, even on this gross scale. A final concern is inherent to risk matrices themselves; they may, in certain cases, produce inaccurate levels (e.g. estimating the risk as low when it should be moderate, etc.) (Cox 2008). While this is particularly true in matrices where the dimensions of risk combine multiplicatively (as with likelihood and consequence) but can be a concern here as well. As an example, imagine two new hazards, one with 501 deaths and 11 injuries and a second with 11 deaths and 501 injuries. In this case, the risk matrices developed here would show the risk of PTSD for the first as moderate (combining high lives lost by low injuries) and the second as high (combining moderate lives lost by high injuries), although we may expect the opposite to be true. While there are no such hazards in the set used in this analysis, it does highlight several concerns. These risk matrices may result in inaccurate levels of risk for hazards where one attribute is high and the other is low and for hazards with attributes that are both very close to and very far from the cut‐points. These concerns are reflected in my hazard set: one hazard (oil spills) presents as high on one dimension (duration of economic damages) and low on another (combined damage); two hazards present with estimates close to the cut‐points (terrorist explosive bombings, which is close to the cut‐point for lives lost, and earthquakes, which is close to the cut‐point on economic damages); and one hazard presents with estimates far out of the range of the other hazards (pandemic influenza, with estimates of average lives lost 40 times higher than the threshold values). I attenuated these concerns regarding the inaccuracy of the risk matrices to any one component by building redundancy into the approach. By using two attributes related to health and two attributes related to economic costs and two dimensions of mental health, the estimate is less sensitive to any one 147 particular aspect of the risk. For example, the estimate of average lives lost per year from terrorist explosive bombings is ten, which is exactly at the cut‐point between low and moderate levels of lives lost. In this study, the cut‐points were rounded down so explosive bombings had low levels of lives lost, which was applied in the risk matrix to give low levels of PTSD and low levels of combined psychological consequences per year on average. However, if we instead treated the level of lives lost as moderate, the results would still be the same; combining moderate lives lost with low more severe injuries per year would still result in low levels for PTSD and low levels of combined psychological consequences. Similarly, none of the four hazards where a possible concern was identified (oil spills, terrorist explosive bombings, earthquakes, and pandemic influenza, as above) were sensitive to either the specific cut‐
points chosen or the format of the risk matrix. But while this approach did eliminate the sensitivity of the outcome to the initial cut‐points for the hazards used in this particular study, it is still a concern associated with the approach, and researchers should be aware of these concerns when extending this approach to other hazards or contexts. While qualitative estimates of the expected annual psychological consequences of disasters remain challenging, qualitative estimates can provide some information. An approach based on the factors associated with PTSD and severe depression can be used to estimate the psychological harm of one type of disaster relative to another using commonly available, open‐source information. This dissertation presents one possible approach to generate such a relative level of concern regarding the average annual psychological consequences of homeland security events. Given the importance of psychological consequences as an aspect of homeland security events, consideration should be given to including relative levels of psychological consequence along with other aspects of consequence when assessing homeland security risk. 148 References
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DerivingComparableQuantitativeAttributesof
HomelandSecurityHazardsfromAvailableOpen‐sourceData
This appendix documents the selection of estimates used to describe the various hazards. The specific estimates that were used in the risk summary sheets are fully documented in those risk summary sheets themselves, which are collected in Appendix D. However, the risk summary sheets do not describe the estimates that were not used or the methods by which one estimate was chosen over another. This section provides some additional detail on how the particular estimates used in the summary sheets were developed from the range of estimates that were available. The initial section describes the considerations that went into estimating the quantitative attributes. The particular rationale for estimating each attribute of risk is summarized in Table then described in greater detail with each individual attribute by hazard. The structure of this appendix includes general considerations, a summary of rationale for each hazard and attribute, then additional detail on the estimates for each attribute by hazard. First is a discussion of the approach generally, and considerations that went into preparing estimates. Then specific estimates are detailed, starting with a summary in Table XXIV and then continuing in detail by hazard. Each hazard is summarized in a table that describes what was selected for the best estimate, why it was selected, the other possibilities that were available, and the rationale for the lower and upper bounds. Then additional detail on that hazard is added by attribute, describing the considerations of data and methods that went into the selection of the best, low, and high estimates. The descriptions begin with earthquakes and other natural disasters, and then continue with terrorist events and major accidents. 157 ConsiderationsinEstimatingQuantitativeAttributesofRiskforHomeland
SecurityHazards
Seventeen attributes were selected to describe homeland security hazards. Ten of these hazards were described qualitatively for conceptual reasons detailed in Chapter 3. The other seven hazards could be described quantitatively. These included quantifiable attributes of lives lost, injuries or illnesses, economic damage, and individuals displaced from their homes. Two forms were selected in which these attributes could be presented: expected consequences per year on average or as greatest consequences in a single event. The preferred format for estimates of expected consequences per year on average was to have a best estimate within lower and upper bounds. One attribute that described expected consequence per year on average— average individuals displaced per year— did not have sufficient data to support a best estimate, with only one or two data sources for some of the hazards. Instead, no best estimate was presented for average individuals displaced per year, only lower and upper bounds. The preferred format for greatest consequences in a single event (i.e., greatest lives lost in a single event and greatest economic damages in a single event) was also presented as a range with no best estimate, but this was for conceptual reasons not reasons of data sufficiency. See Appendix C for the specific definitions used for the attributes of risk. Estimating the consequences for homeland security risks requires significant judgment. Several procedures were used to address the uncertainty in the estimates. First, estimates were not presented as a single best point estimated but included a lower and upper bound for each estimate. When calculating these bounds, I tried to estimate these bounds using data and methods similar to the best estimate when appropriate, although in some cases different data or different methods were required to capture the full range of the estimate. Each of these quantitative estimates was then rounded to one 158 significant figure to avoid overstating the precision of the estimates, and qualitative descriptions were used when quantitative estimates could not be supported. These estimates were compiled by hazard and each hazard sheet individually peer‐reviewed, which led in some cases to revisions of estimates. This process developed one set of useful and defensible estimates of risk for the selected homeland security hazards. Preferred estimates were based on historical data when appropriate; however, the extent to which historical risk is representative of current risk can vary. The risk can evolve, due to advancing technologies, shifts in demographics, or changing political choices. Cyber‐attacks are an example of the importance of advancing technologies. They are an emerging threat based on computer sophistication and interconnectivity beyond what existed even 20 years ago, so using historical data on cyber‐attacks will not necessarily be representative of the current threat. Hurricanes are an example of the importance of demographics shifts, with increased wealth being concentrated in hurricane prone coastal areas. Terrorist explosive bombings are an example of changing political realities, resulting from the choices of intelligent adversaries, which can shift over time. The residual risk can also evolve as new approaches are brought to address existing risks. For example, hurricane and tornado deaths in the U.S. decreased significantly with the implementation of advance warning technologies, so using data prior to those technologies will not represent the current risk. When making estimates based on averages from historical data, it is important to consider the frequency of events. Large tornadoes occur every year while large earthquakes occur only a few times a century, suggesting that average consequence per year requires more years of data to create an appropriate estimate for earthquakes than it does for tornadoes. Often, the bounds can be drawn from the same set of data as the best estimate. For example, there were several sources of data available for making estimates of lives lost in a hurricane, including NOAA data, EM‐DAT data, and other data 159 sources. The NOAA data was chosen as a strong data source. Estimates of lives lost were made using 30 year averages for the best case. Rather than switching to another dataset, I exploited the variation in the NOAA data, taking the low and high 30 year averages from within the 70 year period. The frequency of events can also be important when considering estimates of greatest consequences per event. For rare events, the largest event which occurred may not reflect the largest event which can occur. This may require the use of scenarios or events in other countries as analogues for the current risk. For example, the greatest number of lives lost from toxic industrial chemical accidents in the U.S. is around 600, but the greatest number of lives lost anywhere in the world numbers in the thousands. Some risks could not be described using historical data, either because they have rarely or never occurred in the United States or because the historical data are not recorded. In these cases, one approach is to create an expected value from combining likelihood with consequence. This is more effective for risks that reflect a small number of high consequence events than for risks that include a continuum of both infrequent, high consequence events (e.g., earthquakes) and more frequent, lower consequence events (e.g., tornadoes). When combining likelihood and consequence, each aspect is determined individually. The approach to estimate these aspects follows the approach for estimating the risks generally, preferring historical data but only when the historical data is considered representative of the current risk. The consequences of a hazard in this approach may be drawn from actual events or scenarios, while the likelihood may be drawn from actual events, modeled estimates, or expert opinions. Establishing bounds for estimates of combined likelihood and consequence involved varying likelihood for a fixed consequence or varying consequence for a fixed likelihood. Varying both likelihood and consequence would compound the uncertainty of the bounds beyond what it deserves. For 160 example, there is some uncertainty as to both the likelihood of a pandemic in a given year and in the consequence of a pandemic, but they are inversely related. There were three large pandemics in the U.S. in the past 100 years, and other smaller pandemics. If those three largest are the risks about which we are concerned then the evidence supports a best estimate of 3.3% a year. However, there were also smaller pandemics, and if those are included in this consideration (raising the likelihood to 7% per year) then the consequences of the typical event would be lower. Increasing the likelihood to 7% without decreasing the consequences that we would multiply it by would overestimate the expected consequence, which can be useful when setting a bound; but increasing the likelihood and using a higher consequence event for consequence would overstate the risk. Terrorist nuclear attacks present another example where varying both the likelihood and consequence would not reflect the actual risk. The best estimate for the number of people killed per year due to terrorist nuclear attacks was calculated as the product of best estimates of likelihood and consequence. It was believed that while a 10 kiloton warhead similar to those used in Hiroshima and Nagasaki would be the warhead that terrorist would be most likely to obtain and use, larger warheads are possible but less likely. Accordingly, the distribution of possible values for the number of deaths is skewed. Logically, if we think that the best estimate should be based on the larger consequence, we compensate for the smaller likelihood of such an event taking place. Another approach for quantifying estimates where annual data are not available is applying a multiplier to known estimates. For example, counts of injuries per year for hurricanes do not reflect the distinction between more severe and less severe injuries. Instead, an estimate of the ratio of more severe to less severe injuries or illnesses was developed from specific hurricanes and applied to data on the counts of general injuries or illnesses which are recorded. These multipliers can also be applied when there is a relationship between consequences. For example, there is no estimate for the greatest economic damages to represent a toxic industrial chemical leak the size of Bhopal but occurring in a 161 Western nation. Instead, the ratio of the best estimate of lives lost to the high estimate of lives lost is calculated and applied to the best estimate of average economic damages to derive a high estimate of average economic damages. 162 Table XXIV‐ Sources and Methods for Estimates of Risk by Hazard and Attribute Average number of deaths per year Greatest number of deaths in a single episode Average more severe injuries or illnesses per year Average less severe injuries or illnesses per year Average economic damages per year Greatest economic damages in a single episode Average individuals displaced per year Earthquake Gov. data Hurricane Average 30‐
year average Largest actual events Tornadoe Projected time‐trends Gov. data Applied multiplier to lives lost Applied multiplier to lives lost Gov. data Applied multiplier to lives lost Applied multiplier to lives lost Gov. data Average 10‐
year average Gov. data
Largest scenarios Largest actual events Largest actual events Few estimates, covered range Applied multiplier to lives lost Few estimates, applied multiplier Largest scenarios Largest actual events Pandemic Influenza Historical L, average of scenarios C Largest scenarios Historical frequency L, average of scenarios C Historical frequency L, average of scenarios C Historical frequency L, average of scenarios C Largest scenarios Consensus no mechanism Consensus no mechanism Applied multiplier to lives lost Historical L, Historical C Average 10 year average Consensus no mechanism Applied multiplier to lives lost Historical L, Historical C 10 year average Applied multiplier to lives lost Largest actual to largest scenario Combine expert and historical L, middle NPS C Largest scenario to largest analogue Cost per gallon times average gallons spilled Largest actual events Applied multiplier to lives lost Consensus no mechanism Terrorist Explosive Bombing Average 10 year average Largest actual to largest scenario Average 10 year average Expert L, expert scenario C Best scenario to largest scenario Best scenario to largest scenario Expert L, low and high scenario C Expert L, low and high scenarios C Anthrax Attack Expert L, average of scenarios C Best scenario to largest scenario Expert L, average of scenarios C Expert L, average of scenarios C 163 Cyber‐
attack Consensus no mechanism Largest analogous event Toxic Industrial Chemical Accident Average 10 year average Largest actual events Terrorist Nuclear Detonation Expert L, average of scenarios C Best scenario to largest scenario Expert L, average of scenarios and multiplier C Expert L, average of scenarios and multiplier C Expert L, average of scenarios C Largest actual to multiplier to largest analogue Applied multiplier to lives lost Oil Spill Historical L, Historical C Largest actual events Historical L, Historical C Earthquakes
Table XXV‐ Sources and Methods for Estimates of Earthquake Risk Attribute
Average
Lives Lost
Greatest
Lives Lost
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Average
Economic
Damage
Greatest
Economic
Damage
Individuals
Displaced
per Year
Summary of Available
Data
Significant historical
record, with “state of the
art” estimates modeled
on historical data
Historical data on
earthquakes preesnting
moderate damage,
modeled data for
earthquakes with heavy
damage
Significant historical
record, with “state of the
art” estimates modeled
on historical data
Significant historical
record, with “state of the
art” estimates modeled
on historical data
Significant historical
record, with “state of the
art” estimates modeled
on historical data
Historical data on
earthquakes preesnting
moderate damage,
modeled data for
earthquakes with heavy
damage
Significant historical
record, with “state of the
art” estimates modeled
on historical data
Low Estimate
Best Estimate
High Estimate
Best likelihood times
a low estimate for a
large California
earthquake
Best estimate based
on FEMA expected
value estimates.
Best likelihood times
a high estimate for a
large California
earthquake
Selection of an
estimate for a less
damaging but more
likely to earthquake
scenario
N.A.
Field et al. estimate
for earthquake under
Los Angeles
Best likelihood times
a low estimate for a
large California
earthquake
Best likelihood times
a low estimate for a
large California
earthquake
EM-DAT average of
costs from notable
earthquakes, 19802010
Best estimate based
on FEMA expected
value estimates.
Best estimate based
on FEMA expected
value estimates.
Best likelihood but
high estimate of
consequences
Losses from the
largest historical
earthquake, adjusted
for inflation
N.A.
Field et al. estimate of
earthquake under LA
EM-DAT average of
displacements from
notable earthquakes,
1980-2010
N.A.
Best likelihood with
high estimate of
consequences
Best estimate based
on FEMA expected
value estimates.
Best likelihood times
a high estimate for a
large California
earthquake
Best likelihood times
a high estimate for a
large California
earthquake
Earthquakes are a regularly occurring natural phenomenon, with a number of small earthquakes occurring every year, a small number of earthquakes where the effects can be felt every decade, and several earthquakes with significant numbers of deaths every century. Their occurrence has very little to do with human actions and can be described probabilistically. Given their frequency, they can be described by historical data over a time‐frame of several decades. 164 AverageLivesLostperYear
Best estimates of expected value per year for lives lost, injuries or illnesses, and economic damages were based on FEMA data (FEMA 2008a). A National Academies report on risk analysis in DHS describes DHS’s models of natural disasters as “near the state of the art,” and the use of FEMA estimates was based on that claim (Committee to Review the DHS's Approach to Risk Analysis 2010). This best estimate was 100 deaths per year, based on estimates by FEMA (FEMA 2008a). However, the FEMA estimates were not sufficient to create a range of possible values from low to high. To create a range of possible values, a combination of likelihood and consequence was used. As was standard in such calculations, the bounding estimates only varied likelihood or consequence, and not both. In this case, a single likelihood was used and applied to a range of consequences. Estimates for likelihood of an earthquake in a given year range between 3.3% and 12%, from which a best estimate of 6.8% was selected. Estimates for the likelihood of an earthquake came from historical data and identified models based on that data. These likelihood estimates also depend on the size of the earthquake— the likelihood of an earthquake with a magnitude 7.5 or larger is smaller than the likelihood of an earthquake with a magnitude of 6.5 or larger. I identified estimates of likelihood for earthquakes of magnitude 6.5 or larger, with less consideration of estimates of likelihood representing earthquakes 7.5 or higher. The low estimate of 3.3% represents the single major earthquake in the past 30 years. The high estimate of 12% comes from a paper that reports a U.S. Geological Survey estimate. An estimate of 6.8% likelihood of a major earthquake in a given year was adopted based on USGS estimates (USGS). Estimates used to represent the consequences of a typical earthquake in a given year range from 60 deaths (from the Northridge earthquake of 1994) to 4,500 (from Steinbrugge et al.’s estimates of a large California earthquake) (Steinbrugge et al. 1987). Larger estimates of earthquakes do exist— 165 up to 18,000 fatalities for a scenario with an earthquake directly under Los Angeles— but these were considered to be representative of outlier events and not considered as representative of a typical major earthquake. To create my low and high bounds of deaths per year on average, I applied the likelihood of 6.8% for an earthquake in a given year on average to low and high estimates of lives lost per earthquake. These gave a lower bound of 2 deaths per year and an upper bound of 300 deaths per year. These estimates cover most of the possibilities of likelihood and consequence. The only portions of the range that are not included in the bounds using this approach come when considering the very high consequence events that are not typical of historical events, or when considering a high estimate of both likelihood and consequence or a low estimate of both likelihood and consequence. The possible estimates of expected lives lost are presented in Figure 30. Each square represents the product of likelihood and consequence. Estimates that are within the selected bounds are represented by color. As the figure illustrates, most combinations of likelihood and consequence are within these bounds; only when a low likelihood and a low consequence are combined, or when a high likelihood and high consequence are combined, are the estimates outside the bounds. These low/low and high/high combinations were believed to be not representative of the average risk. 166 consequences
Figure 30‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Earthquakes likelihood
2%
3%
4%
5%
6%
7%
8%
9% 10% 11% 12%
33 0.66 0.99 1.32 1.65 1.98 2.31 2.64 2.97
3.3 3.63 3.96
60
1.2
1.8
2.4
3
3.6
4.2
4.8
5.4
6
6.6
7.2
1400
28
42
56
70
84
98
112
126
140
154
168
1500
30
45
60
75
90
105
120
135
150
165
180
1800
36
54
72
90
108
126
144
162
180
198
216
3000
60
90
120
150
180
210
240
270
300
330
360
4500
90
135
180
225
270
315
360
405
450
495
540
14000
280
420
560
700
840
980 1120 1260 1400 1540 1680
18000
360
540
720
900 1080 1260 1440 1620 1800 1980 2160
Between low and best estimates
Between best and high estimates
GreatestLivesLostinaSingleEvent
Recent earthquakes in the U.S. have not reflected worst case scenarios. One of the largest recent earthquakes occurred in Reseda, California in 1994 (the Northridge Earthquake); even though the earthquake registered 6.7 on the Richter scale, there were fewer than 100 deaths. Larger earthquakes have occurred— including the San Francisco Earthquake of 1906 that killed over 3,000 people— but not recently. Scenarios present estimates of lives lost larger than those in the historical record, including estimates of 4,500 fatalities for a 7.5 magnitude earthquake in the San Francisco Bay area (Steinbrugge et al. 1987) and 18,000 fatalities for an earthquake directly under Los Angeles (Field et al. 2005). Estimates of lives lost in historical events and scenarios of possible events are presented in Figure 31. Because earthquakes occur so rarely, I perceived these scenarios as more representative of the worst case scenarios than the historical record. These scenario estimates (rounded to the nearest significant digit) of 5,000 to 20,000 deaths were selected as the greatest estimate of lives lost. 167 Figure 31‐ Scenarios of Lives Lost in Major Earthquakes 20000
18000
Deaths included in greatest
deaths estimate
16000
14000
12000
10000
8000
6000
Deaths included in best estimate average
4000
2000
0
AverageMoreSevereandLessSevereInjuriesorillnessesperYear
Of the estimates of injuries and illnesses, most but not all included the distinction that was used in my definitions, between hospitalized and non‐hospitalized injuries. This distinction was included in 3 of the 5 estimates of average injury estimates and 5 of the 8 estimates of injuries in a single event. As with lives lost, I adopted FEMA’s estimates as “near state of the art” for a best estimate of injuries or illnesses per year on average. FEMA estimated 70 more severe and 3,000 less severe injuries or illnesses per year as a best estimate (FEMA 2008). Also as with lives lost, there were not enough additional annual average estimates to establish a range, so I calculated the range for injuries or illnesses from earthquakes using the combination of likelihood and consequence. The best estimate of a 6.8% likelihood of an earthquake occurring in a given year (discussed in the section on average lives lost) was applied to a range of consequences. These consequences range from 138 hospitalizations and 8,000 non‐hospitalized injuries in the Northridge earthquake to 13,500 hospitalizations and 135,000 non‐hospitalized injuries from the high estimate from Steinbrugge et al. (Steinbrugge et al. 1987). These estimates of consequence were multiplied by a 6.8% likelihood of an earthquake in any given year (described in the section on average lives lost per 168 year from earthquakes) to generate the low and high estimates of injuries for earthquakes. More severe injuries or illnesses ranged from 10 per year to 900 per year while less severe injuries or illnesses ranged from 500 per year to 9,000 per year on average. AverageEconomicDamageperYear
As compared the average yearly deaths, there were enough estimates of average yearly economic damage from earthquakes to establish a range using estimates of expected value and not separately combining likelihood and consequence. Seven estimates of average economic damage per year were identified (see Figure 32). My best estimate for the average yearly costs of earthquakes is $5 billion, drawn from FEMA’s HAZUS‐MH model. (FEMA 2001) As before, the DHS natural disaster estimates were selected because they are considered “near state of the art” (Committee to Review the DHS's Approach to Risk Analysis 2010). The lower estimate of $1.2 billion comes from the average damages from earthquakes in EM‐
DAT for the previous 30 years (EM‐DAT 2011). The higher estimate of $9 billion comes from Petak and Atkisson. (Petak and Atkisson 1985). Figure 32‐ Estimates of Average Economic Damage per Year from Earthquakes $10,000,000,000
Damage estimates used
$1,000,000,000
$100,000,000
169 GreatestEconomicDamageinaSingleEvent
There are a number of estimates of economic damage from an earthquake, including both records of historical events and estimates from scenarios. The economic damage relates to the size of the earthquake and the location where it hits. The largest estimates were selected to reflect the range of greatest economic damage. At the lower end of the range, I used the largest damages that actually occurred in the United States— $60 billion for the historical costs of the Northridge earthquake. It is certain that an earthquake can have that much damage because it has occurred. Scenarios present higher estimates, and the largest of the scenarios can be used for an upper bound. The largest scenario was selected as an upper bound— a $1 trillion scenario from an earthquake directly under Los Angeles with fire damage (Field et al. 2005; Jones et al. 2008). AverageIndividualsDisplacedperYear
Estimates of individuals displaced are less common than other attributes for describing earthquakes. These included estimates of average exposure from FEMA and EM‐DAT, plus estimates per event from the National Planning Scenarios and other sources. The low estimate was 700, the average number of earthquake related displaced individuals over the last 30 years from EM‐DAT data. The high estimate was 20,000, calculating by bringing the number of individuals displaced in a San Francisco earthquake from the National Planning Scenarios and a 6.8% likelihood of an earthquake in California (see above). 170 Hurricanes
Table XXVI‐ Sources and Methods for Estimates of Hurricane Risk Attribute
Average
Lives Lost
Greatest
Lives Lost
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Average
Economic
Damage
Summary of Available
Data
More than a century of
historical data, with
methodological
adjustments for living
patterns
More than a century of
historical data, with
methodological
adjustments for living
patterns
More than a decade of
historical data; also 30
years of data on
injuries/death from large
hurricanes
More than a decade of
historical data; also 30
years of data on
injuries/death from large
hurricanes
More than a century of
historical data, with
methodological
adjustments for living
patterns and wealth
Low Estimate
Best Estimate
High Estimate
Smallest 30 year
average from the past
70 years of NOAA
data
Average of the 30year averages from
the past 70 years of
NOAA data
Largest 30 year
average from the past
70 years of NOAA
data
Low estimate for
official deaths from
largest recent
hurricane (Katrina)
N.A.
High estimate for
official deaths from
largest recent
hurricane (Katrina)
Applied a
proportional
multiplier to low lives
lost
Applied a
proportional
multiplier to best
lives lost
Applied a
proportional
multiplier to high
lives lost
Applied a
proportional
multiplier to low lives
lost
Applied a
proportional
multiplier
Applied a
proportional
multiplier to high
lives lost
Low of the 10-year
averages from the
past 70 years adjusted
for wealth
Average of the 10year averages from
the past 70 years
adjusted for wealth
High of the 10-year
averages from the
past 70 years adjusted
for wealth
Greatest
Economic
Damage
More than a century of
historical data, with
methodological
adjustments for living
patterns and wealth
Hurricane with the
greatest wealthadjusted damage in
recent times, the 1992
Hurricane Andrew
N.A.
Individuals
Displaced
per Year
Data on largest
Applied a
proportional
multiplier to lives lost
N.A.
Hurricane with
greatest wealth
adjusted damage ever,
the 1926 Miami
hurricane. Similar to
the 2005 Hurricane
Katrina
Applied a
proportional
multiplier to lives lost
Hurricanes are a regularly occurring natural phenomenon, with a small number of major hurricanes occurring every year and a small number of hurricanes leading to significant numbers of deaths every decade. Their occurrence has very little to do with human actions and can be described 171 probabilistically. Given their frequency, they can be well described by historical data over a time‐frame of several decades. AverageLivesLostperYear
As hurricanes are common in the U.S., with several occurring each year, risk estimates can be drawn from historical data from the National Oceanic and Atmospheric Administration (NOAA). Deaths from hurricanes had been on the decline until Hurricane Katrina in 2005, an outlier that shifted the averages considerably (see Figure 33). Because of the importance of these rare but high consequence events, I chose a 30 year average to examine the data so as to smooth out the effect of outliers. The average of these 30 year averages is used as the best estimate, while the low 30 year average and the high 30 year average were used as the low and high bounds respectively. This led to a best estimate of 40 lives lost per year on average from hurricanes, between a low of 10 and a high of 60. Figure 33‐ Deaths from Hurricanes in the U.S. 1940‐2009 1200
1000
800
600
400
200
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
0
GreatestLivesLostinaSingleEvent
The estimate of greatest lives lost in a single event came from historic estimates of lives lost from Hurricane Katrina in 2005. Most estimates for the number of deaths from Hurricane Katrina are between 1,000 and 2,000, but some estimates have been as high as 4,000. Only one U.S. hurricane 172 exceeded this range— the Galveston Hurricane of 1900— but given that the conditions of 1900 were much different than today it was not judged representative of today’s risk. All other hurricanes that have struck the U.S., even those prior to hurricane forecasting and warning technologies, are consistent with Hurricane Katrina as the estimate of greatest fatality. I selected a greatest number of lives lost in a hurricane is between 2,000 and 4,000 based on Hurricane Katrina. AverageMoreSevereandLessSevereInjuriesorillnessesperYear
The estimates of the number of injuries or illnesses from hurricanes are not as strong as the estimates of the number of deaths. Only two sources for the number of injuries or illnesses per year on average were immediately identified, and neither included the distinction between more severe and less severe injuries or illnesses. Because of this limitation, I did not derive estimates of average injuries or illnesses from the historical data. Instead, the estimates for more and less severe injuries or illnesses were calculated by applying a ratio of injuries to deaths to the estimate of deaths per year on average. I then also applied a multiplier for the ratio of more severe to less severe injuries or illnesses based on hospitalizations. The estimate of injuries per life lost came from specific cases— the Hurricanes Andrew, Hugo, Elena, and Gloria (Shultz et al. 2005). The estimates of the percent of injuries that resulted in hospitalizations came from Hurricane Isabella (Gagnon et al. 2005). This led to an estimate of 600 more severe injuries or illnesses, in a range of 200 and 1,000 more severe injuries or illnesses, and 1,000 less severe injuries or illnesses, in a range of 400 and 2,000 less severe injuries illnesses per year. AverageEconomicDamageperYear
Similar to lives lost, best estimates of average economic damage were drawn from historical data. However, NOAA data were not used— because people have increasingly moved to coastal areas over the last 70 years, a wealth‐adjusted estimate of the historical damage was used to represent the 173 present exposure (Pielke Jr et al. 2008). Additionally, because buildings cannot evacuate from hurricanes as people can, the economic damage from hurricanes is less dependent on outliers. For this reason, I used a span of 10 years to average historical data. The average of these 10 year averages was used for the best estimate, while the low 10 year average and the high 10 year average were used as the low and high bounds respectively. This led to estimates of $10 billion of economic damage per year on average, between estimates of $2 billion on the lower bound and $20 billion on the upper bound. GreatestEconomicDamageinaSingleEvent
As with average economic damage, greatest economic damages in a single event were drawn from wealth‐adjusted historical data. At the high end of the range is a wealth‐adjusted estimate from the Miami hurricane of 1926 ($200 billion), which is very similar to the estimates for Hurricane Katrina in 2005. At the low end of the range are estimates from Hurricane Andrew in 1992 ($60 billion), the next largest hurricane in the past 30 years. All these wealth‐adjusted estimates come from Pielke et al. (Pielke Jr et al. 2008). AverageIndividualsDisplacedperYear
There were few estimates of individuals displaced by hurricanes on an average per year basis. Instead, estimates of people displaced per dollar of economic damage were applied to the estimates of economic damage per year to calculate estimates of individuals displaced per year. The ratio of people displaced from their homes to economic damage was derived using the average of several studies looking at Hurricanes Andrew, Ike, Katrina, and Hugo (Comerio 1997; Gabe et al. 2005; FEMA 2008b; Blake et al. 2011). This led to a range of between 10,000 and 100,000 displaced individuals per year on average. 174 Tornadoes
Table XXVII‐ Sources and Methods for Estimates of Tornado Risk Attribute
Average
Lives Lost
Greatest
Lives Lost
Summary of Available
Data
Significant historical
data, with thirty years of
annual data
Significant historical
data, with over 100 years
of single incident data
Low Estimate
Best Estimate
High Estimate
Low estimate
projection based on
time trends
Best estimate
projection based on
time trends
High estimate
projection based on
time trends
Largest tornado of the
last 20 years
N.A.
Largest tornado in all
years in the U.S.
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Average
Economic
Damage
Greatest
Economic
Damage
Significant historical
data, with thirty years of
annual data
Injury to death ratio
applied to low
estimate
Injury to death ratio
applied to best
estimate
Injury to death ratio
applied to high
estimate
Significant historical
data, with thirty years of
annual data
Significant historical
data, with thirty years of
annual data
Significant historical
data, with over 100 years
of single incident data
Injury to death ratio
applied to low
estimate
Injury to death ratio
applied to best
estimate
Injury to death ratio
applied to high
estimate
Estimates from 20002008
Estimates from recent
data
Estimates from 19502009
Individuals
Displaced
per Year
Significant historical
data, with thirty years of
annual data
Average of most
costly tornadoes since
1950
Applied estimate
proportionally based
on ratio of low to best
lives lost
N.A.
N.A.
Greatest event
damage estimate from
NOAA
Applied estimate
proportionally based
on ratio of high to
best lives lost
Tornadoes are a regularly occurring natural phenomenon, with a few deadly tornadoes and many more less severe tornadoes occurring every year. Their occurrence has very little to do with human actions and can be described probabilistically. Given their frequency, they can be well described by historical data over a time‐frame of decades. AverageLivesLostperYear
Estimates for the average lives lost per year come from historical data adjusted for contemporary trends. Due to advances in weather forecasting and warning systems, the number of deaths from tornadoes has declined significantly over the past 70 years (see Figure 34) and estimates of the current risk developed from historical data need to take this trend into account. Projections by 175 Brooks and Doswell take these trends into account and calculate a range of estimates for the average risk per year (Brooks and Doswell III 2001). I adopted the estimates and bounds from Brooks and Doswell as my estimates of lives lost per year on average, which includes a low (10), best (40), and high estimate (100) for deaths per year on average. This best estimate is lower than estimates taken directly from the historical data, but this should be expected as historical estimates that do not take these downward trends into account will overstate the risk. Figure 34‐ Average Deaths per Year from Tornadoes (Plotted by the Last Year of the Averaged Period) 250
200
150
30‐year
average
100
10‐year
average
50
0
1950
1960
1970
1980
1990
2000
2010
GreatestLivesLostinaSingleEvent
Estimates come from the largest historical events. The low end of the range (300 deaths) represents the largest number of deaths in a single event in recent history, a combined series of tornadoes in 2011. The high end of the range (700 deaths) represents the largest number of deaths in the U.S. ever, the Tri‐State Tornado of 1929. Given advances in forecasting and warning technology, a tornado as deadly as the 1929 tornado is unlikely to be experienced again, suggesting that current greatest number of lives is between the two bounds. 176 AverageMoreSevereandLessSevereInjuriesorillnessesperYear
The best estimate of this attribute came from the average number of injuries from tornadoes in recent history recorded by NOAA, using available data from 2000 to 2008. NOAA estimates of injuries were not available further back than 2000, so low and high bounds were generated by multiplying the ratio of injuries to lives lost from 2000‐2008 by the low and high estimate of average number of lives lost per year. This estimated average number of injuries per year was multiplied by the fraction of injuries that could be classified as more severe and as less severe. The ratio of more severe to less severe injuries came from a study from Brown (Brown et al. 2002). This led to a best estimate of 200 more severe and 700 less severe injuries or illnesses per year on average due to tornadoes, between a range of 200 and 600 for more severe and 600 and 2,000 less severe injuries or illnesses per year on average. AverageEconomicDamageperYear
Estimates of tornado damage come from NOAA and EM‐DAT. Older estimates of tornado damage do not reflect changes in wealth in areas where tornadoes hit. So instead, I utilized more recent estimates and estimates that adjusted for wealth. A range of estimates from good sources were identified, including NOAA, EM‐DAT, and the Extreme Weather Sourcebook. The lowest of these was the recent data, from a NOAA estimate of recent economic damages from 2000‐2008, presenting an estimate of $900 million. In the middle of this range were wealth‐adjusted estimates 1950‐1999 and 1950‐2009. These estimates were in the range of $1‐1.5 billion. At the upper end of the range were estimates from EM‐DAT, around $2 billion. The best estimate came from the lower of the wealth‐
adjusted averages, rounded down to one significant figure at $1 billion, while the high estimate came from the higher of the wealth‐adjusted averages, rounded up to one significant figure at $2 billion. This upper number was also consistent with the EM‐DAT figure when rounded to one significant figure. 177 GreatestEconomicDamageinaSingleEvent
The estimates of greatest economic damage come from historical events. The lower bound ($900 million) represents the average of the ten most costly tornadoes in the U.S. since 1950. The upper bound ($3 billion) represents the largest estimates of tornado damage, including the largest NOAA estimate and estimates reflecting a tornado hitting the Dallas‐Ft. Worth area (Rae and Stefkovich 2000; Ross and Lott 2003). AverageIndividualsDisplacedperYear
A single estimate of the number of individuals displaced per year from tornadoes was identified (Sanderson 1989). I took this as a best estimate of individuals displaced but then created a range for displacement based on the range of low to high lives lost. The ratio of best estimate of individuals displaced to the best estimate of lives lost was calculated, then applied to the low and high estimates of lives lost per year on average to develop a range for individuals displaced. This gave a range of between 30,000 and 200,000 individuals displaced per year on average. 178 PandemicInfluenza
Table XXVIII‐ Sources and Methods for Estimates of Pandemic Flu Risk Attribute
Summary of Available
Data
Low Estimate
Best Estimate
High Estimate
Average
Lives Lost
Some historical data on
consistent with an
infrequent event,
significant modeled
consequence data
Low historical
frequency times same
consequence estimate
as best
Historical frequency
of pandemics in past
100 years times midpoint of consequence
estimates from
National Planning
Scenarios
High historical
frequency times same
consequence estimate
as best
Greatest
Lives Lost
Some historical data on
consistent with an
infrequent event,
significant modeled
consequence data
Median estimate for a
Spanish-style flu
today
N.A.
Death rate from the
Spanish Flu if applied
to today’s population
Average
More
Severe
Injuries
Some historical data on
consistent with an
infrequent event,
significant modeled
consequence data
Low historical
frequency times same
consequence estimate
as best
Average
Less Severe
Injuries
Some historical data on
consistent with an
infrequent event,
significant modeled
consequence data
Low historical
frequency times same
consequence estimate
as best
Average
Economic
Damage
Significant modeled
consequence data
Low historical
frequency times same
consequence estimate
as best
Significant modeled
consequence data
No mechanism for direct
displacement
Greatest
Economic
Damage
Individuals
Displaced
per Year
Historical frequency
of pandemics in past
100 years times midpoint of consequence
estimates from
National Planning
Scenarios
Historical frequency
of pandemics in past
100 years times midpoint of consequence
estimates from
National Planning
Scenarios
Historical frequency
of pandemics in past
100 years times midpoint of consequence
estimates from
National Planning
Scenarios and CDC
High historical
frequency times same
consequence estimate
as best
Low estimate from
CDC
N.A.
High estimate from
national planning
scenarios
No mechanism for
direct displacement
N.A.
No mechanism for
direct displacement
High historical
frequency times same
consequence estimate
as best
High historical
frequency times same
consequence estimate
as best
Pandemic influenza is a naturally occurring phenomenon, but society plays an important role in its transmission. Influenza occurs regularly at low levels throughout the population, but occasionally an 179 influenza virus emerges for which humans have little immunity and is easily transmitted from person to person. This results in rare, high consequence pandemics in which a large number of people become sick and a sizable number of those even die. There are many estimates of consequences from influenza pandemics, including historical consequences and models reflecting theorized contemporary infection rates and mortality rates. Estimates of likelihood are less certain, with estimates from historical frequency and some modeled estimates. The occurrence of a pandemic is more reflective of human activities than most naturally occurring disasters, but is still more probabilistic than any of the intentional scenarios. AverageLivesLostperYear
Given the dependence on rare events, the average number of lives lost per year was calculated as the product of the likelihood of a pandemic occurring in a given year times the consequences of a pandemic should one occur. Like earthquakes, the consequences of a pandemic are inversely related to their likelihood, so larger pandemics are possible but less likely than average pandemics. As such, it would not be appropriate when establishing the bounds to apply both the high likelihood and high consequence to derive a high estimate or the low likelihood and low consequence for a low estimate. Varying both would overstate the extent of the bounds substantially. Instead, following my convention, only one of the two is varied. In this case, the likelihood of the pandemic was varied, as the range of estimates for consequences includes some estimates which are very unlikely. Three influenza pandemics occurred in the twentieth century, with several other smaller influenza outbreaks. The historical frequency of 3% a year was selected as my best estimate of likelihood. This estimate was within a range of 1.67%‐10%, based on the National Planning Scenarios (HSC/DHS 2005). 180 A range of estimates for the consequences of a pandemic were identified (Figure 36). This included very low estimates (the 12,000 people killed by the 2011 swine flu, which was fewer people than are killed annually by the seasonal flu) and very high estimates (the 1% of the population that died in the historic Spanish Flu of 1918). As I was applying a range of likelihoods to a typical consequence, I attempted to identify moderate estimates of consequence. The estimate used was the mid‐point between the low and high estimates for pandemic influenza in the National Planning Scenarios. This was consistent with the mid‐point of estimates from the Centers for Disease Control and Prevention (CDCa, CDCb, CDCc, CDCd) and the Department of Health and human Services (HHS), and moderate or medium estimates from a range of sources (Global Security ; HHS ; Meltzer et al. 1999; HSC/DHS 2005; McKibbin and Sidorenko 2006). The range of likelihoods was applied to the typical consequence to get low, best, and high estimates of lives lost per year on average. This provided a range of between 2,000 and 10,000 lives lost per year on average, with a best estimate of 4,000 lives lost per year on average. By comparison, the range of estimates covers all but the lowest estimates of likelihood applied to the lowest estimates of consequence and the highest estimates of likelihood applied to the highest estimates of consequence (see Figure 35). Average estimates of consequence are covered by the range for all estimates of likelihood. 181 consequence
Figure 35‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Pandemic Influenza likelihood
1.67%
3%
6%
10%
12,000
200
400
700
1,200
20,200
337
673
1,178
2,020
24,111
402
804
1,406
2,411
42,000
700
1,400
2,450
4,200
54,400
907
1,813
3,173
5,440
56,000
933
1,867
3,267
5,600
74,700
1,245
2,490
4,358
7,470
87,000
1,450
2,900
5,075
8,700
89,000
1,483
2,967
5,192
8,900
100,000
1,667
3,333
5,833
10,000
121,933
2,032
4,064
7,113
12,193
122,200
2,037
4,073
7,128
12,220
127,200
2,120
4,240
7,420
12,720
180,000
3,000
6,000
10,500
18,000
201,900
3,365
6,730
11,778
20,190
202,000
3,367
6,733
11,783
20,200
207,000
3,450
6,900
12,075
20,700
209,000
3,483
6,967
12,192
20,900
285,300
4,755
9,510
16,643
28,530
297,883
4,965
9,929
17,377
29,788
456,000
7,600
15,200
26,600
45,600
500,000
8,333
16,667
29,167
50,000
675,000
11,250
22,500
39,375
67,500
1,903,000
31,717
63,433
111,008
190,300
1,950,000
32,500
65,000
113,750
195,000 GreatestLivesLostinaSingleEvent
Estimates for the lives lost in a single pandemic vary greatly, as described in the previous section (also see Figure 36). The most severe of these was the 1918 Spanish Flu, representing the worst case scenario. At the high end, I applied the actual death rate from the Spanish Flu (0.65%) to the current population of the United States to get an estimate of 2 million deaths. However, advances in medicine and public health may indicate that a Spanish Flu‐style influenza occurring today would not be as deadly. An estimate of the death rate from a Spanish Flu‐style influenza taking into account medical advances (300,000 deaths) is used for the low end of this range (Murray et al. 2007). 182 Figure 36‐ Identified Estimates of Lives Lost in a Flu Pandemic 10,000,000
Deaths included in greatest
deaths estimate
1,000,000
100,000
Deaths included in best estimate average
10,000
1,000
AverageMoreSevereandLessSevereInjuriesorillnessesperYear
The estimates of more severe and less severe injuries or illnesses due to pandemic influenza were calculated by multiplying likelihood of an outbreak in a given year times the consequences of an event if one were to occur. Estimates of likelihood ranges from 1.67% to 10%, with a best estimate of likelihood of 3%, as described in the previous section on average lives lost per year. Estimates distinguishing between more severe and less severe illnesses were more readily available for pandemic influenza than for many of the hazards because of the way the terms were defined. The distinction between hospitalized and non‐hospitalized infections is frequently reported in pandemic models. 23 scenarios reported some estimate of the number infected, of which 17 identified the number of hospitalized infections. Consistent with the approach used in the average number of lives lost per year, the mid‐point between the low and high estimates from the National Planning Scenarios was selected as the estimate of both more severe and less severe illnesses. This provided estimates of 20,000 more severe and 2,000,000 less severe injuries or illnesses per year on average, between a range of 9,000 and 50,000 more severe and between 1,000,000 and 7,000,000 less severe 183 injuries or illnesses per year on average. These estimates were consistent with other estimates of moderate or medium scenarios from other sources, as well as the midpoint of low and high estimates from the CDC. AverageEconomicDamageperYear
The estimates of average economic damage per year due to pandemic influenza were calculated by multiplying likelihood of an outbreak in a given year by the consequences of an event if one were to occur. Estimates of likelihood ranges from 1.67% to 10%, with a best estimate of likelihood of 3%, as described in the previous section on average lives lost per year. Consistent with the approach used in the average number of lives lost per year, the mid‐point between the low and high estimates from the National Planning Scenarios was selected as the estimate of economic damage. This resulted in a best estimate of $4 billion per year on average, between a range of $2 billion and $10 billion in economic damage per year on average. These estimates were consistent with other estimates of moderate or medium scenarios from other sources, as well as the midpoint of low and high estimates from the CDC. GreatestEconomicDamageinaSingleEvent
The greatest economic damages in a single event were drawn from National Planning Scenarios and CDC estimates. The low end of the greatest economic damage reflects the low end of CDC estimates, $70 billion. The high end represents the high end of the National Planning Scenario estimates, $200 billion. These two different sources were used because they expanded the range; either alone could provide bounds that do not include the actual damages. As compared to the greatest lives lost estimates, these represent the economic damage for a typical event, not an extreme event such as the Spanish Flu. McKibben and Sidorenko present estimates for a Spanish Flu‐style event but 184 these estimates are highly dependent on secondary economic effects which are not included in my definition of economic damage (McKibbin and Sidorenko 2006). The National Planning Scenario and CDC estimates are more reflective of the business interruption associated with the events and as such are used as the estimates. AverageIndividualsDisplacedperYear
There is no mechanism by which it would be expected that influenza would displace people from their homes. This attribute was defined such that it did not include short‐term evacuations (which quarantines might cause) only long‐term displacements. As such, the estimate of individuals displaced per year on average was zero. 185 AnthraxAttacks
Table XXIX‐ Sources and Methods for Estimates of Anthrax Attack Risk Attribute
Summary of Available
Data
Low Estimate
Best Estimate
High Estimate
Average
Lives Lost
Limited historical data,
limited analogous events,
some modeled estimates,
expert opinions of
likelihood
Most similar scenario
times low likelihood
Likelihood times
consequence- average
of most similar to
what had been done
before times
likelihood estimates
Most similar scenario
times high likelihood
Greatest
Lives Lost
Limited historical data,
limited analogous events,
some modeled estimates
Number of deaths in a
calculated scenario
similar to what has
been done
N.A.
Number of deaths in a
scenario unlike what
has been done but is
still plausible
Average
More
Severe
Injuries
Limited historical data,
limited analogous events,
some modeled estimates,
expert opinions of
likelihood
Most similar scenario
times low likelihood
Average
Less Severe
Injuries
Limited historical data,
limited analogous events,
some modeled estimates,
expert opinions of
likelihood
Most similar scenario
times low likelihood
Average
Economic
Damage
Limited historical data,
limited analogous events,
some modeled estimates,
expert opinions of
likelihood
Most similar scenario
times low likelihood
Limited historical data,
limited analogous events,
some modeled estimates
Limited modeled
estimates
Greatest
Economic
Damage
Individuals
Displaced
per Year
Likelihood times
consequence- average
of most similar to
what had been done
before times
likelihood estimates
Likelihood times
consequence- average
of most similar to
what had been done
before times
likelihood estimates
Likelihood times
consequence- average
of most similar to
what had been done
before times
likelihood estimates
Most similar scenario
times high likelihood
Estimate of greatest
historical event
N.A.
Estimate of greatest
theoretical event
Estimate of
displacement times
low likelihood
N.A.
Estimate of
displacement times
high likelihood
Most similar scenario
times high likelihood
Most similar scenario
times high likelihood
Anthrax attacks by terrorists represent a kind of biological attack which has occurred but never yet at the level of feared scenarios. As such, data of historical occurrences may not reflect the risk, and we must turn to modeled scenarios and expert opinion. There are a number of ways anthrax could be dispersed— by mail, by truck‐based sprayers, by airplane— leading to a wide range of possible scenarios. These scenarios have been explored to some extent but not to the extent of the terrorist use 186 of nuclear devices. The likelihood of an anthrax attack is unclear, representing modeled estimates and expert opinion. AverageLivesLostperYear
Estimates for the average lives lost per year from anthrax attacks were not directly available but had to be calculated by multiplying the likelihood of an anthrax attack occurring in a given year by the consequences of an anthrax attack should one occur. Consistent with other cases where likelihood was multiplied by consequence, a range was established by varying only one aspect, in this case applying a range of likelihoods to a single consequence selected as most likely to a future attack. Estimates of consequence for anthrax attacks present special challenges. There are four known events which involved anthrax. The first was an accidental release of military‐grade Anthrax from a military facility in Sverdlovsk. Approximately 100 people were killed from a plume lasting only a few hours, with untold others infected. The Amerithrax case was a much smaller release, resulting in 11 people infected and 5 dead. Spores were mailed to several locations, with contamination resulting to both the offices receiving the letters and the postal infrastructure. The perpetrator is believed to have been a U.S. defensive bioweapons researcher and it is unclear whether the intent of the attack was to cause mass casualties. Finally, a liquid‐suspension of Anthrax was aerosolized from a building in Japan by the Aum Shinrikyo cult in 1993. This attack was intended to kill large numbers, but several factors— including the strain used and problems with the mechanism and time of dispersion— prevented any fatalities (Cordesman 2005; Kellman 2007). Additionally, Clayton Waagner was a bank robber and antiabortion activist who sent letters claiming to contain Anthrax to 550 abortion clinics and doctors in 2001. The letters contained B. thuringiensis, a bacteria used as an insecticide that is very similar to but distinct from B. anthracis, but present one scenario for an Anthrax attack. Had his letters contained Anthrax, it is estimated that 2200 people would have been infected with 500 dead (Sarasin 2006). However, there is significant concern that the events that have occurred are not representative of what could happen in 187 the future. Scenarios described for anthrax attacks often include wide dissemination and tens of thousands of deaths. As a best estimate for consequence, scenarios that were similar to these actual events were used. In particular, I adopted scenarios which contained one but only one advance in the attack scenario. For example, a scenario could be based on an actual attack but with a more deadly strain of bacteria, or with a better dispersal device, but not with a more deadly strain of bacteria and a better dispersal device. Three estimates were identified. First, a scenario of a 2,750 deaths arising from 550 Anthrax letters, similar to the hoax undertaken by Clayton Waagner had he used B. anthracis rather than B. thuringiensis, identified as a likely scenario by Sarasin. Second, the DHS National Planning Scenario (NPS), but limited to one city exposed rather than the five that are used in the NPS, suggesting 1320 deaths. Third, a sprayer scenario like Aum Shinrikyo but with a more effective bacteria, leading to 4000 deaths as suggested by Inglesby (Inglesby 1999; HSC/DHS 2005). The average of these three estimates was taken to represent a most likely scenario. This estimate is at the low end of the range when compared to estimates of wide‐scale release, of which there are many, but high compared to the events that have actually occurred. Additionally, the estimates for the likelihood of an anthrax attack are wide ranging. In early 2005, Senator Lugar solicited expert opinion of the likelihood of a biological attack somewhere in the world in the next ten years, and answers ranged from near zero to near certainty (Lugar 2005). Another set of estimates came from a Sandia model (Sandia 1999). For one estimate, the Sandia model presented an estimate of likelihood for an anthrax attack. Additionally, the Sandia model suggested that an anthrax attack was seven times as likely as a nuclear attack; as there are more estimates of the likelihood of an nuclear attack, we can apply this multiplier can be applied to the range of estimates of the likelihood of a nuclear attack to calculate estimates of likelihood of an anthrax attack. These 188 estimates give a range for the likelihood of an anthrax attack in a given year as between 0.07% and 25%. This estimate of 25% is assuredly too high—at the time the estimate was made in 2011 ten years had passed since the previous event without any anthrax attacks occurring— but it can serve as an upper bound. As a best estimate I apply the multiplier from the Sandia estimate that an anthrax attack is seven times as likely as a nuclear attack to the best estimate of nuclear attacks (see the section on terrorist nuclear detonations for more details). This gives a best estimate of 0.7% for the likelihood of an anthrax attack in a given year, within a range of 0.07% to 25% in a year. Multiplying the estimate of consequence by a range of likelihoods gives the range of expected average lives lost per year, with a range of 2 to 700 lives lost per year on average and a best estimate of 20 lives lost per year on average. These expected averages cover almost all scenarios (see Figure 37) except for the very smallest consequences (such as the actual anthrax attacks in 2001) and the largest consequences (representative of a military‐style warhead). consequence
Figure 37‐ Estimates of Expected Lives Lost per Year on Average Include in the Range of Selected Estimates for Anthrax Attacks likelihood
0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 0.25
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.3
1320
1.3
3
4
5
7
8
9
11
12
13
330
2750
3
6
8
11
14
17
19
22
25
28
688
4000
4
8
12
16
20
24
28
32
36
40 1000
6600
7
13
20
26
33
40
46
53
59
66 1650
13342
13
27
40
53
67
80
93
107
120
133 3336
20000
20
40
60
80
100
120
140
160
180
200 5000
200000
200
400
600
800 1000 1200 1400 1600 1800 2000 50000
Between low and best estimates
Between best and high estimates
GreatestLivesLostinaSingleEvent
The estimate of greatest number killed in a single anthrax attack is presented as a range. The low estimate uses the best estimate of consequences, the number that would be killed in an attack with one but only one advance in sophistication (3,000 deaths) as discussed above. The high estimate uses 189 the largest scenario with people exposed through a crop‐duster or sprayer (20,000 deaths). There are estimates that are even higher involving exposures related to a warhead release were not used as they were not believed to be representative of the attacks of which terrorists are capable. AverageMoreSevereandLessSevereInjuriesorillnessesperYear
Estimates of likelihood were multiplied by consequences from these scenarios. Estimates of likelihood are discussed in above in the section on average lives lost in anthrax attacks. Estimates of consequences come from a range of sources. There are few estimates that make the distinction between hospitalized and non‐hospitalized illnesses. The Amerithrax case of anthrax letters in 2001 led to 17 people hospitalized— the number of people who were infected but did not present to the hospital is unclear. While this is the largest actual use of anthrax, there are larger scenarios are seen as more representative of the event of concern. Other estimates include a more widespread letter scenario and other moderate scenarios (Inglesby 1999; Carroll et al. 2004; HSC/DHS 2005; Sarasin 2006). As with lives lost, the consequences of more severe and less severe illnesses come from the three moderate scenarios that reflect one but only one advance in the attack scenario. Higher estimates, including consideration of advanced dispersion mechanisms coordinated in several cities and scenarios using warheads, are not included in the estimates for injuries or illnesses per year. Multiplying the identified range of likelihoods by the estimates consequences led to estimates of 60 more severe and 300 less severe injuries or illnesses per year on average, within a range of between 6 to 2,000 more severe and between 30 and 10,000 less severe injuries or illnesses per year on average. AverageEconomicDamageperYear
As with other estimates, likelihood was multiplied by consequence to produce estimates of expected average economic damage per year due to anthrax attacks. Estimates of likelihood were presented in other sections, while estimates of consequence will be discussed here. 190 A small number of estimates of economic damage from anthrax attacks were identified. At the low end of the range of estimates was $1 billion from a RAND scenario for indoor release (Carroll et al. 2004). Higher estimates ranged from $100 billion (for a RAND outdoor scenario) to $3 trillion (for a coordinated multi‐site sprayer scenario in the National Planning Scenarios) (Carroll et al. 2004; HSC/DHS 2005). The RAND indoor scenario was selected as the typical consequence measure because it represented one increase in sophistication of the attacks, consistent with the approach used for lives lost and injuries. Other scenarios represented more than one increase in sophistication and are not used for the estimation of average risk but are considered for estimates of the greatest economic damage in a single event. These estimates of consequence were multiplied by the estimates of likelihood to develop a range of estimates of economic damage. The best estimate of economic damage per year on average is $7 million, between a low of $800 thousand and a high of $300 million per year on average. GreatestEconomicDamageinaSingleEvent
A range of economic damages was identified, from $1 billion to $3 trillion (Carroll et al. 2004; HSC/DHS 2005; Sarasin 2006; Risk Management Solutions 2010). However, most of these estimates did not reflect the chosen definition for economic damages, which was specifically focused on the physical damage and business disruption rather than on secondary economic effects. For this reason, consideration was limited to two RAND estimates from which I could distinguish the physical damage and business disruption from the secondary effects. When limiting my consideration to these two estimates, I selected the smaller estimate ($300 million) as a lower bound and the larger estimate as the higher bound ($100 billion). 191 Averageindividualsdisplacedperyear
As with estimates of average values per year for lives lost, illnesses, and economic damage, I calculated the average individuals displaced due to anthrax attacks by multiplying a range of likelihoods by a representative consequence. The estimates of likelihood are discussed in the section on average lives lost per year. The estimate of representative consequence is discussed here. Consistent with estimates of other attributes for anthrax attacks, consequences were sought based on an event that would be considered most likely for an anthrax attack rather than the average of scenarios regardless of how likely they were. Scenarios that involved one but only one improvement in the attack (either broadening the mailings of the previous attacks or small attacks using improved sprayers) were considered most likely. Only one estimate of consequence was available based on this kind of scenario— the National Planning Scenario estimate adjusted for an attack in only one city (HSC/DHS 2005). This best estimate of the displacement should an event occur was multiplied by a range of likelihoods for whether an event would occur to get a range of estimates for the average number of individuals displaced per year between 20 and 6,000. 192 TerroristNuclearDetonation
Table XXX‐ Sources and Methods for Estimates of Terrorist Nuclear Detonation Risk Summary of Available
Data
Low Estimate
Best Estimate
High Estimate
Average
Lives Lost
Limited historical data,
significant modeled
estimates, expert
opinions of likelihood
Best consequence
times low likelihood,
likelihood from a
Sandia modeled
estimate
LxC. Consequence
an average of
estimates from 1015kT detonations,
likelihood from
experts cited in
Jenkins
Best consequence
times high likelihood,
high likelihood from
Lugar report
Greatest
Lives Lost
Limited historical data,
significant modeled
estimates
Estimate from
historical example
representing 10kT
N.A.
Estimate for 150kT
detonations
Attribute
Average
More
Severe
Injuries
Limited historical data,
significant modeled
estimates, expert
opinions of likelihood
Best consequence
times low likelihood,
likelihood from a
Sandia modeled
estimate
Average
Less Severe
Injuries
Limited historical data,
significant modeled
estimates, expert
opinions of likelihood
Best consequence
times low likelihood,
likelihood from a
Sandia modeled
estimate
Average
Economic
Damage
Limited historical data,
significant modeled
estimates, expert
opinions of likelihood
Best consequence
times low likelihood,
likelihood from a
Sandia modeled
estimate
Greatest
Economic
Damage
Limited historical data,
significant modeled
estimates
Individuals
Displaced
per Year
Limited historical data,
some modeled estimates,
expert opinions of
likelihood
Estimate from
historical example
representing 10kT
Average of
consequence times
low likelihood,
likelihood from a
Sandia modeled
estimate
193 LxC. Consequence
an average of
estimates from 1015kT detonations,
likelihood from
experts cited in
Jenkins, applied a
multiplier for percent
hospitalized averaged
from two sources
LxC. Consequence
an average of
estimates from 1015kT detonations,
likelihood from
experts cited in
Jenkins, applied a
multiplier for percent
hospitalized averaged
from two sources
LxC. Consequence
an average of
estimates from 1015kT detonations,
likelihood from
experts cited in
Jenkins,
Best consequence
times high likelihood,
high likelihood from
Lugar report
Best consequence
times high likelihood,
high likelihood from
Lugar report
Best consequence
times high likelihood,
high likelihood from
Lugar report
N.A.
Estimate for 150kT
detonations
N.A.
Average of
consequence times
high likelihood, high
likelihood from Lugar
report
Nuclear devices have only been used twice, and both times by the military, never by a terrorist group. In fact, no terrorist group has ever been known to have obtained or come close to obtaining a nuclear weapon. However, the consequences of the use of a nuclear weapon are so high that even a low likelihood of use can be a serious concern. Data on the consequences of a nuclear detonation in a U.S. city are well modeled and there are multiple scenarios available. The data on the likelihood of an event is much less certain, relying on expert opinion. AverageLivesLostperYear
There little experience of actual nuclear detonations, occurring only twice, with both occurring due to military actions rather than terrorist actions. There is therefore not enough data to estimate the average consequences per year directly. Instead, expected values must be decomposed into likelihood times consequence and estimate both separately. As with other risk estimates where I decomposed likelihood and consequence, only one aspect (either likelihood or consequence) was varied to establish the range. For nuclear detonations, I selected a most likely consequence and applied a low, best, and high estimate of likelihood to it. First, I identified estimates of the most likely consequences of an event. Consequences of nuclear detonation scenarios vary (see Figure 40), depending on the size and sophistication of the warhead, the area targeted, the altitude of the detonation, and other factors. I first focused consideration on weapons in the 5‐15 kiloton range. While terrorist nuclear scenarios may include warheads in the 100‐200 kiloton range or even the megaton range, most experts believe that a terrorist weapon will be in the 5‐15 kiloton range (approximately the size of the detonations at Hiroshima and Nagasaki). This represents a less sophisticated weapon that terrorists could plausibly build themselves with the right materials. Historical estimates from Hiroshima and Nagasaki were available, as well as modeled estimates and scenarios covering a range of cities including New York City, Washington DC, Los 194 Angeles, and Chicago, as well as generalized locations. I used the average of the identified consequence estimates in the 5‐15 kiloton range for the best estimate of consequence. This best consequence was multiplied by a range of likelihoods. Terrorist use of nuclear weapons is an emerging threat, so while there have been no terrorist nuclear attacks in the past, this does not mean that the probability of an attack in a given year is zero. Instead, I relied on theoretical probabilities, derived from structured and unstructured expert opinion. These estimates of likelihood vary widely (see Figure 38). In early 2005, Senator Lugar solicited expert opinion regarding the likelihood of a nuclear attack somewhere in the world in the next ten years, and answers ranged from near zero to near certainty (Lugar 2005). Figure 38‐ Estimates of Likelihood of a Terrorist Nuclear Detonation in the U.S. in a Given Year 1.00000
High estimate
0.10000
0.01000
0.00100
Best estimate
0.00010
Low estimate
0.00001
From the range of estimates, I selected a best estimate of 1% in the next ten years, approximately equivalent to 0.1% likelihood of an attack in a single year. This is very much in the low end of the range of estimates. This estimate is adopted following the convention of Brian Michael Jenkins, who suggests that the estimates of U.S. experts in the 2000’s were biased due to proximity to the attacks on Sept. 11, 2001 as well as political considerations. This is within a range of likelihood from 195 0.01% to 25%. The low estimate reflects modeled estimates from Sandia, while the high estimate comes from a quantification of “near certainty” from expert opinion. These high estimates are unquestionably too high— similar predictions of near certainty were made in the 1990s and never came to pass— but can be useful in setting an upper bound. The estimate of consequence for a most likely event was multiplied by the range of likelihoods to generate a range of estimates for the expected average number killed per year due to terrorist nuclear detonations (see Figure 39). A best estimate for the average number of lives lost per year of 200 was selected, within a range of 20 and 50,000. This wide range of estimates covers almost all combinations of identified likelihood and consequence except for the combination of low consequence events and very low likelihoods and the combination of high consequence events representing military warheads and high likelihoods. 196 consequence
Figure 39‐ Estimates of Expected Lives Lost per Year on Average Included in the Range of Selected Estimates for Terrorist Nuclear Detonations likelihood
0.01%
0.10%
1.05%
2.21%
3.37%
25.89%
24973
2
25
262
551
841
6465
27071
3
27
284
597
911
7008
31100
3
31
326
686
1047
8051
38000
4
38
398
839
1279
9837
60000
6
60
629
1324
2020
15532
68000
7
68
713
1501
2290
17603
99000
10
99
1038
2185
3333
25628
100000
10
100
1048
2207
3367
25887
105000
11
105
1100
2317
3535
27181
110600
11
111
1159
2441
3724
28631
115262
12
116
1208
2544
3881
29837
124943
13
126
1309
2757
4207
32343
158000
16
159
1656
3487
5320
40901
205400
21
206
2153
4533
6916
53171
213675
21
215
2239
4715
7194
55313
230000
23
231
2411
5075
7744
59539
238584
24
240
2501
5265
8033
61761
258622
26
260
2711
5707
8708
66948
262000
26
263
2746
5782
8821
67823
300000
30
301
3144
6620
10101
77660
500000
50
502
5240
11034
16835
129433
807359
81
811
8462
17816
27183
208997
830000
83
834
8699
18316
27945
214858
Between low and best estimates
Between best and high estimates
GreatestLivesLostinaSingleEvent
Identified estimates of the number of lives lost in a single terrorist nuclear detonation were discussed in the previous section on average lives lost per year (also see Figure 40). For the low estimate of the greatest number of lives lost in a single event, I selected an estimate of 100,000, representing the largest historical event, the nuclear detonations of World War II. For the high estimate of the greatest number of lives lost in a single event, I selected the largest scenario from detonations in the 150 kiloton range which I identified as 800,000 deaths. Detonations from the megaton range were considered implausible for a terrorist to accomplish and were not used. 197 Figure 40‐ Estimates of Lives Lost from a Terrorist Nuclear Detonation Should One Occur 1000000
Greatest plausible event
100000
Deaths included in best estimate average
Greatest actual
event
10000
AverageMoreSevereandLessSevereInjuriesorillnessesperYear
As with other attributes, likelihood was multiplied by consequence to produce estimates of expected average more severe and less severe injuries or illnesses per year due to a terrorist nuclear detonation. Estimates of likelihood were presented in other sections. Estimates of consequence included a large number of scenarios were identified with estimates of injuries from a nuclear detonation, although not all of them included a distinction between more severe/hospitalizations and less severe/non‐hospitalized injuries. Consistent with average lives lost, estimates of events between 5 and 15 kiloton were used as representative of the kinds of events that were most plausible for a terrorist. 20 estimates in this range were available, of which the average was taken as the consequence estimate. This led to estimates of 200 more severe and 100 less severe injuries or illnesses per year, within a range of 20 to 50,000 more severe and 10 to 40,000 less severe injuries or illnesses per year on average. AverageEconomicDamageperYear
As with other attributes, likelihood was multiplied by consequence to produce estimates of expected average economic damage per year (see Figure 41) due to terrorist nuclear detonations. 198 Estimates of likelihood were presented in a previous section. Estimates of consequence will be discussed here. Consistent with average lives lost and average injuries or illnesses, estimates involving 5‐15 kiloton detonations were selected as representative of the kinds of events that are most plausible for terrorists to accomplish. Nine estimates of economic consequence from 5‐15 kiloton warheads were identified, of which the average was taken to develop an estimate of best consequence. Multiplying these estimates of consequence by estimates of likelihood gave a best estimate of $3 billion per year on average, within a range of $300 million and $900 billion per year on average. This covered all typical events, but did not cover estimates representing low likelihood and low consequence (which will understate the risk) or high likelihood and high consequence (which will overstate the risk). consequence (in billions)
Figure 41‐ Estimates of Expected Economic Damage per Year on Average Included in the Range of Selected Estimates for Terrorist Nuclear Detonations likelihood
0.01%
0.1%
1%
2%
3%
26%
$
134 $
0.0 $
0.1 $
1 $
3 $
5 $
35
$
158 $
0.0 $
0.2 $
2 $
3 $
5 $
41
$
200 $
0.0 $
0.2 $
2 $
4 $
7 $
52
$
218 $
0.0 $
0.2 $
2 $
5 $
7 $
56
$
260 $
0.0 $
0.3 $
3 $
6 $
9 $
67
$
886 $
0.1 $
0.9 $
9 $
20 $
30 $
229
$
1,000 $
0.1 $
1 $
10 $
22 $
34 $
259
$
1,000 $
0.1 $
1 $
10 $
22 $
34 $
259
$
1,154 $
0.1 $
1 $
12 $
25 $
39 $
299
$
1,441 $
0.1 $
1 $
15 $
32 $
49 $
373
52 $
110 $
168 $
1,294
0.5 $
5 $
$
5,000 $
$
5,056 $
0.5 $
5 $
53 $
112 $
170 $
1,309
$
7,629 $
0.8 $
8 $
80 $
168 $
257 $
1,975
$
8,226 $
0.8 $
8 $
86 $
182 $
277 $
2,129
$
14,000 $
1 $
14 $
147 $
309 $
471 $
3,624
$
43,537 $
4 $
44 $
456 $
961 $
1,466 $ 11,270
Between low and best estimates
Between best and high estimates
199 GreatestEconomicDamageinaSingleEvent
As compared to the estimate for greatest lives lost in a single event, the economic damages from Hiroshima and Nagasaki are neither available nor representative of risks as they would exist in the U.S. today. Instead, a low estimate of $1 trillion was drawn from lower estimates of a 10 kiloton detonation, including Bunn (2006), Meade and Molander (2006), and others consistent with detonations in Los Angeles or Chicago (HHS ; Bunn 2006; Meade and Molander 2006; Schanzer et al. 2009). The high estimate of $10 trillion was drawn from high expert estimates and estimates of a 10 kiloton detonation in Manhattan (Jenkins 2009; Schanzer et al. 2009). AverageIndividualsDisplacedperYear
As with other attributes, likelihood was multiplied by consequence to produce estimates of expected average individuals displaced per year from terrorist nuclear detonations. Estimates of likelihood were presented in a previous section. Estimates of consequence will be discussed here. Five estimates of the number of individuals displaced by nuclear detonations of between 5 and 15 kiloton were identified. These estimates of the number displaced per year on average ranged from 310,000 to 3,000,000. The average of these five estimates was calculated and used as the consequence estimate. Combining this consequence estimate by the range of likelihoods gave the expected average individuals displaced per year (Figure 42). 200 consequence
Figure 42‐ Estimates of Expected Average Individuals Displaced per Year Included in the Range of Selected Estimates for Terrorist Nuclear Detonations likelihood
0.01%
0.1%
1%
2%
3%
26%
310,000
31
311
3,249
6,841
10,437
80,248
450,000
45
452
4,716
9,930
15,151
116,489
500,000
50
502
5,240
11,034
16,835
129,433
2,000,000
200
2,009
20,961
44,134
67,338
517,731
3,000,000
300
3,014
31,442
66,202
101,008
776,597
Between low and high estimates
201 TerroristExplosiveBombings
Table XXXI‐ Sources and Methods for Estimates of Terrorist Explosive Bombing Risk Attribute
Summary of Available
Data
Low Estimate
Best Estimate
High Estimate
Average
Lives Lost
Significant historical data
in domestic and
international contexts
Lowest 10 year
average in U.S. 19802009 from RAND
DWTI
Average 10 year
average in U.S. 19802009 from RAND
DWTI
Greatest
Lives Lost
Significant historical data
in domestic and
international contexts
Largest actual event
N.A.
10 year average of
Israel and West Bank
1980-2009 from
RAND DWTI
Deaths from one
building of WTCs in
9/11, largest
theoretical event
Lowest 10 year
average in U.S. 19802009 from RAND
DWTI, multiplier for
proportion more/less
severe
Lowest 10 year
average in U.S. 19802009 from RAND
DWTI, multiplier for
proportion more/less
severe
Best inflated by the
factor of low
deaths/best deaths
Average 10 year
average in U.S. 19802009 from RAND
DWTI, multiplier for
proportion more/less
severe
Average 10 year
average in U.S. 19802009 from RAND
DWTI, multiplier for
proportion more/less
severe
Average of estimates
for expected annual
cost
Average
More
Severe
Injuries
Significant historical data
in domestic and
international contexts
Average
Less Severe
Injuries
Significant historical data
in domestic and
international contexts
Average
Economic
Damage
Significant historical data
in domestic and
international contexts
Greatest
Economic
Damage
Significant historical data
in domestic and
international contexts
Largest actual event
N.A.
Individuals
Displaced
per Year
Limited historical data in
domestic and
international contexts
Applied proportional
multiplier to low lives
lost
N.A.
10 year average of
Israel and West Bank
1980-2009 from
RAND DWTI
10 year average of
Israel and West Bank
1980-2009 from
RAND DWTI
Best inflated by the
factor of high
deaths/best deaths
Cost from one
building of WTCs in
9/11, largest
theoretical event
Applied proportional
multiplier to high
lives lost
Terrorist explosive bombings are one of the few terrorist scenarios in which there is a large amount of historical data from which to draw estimates. For this reason, estimates of expected averages can be calculated directly rather than relying on multiplying estimates likelihood by estimated consequences. 202 AverageLivesLostperYear
The RAND’s Database of Worldwide Terrorism Incidents (RDWTI) collects data on terrorist events, including their data, country, method, and the consequences. From this data, estimates of the average number of lives lost from terrorist explosive bombings were calculated using a range of different time periods and different countries (see Figure 43). The best match for the current U.S. risk is recent U.S. history. From the U.S data from the RDWTI, ten year averages were calculated for the periods 1980‐2008. The average of the 10‐year averages was selected as the best estimate of lives lost per year on average (10 deaths per year on average), while the lowest of the 10‐year averages was selected as the lower bound (1 death per year on average). However, it is plausible that the previous experience with bombings in the United States may not reflect the upper bound of the current risk in the United States. It is possible that terrorist organizations could increase the tempo of their operations. While there is no recent domestic data reflecting such sustained terrorist bombing campaigns, data from similar countries may be useful to represent the increased threat. When considering data from other countries, non‐Western nations such as Iraq or Afghanistan were considered too dissimilar from the U.S. situation to be used. However, intense bombing campaigns from other Western nations were considered as similar enough to the U.S. situation that they could reflect an intense bombing campaign in the U.S. These situations included the United Kingdom and Northern Ireland in the conflict with the Irish Republican Army, Israel in the first and second Intifada, and Spain in the conflict with the Basque separatists. Of these, I chose the Israeli experience because it had the greatest average fatalities and therefore served as an inclusive upper bound. An estimate of 40 deaths per year on average, representing the average 10‐year average of 203 deaths by bombings in Israel from 1980‐2008, was used as the upper bound of deaths per year from terrorist explosive bombings. Figure 43‐ Estimates of Lives Lost per Year on Average for Terrorist Explosive Bombings 90
80
70
60
Israel
50
Spain
High estimate
40
UK
US
30
20
Best estimate
10
Low estimate
0
GreatestLivesLostinaSingleEvent
A range of estimates was presented for the number of lives lost in a single explosive bombing. At the low end (200 deaths) represents the largest number of deaths that has actually occurred in a bombing in the United States. However, the largest event that has occurred may not be the largest event that can occur. A larger scenario involves the use of explosives to collapse a large building. While the events of Sept. 11, 2001 were not caused by an explosive bombing, the consequences (the collapse of a large building) were similar. I used this analogous event as an upper bound, using the number of people who died in one building of the World Trade Center as representative of the number of possible deaths in a single event (2,000). AverageMoreSevereandLessSevereInjuriesorillnessesperYear
As with average lives lost per year, data from the RDWTI was used to identify the number of injuries per year from explosive bombings. The percentage of those that were more serious and less serious was not clear. The percentages of injuries that were considered more serious (i.e. those that 204 resulted in hospitalization) were drawn from a review by Arnold et al., with 1/3 of injuries considered more serious and 2/3 considered less serious (Arnold et al. 2004). This led to estimates of 30 more serious and 60 less serious injuries or illnesses per year on average, between a range of 1 to 70 more severe and 1 to 100 less severe injuries or illnesses per year on average from terrorist explosive bombings. AverageEconomicDamageperYear
The RDWTI does not record economic damage from events, as ascertaining the economic damages is not a simple process. Instead, data on economic damage were available from the FBI Bomb Damage Center from 1988 to 1998 (Gadson et al. 2002). A ten year average of the economic damage adjusted for inflation ($100 million) was used to calculate the best average economic damages per year. The available data were only sufficient to identify a best estimate and not a range of low to high estimates. Instead, the economic data was modified proportional to the range of lives lost. A ratio of the economic damage per life lost was calculated based on the best estimate for average economic damages and the best estimate for average lives lost. This ratio was applied to the low estimate for lives lost and the high estimate for lives lost to create a range of low to high average economic damage per year. This provided a range of between $10 million and $400 million in economic damage per year on average. GreatestEconomicDamageinaSingleEvent
The greatest economic damage in a single event is presented as a range. The low estimate ($1 billion) represents the largest bombing in the United States, the Oklahoma City bombing of 1995. But the largest event that has occurred may be not the largest event that can occur. A larger scenario involves the use of explosives to collapse a large building. While the events of Sept. 11, 2001 were not caused by an explosive bombing, the consequences (the collapse of a large building) were similar. I used 205 this analogous event as an upper bound, using the economic damage of the collapse of one building of the World Trade Center at approximately $40 billion. AverageIndividualsDisplacedperYear
Annual averages of individuals displaced per year from terrorist explosive bombings were not found. Data were identified on the number of people made homeless only for a specific case, the Oklahoma City bombing of 1995 (Sitterle and Gurwitch 1999). This was used to calculate the ratio of people made homeless per person killed. This ratio was applied to the estimates of low and high average lives lost per year to generate low and high estimates of average individuals displaced per year. This provided an estimate of between 3 and 100 individuals displaced per year on average due to terrorist explosive bombings. 206 Cyber‐attacks
Table XXXII‐ Sources and Methods for Estimates of Cyber‐attack Risk Attribute
Average
Lives Lost
Greatest
Lives Lost
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Summary of Available
Data
Very limited historical
data, limited modeled
data, some analogous
data
Very limited historical
data, limited modeled
data, some analogous
data
Very limited historical
data, limited modeled
data, some analogous
data
Very limited historical
data, limited modeled
data, some analogous
data
Low Estimate
Best Estimate
High Estimate
Lower bound of zero
Historical estimate of
zero
Consequence from
analogous event times
likelihood
Lower bound of zero
N.A.
One consequence
estimate from
analogous event
Lower bound of zero
Historical estimate of
zero
Consequence from
analogous event times
likelihood
Lower bound of zero
Historical estimate of
zero
Consequence from
analogous event times
likelihood
LxC. Did not have
good data on
likelihood, so single
likelihood and varied
consequence. Best
consequence from
mid-range of NPS.
Average
Economic
Damage
Very limited historical
data, limited modeled
data, some analogous
data
LxC. Did not have
good data on
likelihood, so single
likelihood and varied
consequence. Low
consequence from
low estimate of NPS.
Greatest
Economic
Damage
Very limited historical
data, limited modeled
data, some analogous
data
Low consequence
from low estimate of
NPS.
N.A.
Individuals
Displaced
per Year
No known mechanism
No known
mechanism
N.A.
LxC. Did not have
good data on
likelihood, so single
likelihood and varied
consequence. High
consequence from
highest estimate of
costs from analogous
event.
High consequence
from highest estimate
of costs from
analogous event.
No known
mechanism
Cyber‐attacks have fewer estimates from which to draw than other hazards. Cyber‐attacks are an emerging threat, but unlike other emerging threats examined here (viz. terrorist nuclear detonations and anthrax attacks), it is not only the likelihood of an attack that were unclear but also the consequences of an event if it were to occur. Additionally, research on cyber‐events describes a large number of things, including the constant low‐level inundation of spam, viruses, and worms, cyber‐
207 espionage, identity theft, and disruptions of infrastructure through computer networks. My description of cyber‐attacks relates only to this last category, disruption of infrastructure through computer networks, and this distinction made drawing estimates from the literature more difficult. AverageLivesLostperYear
The primary concerns of a cyber‐attack are not related to life or health. The cyber‐attacks against Estonia in 2007, Georgia and Lithuania in 2008, and Kyrgyzstan in 2009 resulted in no deaths, nor are there recorded deaths from the multitude of cyber‐events generally. Additionally, the National Planning Scenarios include no estimates of lives lost. For this reason, the best and low estimates for the lives lost from a cyber‐attack were set at 0. However, historical data are not sufficient to establish an upper bound. Instead, the likelihood of a cyber‐attack was multiplied by the high consequences of a cyber‐attack if one should occur to develop the upper bound of the average lives lost per year. To determine a high estimate of consequences of a cyber‐attack, analogous events were examined. While most cyber‐event scenarios represent the shutting down of infrastructure, a cyber‐attack could possibly disrupt infrastructure, such as the disruption of the centrifuges in Iran in 2010. If infrastructure were disrupted rather than shut down, it could lead to accidents and possibly death. Events in which infrastructure were shut down or disrupted due to computer system malfunctions serve as analogous events for cyber‐attacks. Two such events were a computer‐induced subway collision in Washington, DC, and the Northeast Blackout of 2003. Similar numbers of people were killed in each. The number of deaths from these incidents was used as a high end for consequences of lives lost. The consequences of a cyber‐attack were multiplied by the likelihood of a cyber‐attack in a given year. Studies of IT professionals suggest that nearly 80% expect a major attack in the U.S. within the next five years (Baker et al. 2010), although what major‐attack means is unclear. Historical 208 experience, however, does not show any true major attacks, suggesting a likelihood of 0%. I average these two positions, giving equal weight to each, to generate a 10% likelihood of a cyber‐attack in a given year. The likelihood of a cyber‐attack in a given year was multiplied by the consequences of a cyber‐
attack if one were to occur to give an estimate of the upper bound of the average number of lives lost per year. This gave an upper bound of 1 life lost per year on average from cyber‐attacks. GreatestLivesLostinaSingleEvent
There have been no cyber‐attacks that have resulted in lives lost, and most scenarios for cyber‐
attacks contain no consideration of lives lost. However, while most cyber‐event scenarios represent the shutting down of infrastructure, a cyber‐attack could possibly disrupt infrastructure, such as the disruption of the centrifuges in Iran in 2010. If infrastructure were disrupted rather than shut down, it could lead to accidents and possibly death. Events in which infrastructure are shut down or disrupted due to computer system malfunctions serve as analogous events for cyber‐attacks. Examples of such computer‐induced events include a computer‐induced subway collision in Washington, DC, the Davis‐
Besse power plan incident of 2003, the Northeast Blackout of 2003, and the Browns Ferry nuclear power plant failure of 2006. Similar numbers of people were killed in each, giving an estimate of 0 to 10 lives lost per year on average. AverageMoreSevereandLessSevereInjuriesorillnessesperYear
Estimates for the average more and less severe injuries and illnesses per year were developed consistent with the estimates for the average lives lost per year. Low and best estimates were zero based on historical events and the lack of a clear mechanism by which cyber‐attacks would cause injuries. High estimates were based on the likelihood of a cyber‐attack (10% per year as a high estimate, as described in the section on average lives lost due to cyber‐attacks) multiplied by the high 209 consequences of a cyber‐attack would one occur as developed from analogous events. For the estimate of the number injured in an analogous event, I used the number injured in the Washington, DC subway incident described above (Dharapak 2009). I then assigned half of those injuries to severe and half to less severe (representing a uniform prior distribution, which is a typical assumption for Bayesian analysis where there is no information). This resulted in an upper bound of 5 more severe and 5 less severe injuries or illnesses per year on average from cyber‐attacks. AverageEconomicDamageperYear
As compared to the damage from life and health, cyber‐attacks do cause significant economic damage from business interruption. The way that cyber‐attacks have been defined as a hazard in this study does not include cyber‐theft and cyber‐espionage; this leaves no history of cyber‐attacks in the U.S. from which historical averages could be drawn. Instead, estimates of average economic damage were calculated by multiplying the likelihood of a cyber‐attack by the consequences of a cyber‐attack should one occur. There were no data on the likelihood of a cyber‐attack. There were estimates where the computer systems of critical infrastructure have been disrupted— including the Davis‐Besse nuclear power plant in 2003 and the Harpers Ferry nuclear power plant in 2006 — but these were not as widespread as the cyber‐attack scenario used in this study envisions. Instead, estimates of the likelihood of a cyber‐attack were generated from a combination of expert opinions and historical data, as described above in the section on average lives lost per year. The likelihood of a cyber‐attack in a given year used for this calculation was 10%. There are three sources of consequence estimates for cyber‐attacks in the United States: expert scenarios, cyber‐attacks that occurred in other countries, and events with consequences similar to those of cyber‐attacks that occurred in the United States. The National Planning Scenarios identify the costs of 210 cyber‐attacks as being in the “hundreds of millions of dollars” (HSC/DHS 2005). Estimates have reached as high as of $700 billion, representing a power outage for one‐third of the country for three months (Meserve 2007); I viewed this estimate as being possible but implausible compared to events that have occurred— no cyber‐attacks have caused power outages, no power outages have covered half of the country, and no wide spread power outages have lasted three months. Cyber‐attacks that have occurred in other countries focused on denial of service for government computers and the economic damage has been very hard to estimate (Dowdy 2012). The event most commonly applied as analogous with regards to economic losses is the Northeast Blackout of 2003, with estimates ranging from $4.5 billion to $10 billion (Cashell et al. 2004). Analogous events of lesser severity include not only the nuclear power plant disruptions described above (costs of less than $1 million), but also a 3‐day electrical outage in the Midwest (estimated costs of $22 million) and a 10‐day oil and gas disruption (estimated costs of $405 million). The estimates of the National Planning Scenario were selected as the best estimate of consequence because they most directly address the hazard as it has been defined in this exercise. The midpoint of the low and high estimates from the National Planning Scenarios was selected as the best estimate of consequence while the low estimate from the National Planning Scenarios was selected as the low estimate of consequence. The high estimate of consequence was drawn from the analogous case of the Northeast Blackout of 2003, representing a worst case scenario. These estimates of consequence were multiplied by the estimate of likelihood to provide estimates of average expected economic damages in a single event. This resulted in a range of$50 million in economic damages per year on average, between a range of $10 million and $1 billion per year on average from cyber‐attacks. 211 GreatestEconomicDamageinaSingleEvent
A range of estimates was provided for greatest economic damage from a single event (see Figure 44). As discussed in the previous section, the low estimate of a cyber‐attack from the National Planning Scenarios was used for the low estimate of economic damage from a single event ($100 million), the damages from the analogous case of the Northeast Blackout of 2003 were used for the high estimate of economic damage from a single event ($10 billion), while the estimate of $700 billion was not included. Figure 44‐ Estimates of Economic Damage from a Cyber‐attack $1,000,000,000,000
$100,000,000,000
Northeast blackout
$10,000,000,000
$1,000,000,000
$100,000,000
National
Planning Scenarios
$10,000,000
Harpers Ferry (actual event)
$1,000,000
$100,000
AverageIndividualsDisplacedperYear
There were no identified mechanisms by which cyber‐attacks lead to homelessness. 212 ToxicIndustrialChemicalAccident
Table XXXIII‐ Sources and Methods for Estimates of Toxic Industrial Chemical Risk Attribute
Average
Lives Lost
Greatest
Lives Lost
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Average
Economic
Damage
Greatest
Economic
Damage
Individuals
Displaced
per Year
Summary of Available
Data
Limited annual historical
data, significant historical
incident data in domestic
and international
contexts, limited
estimates of likelihood
Limited annual historical
data, significant historical
incident data in domestic
and international contexts
Limited annual historical
data, significant historical
incident data in domestic
and international
contexts, limited
estimates of likelihood
Limited annual historical
data, significant historical
incident data in domestic
and international
contexts, limited
estimates of likelihood
Limited annual historical
data, some historical
incident data in domestic
and international
contexts, limited
estimates of likelihood
Limited annual historical
data, some historical
incident data in domestic
and international contexts
Limited annual historical
data, some historical
incident data in domestic
and international
contexts, limited
estimates of likelihood
Low Estimate
Best Estimate
High Estimate
Low 10 year average
of the last 30 years
from EM-DAT
Average 10 year
average of the past 30
years from EM-DAT
LxC. Consequence
of largest event
(Bhopal) times an
arbitrary 1%
likelihood serving as
a bounding estimate.
Largest event in the
U.S.
N.A.
Largest event ever
Multiplier of severe
injuries to low lives
lost
Multiplier of severe
injuries to average
lives lost. Multiplier
from Bhopal.
Multiplier of severe
injuries to high lives
lost
Multiplier of less
severe injuries to low
lives lost
Multiplier of less
severe injuries to
average lives lost.
Multiplier from
Bhopal.
Multiplier of less
severe injuries to high
lives lost
Applied multiplier
based on NPS and
actual economic
damages from a
chemical release of
that kind
Applied multiplier
based on NPS and
actual economic
damages from a
chemical release of
that kind
N.A.
Greatest economic
damages from a
chemical release
N.A.
Multiplier of
evacuees per death
applied to low
number of deaths.
Evacuees per death
based on accidents in
the U.S.
Applied multiplier
based on NPS and
actual economic
damages from a
chemical release of
that kind
Greatest economic
damages from a
chemical release in
the U.S.
Multiplier of
evacuees per death
applied to low
number of deaths.
Evacuees per death
based on accidents in
the U.S.
Much of the data on toxic industrial chemical accidents in the United States involve one of two kinds of events that are excluded from the definition of the hazard used in this report: accidents where 213 employees working with the chemicals are exposed but the exposure to the public is minimal; and accidents that are very low level chemical releases such as a gasoline spill associated with a car accident. However, there are still two kinds of data that are useful to informing the analyses of toxic industrial chemical accidents: recorded data on moderate‐scale events, and scenarios of large‐scale events that have not yet occurred in the U.S. but which may occur in the U.S. These data were used to inform estimates of the attributes of risk for toxic industrial chemical accidents. AverageLivesLostperYear
Of the data sources reflecting chemical accidents in the United States, only the EM‐DAT data reflected a definition appropriate to the hazard as defined in this study. Toxic industrial chemical accidents in this study focused on the kinds of high casualty public exposures about which DHS is concerned, and not the kinds of small scale exposures (e.g. a gasoline spill from a car accident, handled by local authorities) or non‐public exposures (e.g. a chemical spill exposing individuals in a workplace, handled by OSHA) handled by other agencies. The EM‐DAT data records incidents with 10 or more people killed, 100 or more people affected, or where a state of emergency is declared; this reflects the kinds of high casualty public exposures about which our definition is concerned. Thirty years of data were used, from 1981‐2010, from which 10‐year averages were drawn. The average of the 10‐year averages was selected as the best estimate for average lives lost per year (8 lives lost per year on average), while the lowest 10‐year average was selected as the low estimate for average lives lost per year (5 lives lost per year on average). However, toxic industrial chemical accidents include high consequence low likelihood events, which are not be reflected in this data, and therefore estimates from historical data may not be sufficient to establish an upper bound. For an upper bound, estimates were derived by multiplying likelihood by consequence. Estimates of consequence for toxic industrial chemical accidents reflect 214 several events: factory explosions, train derailments, and chemical leaks. Domestic incidents have had fatalities from the tens up to the hundreds, while international incidents have ranged up to the thousands. As an upper bound of expected average lives lost per year I used the largest amount of lives lost in any event, estimates from the Bhopal, India release. This is multiplied by a likelihood of 1% in a given year; this is an ad hoc assumption but one I viewed to be a high bound given the absence of any events of this size in any Western nation. This led to a high bound of 200 lives lost per year on average. GreatestLivesLostinaSingleEvent
Toxic industrial chemical accidents have actually occurred both domestically and abroad (see identified estimates in Figure 45). The largest events that have occurred in the United States have reached into hundreds of lives lost, but these may not reach the potential for the largest events that could possibly occur (National Research Council 2006). The largest event internationally occurred in Bhopal, India in 1984, where thousands were killed. These are the kinds of events which are reflected in toxic industrial chemical scenarios, including the National Planning Scenarios and others (HSC/DHS 2005; National Research Council 2006; Barrett 2009). Figure 45‐ Estimates of Lives Lost from Toxic Industrial Chemical Accidents 100000
10000
1000
scenarios
U.S.
foreign countries
100
10
1
215 The greatest number of deaths in a single toxic industrial chemical accident is presented as a range. At the low end of the range is the largest estimate of fatalities for an event that has actually occurred in the United States (in this case, 600, in Texas in 1947), while at the high end of the range is the largest estimate of fatalities from an event anywhere (in this case, 20,000, in Bhopal, India in 1984). AverageMoreSevereandLessSevereInjuriesorillnessesperYear
Data on injuries or illnesses from toxic industrial chemical accidents were not sufficient in EM‐
DAT to create estimates in the same fashion as with lives lost. Additionally, there was not sufficient data on the historical frequency of large scale industrial chemical accidents to establish estimates of likelihood for anything other than the bounds. At the upper bound, I applied the number of injuries from Bhopal to the bounding likelihood of 1%, consistent with the approach of average lives lost per year. Low and best estimates for average more severe and less severe injuries or illnesses per year require a different approach. There are data to establish the ratio of injuries per life lost, which can be applied to the estimates of average lives lost per year to calculate averages for more severe and less severe injuries per year. Mannan et al. estimates that there are 6 more hospitalizations per death and around 60 less severe injuries per death from toxic industrial chemical accidents (Mannan et al. 2005). I applied these numbers as multipliers to the low and best estimates of average lives lost per year to generate estimates for the low and best estimates of more severe and less severe injuries or illnesses per year for toxic industrial chemical accidents. This approach led to best estimates of 50 more severe and 500 less severe injuries or illnesses per year, within a range of 20 to 200 for more severe and 300 to 5,000 for less severe injuries or illnesses per year on average. 216 AverageEconomicDamageperYear
There are few sources of data on the economic damages of toxic industrial chemical accidents as they concern DHS (i.e. large scale accidents with substantial public exposure). As with injuries or illnesses, estimates of average economic damages were calculated by applying a multiplier to more reliable estimates of average deaths per year. The multiplier was created by the ratio of economic damages per life lost in the National Planning Scenarios, with estimates of “billions” of dollars and 350 deaths. For the vague “billions” of dollars, an estimate of $12.7 billion was used, reflecting the largest economic damages from a chemical release (a 2002 chemical release in Spain, adjusted for inflation (EM‐DAT 2011). This estimate is consistent with the ratio of damages to lives lost in the U.S. from 1994 to 2001 (Kleindorfer et al. 2003). This multiplier was applied to calculate low, best, and high estimates of economic damage per year. This resulted in a best estimate of $300 million in economic damage per year on average, within a range of $200 million and $7 billion per year on average. GreatestEconomicDamageinaSingleEvent
The largest economic damages from a toxic industrial chemical accident are presented as a range. At the low end are the inflation‐adjusted economic damages from the largest event in the U.S., approximately $2 billion from a Texas processing plant fire in 1989 (EM‐DAT 2011). Larger events have occurred internationally, including an incident in Spain in 2002 that resulted in approximately $13 billion in damages (EM‐DAT 2011). However, my estimate of a largest scenario reflects what would happen if a Bhopal‐style disaster would occur in the United States, for which the largest international event is not a relevant comparison because of the differences in wealth between India and the U.S. Instead, I applied a multiplier related to the ratio of the high estimate of lives lost to the best estimate for lives lost to the best estimate of economic damage (i.e. because the high estimate for lives lost was 26 times as high as the best estimate for lives lost, I made the high estimate for economic damages per year on average 26 217 times higher than the best estimate for economic damage). This resulted in a high estimate of $700 billion in economic damage. AverageIndividualsDisplacedperYear
As with other attributes in this hazard, estimates on the number of people displaced by toxic industrial chemical accidents are not sufficient for generating averages, nor are they sufficient to inform likelihoods as anything but bounding estimates. Instead, a multiplier was used. I calculated the number of people evacuated per life lost from chemical accidents in the U.S. from 1994‐2001 (995 evacuees per life lost) (Kleindorfer et al. 2003), then applied this to the low and high average number of lives lost per year to calculate the average individuals displaced per year. This resulted in an estimate of between 5,000 and 200,000 individuals displaced per year on average for toxic industrial chemical accidents.
218 OilSpills
Table XXXIV‐ Sources and Methods for Estimates of Oil Spill Risk Attribute
Summary of Available
Data
Low Estimate
Best Estimate
High Estimate
Consequences based
on largest deaths from
a spill worldwide,
times 1 in 40 year
likelihood.
Average
Lives Lost
Over thirty years of
annual historical data
Historical 10 year
average of zero
LxC. Estimate of
deaths from the
Deepwater horizon
event. Historical
likelihood of large oil
spills.
Greatest
Lives Lost
Over thirty years of
annual historical data
Highest deaths world
wide
N.A.
Limited historical
incident data
LxC. Estimates of
deaths more severe
injuries from Deep
Water horizon,
historical estimate of
oil spills times 0.5 to
represent decreasing
oil spilled from
PMG/ERC
LxC. Estimates of
more severe injuries
from Deep Water
horizon, historical
estimate of oil spills
Limited historical
incident data
LxC. Estimates of
deaths less severe
injuries from Deep
Water horizon,
historical estimate of
oil spills times 0.5 to
represent decreasing
oil spilled from
PMG/ERC
LxC. Estimates of
less severe injuries
from Deep Water
horizon, historical
estimate of oil spills
Over thirty years of
annual historical data
Based on damage per
gallon spilled times
low estimate of
number of gallons
spilled
Based on damage per
gallon spilled times
average estimate of
number of gallons
spilled
Over thirty years of
incident data
Estimates from
Exxon Valdez
N.A.
Estimates from
Deepwater Horizon
Limited historical
incident data
Based on estimates
from Exxon Valdez
N.A.
Based on estimates
from Exxon Valdez
Average
More
Severe
Injuries
Average
Less Severe
Injuries
Average
Economic
Damage
Greatest
Economic
Damage
Individuals
Displaced
per Year
Highest deaths world
wide
LxC. Estimates of
deaths more severe
injuries from Deep
Water horizon,
historical estimate of
oil spills times 1.5
representing largest
10 year average of
barrels of oil spilled
from PMG/ERC
LxC. Estimates of
deaths less severe
injuries from Deep
Water horizon,
historical estimate of
oil spills times 1.5
representing largest
10 year average of
barrels of oil spilled
from PMG/ERC
Based on damage per
gallon spilled times
high estimate of
number of gallons
spilled
Oil spills have occurred multiple times in the U.S. in the last half century. These include both large numbers of small events and a handful of very large events. These very large events drive much of 219 the discussion. While the low and best estimates can largely be described by the last few decades of historical data, the upper bounds need to incorporate rare but high‐consequence events that may not have occurred in recent history. These upper bounds can be calculated by combining likelihood and consequence. AverageLivesLostperYear
Deaths from oil spills are rare. While some oil spills are associated with fatalities, it is typically an accident that causes both the deaths and the oil spill, rather than the deaths being caused by the oil spill itself (Winter 2010). While chemicals released by oil spills include carcinogens, the volume of water into which they are spilled is so large that the increases in exposure to these chemicals are minimal. For this reason, estimates of deaths were based on fatalities from rare oil spill‐related accidents calculated from estimates of likelihood and consequence. Most oil spills result in no fatalities. The Deepwater Horizon spill of 2010 was associated with 11 deaths, the greatest number domestically. This number was used as the estimate of consequence. The likelihood of an accident as large as the Deepwater Horizon was varied to set the range for the estimates. The best estimate of likelihood was 3 in 40, representing the 3 large oil spills in the U.S. in the past 40 years; multiplying this by 11 deaths gave a best expected average lives lost per year of 1 due to oil spills. The low estimate of likelihood was 1 in 40, representing the one oil spill in the U.S. in the past 40 years with fatalities; multiplying this by 11 have a low expected lives lost per year rounded to 0 due to oil spills. The high estimate uses the same likelihood as the best estimate (3 in 40) but uses the greatest number of fatalities in an oil spill anywhere (167 in an oil rig accident in the North Sea in 1988) rather than the greatest number domestically; multiplying these results in a high estimate of 4 lives lost per year on average from oil spills. 220 GreatestLivesLostinaSingleEvent
As described in the previous section, fatalities occur in only a small fraction of oil spills. A range is presented for greatest lives lost in a single event. For the low end of the range I used the greatest number of people killed in an event in the U.S. or in waters associated with the United States— 11 people killed in the Deepwater Horizon accident of 2010. For the high end of the range I used the greatest number of people killed in an event anywhere— 167 killed aboard the Piper Alpha oil rig in the North Sea in 1988. This was rounded to 200 killed in a single event. AverageMoreSevereandLessSevereInjuriesorillnessesperYear
Estimates of injuries or illnesses per year from major oil spills were available for specific spills but were not available in a format that matched my definition of more/less severe and my definition of the hazard. Instead, I calculated the estimates by multiplying likelihood by consequence. The best estimate of the likelihood of an oil spill was 3 in 40, as described in the section on average lives lost per year for oil spills. The low and high estimates were calculated from multipliers based on the number of gallons spilled. The ratio of the lowest 10‐year average of barrels of oil spilled to the average number was used to calculate the low likelihood and the highest 10‐year average of barrels spilled to the average number was used to calculate the high likelihood (PMG/ERC 2002). Estimates for the number of injuries or illnesses were recorded for the Deepwater Horizon oil spill of 2010 (CDC 2010; CDC 2010; CDC 2010; CDC 2010). Additionally, the percentage of those injuries or illnesses that were more severe also derived from data from the Deepwater Horizon (McCoy and Salerno 2010). The range of likelihoods was applied to this estimate of consequence to give a range of expected average more severe and less severe injuries and illnesses per year. This resulted in a best estimate of 5 221 more severe and 60 less severe injuries or illnesses per year, within a range of 3 to 8 more severe and 30 to 90 less severe injuries or illnesses per year on average. AverageEconomicDamageperYear
While oil spills are rarely associated with fatalities, they are always associated with economic damages. While estimates of lives lost due to oil spills required focusing on high consequence events, calculating the average economic damage per year needs to take into account both the high consequence events and more frequent smaller oil spills. Historical data exist that include both larger and smaller oil spills to calculate historical averages. Two approaches were considered to calculate estimates of economic damage from oil spills. In the first approach, estimates of economic cost per year were identified in the literature for multiple years and turned into averages. In the second approach, estimates of the economic cost per gallon spilled were multiplied by estimates of the total number of gallons spilled per year on average. These two approaches presented a range of estimates (Figure 46). Under the first approach, two sources of estimates of economic damage were identified— those associated with the Government Accountability Office (GAO) and those associated with the U.S. Coast Guard. The estimates developed by a consulting firm under contract for the U.S. Coast Guard delivered estimates of approximately $1 billion per year on average based on various time periods from 1980‐
2002 (Etkin 2004). The GAO estimates were much lower— in the range of $20 million per year— but there are two reasons to believe that the GAO numbers do not represent oil spill costs as I have defined them here. First, the estimates reflect only the largest events and not the large number of smaller events that occur every year that must be addressed by the U.S. Coast Guard, a DHS component. Second, the estimates include only those costs of clean‐up and paid out recovery costs, which may not include all costs of business interruption. 222 Under the second approach, I identified estimates of cost per gallon of oil spilled from the literature (Tebeau 2003), as well as estimates of the number of gallons spilled per year (PMG/ERC 2002). Estimates of the number of gallons of oil spilled included several estimates of the gallons of oil spilled in the U.S. or U.S. waters over the period from 1980‐2002. This range of estimates of number of gallons spilled per year was applied to the estimate of cost per gallon spilled to calculate a range of estimates of cost of oil spilled per year. The best estimates from this approach were similar to averages found in the US Coast Guard data. However, this second approach had a somewhat wider range of estimates that included the estimates from the first approach within its bounds. I adopted this second approach because of these more inclusive bounds. The low, best, and high estimates of gallons per year were derived from the low, best, and high 10‐year averages of gallons of oil spilled, multiplied by the cost per gallon spilled. This resulted in a best estimate of $1 billion in economic damage per year on average, between a range of $1 billion and $4 billion per year on average. Figure 46‐ Estimates of Average Economic Damages per Year $10,000,000,000.00
$1,000,000,000.00
GAO
Estimated range
based on
gallons
spilled
$100,000,000.00
based on
averages
1980‐2002
$10,000,000.00
223 GreatestEconomicDamageinaSingleEvent
A range of estimates was provided for greatest economic damage from a single oil spill. At the low end was an estimate for the economic damages for the largest oil spill from a ship ($4 billion), the oil spill associated with the Exxon Valdez in 1989. At the high end was an estimate of the greatest economic damages from any event in the U.S. ($40 billion), the oil spill associated with the Deepwater Horizon drilling platform in 2010. AverageIndividualsDisplacedperYear
While oil spills in water may lead to short term restrictions, notably around beaches or waterways, they do not commonly involve displacing people from their homes. One exception was the Port Arthur oil spill in 2010, in which 120 people were displaced. Oil spills associated with ruptured on‐
land pipelines can cause more widespread displacement, but these spills are typically the responsibility of the Environmental Protection Agency and not DHS. The estimate of 120 people from Port Arthur was multiplied by 1/40th, representing the single event with displacement in the past 40 years. This led to an estimate of 5 people displaced per year on average. References
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Cambridge, Mass., Harvard University Press. Schanzer, D. H., J. Eyerman and V. De Rugy (2009). Strategic Risk Management in Government: A Look at Homeland Security, IBM Center for the Business of Government. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. Sitterle, K. A. and R. H. Gurwitch (1999). "The terrorist bombing in Oklahoma City." When a community weeps: Case studies in group survivorship: 160‐189. Steinbrugge, K. V., H. J. Degenkolb, G. L. Laverty and J. E. McCarty (1987). Earthquake planning scenario for a magnitude 7.5 earthquake on the Hayward Fault in the San Francisco Bay area, California Dept. of Conservation, Division of Mines and Geology. Tebeau, P. (2003). U.S. Coast Guard‐ Oil Spill Response Research and Development Program‐ A Decade of Achievement. Washington, DC, Potomac Management Group, U.S. Coast Guard. USGS. "FAQs ‐ Probabilities, Seismic Hazard & Earthquake Engineering." Retrieved Jan. 12, 2012, from http://earthquake.usgs.gov/learn/faq/?faqID=42. 230 Winter, D. C. (2010). Interim Report on Causes of the Deepwater Horizon Oil Rig Blowout and Ways to Prevent Such Events. Washington, DC, National Academies. 231 AppendixC. SupportingDocumentstoGuideaDeliberativeRanking
ofRisksintheHomelandSecurityDomain—“NotesontheRisk
Calculations”
NotesontheRiskCalculations
The first page of each risk summary sheet includes a data table summarizing the nature and magnitude of environmental health risks attributable to the risk being described. This table provides quantitative and qualitative data for attributes of each risk that have been demonstrated to be important when asking people to assess their concern about risks. This sheet provides definitions for each of these attributes. DefinitionsofRiskAttributes
Average number of deaths per year. This is the average number of deaths expected per year among residents in the U.S. from current levels of exposure to the hazard. Expected values for regular or annual events fall within a historical range, while infrequent events consider both the severity of the hazard and the likelihood that the hazard will happen in that year. Expected deaths from terrorism contain more uncertainty than expected deaths from natural events, as whether an event occurs is the result of uncertain actions, rather than reflecting an underlying distribution. Greatest number of deaths in a single event. Some homeland security hazards kill only one person at a time, whereas other hazards can kill a group of people all at once. For instance, people who die from lightning strikes die one at a time, but a terrorist attack could claim many lives at once. This statistic represents the greatest number of people who could plausibly be killed in a single event involving a given hazard. Injury or illness. Many homeland security hazards present nonfatal risks. These risks vary in both duration and severity. The risk summary tables describe two categories of cases of nonfatal 232 physical injury or illness per year expected among U.S. residents resulting from one year of exposure to a given hazard. These two categories include more severe injuries or illnesses and less severe injuries or illnesses. These are limited to physical injuries or illnesses, including brain damage but excluding psychological damage. More severe injuries or illnesses are typically defined as ones requiring hospitalization, while less severe may be treated at a hospital but not admitted. Both more severe and less severe include both short‐term and long‐term consequences. Examples are given in Table . The average number of injuries or illnesses in a given year is presented for each of the two categories. Table XXXV‐ Categorization Used to Describe Nonfatal Risks More severe Serious acute and chronic conditions involving hospitalization Less severe Less serious acute and chronic conditions that may include medical care but do not include hospitalization Examples of serious acute conditions include: Examples of acute conditions include: acute meningitis, pneumonia, severe asthma or allergic infectious diseases without hospital stay (e.g. cold, attack, compound fracture, severe food poisoning. flu, ear ache), vaccinations, mild food poisoning, concussions, bruises, and minor burns. Examples of serious chronic conditions include: Examples of less serious chronic conditions loss of limb, mental retardation requiring include: joint damage, loss of finger, mild mental continuous caregiver, blindness, infertility, non‐
retardation, scars and burns affecting movement, fatal cancer, chronic migraine, disfiguring burns, permanent damage to lung, liver, kidney or heart any condition requiring long‐term institutional resulting in less than 20% loss of organ function. care, permanent damage to lungs, liver, kidney, or heart resulting in more than 20% loss of organ function. Psychological damage per year on average. Homeland security disasters have disruptive effects on people’s mental health. These harms can be caused by the trauma of the incident itself, from living with the losses of the event, or from the disruption of treatment or social support following the event. Two of the greatest harms relate to post‐traumatic stress disorder (PTSD) and depression, although most psychological reactions will be less severe reactions not reaching the level of those two 233 syndromes. Psychological damage per year is qualitatively estimated as low, moderate, or high, based on whichever of those two syndromes is deemed worse. Average economic damage per year. Homeland security disasters are often associated with the destruction of property, business disruption and altered spending patterns. Economic costs characterizes the economic costs expected in an average year. Expected values for regular or annual events fall within a historical range, while infrequent events consider both the severity of the hazard and the likelihood that the hazard will happen in that year. Estimates include the costs of direct physical damage and direct business interruption. They do not include indirect costs, such as government spending on activities incited by the action, costs to specific industries or impact on markets. Greatest economic damage in a single event. Homeland security disasters are often associated with the destruction of property, business disruption and altered spending patterns. Greatest economic costs characterizes the economic costs associated with a single event. The low estimate represents of direct damages for a low severity event. The high estimate represents the damages for a high severity event. The best presents the average of the estimated damages for events of the disaster type. Duration of economic damages. Consequences of homeland security risks vary on the amount of time they harm. Some economic harms may last only hours, others may last months to years. To some extent, duration of damages is related to job loss— for example, the contamination of an oil spill contaminating fisheries can damage fishing businesses for years to decades. Physical damage lasts until repairs are made or replacements are put in place. Costs of business interruption last until businesses reopen or the market adjusts. Businesses can reopen when physical damage to the business is repaired, employees return to work, or contamination is cleaned up. Duration of total economic damage extends until the large majority of all damage is repaired or restored. 234 Size of area affected by economic damages. One important aspect of catastrophic damage is the area affected. An IED may impact only a small area, a neighborhood or city, while a hurricane causes damage across many states. Size of area affected is presented categorically in increasing size‐ less than a block, block, neighborhood, city, county, state, region, and nation. Average environmental damage per year. Environmental damage from homeland security events comes largely from the intersection of human contaminants disrupted by the disaster. Two important considerations are the impact on species and aesthetic impact, which largely depend on the spatial extent and longevity of the harms. Environmental damage as a consequence should be considered in expectation, including both the severity and the likelihood of damage. Environmental damage is considered categorically, with low, medium, and high damage. Low damage includes either little damage to natural species or aesthetic damage, or some potential for damage unlikely to be realized. Examples include most terrorist risks. Medium damage includes significant potential for damage but unlikely to be realized (e.g. nuclear explosions), or moderate damage that is likely to occur in a given year (e.g. tornadoes or forest fires). High environmental damage consists of potential for significant damage that is likely to occur in a given year (e.g. hurricanes, floods). Average individuals displaced per year. This represents the number of people who are forced to move due to the disaster each year on average. This characteristic not only presents direct harm to communities, but also contributes to other psychological harms and societal harms. Disruption of government operations. This characteristic describes the amount that government operations are disrupted, with three levels of low, moderate, and high disruption. These categories are presented using an anchored scale across two dimensions: severity and duration. Both severity and duration have categories of low, moderate, and high. Low duration is less than a week, moderate disruption lasts one week to three months, high disruption lasting three months or longer. 235 Severity of disruption is also categorical. High levels of disruption include a loss of order within a large effected area and disruption of emergency responders’ ability to help; moderate levels include an inability to perform governmental functions beyond emergency response in a large area or inability to perform functions including emergency response in a small area; low levels of impact include disruption of non‐emergency response governmental functions in a small area, disruption of non‐essential services or less. Table XXXVI‐ Disruption of Government Operations Severity Low Moderate High High Moderate High High Duration Moderate Low Moderate High Low Low Low Moderate Natural/human‐induced. This statistic characterizes the intent behind the disaster, having implications for both social cohesion and for behavioral responses to the disaster. This characteristic is represented with two categories‐ natural or human‐induced. Ability of an individual to control their exposure. The ability of an individual to control their own exposure varies. Some events, such as hurricanes or pandemic flu, are known in advance and individuals can take actions to lessen their exposure. Other events can be mitigated generally, through purchasing a home in a specific location or buying flood insurance to control exposure to flood risks. Other events can be reduced only by making significant lifestyle choices, such as moving to another city or region. This characteristic is presented with three levels‐ low, moderate, and high. High control involves intentional actions that can avoid or significantly reduce one or more aspect of harm. High control often relates to advance warning of a clearly visible event. Moderate control involves actions 236 that can reduce likelihood of exposure or mitigate the damage to some extent. Low control involves actions that are completely out of the individual’s control or can only be reduced by significant lifestyle changes such as moving away from urban or suburban areas. Time between exposure and health effect. Most of the injuries and deaths in a catastrophic disaster occur at the time of the event, with the exception of infectious diseases, where all health effects arise days after the exposure. However, some catastrophes also have delayed consequences due to chemical or radioactive contamination. This consequence presents in low amount of time and the high amount of time‐ immediate, days, immediate to days, immediate to weeks, immediate to years, and immediate to decades. Quality of scientific understanding. There are two sources of uncertainty in estimating risks of disasters. One involves how well scientists know the relationship between exposure to a hazard and its resulting health impacts. The other involves how well we can predict exposure of US residents to a particular hazard. This statistic characterizes the former. For instance, while scientists do not know the likelihood of a nuclear attack, the consequences of the explosion and subsequent contamination are well understood. This also does not reflect how well individuals in society know the risk. Quality of scientific understanding is represented with three levels‐ low, moderate, or high. Combined uncertainty in deaths, illness, and injury. This measure is a measure designed to incorporate the total uncertainty. It is categorical, with low, moderate, and high combined uncertainty. It is based on the ranges of estimates for average number killed per year, average number of severe injuries per year, average number of less severe injuries per year, and average economic damages per year. For each of these categories, the ratio of the high estimate to the low estimate is calculated. These ratios are then added together to give a combined uncertainty number. If a ratio cannot be calculated because an estimate because the low estimate is zero, the high estimate will be taken as the 237 ratio (i.e., the low estimate will be treated as one for purposes of calculating the ratio). However, if a ratio cannot be calculated because estimates are not available (e.g. the long‐term health effects from carcinogens released in an oil spill), then the combined uncertainty is automatically treated as high. The thresholds for low, moderate, and high are derived from the data. Low combined uncertainty reflects calculated values lower than 100. Moderate combined uncertainty reflects calculated values between 100 and 1000. High combined uncertainty reflects calculated values greater than 1000, or values that cannot be calculated because estimates are not available. 238 AppendixD. RiskSummarySheetsDescribingaSetofHomeland
SecurityHazards
The following section contains the risk summary sheets that were developed to describe the risks. Four page versions of these risk summary sheets containing no endnotes or citations were provided to the participants in this risk ranking sessions. These extended versions include documentation on the sources and methods of estimates that support these summaries. 239 EARTHQUAKE
Shifting tectonic plates create earthquakes – sometimes major shaking of the ground over
seconds to minutes duration. A series of active faults straddles the west coast of the United
States along the Pacific Rim, leaving it particularly vulnerable to major earthquakes. While
earthquakes outside of the west coast are less frequent and often less severe, faults near
Charleston, SC and the New Madrid region of the Mississippi river basin present additional areas
of concern. Severe earthquakes can create widespread damage across an entire metropolitan
area, including high potential for building collapse and disruption of water and power. Shaking
can contribute to injury and death directly, through structural collapse, and through the
disruption of critical infrastructures such as electricity. While scientists have a good
understanding of the geographical areas at risk of major earthquakes over centuries, the
prediction of the day and time, or even decades remains impossible. Still, the understanding of
earthquakes identifies vulnerable areas that are at high risk and need to prepare and mitigate
earthquake risks. Many decades of mitigation efforts have significantly decreased the risk of
death or injury due to a major earthquake in the US.
Risk Characteristics of Earthquakes
Public Health
Average number of deaths per year
Greatest number of casualties in a single event
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damage per year
Greatest economic damage in a single event
Duration of economic damage
Size of area affected by economic damage
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and injuries
Quality of scientific understanding
Combined uncertainty
Low
Best
High
2i
100ii
5,000iv-20,000v
70vii
3,000x
Highxii
300iii
10vi
500ix
$1Bxiii
$5Bxiv
$60Bxvi - $1Txvii
Months to decadesxviii
County to statexix
Moderatexx
700xxi-20,000xxii
Moderatexxiii
900viii
9,000xi
$9Bxv
Natural
Low to moderatexxiv
Immediatexxv
Highxxvi
Moderatexxvii
WHAT IS KNOWN ABOUT RISK OF EARTHQUAKES?
An earthquake is sudden movement along a fault that causes shaking of the earth’s crust.
The earth’s tectonic plates are constantly shifting, and this stores energy along a system of fault
surfaces that extend deep into the crust. Occasionally this energy is released suddenly and
240 abruptly, called an earthquake. While tens of thousands of earthquakes occur in the U.S. every
year, most of these are too small to be felt. The magnitude of an earthquake is a relative measure
of its size. The magnitude measures amplitudes of ground shaking on a logarithmic scale, which
means that each unit of magnitude represents a tenfold increase in shaking amplitude. The
magnitude can be converted into energy release, where each unit of magnitude corresponds to
about 30 times as much energy as the previous number.xxviii For example, a 6.0 magnitude
earthquake releases 30 times as much energy as a 5.0. While earthquakes over 6.0 can be
considered major, earthquakes 100 to 1000 times more powerful do occur, although much less
frequently. Major earthquakes in populated areas are rare, occurring less than once a decade, but
when they occur they cause significant shaking, landslides, and can rupture the earth’s surface,
as well as cause liquefaction of water saturated ground in some circumstances. These can extend
across a city or region, lasting 15 seconds to a minute, followed by an increased likelihood of
additional earthquakes over the next several days, called aftershocks. While scientists can
predict the likelihood of an earthquake over a period of years, it is impossible to predict that an
earthquake will occur on a specific day, month, even decade.
The greatest risk of fatality from earthquakes comes from the collapse of buildings or
other structures.xxix Injuries from earthquakes are far more common than deaths, including
injuries from falls, being hit by objects, or motor vehicle accidents.xxx Motor vehicle accidents
can occur from the shaking while driving or roadway collapse.xxxi Additionally, earthquake
damage can also disrupt important technology, such as hazardous chemical storage, water
systems, electricity, and gas, which can result in contamination, dehydration, spoiled food, and
house fires.xxxii Less severe but more widespread effects of dehydration and exposure are
heightened as damage to shelter drives people outdoors.
Economic damage is composed of physical damage to buildings and structures as
well as business interruption.xxxiii Damage to buildings and their contents is the most significant
cost.xxxiv In a catastrophic earthquake, hundreds of thousands of buildings may be damaged.xxxv
An estimated 19% of structures in an area would have damage over $1000 as a result of a 6.0
magnitude earthquake, while over 75% would have such damage as a result of a 7.0.xxxvi A
typical major earthquake (7.0 magnitude) could cause considerable damage to ordinary
buildings, including partial collapse, fall of chimneys, factory stacks, columns, monuments, and
walls, and slight damage to specially designed buildings.xxxvii Following the 1994 Northridge
earthquake (6.7 magnitude), over 1000 homes were damaged so severely that they could not be
re-entered even to get belongings and several highways collapsed.xxxviii Additional economic
costs come from business disruption following an event.xxxix Common causes of business
interruption are physical damage, power outages,xl and damage to transportation systems, which
may prevent employees, customers, and goods from reaching the business.xli
Earthquakes may cause significant visible changes to the environment across a
geographic area of up to 20,000 square miles as a result of events such as landslidesxlii and
altered courses of rivers and springs.xliii For example, a New Madrid earthquake in the 1800’s
changed the course of the Mississippi river.xliv These changes can have impact on animals,xlv but
ecosystems can largely adapt to environmental impacts of earthquakes.xlvi Still, there is the
potential for contamination due to the release of chemicals being stored, disruption of
infrastructure (including pipelines and tanker trucks), and damage to pollution control
systems.xlvii
241 WHAT IS THE EXPOSURE TO EARTHQUAKES?
Certain areas of the country are at higher risk of a major earthquake based on the
locations of shifting tectonic plates and the proximity of the associated fault systems. The
greatest risks in the United States are along the Pacific Coast, with significant potential for
economic and human damage in the highly populated Los Angeles and San Francisco areas.xlviii
The West Coast plate boundary extends from the Gulf of California up the coast, creating the
potential for significant earthquakes in California, Oregon, Washington, and Alaska.
Additionally, there are other fault systems with the potential for a significant earthquake,
including faults in the Salt Lake City area, the New Madrid fault system in the Midwest, and
additional faults near Charleston, SC.
Major earthquakes are rare, occurring less than once a decade on average. While smaller
earthquakes may occur 60 times a day in California with no significant damage, more severe
events are less frequent. An earthquake causing damage on the scale of the 1994 Northridge
earthquake (6.7 magnitude) occurs approximately once every thirty years. When a large
earthquake occurs, a large area is affected, causing damage across one or more metropolitan
areas.xlix Estimates for the likelihood of a catastrophic earthquake vary, reflecting uncertainty in
the estimates as well as different thresholds for what is considered to be a large earthquake. The
US Geological Survey (USGS) estimates the annual probability of a 6.7M or greater earthquake
is 3.6% for the Los Angeles area and 3.2% for the San Francisco Bay area. The joint probability
of around 7% per year is our best estimate of the likelihood of a major California earthquake in a
year.l Major earthquakes outside of the West Coast of the United States are considerably rarer.
As major earthquakes are rare, the worst historical events in the United States are not
representative of the worst possible events. The best estimate of the largest number of fatalities
in a single event is between 5,000 and 20,000, based on scenarios of a major earthquake directly
under the Los Angeles or San Francisco Bay area.li These estimates are much larger than the
actual deaths from recent earthquakes in the U.S. (the Northridge quake of 1994 killed around
60) but represent catastrophic scenarios.lii
Average yearly deaths can be estimated based on these worst case events. FEMA’s
HAZUS-MH hazard methodology predicts 100 deaths, 100 hospitalizations, and 3,000 minor
injuries each year on average.liii However, using a the Northridge earthquake as an example
suggests lower risks of around 2 deaths, 10 hospital admissions and 500 hospital non-admissions
per year on average.liv Alternatively, basing estimates on other estimates of a more significant
catastrophic earthquake suggests a higher average risk; 300 deaths, 900 hospital admissions, and
9,000 hospital non-admissions.lv
The economic damage from earthquakes averages in the billions of dollars, and a single
catastrophic earthquake can cost up to hundreds of billions of dollars. Our best estimate for the
average yearly costs of earthquakes is $5B, drawn from FEMA’s HAZUS-MH model.lvi This
falls into a range between $4B, based on the historical costs of the Northridge earthquake (6.7),
and $9B, based on the estimated costs of a 7.8 earthquake in the ShakeOut scenario. The most
damaging contemporary earthquake in the U.S. (the Northridge earthquake of 1994) had around
$60B in damage, lvii but this is not the greatest that could be expected from a single event. A
worst-case event with a severe earthquake under a major city such as Los Angeles or San
Francisco could result in damage upwards of $1T.lviii Economic damage would impact a large
area ranging from a county to a state, and could last for years or decades depending on the
availability of resources to fund a recovery. Tens to hundreds of thousands of people could be
242 displaced in an event, with the best estimate of 2,000 displaced a year on average, within a range
of 700 and 20,000 displaced per year.lix
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF EARTHQUAKES?
Actions to address earthquake risks include understanding the risks, damage
mitigation efforts, preparing emergency responders and individuals, as well as pre-planned
response, and recovery efforts.
Earthquake science and fault mapping continue to improve. Using GPS, geological
mapping and seismic instrumentation, scientists have determined the locations of active faults
and estimated the amounts of energy that can be released by these faults.lx The federal
government monitors and reports on earthquakes and their effects through the USGS.lxi This
understanding of risk is useful for planning of land use, emergency response, and insurance
exposure.
Many mitigation efforts address infrastructure collapse. Improved building codes and
standards have decreased the vulnerability to seismic damage, with input from FEMA’s National
Earthquake Hazards program.lxii The federal and state governments have also been involved
with decreasing seismic risk, including encouraging seismic resistant buildings through the
Hazard Mitigation Grants of over $50M per year.lxiii Other damage to infrastructure has been
addressed, including the reinforcement of roads and bridges,lxiv as well as water and gas
utilities.lxv This is likely to mitigate the damage of an earthquake- in a 6.5 magnitude
earthquake, for example, buildings built to older codes will suffer considerable damage with
partial collapse, but damage in specially designed buildings could be much less.lxvi These
measures are cost effective because they decrease injury and death; an estimated 26 deaths and
542 injuries have been reduced annually from Hazard Mitigation Grants.lxvii
Preparedness approaches have also been undertaken. Because major earthquakes are
certain to occur in the U.S., federal, state, and local officials as well as private businesses in
high-risk areas often have plans in place to respond to an event.lxviii These include regional
initiatives such as the San Francisco Bay Earthquake Planlxix and the New Madrid Seismic Zone
Catastrophic Disaster Planning Initiative,lxx and exercises such as the ShakeOut.lxxi FEMA
supports preparedness activities through Emergency Preparedness Grants.lxxii Individuals are
also encouraged to undertake other preparedness activities, including having an earthquake plan
and supplies in high-risk areas. Federal agencies and some states and localities identify and
communicate specific earthquake preparedness goals to the public through websites, television
and radio, exercises, and the Citizen Corps.lxxiii
These plans support response activities. In the event of a catastrophic earthquake, FEMA
will be the lead agency under the National Response Framework, coordinating with other
federal, state, and local agencies. Activities include fire-fighting and emergency services at the
local level along with national Urban Search and Rescue units, and providing shelters and
supplies from state and national agencies (including FEMA) and NGOs (including the American
Red Cross).
Recovery efforts available include private insurance and government funds. FEMA and
some states have disaster relief mechanisms. The primary recovery funds from FEMA are in the
form of loans supplemented by grants for those who do not qualify for loans.lxxiv Private
insurance is another option although deductibles are usually very high. Earthquake insurance is
not typically included in standard homeowner’s policies, but can be bought individually and used
to recover from a disaster.lxxv Assistance is also available under tax relief.lxxvi
243 References
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toid=6f461c99b5ccb110VgnVCM10000089f0870aRCRD&currPage=13cbb969ae282210VgnVCM1
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National Research Council, National Academies Press, Washington, D.C. DHS Office of Inspector General (2010). FEMA's Preparedness for the Next Catastrophic Disaster‐ An Update. U.S. Department of Homeland Security Office of Inspector General. Washingon, DC. 244 Eguchi, R. T., J. D. Goltz, C. E. Taylor, S. E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais (1998). "Direct economic losses in the Northridge earthquake: a three‐year post‐event perspective." Earthquake Spectra 14(2): 245‐264. Elnashai, A. S., L. J. Cleveland, T. Jefferson and J. Harrald (2008). "Impact of Earthquakes on the Central USA." FEMA (2008). HAZUS MH estimated annualized earthquake losses for the United States. Washington, D.C., National Institute of Building Sciences, Federal Emergency Management Agency. FEMA. (2012). "Earthquake." Retrieved Jan. 12, 2012, from http://www.fema.gov/hazard/earthquake/index.shtm. FEMA. (2012). "Earthquake Response and Recovery." Retrieved Jan. 12, 2012, from http://www.fema.gov/hazard/earthquake/response.shtm. FEMA. (2012). "FEMA Earthquake Mitigation Handbook." Retrieved Jan. 12, 2012, from http://www.conservationtech.com/FEMA‐WEB/FEMA‐subweb‐EQ/index.htm. FEMA. (2012). "IS‐8.a Building for the Earthquakes of Tomorrow: Complying with Executive Order 12699." Retrieved Jan. 12, 2012, from http://training.fema.gov/EMIWeb/IS/is8a.asp. Field, E. H., H. A. Seligson, N. Gupta, V. Gupta, T. H. Jordan and K. W. Campbell (2005). "Loss estimates for a Puente Hills blind‐thrust earthquake in Los Angeles, California." Earthquake Spectra 21: 329. Giuliano, G. and J. Golob (1998). "Impacts of the northridge earthquake on transit and highway use." Journal of transportation and statistics 1(2): 1‐20. 245 Gordon, P., H. Richardson, B. Davis, C. Steins and A. Vasishth (1996). "The business interruption effects of the Northridge earthquake." Lusk Center Research Institute, University of Southern California, Los Angeles, CA. Grossi, P. (1999). "Assessing the Benefits and Costs of Earthquake Mitigation." Center for Financial Institutions Working Papers. Hancox, G., N. Perrin and G. Dellow (2002). "Recent studies of historical earthquake‐induced landsliding, ground damage, and MM intensity in New Zealand." Bulletin of the New Zealand Society for Earthquake Engineering 35(2): 59‐95. Horwich, G. (2000). "Economic lessons of the Kobe earthquake." Economic Development and Cultural Change 48(3): 521‐542. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. John, H. (1968). "Earthquake‐initiated changes in the nesting habitat of the dusky Canada goose." The great Alaska earthquake of 1964: 129. Kroll, C. A., J. D. Landis, Q. Shen and S. Stryker (1990). "Economic impacts of the loma prieta earthquake: A focus on small business." Berkeley Planning Journal 5(1): 3958. Laatsch, E. (2007). "FEMA's Statutory Activities." Retrieved Jan. 12, 2012, from http://www.nehrp.gov/pdf/fema_statutory_activities_ppt.pdf. Lindell, M. K. and R. W. Perry (1996). "Addressing gaps in environmental emergency planning: hazardous materials releases during earthquakes." Journal of Environmental Planning and Management 39(4): 529‐543. 246 Multihazard Mitigation Council (2005). Natural Hazard Mitigation Saves: An Independent Study to Assess the Future Savings from Mitigation Activities, National Institute of Building Sciences. 2‐ Study Documentation: 126‐127. NAHB Research Center (1994). Assessment of Damage to Residential Buildings Caused by the Northridge Earthquake. U.S. Department of Housing and Urban Development Office of Policy Development and Research. Washington, DC. 2012. Nichols, J. M. and J. E. Beavers (2003). "Development and calibration of an earthquake fatality function." Earthquake Spectra 19: 605. Parise, M. and R. W. Jibson (2000). "A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake." Engineering Geology 58(3‐4): 251‐270. Peek‐Asa, C., J. F. Kraus, L. B. Bourque, D. Vimalachandra, J. Yu and J. Abrams (1998). "Fatal and hospitalized injuries resulting from the 1994 Northridge earthquake." International Journal of Epidemiology 27(3): 459. Perry, R. W. and M. K. Lindell (1997). "Earthquake planning for government continuity." Environmental Management 21(1): 89‐96. Petak, W. J. and A. A. Atkisson (1985). "Natural hazard losses in the United States: A public problem." Review of Policy Research 4(4): 662‐669. Risk Management Solutions (2009). Catastrophe Modeling and California Earthquake Ris: A 20‐Year Perspective, Risk Management Solutions. 247 Roberts, J. E. (1994). Highway Bridges. Practical Lesson from the Loma Prieta Earthquake. Geotechnical Board and National Research Council. Washington, DC, National Research Council. Rose, A., J. Benavides, S. E. Chang, P. Szczesniak and D. Lim (2002). "The regional economic impact of an earthquake: Direct and indirect effects of electricity lifeline disruptions." Journal of Regional Science 37(3): 437‐458. Rose, A. and D. Lim (2002). "Business interruption losses from natural hazards: conceptual and methodological issues in the case of the Northridge earthquake." Global Environmental Change B: Environmental Hazards 4(1): 1‐14. San Francisco Fire Department. "Neighborhood Emergency Response Team Home." Retrieved Jan. 12, 2012, from http://www.sfgov.org/site/sfnert_index.asp. Science Daily. "Disaster Earthquake Scenario Unveiled For Southern California." Retrieved Jan. 12, 2012, from http://www.sciencedaily.com/releases/2008/05/080522104754.htm. Steinbrugge, K. V., H. J. Degenkolb, G. L. Laverty and J. E. McCarty (1987). Earthquake planning scenario for a magnitude 7.5 earthquake on the Hayward Fault in the San Francisco Bay area, California Dept. of Conservation, Division of Mines and Geology. The Great California Shake Out. "The Great California Shake Out." Retrieved Jan. 12, 2012, from www.shakeout.org. The Great California Shake Out. "Who Is Participating?" Retrieved Jan. 12, 2012, from http://www.shakeout.org/whoisparticipating/ Thilenius, J. F. (1990). "Woody plant succession on earthquake‐uplifted coastal wetlands of the Copper River Delta, Alaska." Forest Ecology and Management 33: 439‐462. 248 USGS. "2008 NSHM Figures." Retrieved Jan. 12, 2012, from http://earthquake.usgs.gov/hazards/products/conterminous/2008/maps/ USGSb. "FAQs ‐ Probabilities, Seismic Hazard & Earthquake Engineering." Retrieved Jan. 12, 2012, from http://earthquake.usgs.gov/learn/faq/?faqID=42. USGSc. "Magnitude/Intensity Comparison." Retrieved Jan. 12, 2012, from http://earthquake.usgs.gov/learn/topics/mag_vs_int.php. Walter, L. (2008). "FEMA Develops Earthquake Disaster Response Initiative." Retrieved Jan. 12, 2012, from http://ehstoday.com/fire_emergencyresponse/disaster‐
planning/FEMA_earthquake_disaster_response_1125/. 249 HURRICANES
Hurricanes are large storms that form over large, warm bodies of water like the Western Atlantic
or Gulf of Mexico. When hurricanes reach land, they bring high winds, tornadoes, and flooding
from both rain and storm surge. Hurricanes regularly strike the southeastern U.S. along the
coasts of the Atlantic Ocean and Gulf of Mexico. A major storm can affect a significant portion
of one or more states, and several major storms make landfall each year. Hurricanes are
typically associated with some casualties and major economic damage. While preventing
hurricanes is impossible, weather forecasting provides opportunities to reduce the consequences
of hurricanes by implementing evacuation plans and taking other preparedness measures days in
advance. Furthermore, because the effects of hurricanes occur predictably, communities can
reduce their risks by implementing building codes and zoning plans to make buildings more
resilient and limit the exposure of buildings in floodplains.
Risk Characteristics of Hurricanes
Public Health and Safety
Average number of deaths per year
Greatest number of casualties in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
10i
40ii
2,000-4,000iv
600vi
1,000ix
Highxi
60iii
200v
400viii
$2Bxii
$10Bxiii
$60-200Bxv
Months to yearsxvi
Counties to statesxvii
Highxviii
10,000xix-100,000xx
Moderate to Highxxi
1,000vii
2,000x
$20Bxiv
Natural
Highxxii
Immediate up to yearsxxiii
Moderate to highxxiv
Lowxxv
WHAT IS KNOWN ABOUT RISK OF HURRICANES?
Hurricanes are natural, highly destructive cyclonic weather systems that primarily affect
coastal areas. Hurricanes form over warm water, and are predominantly a risk to the Atlantic
and Gulf Coast regions of the United States, spanning the coast from Texas to Maine and
including Puerto Rico.xxvi While hurricanes can form in the Eastern Pacific, they rarely do. xxvii
Hurricanes may be 200-600 miles across. As a result, they create damage across a wide area in
250 their path, potentially a large portion of one or several states.xxviii Hurricanes typically form
August through October, but significant hurricanes have occurred as late as December.
Hurricane severity varies along a range rated by wind speed. Tropical depressions with
wind speeds over 74 mph are designated hurricanes, categorized on a scale of increasing severity
from 1 to 5. Category 1 and 2 storms create minimal to moderate damage, while major storms of
Category 3 and higher can create extensive damage.xxix These more intense storms comprise
only 24% of hurricane landfalls in the U.S., but account for 85% of the total damage.xxx
Most direct damage from hurricanes comes from flooding, including both salt-water
storm surge and fresh-water flooding from rains, or from wind. Indirect damage can occur from
disruptions to infrastructure, chemical contaminants and mold from floods, and displacement.
Major hurricanes are extremely expensive events in economic terms. Fatalities are often low, but
can be in the hundreds to thousands for a severe hurricane.
Storm surge is considered the most severe impact of hurricanes, causing immediate
damage in low-lying coastal areas. Storm surge is caused when the low pressure of a hurricane
raises water levels under the storm, causing flooding when it nears land. This flooding can lead
to fatal drowning, either on land directly or by carrying people out to sea.xxxi Drowning due to
storm surge was the most common cause of fatality in hurricanes before 1990; more recently
evacuation measures have decreased this concern.xxxii Storm surge also causes significant
economic damage, both from the physical force of water slamming into buildings and from
flooding.xxxiii A second cause of flooding is rainfall. This fresh-water flooding is typically less
severe than storm surge but fewer precautions are taken to avoid it.xxxiv Almost all deaths from
fresh-water flooding occur from people traveling on flooded roadways.xxxv
Flooding may also lead to two sources of contamination- mold and household, industrial,
and agricultural chemicals. Mold contamination can harm people, particularly those with
weakened immune systems such as the elderly.xxxvi Mold can cause infections, allergies, and an
immune disease called hypersensitivity pneumonitis.xxxvii Chronic mold exposure may also be
related to cancer and liver failure.xxxviii Chemical contamination is less well understood.
Household, industrial, and agricultural chemicals get mixed into the floodwaters and dispersed
over a large area. While many of these chemicals are known to harm health, it is unclear the
extent to which people are exposed to them as the water carries it into rivers and offshore. The
extent to which chemical contamination harms human health is unclear.xxxix However,
environmental damage can be high; for example, Hurricane Katrina had over 100 square miles of
shoreline washed away, 320 million trees killed, lead and arsenic contamination, and an oil spill
as large as the Exxon-Valdez.xl
Wind also contributes to hurricane damage in four ways: directly through blown debris
and structural collapse, and indirectly through scattered debris in the aftermath and systemically
through loss of electricity. In an average year, wind accounts for approximately 10 percent of
hurricane deaths at the time of landfall.xli Wind can cause structural collapse, falling trees or tree
limbs, wind-borne debris, and electrocution by downed power lines.xlii Power outages contribute
to other casualties as well, including carbon monoxide poisoning from portable electric
generators.xliii Deaths and injuries after the hurricane, including accidents when dealing with
debris, exposure to the elements, and loss of power, may also occur but are not included in our
estimates.xliv
Hurricanes also cause economic damage through business disruption. Evacuations are
common, shutting down businesses across several counties for a period of days to weeks. Areas
that are damaged will be disrupted even longer, from months to years, as debris is cleared and
251 repairs are made. There were 20 hurricanes with over $1B in economic costs in the 22 years
between 1983 and 2004.xlv
Hurricanes also cause social and psychological damage. People are often displaced from
their homes, including both short-term evacuations and long-term relocations due to damaged
homes. For example, Hurricane Katrina in 2005 displaced somewhere between 700,000 to 1.2
million people.xlvi The initial flooding lasted for months, but many of the displaced people never
returned. Displacement also disrupts social networks of friends, family, and coworkers, making
it more difficult for evacuees to get new housing or jobs.xlvii The lack of a social network can
also exacerbate the trauma of the disaster, damaging mental health in at least the short run,
including depression and post-traumatic stress disorder (PTSD).xlviii
WHAT IS THE EXPOSURE TO HURRICANE RISK?
As hurricanes are common in the U.S., with several occurring each year, risk estimates
can be drawn from historical data. There are an average of 2 major hurricanes of Category 3 of
higher each year, although there have been as few as 0 and as many as 8.xlix The best estimate of
the average number of people killed each year by hurricanes is 40 (representing the average of 30
year averages over the past 70 years) but the average may be as low as 10 (the lowest 30 year
average) to as high as 60 (the highest 30 year average).l The greatest number of people that
killed in a single event is between 2,000 and 4,000, reflecting the number killed in Hurricane
Katrina in 2005.li Injuries are often underreported, but estimates can be projected from recent
hurricanes with more complete reports. Best estimates are 600 more severe and 1,000 less
severe injuries per year on average (between a low of 200 more severe and 400 less severe, and a
high of 1,000 more severe and 2,000 less severe injuries per year on average).lii
While casualties can be greatly reduced through advance notice and evacuation,
economic damage is still high. In fact, the economic consequences of hurricanes have been
increasing. This is partly due to a short-term increase in the number of hurricanesliii and to
normal cyclical patterns in ocean currents that vary over 25 to 40 year cycles;liv whether global
warming increases the number of major storms is uncertain.lv However, the more significant
trend is a long-term increase in risk as more people are moving to high-risk coastal areas.lvi In
the past 30 years, there has been a 28% increase in population in coastal counties, and people
within those counties are moving closer to the ocean. lvii
Based on historical 10 year averages over the past 70 years, the best estimate of average
economic damage is $10B per year (ranging between a low of $2B per year and a high of $20B
per year).lviii The worst economic damage for a single event is $60B (representing a very large
Hurricane Andrew-like event) to $200B (representing the largest events, like Hurricane Katrina
of 2005 and the Miami hurricane of 1926).lix
The disruption of government services is widespread for non-essential services, and large
storms such as Hurricane Katrina can overwhelm even emergency response services. While
millions are evacuated until the storm can pass, an estimated 60,000 individuals are displaced
from their homes each year for longer periods of time (between a low of 10,000 and a high of
100,000).lx It can take months to years for an area to recover from a severe hurricane, with areas
that were economically weak before the disaster taking longer to recover than those that were
economically vibrant.lxi
252 WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF HURRICANES?
Approaches directed at hurricanes fall into five categories: planning and preparedness,
pre-event evacuation, response efforts, structural and non-structural mitigation efforts, and use of
insurance systems.
Planning and preparedness involves preparing response and evacuation activities.
Hurricane planning exists at both the federal government and state and local level. This includes
stockpiling supplies, having a plan to move those supplies when needed, organizing emergency
responders to remove people from flooded and dangerous areas as well as to help provide
supplies and shelter, and preparing a plan to organize these activities. It also includes leading
exercises to train in response, which have been undertaken to a limited extent.
Unlike most disasters, we can take pre-emptive action before a hurricane. Hurricane
tracking by the federal government gives advance notice days before landfall.lxii This allows for
the activation of emergency plans, which may include evacuation orders or sheltering.lxiii Most
areas have sufficient evacuation plans for typical hurricanes, but there are some limitations with
regards to larger events that require evacuation across several states.lxiv The National Oceanic
and Atmospheric Administration estimates that hurricane tracking may save over 200 lives and
$1B in reduced property damage per year by allowing individuals to secure their windows,
prepare sandbags, and evacuate themselves and high value property.lxv
Response activities occur at the time of the event or in the immediate aftermath, and
include increased medical care and providing food, water, and temporary housing.lxvi Most
immediate response activities are performed by state or local emergency responders, although
the federal government may provide emergency funding for response and recovery through the
Stafford Act. The federal government can serve in a coordination role for large hurricanes, as
well as adding Urban Search and Rescue teams and medical response.lxvii
Knowledge of hurricane risks supports mitigation activities. The federal government
prepares maps identifying flood plains.lxviii This allows for both risk avoidance and mitigation
in both private and government action. Information by the government helps people and
businesses make informed decisions as to whether they want to live in areas susceptible to storm
surge. The federal government also provides information on steps individuals can take to
strengthen their homes for hurricanes,lxix and businesses are encouraged prepare contingency
plans for emergencies.lxx State and local governments also engage in zoning to enforce higher
building codes in high risk areas.lxxi The Army Corps of Engineers mitigates risk by making
material improvements to flood plains, wetlands, and levees to protect high risk areas.lxxii
Finally, hurricane risk can also be managed through insurance. Wind damage is often
included in homeowner’s policies, but flood insurance must be bought separately through the
National Flood Insurance Program.lxxiii Homeowners at high risk often find it difficult to obtain
insurance through the private market, although some state programs exist to pool the risk.lxxiv
The extent to which mitigation activities have decreased economic damages of hurricanes
is unclear. Not only is it is unclear whether mitigation efforts are cost effective in and of
themselves,lxxv having mitigation efforts in place may encourage more people to move to highrisk areas,lxxvi which is driving the increase in physical damage from hurricane. However
government actions, especially pre-event tracking and evacuations, have decreased the number
of hurricane deaths, with the number of deaths per year on average dropping from the hundreds
in the early 1900’s to tens by 2000.lxxvii Whether this success in the average year leads to smaller
or greater casualties in exceptional events (such as Hurricane Katrina) is unclear.
253 References
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Tornadoes are deadly windstorms associated with severe thunderstorms, which cause devastation
in an area from a few blocks to counties. Typically, 20 severe tornadoes occur in the United
States each year. The Great Plains are known as “Tornado Alley” for their frequency of
tornadoes, but more deaths occur in the Gulf region where tornadoes are more likely to strike at
night while people are asleep and more vulnerable. Tornadoes cannot be prevented, but their
damage can be mitigated. The government monitors tornado activity and can provide advance
warnings of tornadoes, allowing individuals to take shelter and minimize their risks of injury.
The government also tried to educate the public about the best ways to take shelter. With
warning and sheltering, injuries and deaths can be minimized, but this does not stop the severe
economic damage and governmental disruption. Economic damages are reduced through housing
codes and shared through insurance. State and local emergency agencies lead the response
activities for most tornadoes supported by FEMA response and recovery functions.
Risk Characteristics of Tornadoes
Public Health and Safety
Average number of deaths per year
Greatest number of casualties in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
10i
40ii
300iv-700v
200vii
700x
Moderatexii
100iii
200vi
600ix
$900Mxiii
$1Bxiv
$900Mxvi-$3Bxvii
Weeks to years
Blocks to countiesxviii
Lowxix
30,000-200,000xx
Moderatexxi
600viii
2000xi
$2Bxv
Natural
Moderatexxii
Immediatexxiii
Highxxiv
Lowxxv
WHAT IS KNOWN ABOUT THE RISK OF TORNADOES?
Tornadoes are a circular windstorm generally accompanying a thunderstorm. When dry
air meets warm, wet air extreme weather can occur, including lightning, hail, rain, and tornadoes.
The very high winds of a tornado leave a path of damage where it touches down, with damage
covering areas as small as a few blocks long or as large as hundreds or thousands of square
261 miles. In this path, physical damage can be intense with buildings completely demolished,
especially for the rarer, more powerful storms.
Tornado damage varies depending on the strength of the wind, and most damage is
caused by the very small number of severe events. Tornado damage is measured on the
Enhanced Fujita (EF) Scale, based on gust wind speed.xxvi Weaker tornadoes, with gust speeds
of 65-110 mph (EF 0 and 1), represent 69% of all tornadoes but rarely result in fatalities. xxvii
Strong tornadoes (EF 2 and 3) represent nearly 30% of all tornadoes and 30% of tornado deaths.
xxviii
Violent tornadoes with gust speeds over 200 mph (EF 4 and 5) are rare, representing only
2% of tornadoes, but causing 70% of tornado deaths. xxix Increased wind speed is also associated
with a wider and longer path, so more severe winds also impact a larger area. xxx A typical
tornado disaster scenario involves an EF4 or EF5 tornado striking a heavily populated area.xxxi
Tornadoes present a health risk both during and after the event. Most deaths occur during
the tornado,xxxii but about half of tornado injuries occur in the aftermath. During a tornado, high
winds present a direct hazard, while secondary effects at the time of a tornado include flash
floods, lightning, and hail. Wind-related fatalities most often result from head or spine injuries
when a person is thrown through the air by the wind.xxxiii A secondary source of head and spine
injuries, as well as crush trauma, come from airborne objects thrown into people’s heads and
collapsing buildings.xxxiv Head-injuries, fractures, and lacerations from flying objects cause most
of the serious injuries; these are mostly short-term injuries, but disabling fractures, paralysis, and
soft-tissue trauma such as kidney trauma are also possible.xxxv Following a tornado, the
wreckage presents a significant additional hazard.xxxvi Electrical disruptions and flooding can
lead to electrocutions and toxic mold;xxxvii structural damage can lead to lacerations and puncture
wounds, then infection;xxxviii gas leaks can lead to asphyxiation from gases and carbon monoxide
and burns from structural fires;xxxix and exposure to the elements can lead to sunburn, heat
exposure, and dehydration.xl
Individual risk factors may also influence harm. Elderly people are often more likely to
be injured or killed.xli Location is also important, as individuals in permanent, single-family
homes are at far less risk than those in mobile homes or automobiles, which are often weaker and
more likely to be lifted off the ground.xlii
Tornadoes can cause extensive damage to the infrastructure of the affected area,
including physical damage to dwellings, businesses, government buildings and infrastructure
such as roads and bridges.xliii Typical damage to a family home would range from some loss of
roofing and broken windows for an EF1, the entire house shifted on its foundation, significant
loss of roofing and some wall collapse for an EF2, most to all walls collapsed for an EF 3 or
higher.xliv Weaker buildings, such as mobile homes and barns, can face complete damage from
less intense winds,xlv while stronger buildings, such as high-rise buildings and institutional
buildings such as hospitals, require stronger winds to create the same damage.xlvi This physical
damage and the resulting debris can also interrupt business activity, shutting down businesses for
several days. The economic damages of tornadoes have increased over the past century,
reflecting increases in the value of properties rather than increases in the severity of the
storms.xlvii Damage can cover an area from several blocks to several counties, based on the size
of the areas affected. The median tornado path width is approximately 90 ft., with the largest
recorded at 2.5 miles.xlviii Tornado path lengths average 3 miles, but paths of over 200 miles
have been documented.xlix
262 WHAT IS THE EXPOSURE TO TORNADOES?
The U.S. averages 1,300 tornadoes in a year, although the number of severe tornadoes is
far lower, averaging fewer than 20.l The number of reported tornadoes has been increasing over
the last century, largely reflecting methodological issues rather than increases in the actual
numbers of tornadoes.li
Specific factors may increase the exposure to tornado risk. Tornadoes are most likely to
occur May through September in the afternoon or evening.lii While tornadoes have been
recorded in every state,liii the Great Plains, Gulf Coast states, and Midwest face the highest risk
of tornadoes.liv While the Great Plains (“Tornado Alley”) are known for having more tornadoes,
Gulf Coast states have a higher risk of fatality as tornadoes there are more likely to happen at
night and at unexpected times of the year when people are more vulnerable.lv Tornadoes are
more common in rural areas than urban areas,lvi but touchdowns have occurred in downtown
areas.lvii The probability that a tornado will strike any specific area is low, even in high risk
areas.lviii
Health consequences can be estimated from the large number of tornadoes that occur
every year. The average number of people killed in the U.S. each year has been declining, and
our best estimates of 40 deaths per year on average reflect those trends (between a low estimate
of 10 deaths per year and the high estimate of 100 deaths per year on average).lix The greatest
number killed in a tornado in the U.S. is approximately 700, occurring in 1925; as significant
advances in safety since then make this death toll unlikely to be matched, this is presented as an
upper bound.lx A more conservative estimate for the greatest number killed in a single tornado is
300, representing the deadliest modern tornado, a supercell of activity that occurred in 2011.lxi
Injury data is more limited, but our best estimate for the average number of injuries per year is
200 for more severe injuries (within a range of 200 to 600) and 700 less severe (within a range of
600 to 2,000 per year on average).lxii
The expected economic damage per year on average is estimated as $1B (within a range
of $900M to 2B per year on average).lxiii This reflects the damage from multiple tornadoes each
year. The greatest economic damages from a worst case single tornado are also in this range,
with estimated damages from $900M to $3B.lxiv The economic damage affects an area from
blocks to counties, taking weeks to years to recover. Environmental damage is low, with little
contamination and disruption of species.
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF TORNADOES?
While the number of tornadoes recorded hitting populated areas has steadily increased
over the past 100 years, the risk of dying from a tornado has steadily decreased. lxv This is due in
large part to two things- improved forecasting and warning, and improved structural integrity of
buildings.lxvi Public education, response and recovery, and research activities are other actions
currently underway.
Warnings for imminent tornadoes are a function of the federal government through the
National Weather Service. lxvii The National Weather Service is equipped with radar and satellite
technology for observing and forecasting tornadoes,lxviii and typically provide 10 to 20 minutes
warning of a developing tornado and often more for larger and deadlier tornadoes.lxix Research
continues to improve forecasting. Warning is communicated through radio, television, and civil
defense sirens.lxx This advance warning provides effective communication that a tornado is
imminent and that people should take shelter. This advance sheltering significantly decreases
injuries and death.lxxi Individuals can also minimize fatality and injury by taking physical
263 precautions, including going to shelters or to basements and covering their body and head with
blankets or pillows.lxxii Warnings rely on individuals taking action based on those warnings, so
educating the public of individual-level actions that can be taken to minimize risk can be highly
effective at reducing injury and death.lxxiii Risk communication can be done by making
information available through government websites and brochures, or more actively
communicating in schools and through state actions encouraging tornado drills and
communicating actions to take in the face of a tornado.lxxiv Emergency managers may also
conduct live and tabletop exercises to prepare to respond to a tornado.
Mitigation efforts are those designed to reduce the damage when a tornado occurs. These
include the building of shelters for human health as well as improved design and construction to
minimize property damage. The International Residential Codes 2000 and newer and similar
codes have been raised to survive high winds.lxxv Individuals can also spread their risk through
insurance. Tornadoes and related weather events are responsible for more than half of all insured
losses.lxxvi However, the effectiveness of the federal government in mitigation is unclear.
Federal community block grants can be used for tornado shelters.lxxvii But the National
Windstorm Impact Reduction Program Act of 2004, designed to provide a more active federal
role in wind disaster mitigation, had difficulties in implementation.lxxviii
Most tornadoes present little damage, and response is handled by state and local
authorities. The most catastrophic tornadoes may be declared national disasters, a designation
that brings federal assistance for both response and recovery activities.lxxix Federal assistance is
led by FEMA partnering with other government agencies and the American Red Cross.lxxx
Immediate response involves emergency response and medical care as well as restoring order.
Following that, response activities involve getting survivors water, food, and shelter, and
cleaning up the damage.lxxxi Recovery activities to after a tornado can involve longer term
shelter and money to rebuild, lxxxii and may include partnering with organizations such as the
Small Business Administration. lxxxiii Other recovery programs include federal crop insurance and
emergency disaster loans.lxxxiv
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Influenza (flu) pandemics occur when a significantly new flu strain emerges as a result of a
genetic shift that makes it unfamiliar to human immune systems, and as a result more infectious
and possibly more severe. Three flu pandemics occurred during the 20th Century. When a flu
virus emerges for which there is little to no immunity in the human population, it can spread
swiftly, significantly impacting human health worldwide. Typical estimates are that
approximately 30 percent of a country’s population will be infected, which corresponds to
roughly 100 million individuals in the United States alone. The symptoms range from none
(asymptomatic infection) to milder flu symptoms (cough, aches and fever and sometimes
vomiting and diarrhea), to more serious respiratory disease, complications and even death.
While most infected persons recover after several days, some die, and even a low death rate can
result in thousands of deaths. The illness can indirectly affect businesses, government, schools,
and health care systems. Modern strategies focus on monitoring flu viruses, containing
transmission early within communities, and preventing or delaying transmission through vaccine
development and distribution. Anti-viral drugs are used to treat or prevent flu illness.
Risk Characteristics of Pandemic
Influenza
Public Health and Safety
Average number of deaths per year
Greatest number of deaths in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Middle
High
2,000i
4,000ii
300,000iv-2,000,000v
20,000vii
2,000,000x
Highxii
10,000iii
9,000vi
1,000,000ix
$2Bxiii
$4Bxiv
$70 -$200Bxvii
Months to yearsxviii
Nation/worldxix
Lowxx
0xxi
Moderate to Highxxii
50,000viii
7,000,000xi
$10Bxv
xvi
Natural
Lowxxiii
Days
Highxxiv
Lowxxv
WHAT IS KNOWN ABOUT RISK OF PANDEMIC INFLUENZA?
Pandemic influenza occurs when an influenza virus emerges for which humans
have little immunity and that is easily transmitted from person to person. When a pandemic
271 strikes, it affects people in two ways- directly through individual illness or death, and indirectly
through societal disruption. Influenza strains of recent pandemic interest include the swine flu
(H1N1) virus that reached pandemic levels in 2009 and the avian flu (H5N1) virus that causes
more severe illness but does not yet spread easily from person to person. The severity of illness
caused by pandemic flu cannot be predicted in advance.xxvi The 2009 H1N1 pandemic resulted
in high rates of infection (and some deaths) but mostly milder than expected illness. xxvii
Seasonal flu causes significant illness and an estimated 36,000 deaths each year in the
United States alone. Pandemics are rare - three occurred in the twentieth century (1918, 1957,
1968), plus a handful of significant outbreaks beyond typical seasonal flu.xxviii Whereas
pandemics are highly likely to occur because of the inherent propensity of flu viruses to mutate,
their specific timing is highly uncertain.xxix Emerging pandemics can spread quickly, infecting
millions to billions of people worldwide.xxx Evidence suggests influenza may be passed through
airborne droplets, direct contact, and fluids, and possibly through aerosolized airborne
transmission as well.xxxi Both the rate of infection and the rate of serious illness vary with
different flu virus strains. A more infectious pandemic may infect nearly all of the U.S.
population, with up to half hospitalized.xxxii
Infection rates and severity of pandemic flu symptoms vary widely, depending on the
strain. Infected individuals suffer effects similar to a seasonal flu (cough, aches and fevers and
possible also vomiting and diarrheaxxxiii), but with the potential for causing more severe disease,
including bronchitis, viral pneumonia, ear and sinus infections and Reye’s syndrome.xxxiv Most
people who get the flu suffer these symptoms only a couple of days. However, influenzarelated deaths are possible, especially when the person already has other medical conditions such
as asthma, chronic obstructive pulmonary disease (COPD), other diseases of the heart or lungs,
diabetes, or a weakened immune system (e.g., due to cancer or cancer treatment, HIV); deaths
may also occur more frequently among the very young or very old .xxxv In many cases, the
exacerbating effects of influenza infection are acute- for example, people who already have
breathing difficulties due to asthma or COPD will have a worse time dealing with the flu, with
increased likelihood of hospitalization and death, but will return to their baseline level of health
when the flu passes.xxxvi In other cases, the effects can be lasting- for example, people with heart
disease who get the flu are at higher risk of heart failure or stroke.xxxvii However, pandemic flu
may also be deadly for previously healthy individuals, as was the case during the 1918 Spanish
flu pandemic.xxxviii
Widespread infection may also have significant societal and economic consequences.
Large numbers of people would get sick at the same time, and people who are well will stay
home to avoid getting sick or take care of their sick family members. The health care system
would likely be overwhelmed with individuals seeking help,xxxix especially if health care workers
are absent with the flu or taking care of ill family members.xl Measures that may be
implemented in the early (but not later) stages of a pandemic to limit transmission, such as
quarantine, school closures,xli and restricted public gatherings or transportationxlii, also disrupt
daily life. Individuals will miss work due to illness and to care for sick family members,xliii
disrupting business’s ability to operate. Businesses may also intentionally close to minimize
infection among their employees. xliv Businesses could be disrupted both by the consequences of
illness and the response to a pandemic. The decrease in the ability of business to supply goods
and services, as well as decreases in demand for goods and services, would disrupt the economy,
both in the U.S. and overseas. xlv These disruptions would last from a few months to around a
year and a half.
272 WHAT IS THE EXPOSURE TO PANDEMIC INFLUENZA?
The exposure to pandemic influenza is driven by rare, high consequence events. Three
influenza pandemics occurred in the twentieth century.xlvi The first flu pandemic of the current
century occurred in 2009. The likelihood of a moderate to severe pandemic is estimated between
2% and 10% annually,xlvii with a best estimate of 3% a year based on the frequency of historical
events. While the likelihood that another pandemic will occur is high, its timing is uncertain.
When influenza pandemics occur, they spread across the world and all Americans may be
exposed. Infection rates vary widely, depending on the strain; most planners use an infection
rate of 30% for planning purposes but infection rates can range from 3 to 60%.xlviii Some
populations are at particularly high risk for pandemic flu, both with regards to their exposure and
their vulnerability. Health care workers are more likely to be exposed, which is why preventive
measures (such as vaccination and personal protective equipment) for them are especially
important. School children are another group likely to both get infected and infect others.xlix
Younger children, the elderly, and people with compromised immune systems are also at high
risk of severe symptoms,l although occasionally a pandemic may be just as dangerous for healthy
adults.li
The severity of pandemic flu symptoms also vary widely, depending on the strain. The
2009 H1N1 “Swine Flu” killed fewer people in the U.S. than the estimated 36,000 annual deaths
due to the seasonal flu,lii while the historic Spanish Flu of 1918 killed 675,000 Americans, a
death rate of nearly one percent of the total population.liii If an outbreak with a similar death rate
were to occur today, approximately 3,000,000 Americans would die.liv Advances in medicine
and public health would probably result in a lower death rate, with a Spanish Flu-style outbreak
resulting in 300,000 deaths.lv Estimated deaths from a more typical pandemic range from 70,000
to 200,000.lvi Multiplying these consequence estimates by the range of likelihoods gives our
expected number of people killed by a flu pandemic in an average year which range from 2,000
to 10,000, with a best estimate of 6,000.lvii An additional 20,000 people will be hospitalized each
year on average (with an estimated range between 9,000 and 50,000)lviii and 3 million people will
get sick but not be hospitalized each year on average (with an estimated range between
1,000,000 and 7,000,000).lix
Economic damages are also expected to be high. An outbreak can cause significant
business disruption, with economic damages for a single event between $70 to $200 billion.lx
This suggests expected costs per year on average to be between $1 billion and $7 billion, with a
best estimate of $2 billion.lxi Business disruptions occur across the entire nation over the course
of months to years. The functioning of the government would also be widely disrupted. Nonemergency services would be particularly affected, as may not be vaccinated, but medical
services may be overwhelmed by the influx of flu patents, disrupting other medical services.
However, there would be low social displacement, as there is no contamination to cause people
to leave their homes. The lack of contamination also makes the environmental impact low.
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF PANDEMIC
INFLUENZA?
Efforts to reduce the risk of pandemic flu include virus monitoring, building
vaccine manufacturing capacity, and “non-pharmaceutical” interventions such as public
communications and community measures to promote personal hygiene, personal protection, and
social distancing. The U.S. government and World Health Organization have also supported
development of public sector response plans and exercises.
273 Influenza viruses and disease are monitored both domestically (by the CDC) and
internationally (by the World Health Organization).lxii Monitoring is useful for tracking the
emergence and spread of new flu strains and for preparing vaccines each year and in response to
a pandemic.
Flu vaccines effectively prevent infection, although there is a small rate of serious side
lxiii
effects.
Vaccines are manufactured by the private sector, although the government has a role
in their development, procurement, and distribution. As the government tracks flu viruses and
determines what the composition of an annual or pandemic flu vaccine should be, it shares the
seed virus with vaccine manufacturers. This allows manufacturers to grow the virus and develop
the vaccine. Making a new vaccine (each year and in response to a pandemic) requires several
months from the determination of the strain(s) to be included. Often vaccines cannot be
produced in sufficient numbers and distributed in time to all people in need.lxiv Vaccines are
typically provided mainly through the private sector. People may get them through their
employers, health plans, or can purchase it at pharmacies and grocery stores. The government
does purchase and provide vaccines through the Vaccines for Children program. Because the
virus inherently mutates rapidly, developing and stockpiling of vaccine in advance of a pandemic
is challenging—the government did stockpile an H5N1 vaccine, but it is unclear how well that
vaccine would work with an H5N1 strain that eventually does mutate to become easily
transmissible among humans. When a pandemic vaccine first becomes available, as was the case
in the 2009 H1N1 pandemic, analysis of those most at risk determines who receives vaccination
on a priority basis. Health workers and other high-risk populations (such as children, pregnant
women or the elderly) are often prioritized for that reason.lxv
Keeping health services functional is especially important in a pandemic, and federal
resources have been invested to better prepare communities for emerging public health incidents
such as a flu pandemic. In a pandemic, the public health community undertakes additional
actions under coordination from the CDC and HHS.lxvi In surge conditions that overwhelm
standard care, hospitals may need to undertake new protocols to treat those infected and to
prevent those infected from infecting others.lxvii This may include patient placement and limiting
patient movement, transport, and visitors.lxviii The government contributes funding and guidance
for communities and the public health sector for pandemic flu planning and testing those plans
through drills and exercises.lxix Public health actions also include non-medical actions such as
restrictions of public gatherings or travel advisories, case isolation, household quarantine, or
school or governmental closures.lxx Such measures are unlikely to reduce the overall number of
people infected, but can delay transmission and reduce the peak infection rate.lxxi
The government has also provided guidance to encourage the private sector to prepare
pandemic plans. These plans should address both preventative measures (e.g., increased
personal hygiene including hand sanitizers, sending sick workers home, allowing telecommuting,
or temporarily shutting the business) and prepare for the consequences of a pandemic (e.g.,
preparing for absences, disruption of supply chains, and possible decreases in sales).lxxii
However, it is believed that most of U.S. companies do not have adequate plans in place.lxxiii
Finally, the government communicates risks to individuals so the public will know what
they can do to limit their exposure.lxxiv Individuals have some ability to limit exposure by
practicing good personal hygiene (frequent hand washing, “cough etiquette”) and social
distancing (avoiding public contact).
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s/animals.htm. Randall, T. and A. Nussbaum. (2009). "Hospitals May Face Severe Disruption from Swine Flu (Update1)." Retrieved Jan. 12, 2012, from http://www.bloomberg.com/apps/news?pid=20601087&sid=abCN9aBVJIkg. 279 Reuters. "FACTBOX: Economic Costs of a Flu Pandemic." Retrieved Jan. 12, 2012, from http://www.reuters.com/article/idUSTRE53O0WO20090425. Robelen, E. W. (2009). "Swine Flu Disruption Has School Officials Looking for Lessons." Retrieved Jan. 12, 2012, from http://www.edweek.org/ew/articles/2009/05/13/31swineadmin.h28.html. Rosenthal, E. and K. Bradsher (2006). Is Business Ready for a Flu Pandemic. New York Times. New York, NY. Sandman, P. M. and J. Lanard. (2004). "Pandemic influenza risk communication: The teachable moment." Retrieved Feb. 27, 2012, from http://www.psandman.com/col/pandemic.htm. Simonsen, L., T. A. Reichert, C. Viboud, W. C. Blackwelder, R. J. Taylor and M. A. Miller (2005). "Impact of influenza vaccination on seasonal mortality in the US elderly population." Archives of Internal Medicine 165(3): 265. WebMD. "Flu Complications." Retrieved Jan. 12, 2012, from http://www.webmd.com/cold‐and‐
flu/flu‐guide/flu‐complications. WHO (2002). WHO Manual on Animal Influenza Diagnosis and Surveillance. World Health Organization Department of Communicable Disease Surveillance and Response and WHO Global Influenza Programme 280 ANTHRAX RELEASE
Anthrax spores cause a highly lethal non-communicable disease and could be used as a weapon
to cause hundreds to tens of thousands of deaths. Exposed areas may remain contaminated for
months or years, creating further risk for economic and governmental disruption. No large-scale
release has yet occurred, and experts disagree about both the consequence and likelihood of an
anthrax attack. While anthrax has been pursued by terrorist organizations, it has never been used
in a large-scale attack, and estimates of an actual event in the next decade range from very
unlikely to near certainty. The US currently spends over $7B annually on biodefense, which
includes defense against anthrax and other diseases. This includes applied scientific research,
securing and monitoring anthrax samples and dual-use equipment, systems to identify releases,
and preparing to respond to a release in both the public health and law enforcement sectors.
Additional efforts include military and intelligence actions on terrorist actors to prevent WMD
attacks are also ongoing.
Risk Characteristics of Anthrax
Releases
Public Health and Safety
Average number of deaths per year
Greatest number of casualties in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
2i
20ii
3,000 -20,000v
60vii
300x
Moderatexii
700iii
iv
6vi
30ix
$800kxiii
$7Mxiv
$300Mxvi-$100Bxvii
Monthsxviii
Neighborhood to Cityxix
Lowxx
20xxi-6000xxii
Moderatexxiii
2,000viii
10,000xi
$300Mxv
Human-induced
Lowxxiv
Days to Weeksxxv
Highxxvi
Highxxvii
WHAT IS KNOWN ABOUT RISK OF ANTHRAX RELEASE?
Anthrax is the common name for the bacteria Bacillus anthracis and infections of that
type of bacteria. Anthrax infections are transmitted by spores, and are almost never transmitted
from person to person. Once a person is infected, anthrax bacteria multiply quickly while
281 releasing a lethal toxin. Infections can result from three kinds of exposure- through cuts or
scrapes in skin, inhalation, or swallowing.
Anthrax exists naturally in areas with wild animals and livestock, presenting only a minor
hazard to people through cuts or scrapes; however, the intentional release of anthrax can be very
deadly. Anthrax can have different levels of lethality, depending on the particular strain and how
it has been prepared.xxviii Spores can be weaponized by improving their ability to be inhaled and
the distance they will carry in the air. Additionally, an effective device to spread spores (similar
to a truck used to spray for mosquitoes) could greatly increase the number of people exposed. In
October, 2001, anthrax spores sent through the U.S. mail caused 22 cases of anthrax. By
comparison, weaponized spores released into the air by an effective device can infect hundreds
to hundreds of thousands of individuals. The size of the attack and the ability to optimize the
spores for infection are the most important considerations for mortality.
Infections from spores entering through a cut in the skin are the most common but are
rarely fatal when proper medication is used, with infections leading to ulcerated skin and some
dying tissue. Inhalation anthrax initially presents similar to a common cold, but progresses to
severe inhalation problems and toxic shock. Intestinal anthrax presents similar to food
poisoning, damaging the digestive tract with lesions in the throat to vomiting of blood and
diarrhea. Both inhalation and intestinal anthrax are deadly, even for those aggressively treated
with antibiotics. The CDC recommends that individuals exposed to anthrax spores but not yet
showing an infection take an anthrax vaccine (BioThraxT), as well as 60 days of oral antibiotics
to prevent infection while the vaccine takes effect. Those who have already been infected are
given antibiotics, such as ciprofloxacin (known as cipro), doxycycline or levofloxacin.xxix The
death rate for those infected through scrapes or cuts is only 1% when treated and 20% for those
who are not treated. People are much more likely to die when swallowing or inhaling the spores,
with death rates ranging from 25% to 60% when treated, and 75-90% when untreated.xxx
Anthrax attacks do not cause physical damage to property, but do have economic costs
from contamination. An area exposed to anthrax spores will be contaminated until it is cleaned
up, forcing people out of homes and businesses. This will hurt businesses, as workers and
customers will avoid businesses in contaminated areas and deliveries will be disrupted.
The disruption of some government services is likely- when anthrax was mailed to
Congressional offices and the press in the fall of 2001, Congress was suspended, the Supreme
Court was evacuated, and people were afraid to open their mail.xxxi People would be evacuated
from their homes if a wide area is contaminated, but evacuations would be limited if the release
is indoors. These evacuations could last months to years while the areas are decontaminated; it
could even prove too costly to decontaminate their homes to allow them to return them to their
homes at all. Environmental damage would be low, with the potential for some animals in the
infected area to die but no physical damage to the appearance of the environment.
WHAT IS THE EXPOSURE TO ANTHRAX RELEASE?
Estimating the likelihood of an attack or at-risk populations is difficult, as it depends on
the intent and capabilities of intentional actors rather than some probabilistic event. Historically,
anthrax attacks are infrequent.xxxii Only two cases of intentional anthrax release are known
worldwide, one of which may have been intended as a warning rather than an attack, while the
other failed. Terrorist organizations (including Al Qaida) have pursued or expressed an interest
in using anthrax for mass casualties.xxxiii Domestic terrorists have shown an interest in chemical
and biological weapons, but are believed to be more likely to use them for assassination than a
282 mass casualty event.xxxiv But while terrorists may want them, they have not used them and have
been using explosives instead, which may reflect a lack of familiarity or interest in biological
attacks, which may be risky for the terrorists to make, uncertain in their effects, and less lethal
than bombs.xxxv Still, as technological advances in the life sciences make it easier to prepare a
biological attack, anthrax may be one of the most likely agents to be used.xxxvi Expert opinions
of the likelihood of an anthrax attack over the next ten years range from a near certainty to
almost no chance.xxxvii Our best estimate of the likelihood of an anthrax attack is a 20% chance
in 10 years, approximately 2% for any given year, representing the median expert opinion.xxxviii
This fits within a range of 0.5% to 25% for any given year. Terrorists would likely target highly
concentrated urban areas with additional economic or symbolic value.
Research on the consequences of an anthrax release is limited. Most research has
reflected a government-sized attack, using weaponized strains and an efficient dispersion
device,xxxix where estimates of deaths range from hundreds of thousands to millions of people.xl
These scenarios are likely to be not representative of terrorist releases. Based on a small number
of accidental and intentional releases,xli attempts,xlii and hoaxes,xliii an actual terrorist event is
estimated to be much smaller, with deaths rising perhaps to the hundreds.xliv However, experts
believe that killing tens of thousands is not beyond the capacity of terrorists.xlv
The number of deaths and illnesses for an attack depends on the number of people
exposed, which in turn depends on how well the anthrax has been prepared, how well it is
dispersed, and where and when it is dispersed. The most successful anthrax attack ever carried
out occurred in the U.S. in 2001, where only 22 people were infected and 5 of those died.
Hypothetical scenarios involve a more severe event, ranging from 3,000 killed in a wide-scale
letter attack to 20,000 for terrorists using an efficient spraying system.xlvi Estimates with
hundreds of thousands to millions of deaths are not considered here, as they reflect the kind of
attack that a nation could do but terrorists likely could not.xlvii This gives a best estimate of 20
deaths per year on average (within a range of 2 to 700) with severe illnesses represented by the
60 getting sick per year on average (within a range of 6 to 2,000) and less severe illnesses
represented by the 300 expected to receive vaccinations but not getting sick per year on average
(within a range of 30 to 10,000).xlviii
Similarly, estimates of economic damages vary depending on the kind of attack.
Estimates of economic damages in an anthrax attack range from hundreds of millions of dollars
(in an event similar to the attacks of 2001) to a hundred billion (for spraying anthrax in a
crowded outdoor venue).xlix Estimates as high as several hundred billion dollars are not
considered here, as they represent the kinds of sophisticated weaponization and dispersion
expected from a foreign nation but considered to be beyond the abilities of terrorists.l Based on
the best consequence estimates for a single event and the range of likelihoods, our best estimate
for the average economic damages per year is $7M (within a range of $1M to $300M).li
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF ANTHRAX RELEASE?
The US already spends over $7B/year on biodefense, of which anthrax preparedness is
only a part along with preparing for other biological outbreaks. Biodefense includes spending on
natural resources, agriculture, health, Medicare, social security, and veterans’ benefits.lii These
include mitigation, preparedness, response, and recovery activities, organized under the National
Strategy for Countering Biological Threats and the National Response Framework.liii There are
also military or intelligence activities against terrorist organizations which would include
stopping terrorist attacks.
283 Efforts to reduce the ability to make anthrax or mitigate its consequences are limited. It
is hard to limit the spread of technology or information, as many tools used to prepare anthrax
have a legitimate use as biological equipment for medicines or research. While there are
attempts to limit the spread of dangerous pathogens, some strains of anthrax are still commonly
available.liv Informal bodies give advice, guidance, and leadership on such issues both
domestically (through the National Science Advisory Board for Biosecurity) and internationally
(through the Australia Group).lv Risk analyses for homeland security threats also exist, but are
limited in their treatment of adversarial events.lvi
The government also acts to prevent the use of anthrax and other weapons of mass
destruction both directly and indirectly, using law enforcement, intelligence agencies, and the
military. The military and intelligence community undertake both general actions to improve
safeguards and reduce the threat of anthrax and other attacks, and identify and disrupt specific
attacks.lvii These actions may also be taken in coordination with other countries, with more than
90 countries committed to interdict weapons of mass destruction-related shipments.lviii The
government also tries to prevent the use of anthrax using the promise of retaliation to deter its
use as a terrorist weapon; the National Biological Forensics Analysis Center continues to
develop techniques to identify the groups responsible for a specific attack.lix
Detection is a key part of responding to a biological event, and there are several systems
to aid detection. First, Biowatch is a system of detection points across urban areas, which can
detect anthrax spores, but it is limited in the number of cites it can identify including limited
ability to detect indoor releases.lx Some organizations have their own systems to detect the
spores, including the US Postal Service.lxi If the release itself is not detected, an anthrax attack
could be identified by the public health system as people begin to show symptoms. Public health
surveillance is driven through the Centers for Disease Control and Prevention (CDC) and the
Public Health Information Network.lxii
Following detection is public health response. In an anthrax attack, the Department of
Health and Human Services will coordinate the activities of the federal government with state
and local governments and the public health community of hospitals and doctor’s offices. The
first responders are local hospitals and emergency responders. Plans are in place for responders
to be vaccinated as soon as an outbreak is identified using a vaccine/antibiotic combination to
prevent infection.lxiii In addition to the usual medical capabilities of hospitals, plans should be in
place at the local level to respond to a public health emergency, which includes both protocols
and equipment such as biohazard suits. Similarly, security grants allow law enforcement
agencies to purchase biohazard gear.lxiv Exercises on public health emergencies help the medical
and emergency management communities prepare to coordinate for response.lxv
The federal government also supplies equipment and medicines for a public health
emergency. The CDC’s Strategic National Stockpile has large amounts of medicine and supplies
for public health emergencies,lxvi including enough anthrax vaccines and treatments to respond to
attacks in several large cities at the same time.lxvii As the event emerges, medicines and supplies
for general threats would be distributed for an immediate response within 12 hours, and vendors
would distribute supplies specific to the threat (in this case, anthrax) in 24 to 36 hours.
Additionally, the CDC’s Cities Readiness Initiative helps those cities at the greatest risk for
attack with their own stockpiles. The government purchased many of these stockpiles under
Project BioShield, a government initiative to prepare for bioterrorism, which also included R&D
of new pharmaceuticals and changes of government authority in a bioterror crisis.
284 References
Bowman, S. (2002). Weapons of mass destruction: The terrorist threat. Library of Congress Congressional Research Service. Washington, DC. Carroll, S. J., T. LaTourrette, B. G. Chow, G. S. Jones and C. Martin (2004). "Assessing the Effectiveness of the Terrorism Risk Insurance Act." CDC. "Anthrax Q&A: Signs and Symptoms." Emergency Preparedness and Response Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/agent/anthrax/faq/signs.asp. CDC. "CDC Emergency Operations Center (EOC)." Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/cdcpreparedness/eoc/. CDC. "Fact Sheet: Anthrax Information for Health Care Providers." Emergency Preparedness and Response Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/agent/anthrax/anthrax‐hcp‐
factsheet.asp CDC. "Questions and Answers About Anthrax." Emergency Preparedness and Response Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/agent/anthrax/faq/ CDC. "Strategic National Stockpile (SNS)." Retrieved Jan. 10, 2012, from http://www.cdc.gov/phpr/stockpile/stockpile.htm. CDC. (2001). "Interim Recommendations for the Selection and Use of Protective Clothing and Respirtators Against Biological Agents." Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/documentsapp/Anthrax/Protective/10242001Protect.asp. 285 CDC (2011). Public Health Emergency Response Guide for State, Local, and Tribal Public Health Directors‐ Version 2.0. U.S. Department of Health and Human Services Centers for Disease Control and Prevention. Washington, DC. Center for Biosecurity of UPMC. "Anthrax Fact Sheet." Retrieved Jan. 10, 2012, from http://www.upmc‐biosecurity.org/website/our_work/biological‐threats‐and‐
epidemics/fact_sheets/anthrax.html. Cordesman, A. H. (2005). The challenge of biological terrorism. Significant issues series v. 27, no. 10. Center for Strategic and International Studies. Washington, D.C., CSIS Press: xiii, 208 p. Counterproliferation Program Review Committee (2009). Report on Activities and Programs for Countering Proliferation and NBC Terrorism. Washington, D.C. Gerstein, D. M. (2009). Bioterror in the 21st century : emerging threats in a new global environment. Annapolis, Md., Naval Institute Press. Graham, B., J. M. Talent and G. T. Allison (2008). World at risk: the report of the Commission on the Prevention of WMD Proliferation and Terrorism, Vintage. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. Inglesby, T. (1999). "Anthrax: A possible case history." Emerging Infectious Diseases 5(4): 556. Inglesby, T., D. Henderson, J. Bartlett, M. Ascher, E. Eitzen, A. Friedlander, J. Hauer, J. McDade, M. Osterholm and T. O'Toole (1999). "Anthrax as a biological weapon: medical and public health management." Jama 281(18): 1735. Jenkins, B. M. (2008). Will terrorists go nuclear? Amherst, NY, Prometheus Books. 286 Kaufmann, A. F., M. I. Meltzer and G. P. Schmid (1997). "The economic impact of a bioterrorist attack: are prevention and postattack intervention programs justifiable?" Emerging Infectious Diseases 3(2): 83. Kellman, B. (2007). Bioviolence : preventing biological terror and crime. New York, Cambridge University Press. Leitenberg, M. (2005). Assessing the biological weapons and bioterrorism threat, Strategic Studies Institute, US Army War College. Lugar, R. G. (2005). The Lugar Survey on Proliferation Threats and Responses. Washington, DC, US Senate. National Security Council (2009). National Strategy for Countering Biological Threats. Washington, D.C., Defense Technical Information Center. Parnell, G. S., L. L. Borio, G. G. Brown, D. Banks and A. G. Wilson (2008). "Scientists urge DHS to improve Bioterrorism Risk Assessment." Biosecur Bioterror 6(4): 353‐356. Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism : containing and preventing biological threats. Amsterdam ; Boston, Butterworth‐Heinemann. Sandia (1999). Osama bin Laden: A Case Study. Sandia National Laboratories. Livermore, CA. Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. Stern, M. (2008). "Experts Divided Over Risk of Bio‐Terrorist Attack." Retrieved Jan. 10, 2012, from http://www.propublica.org/article/experts‐divided‐over‐risk‐of‐bioterrorist‐attack. 287 Takahashi, H., P. Keim, A. Kaufmann, C. Keys, K. Smith, K. Taniguchi, S. Inouye and T. Kurata (2004). "Bacillus anthracis incident, Kameido, Tokyo, 1993." Emerging Infectious Diseases 10(1). Wright, J. G., C. P. Quinn, S. Shadomy and N. Messonnier. (2009). "Use of Anthrax Vaccine in the United States." Morbidity and Mortality Weekly Report Retrieved Jan. 10, 2012, from http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5906a1.htm 288 TERRORIST NUCLEAR DETONATION
Nuclear detonations are extremely large explosions, the equivalent of thousands to millions of
tons of conventional explosives, which also produce large amounts of both thermal and ionizing
radiation and potentially long-lived contamination from radioactive fallout. A ground-level
nuclear explosion in an urban area could kill hundreds of thousands of people and leave
additional hundreds of thousands injured or ill. Areas of hundreds to thousands of square miles
could be contaminated with radioactive fallout for months to years. Direct economic damages
could be in the hundreds of billions of dollars, with total economic impacts in the trillions. The
likelihood of a nuclear detonation by terrorists is entirely unclear; expert opinions of use
somewhere in the world in the next ten years range from near zero to 100%. The government
acts to prevent nuclear terrorism through securing nuclear materials and weapons, limiting the
spread of nuclear technologies, disrupting terrorist organizations, and active sensors in U.S.
cities. The response plans also exist, although the impact of a nuclear explosion would almost
certainly overwhelm immediate response capacity.
Risk Characteristics of Terrorist
Nuclear Detonations
Public Health and Safety
Average number of deaths per year
Greatest number of deaths in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
20i
200ii
100,000iv - 800,000v
200vii
100x
Highxii
50,000iii
20vi
10ix
$300Mxiii
$3Bxiv
$1Txvi-$10Txvii
Years
Nation/World
Moderatexviii
100xix-300,000xx
High
50,000viii
40,000xi
$900Bxv
Human-induced
Lowxxi
Immediate up to decadesxxii
Highxxiii
Highxxiv
WHAT IS KNOWN ABOUT RISK OF TERRORIST NUCLEAR DETONATIONS?
Nuclear explosions combine the enormous physical destructiveness of blast with prompt
nuclear and thermal radiation. Additionally, if the nuclear device detonates near ground level, it
can create wide-spread radioactive fallout contamination that can last generations.
289 The destructive power of a nuclear explosion can range widely depending on the size and
sophistication of the weapon. At the lower end, terrorists could use a device with a yield
equivalent to 5-15 kilotons of TNT, around as powerful as the atomic bombs used against
Hiroshima and Nagasaki.xxv They could conceivably build their own if they could obtain their
own nuclear material or could obtain such a weapon either by gift or theft from a nuclear-armed
nation. We consider this scenario to be most likely and use a warhead with a 10-15 kt yield for
most of our calculations. Terrorists could also conceivably illicitly acquire a second or third
generation warhead with a yield in the 100-200 kt range. Larger thermonuclear warheads with
explosive yields in the megaton range are likely in most cases to be too large (~ 1000 lb. or
more) for terrorists to readily transport. A crude terrorist weapon might also produce a “fizzle”,
an incomplete explosion with a yield measured in tons as opposed to kilotons of TNT. The
explosive power of a fizzle could still have blast effects much larger than the Oklahoma City
bombing, plus additional thermal and nuclear effects.xxvi
Closest to “ground zero,” the blast itself is predominant, killing through physical trauma,
asphyxiation, and building collapse.xxvii The blast is composed of two effects - static
overpressure due to the compression of the air, and dynamic pressure, the wind associated with
the movement of hot compressed gas. The blast itself is estimated to kill 50% of individuals
within a range up to a half mile.xxviii Most buildings would be destroyed within a half mile
radius, and uninhabitable within a mile of the explosion.xxix
Further from the blast, more people are killed as a result of the thermal radiation through
both direct “flash burns” from the initial thermal pulse and structural fires following the initial
heat.xxx These effects are estimated to kill 50% of people up a range of approximately a mile,
depending on target and weather conditions,xxxi while the resulting fires may be much wider.
Radiation damage comes in two forms- radiation from the initial explosion and from
radioactive byproducts of the explosion. The nuclear explosion produces an initial flash of
highly energetic ionizing radiation killing at a range similar to that of the thermal flash.xxxii
Explosions at ground level (as most terrorist scenarios assume) irradiate large amounts of the
surrounding material which is carried upward by the heat of the blast (the classic mushroom
cloud) and then falls back to the ground as radioactive fallout.xxxiii This fallout presents an
immediate danger for the first few days but over matter of weeks becomes less dangerous except
in some “hot spots.” Still, areas of up to hundreds of square kilometers could be contaminated
for years to decades.xxxiv
Acute Radiation Syndrome develops when highly energetic radiation damages cell DNA
and proteins. Symptoms begin with nausea and vomiting for up to 48 hours, after which
symptoms stop for an apparent recovery. Deeper damage reveals itself hours to weeks later. At
high doses of radiation, damage to the central nervous system, stomach, and intestines results in
death within days to weeks. At moderate doses, impairment of the blood leads to internal
bleeding and infection weeks after exposure that may be fatal depending on severity of exposure
and the initial medical care. Survivors of initial radiation, including those exposed at doses too
low to develop Acute Radiation Syndrome, may suffer long-term illness, including higher rates
of cancer,xxxv heart disease, stroke, and digestive and respiratory diseases.xxxvi
Nuclear attacks could have enormous economic costs. Hundreds of thousands of
buildings in an urban area would likely be destroyed by blast and fire, and tens to thousands of
square miles contaminated by fallout.xxxvii These effects could cascade throughout the economy,
including through the likely impact on critical infrastructures disrupting elements of global
trade.xxxviii Transportation and finance sectors are particularly vulnerable.xxxix Government could
290 be severely disrupted, in particular if an attack on Washington, DC essentially decapitated the
federal government. Social instability following an attack could also be widespread, with
uncertain consequences. Due to the enormity of a nuclear attack, the world would change
dramatically after an event, but how those changes would specifically manifest is unclear.
WHAT IS THE EXPOSURE TO TERRORIST NUCLEAR DETONATIONS?
Nuclear detonations are a discontinuous event- an attack either occurs or does not based
on the choices of an adversary and the defenses in place to stop them. If an event were to occur,
the world would change so drastically that the likelihood that another event would occur would
change. Because of this, representing any average exposure is problematic. The average
exposure never exists; there is some chance that there will be a high consequence event and some
chance there will be no event. Our average represents an expected value based on the current
likelihood of an event in a year times the consequences of an event if it were to occur.
Experts are split on how likely it is that terrorists will actively seek a nuclear weapon, can
obtain or create a nuclear weapon if they seek it, and use it if they possess one.xl Some experts
present the likelihood of a nuclear event as a certainty (“when, not if”), while others are
skeptical.xli In early 2005, Sen. Lugar solicited expert opinion of the likelihood of a nuclear
attack somewhere in the world in the next ten years, and answers ranged from 0% to 100%.xlii
These provide a wide range for our estimates for the likelihood of a nuclear terrorist attack in a
given year, with a low of 1 in 10,000 and a high of 26%. Our best estimate for the likelihood of
a nuclear attack in a single year is 0.1%, reflecting some expert opinion and event tree
modeling.xliii We emphasize that this best estimate or any best estimate is highly speculative.
Terrorists would likely select a target to maximize casualties, economic damage, and
symbolic importance. This suggests that major urban areas, such as previously targeted New
York and Washington, DC, face the greatest threat. Alternatively, major ports of entry (seaports,
airports, and land crossings) may be targeted, either to disrupt transportation and trade or because
those checkpoints serve as a last line of defense and may be as close as terrorists can get a
warhead from overseas.
Given the enormity of the potential damage, even an unlikely event can present very high
annual expected damage. The greatest number of people killed in a nuclear explosion in an
urban area ranges from 100,000 for a small 10 kt bomb to 800,000 for a 150 kt bomb.xliv Using
the estimates from the more likely10 kt scenario, this gives us a best estimate of 200 fatalities for
the expected value of a given year.xlv However, given the range of uncertainty over how likely a
nuclear explosion would be, a low probability would suggest an expected value of 20 fatalities,
while the highest probabilities would suggest an expected value of 50,000 fatalities.xlvi Estimates
of injuries are generally around the same size as fatalities. Our best estimate of injuries is 200
serious and 100 less serious injuries for a year,xlvii but depending on the likelihood of a nuclear
explosion, these could be as low as 20 serious and 10 less serious injuries or as high as 50,000
serious and 40,000 less serious injuries for a year.xlviii
Economic damage is also expected to be high. Our estimate for greatest economic
damage from a single event is between $1 trillionxlix and $10 trillion.l This reflects an explosion
in Manhattan, with a low estimate reflecting a 10 kt explosion and the high estimate reflects a
150 kiloton explosion; damages in other cities would be smaller. These consequence estimates
suggest a best estimate of $3 billion for the expected value of economic damages in a given
year,li but depending on the likelihood of a nuclear attack, the average expected economic
damages could be as low as $300 millionlii or as high as $900 billion for a given year.liii
291 WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF TERRORIST NUCLEAR
DETONATIONS?
In recognition of the nuclear terrorism threat, the U.S. has led a global effort to secure
existing warheads, to prevent the spread of technical knowledge and nuclear materials to build a
warhead, and to prevent nuclear weapons delivery to inside the borders of the U.S. and its
partners and allies.
One layer of security is keeping nuclear weapons out of the hands of those who would
use them. This includes securing existing weapons, securing the materials and expertise to make
new weapons, and taking or threatening action against those who would want to obtain nuclear
weapons. For example, the Nunn-Lugar Amendment and the Cooperative Threat Reduction
program are cooperative agreements with the former Soviet Union to secure or dismantle excess
nuclear warheads. These programs have been successful at making the theft of former Soviet
warheads less likely.liv As military warheads are secured, terrorists are less likely to acquire
higher yield warheads (e.g., over 10 kilotons).
Securing nuclear materials and expertise has been less successful. lv The U.S. continues
to push other countries to secure all nuclear materials, to mixed results. U.S. attempts to limit
nuclear weapons technology include the Defense Nuclear Proliferation program and programs to
provide foreign weapons scientists with funding. The UN’s International Atomic Energy
Agency also seeks to limit proliferation internationally under the Non-Proliferation Treaty.lvi
While 90% of the nuclear materials in the former Soviet Union have been secured,lvii this still
leaves enough nuclear materials to make over 100,000 nuclear weapons.lviii Additionally, India
and Pakistan have been steadily building up nuclear forces since testing in 1998, North Korea
tested a nuclear weapon in 2006, and Iran is believed to be building a nuclear infrastructure for
weapons production. Finally, the U.S. also directly acts to disrupt the market for fissile material
through military and intelligence actions including stings.lix Whether terrorists will be able to
obtain nuclear weapons from these or other nations or acquire enough unsecured nuclear
materials to make their own nuclear weapons is unclear.
Another layer of security is preventing the nuclear weapons from getting to their targets.
Customs and Border Protection and the Domestic Nuclear Detection Office use a layered
security plan to prevent weapons from entering the country, including reviews of overseas
manifests for suspicious shipments, partnerships with the shipping industry, and radiation
detectors at ports of entry, selected foreign ports, and a mobile sea container system.lx Radiation
detection equipment is also being expanded along ground entry locations.lxi Other radiation
detection equipment focuses on preventing attacks by detecting nuclear weapons in high risk
cities such as New York and Washington, DC.lxii
A final preventative measure is a threat of retaliation. To deter other nations, the U.S. has
pledged to respond to a nuclear attack with massive retaliation. In order to make this a credible
threat, the U.S. needs to be able to identify who is responsible for the nuclear weapons if an
attack is anonymous. Nuclear forensic technologies are being developed to hopefully someday
identify the source of the fissile material used in a nuclear attack, but such techniques will be
critically dependent on the international establishment of a library of fissile nuclear material and
its source – a formidable undertaking.lxiii
Plans are also in place to respond to a nuclear event as part of the National Response
Framework, including separate plans in the public health system, the activation of Nuclear
Emergency Search Teams, and emergency response at the local, state, and federal level.lxiv Still,
a nuclear attack would likely initially overwhelm emergency response capabilities.lxv
292 References
(2010). Planning Guidance for Response to a Nuclear Detonation. National Security Staff Interagency Policy Coordination Subcommittee for Preparedness and Response to Radiological and Nuclear Threats. Washington, DC. American Physical Society/American Association for the Advancement of Science (2008). Nuclear Forensics: Role, State of the Art, and Program Needs, Joint Working Group of the American Physical Society and the American Association for the Advancement of Science. Barnett, D. J., C. L. Parker, D. W. Blodgett, R. K. Wierzba and J. M. Links (2006). "Understanding radiologic and nuclear terrorism as public health threats: preparedness and response perspectives." Journal of Nuclear Medicine 47(10): 1653. Bunn, M. (2006). "A mathematical model of the risk of nuclear terrorism." The ANNALS of the American Academy of Political and Social Science 607(1): 103. Bunn, M. (2010). Securing the Bomb 2010: Securing All Nuclear Materials in Four Years, Project on Managing the Atom, Harvard University. Bushberg, J. T., L. A. Kroger, M. B. Hartman and E. M. Leidholdt (2007). "Nuclear/radiological terrorism: Emergency department management of radiation casualties." Journal of Emergency Medicine 32(1): 71‐85. Carter, A. B., M. M. May and W. J. Perry (2007). "The day after: action following a nuclear blast in a US city." The Washington Quarterly 30(4): 19‐32. 293 CBP. (2005). "Inspections and Surveillance Technologies‐ Extended." Retrieved Jan. 11, 2012, from http://www.cbp.gov/xp/cgov/newsroom/fact_sheets/port_security/fact_sheet_cbp_securing.x
ml. CISAC Understanding the Risks and Realities of Nuclear Terrorism. Center for International Security and Cooperation, Stanford University. Executive Office of the President (2002). National Strategy to Combat Weapons of Mass Destruction. Executive Office of the President. Washington, DC. Faherty, C. (2007). Police Test Technology to Safeguard City from Nuclear Attacks. The Sun. New York. Ferguson, C. and W. Potter (2004). Improvised nuclear devices and nuclear terrorism, Weapons of Mass Destruction Commission. GAO (2009). Nuclear Detection: Domestic Nuclear Detection Office Should Improve Planning to Better Address Gaps and Vulnerabilities. G. A. Office. Washington, DC. GAO (2010). Combatting Nuclear Terrorism: Actions Needed to Better Prepare to Recover from Possible Attacks Using Radiological or Nuclear Materials. G. A. Office. Glasstone, S. and P. J. Dolan (1977). "The Effects of Nuclear War." Washington: Department of Defense,. Gustafson, T. (2007). "Radiological and Nuclear Detection Devices." Retrieved Jan. 11, 2012, from http://www.nti.org/analysis/articles/radiological‐nuclear‐detection‐devices/. HHS. "Nuclear Explosions: Weapons, Improvised Nuclear Devices." Radiation Emergency Medical Management Retrieved Dec. 13, 2011, from http://www.remm.nlm.gov/nuclearexplosion.htm. 294 Holdstock, D. and L. Waterston (2000). "Nuclear weapons, a continuing threat to health." The Lancet 355(9214): 1544‐1547. Homeland Security Council Interagency Policy Coordination Subcommittee for Preparedness Response to Radiological Nuclear Threats (2009). Planning Guidance for Response to a Nuclear Detonation. McLean, VA. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. Jenkins, B. (2009). "5.2 The Impact of Cataclysmic Events." Retrieved April 14, 2011, from http://www.jhuapl.edu/urw_symposium/Proceedings/2009/Authors/Jenkins.pdf Jenkins, B. M. (1975). Will terrorists go nuclear?, California Seminar on Arms Control and Foreign Policy. Jenkins, B. M. (2008). Will terrorists go nuclear? Amherst, NY, Prometheus Books. Lugar, R. G. (2005). The Lugar Survey on Proliferation Threats and Responses. Washington, DC, US Senate. Masse, T. "Nuclear Terrorism Redux: Conventionalists, Skeptics, and the Margin of Safety." Orbis. Meade, C. and R. Molander (2006). Considering the effects of a catastrophic terrorist attack. Washington, DC, RAND. Medalia, J. and S. Library Of Congress Washington Dc Congressional Research (2005). "Nuclear Terrorism: A Brief Review of Threats and Responses. CRS Report for Congress." Mettler Jr, F. A. and G. L. Voelz (2002). "Major radiation exposure‐‐what to expect and how to respond." The New England Journal of Medicine (CME) 346(20): 1554. 295 Oak Ridge Associated Universities. "Radiological and Nuclear Terrorism: Medical Response to Mass Casualties." Retrieved Jan. 11, 2012, from http://www.orau.gov/hsc/RadMassCasualties/content/text_version.htm. Ohkita, T. "Health effects on individuals and health services of the Hiroshima and Nagasaki bombs." Effects of Nuclear War on Health and Health Services: Report of the International Committee of Experts in Medical Sciences and Public Health to Implement Resolution WHA3438. Annex4. Preston, D. L., S. Kusumi, M. Tomonaga, S. Izumi, E. Ron, A. Kuramoto, N. Kamada, H. Dohy, T. Matsui and H. Nonaka (1994). "Cancer incidence in atomic bomb survivors. Part III: Leukemia, lymphoma and multiple myeloma, 1950‐1987." Radiation Research 137(2): 68‐97. Preston, D. L., Y. Shimizu, D. A. Pierce, A. Suyama and K. Mabuchi (2003). "Studies of mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950‐1997." Radiation Research 160(4): 381‐407. Remick, A. L., J. L. Crapo and C. R. Woodruff (2005). "US national response assets for radiological incidents." Health Physics 89(5): 471. Risk Management Solutions (2010). Modeled Losses. H. Willis. Rodionov, S. (2002). Could Terrorists Produce Low‐Yield Nuclear Weapons? High Impact Terrorism: Proceesings of a Russian‐American Workshop. S. S. Hecker. Washington, DC, National Academies Press. Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, Sandia National Laboratories. 296 Schanzer, D. H., J. Eyerman and V. De Rugy (2009). Strategic Risk Management in Government: A Look at Homeland Security, IBM Center for the Business of Government. Toon, O. B., R. P. Turco, A. Robock, C. Bardeen, L. Oman and G. L. Stenchikov (2007). "Atmospheric effects and societal consequences of regional scale nuclear conflicts and acts of individual nuclear terrorism." Atmospheric Chemistry and Physics 7(8): 1973‐2002. Wilson, S. and M. B. Sheridan (2010). Obama leads summit effort to secure nuclear materials. Washington Post. Washington, DC. 297 TERRORIST EXPLOSIVE BOMBINGS
Explosives are historically the most common weapon used in terrorist attacks. Explosives
release a large amount of energy very suddenly and violently, presenting very concentrated
damage over an area from hundreds of square feet to several blocks. This scenario refers to
mass-casualty bombings where a bomb (or many bombs in parallel or near succession) is placed
by terrorists with the intent to injure or kill tens, hundreds, or thousands of people. In the U.S.,
three mass-casualty bombings have occurred in the past 20 years. Typical mass-casualty
bombings may kill hundreds and cause hundreds of millions of dollars of damage.
Risk Characteristics of Terrorist
Explosive Bombings
Public Health and Safety
Average number of deaths per year
Greatest number of deaths in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
1i
10ii
200-2,000iv
30vi
60ix
Lowxi
40iii
1v
1viii
$10Mxii
$100Mxiii
$1B – 40Bxv
Weeks to monthsxvi
Less than a block to city
Low
3xvii-100xviii
Low to moderatexix
70vii
100x
$400Mxiv
Human-induced
Lowxx
Immediatexxi
Highxxii
Moderatexxiii
WHAT IS KNOWN ABOUT RISK OF EXPLOSIVES?
Explosives release a large amount of energy very suddenly and violently, presenting very
concentrated damage over an area from hundreds of square feet to several blocks. This hazard
specifically refers to mass-casualty bombings where a bomb (or many bombs in parallel or near
succession) is placed by terrorists with the intent kill large numbers of people and destroy
buildings or infrastructure. We do not include the approximately one thousand letter bombs and
smaller events that come to law enforcement every year. Typical scenarios range from a suicide
bomber detonating in a crowded area or an improvised explosive device with the equivalent
explosive power equal to around a pound of TNT, to a truck bomb with the equivalent of
thousands of pounds of TNT. The U.S. has had 51 terrorist bombings in the past 20 years,
298 including three mass-casualty events – the bombing of the World Trade Center in 1993, the
Oklahoma City Federal Building in 1995, and the Centennial Olympic Park in Atlanta, Georgia,
in 1996.
Explosions are the violent result of the sudden release of energy from chemical reactions
of specific materials. Some explosive materials have both military and commercial uses; these
include dynamite and plastic explosives such as SEMTEX. These materials are typically
restricted, although they can be obtained by theft or fraud, or can be smuggled into the country.
Other explosive materials can be made from other materials that are more readily available, such
as fertilizer and fuel oil.
An explosion is a large energy release, with heat and a concussive shock wave associated
with additional shrapnel when pieces of the bomb or debris are accelerated by the explosion.
The pressure from the explosion and shockwaves can be powerful enough to cause buildings to
collapse. While heat from the initial explosion can cause burns, some explosives have additional
incendiary chemicals added to the bomb to increase the fire damage following an explosion.xxiv
People can be injured or killed either from the bomb itself or from resulting structural
collapse. The energy of the bomb creates large differences in pressure which causes direct
damage to hollow organs, including ruptured eardrums and damage to the lungs.xxv The damage
from pressure is more severe in confined spaces like buses, trains, or indoors, where it is difficult
for the pressure to disperse.xxvi The initial energy also has a concussive force to propel shrapnel,
small bits of debris including pieces of the bomb that are accelerated to speeds of thousands of
feet per second.xxvii Smaller pieces of debris can puncture a person like a bullet, while larger
pieces of debris can break bones, amputate limbs, or cause brain injuries. xxviii In addition to
throwing objects into people, a blast wind traveling at hundreds of feet per second can throw
people into objects,xxix which can cause in penetrating or blunt force trauma, fractures,
amputations, and brain injuries.xxx In addition, the explosion can damage buildings and other
structures to the point of collapse, which can land on the victim and cause additional penetrating
and blunt force injuries as well as crush injuries.xxxi Burns are also common, either directly from
the initial heat of the explosion or resulting fires.xxxii
Economic damages include direct effects of physical damage and business disruption,
and indirect effects on the economy. Physical damage can range from the destruction of a single
building, bus, or train, to the several blocks of property damaged when a large explosion causes a
building to collapse. While no bombings in the United States have caused an entire large
building to collapse, it is a plausible worst case scenario. In addition to property damage, there
may be significant business disruption, as businesses close either because of physical damage,
temporary safety and security measures following the bombing, or perceived risk. This business
disruption may be larger than the area actually damaged- for example, a suicide bomber in the
street can cause businesses to be shut down across the entire block while the damage is assessed
and repaired.
WHAT IS THE EXPOSURE TO EXPLOSIVES
Explosives attacks have occurred in the U.S. As discussed above, bombings are common
(over 1,000 a year), but mass-casualty events are rare, occurring approximately once or twice a
decade. Terrorist explosions are not new, with historical incidents including the bomb on Wall
Street in 1920, Chicago’s Haymarket Square in 1886, even London’s failed Gunpowder Plot in
1605. However, technological advances have made explosives more deadly and accessiblexxxiii
and the past several decades have shown an increasing terrorist intent to create mass-casualty
299 events that could be considered catastrophic.xxxiv In the past 20 years, the U.S. has experienced
three mass-casualty IED attacks and several other failed attempts.
Estimating the likelihood of an attack or at-risk populations is difficult, as it depends on
the intent and capabilities of intentional actors rather than some probabilistic event. Adversaries
have complex motivations and hide their intent, making the likelihood of an attack hard to
predict both in general and in specific. Unlike probabilistic events, adversaries generally adapt
to some extent to security measures, and the likelihood of a specific attack or target may change.
As an intentional attack, the greatest risk is for those areas that adversaries would select
as targets. Different terrorist groups may consider different things important when selecting
targets. Some may intend to assassinate particular people, to create mass destruction, or to
attract attention with little actual consequences. xxxv However, groups that pursue mass-casualty
bombings (including foreign terrorists such as Al-Qaeda and its affiliates as well as domestic
terrorists such as Timothy McVeigh) are interested in inflicting high numbers of casualties.xxxvi
Other considerations may include the symbolic value of those targets to the terrorists both for
propaganda and fear. On a national level, this suggests that the risk of a deadly attack is higher
in some urban areas (including New York, Washington, DC, and Los Angeles). xxxvii Within a
particular city, symbolic or crowded sites may have relatively higher risks of fatal bombings as
well as at symbolic sites and government offices. However, this does not mean that lower risk
cities and targets have no risk; terrorists may also select targets to minimize the risks to
themselves, or attack targets that are close to them or with which they are familiar. xxxviii This
suggests that airports and airplanes are also likely targets, as they have been attacked before, but
it also areas close to where the terrorists live or work.xxxix Terrorists have also shown a
willingness to shift to more vulnerable (“soft”) targets in response to security measures.xl As
security increases for higher risk targets, the risk to lower risk targets may increase.
Most bombings have no casualtiesxli but a large scale bombing can kill hundreds of
people and injure hundreds to thousands more.xlii Our estimate for the average number killed
per year is 10, based on the U.S. average over the last 20 years.xliii The low estimate of the
average number killed per year by terrorist bombings is less than 1, based on the average over
the last 10 years in the U.S.xliv The high estimate of the average number killed from masscasualty terrorist bombings per year is 40, which represents the number and severity of bombs in
the U.S. increasing to the point that it is similar to the number in Israel each year.xlv Injuries
were estimated the same way, with a best estimate of 30 more severe injuries a year (within a
range of 1 to 70 more severe injuries a year) and 60 less severe injuries a year (within a range of
1 to 100 less severe injuries a year).xlvi
The greatest economic damage in a single attack would be result of the partial or
complete destruction of a building. The greatest actual economic damages from a bombing in
the U.S. are valued at $1 billion following the partial destruction associated with the bombing of
the Murrah Federal building in Oklahoma City in 1995.xlvii However, this is not the worst that a
bombing could realistically be. At worst, a bombing could conceivably completely destroy a
building. While the acts of Sept. 11, 2001 are not a bombing, the consequences could be similar
to a theoretical worst case bombing scenario in which the building collapses. For this reason, we
use $40B (one third of the economic damages from the attack of Sept. 11, 2001) as our high
estimate for the greatest economic damage in a single event.xlviii The average expected economic
damages are driven by these high casualty events. Between 1988 and 1998, the U.S. averaged
$100M per year in damage from bombings, 90% of which was due solely to the World Trade
Center bombing in 1993 and the Oklahoma City bombing in 1995.xlix This best estimate of
300 $100M of damage per year on average is a range between $10M and $400M per year on
average.l
The size of the area affected by the physical damage is small, ranging from a bus or a
corner of a building to a few blocks. The small area displaces a small number of households, and
often has little effect on government function. However, as an attack may target government
facilities, the upper-bound potential for governmental disruption is high. Another reduced
consequence of the small area damaged, compounded by the targeting of urban areas and the
comparatively little contamination, is that environmental damage is low.
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF EXPLOSIVES?
The United States is alert to possible explosives attacks following 9/11. Activities to
address terrorist use of IEDs largely reflect spending on counterterrorism generally. This
includes domestic and foreign counterterrorism, security and mitigation efforts, and response
preparedness. Most of these activities are focused on the adversary, but some counterterrorism
actions are specifically directed to explosives.
Domestic counterterrorism includes activities at the federal, state, and local levels to
identify and intercept terrorists before they act. Domestic counterterrorism was reorganized
following the events of 9/11. The Federal Bureau of Investigation is the lead entity on terrorism
cases, in coordination with the Intelligence Community, the Department of Homeland Security,
and state and local homeland security and law enforcement. Specific to explosives, the Bureau
of Alcohol, Tobacco, Firearms and Explosives enforce legislation on explosive materials.li
Foreign activities include counterterrorism by the military and intelligence agencies as well as
actions to reduce terrorism through diplomacy and aid.lii It is unclear whether foreign terrorists’
interest in explosive attacks on American soil has increased or diminished,liii but there is reason
to believe that their ability to operate in the US may be diminished but not eliminatedliv
Security has increased in areas that are perceived at risk of terrorist attacks. This includes
increased police and other government security as well as private security personnel at
checkpoints, buildings, and high-profile targets such as Times Square. Target specific security is
also useful, including fences and barricades to prevent access, and surveillance and unobstructed
space to prevent concealment of a device.lv Explosives security is coordinated by the
Department of Homeland Security’s Office of Bombing Prevention.lvi Securing airplanes and
transit systems is also important for explosives, with the Transportation Security Administration
central to those efforts. Efforts include checking the names of passengers, screening passengers
with metal detectors and in some cases whole body imaging, scanning baggage and explosives
trace detection, and scanning cargo. It is not clear whether these additional security measures
have been successful- while there have been several nearly successful attempts to bomb
passenger aircraft in or going to the U.S. since the deployment of the Department of Homeland
Security, none of these attempts were successful.
Mitigation activities reduce damage if an event occurs. Barricades serve as mitigation as
well as security, by absorbing the force of a blast or forcing adversaries to use a less effective
attack. Protecting buildings is an important part of mitigation; actions include recommended or
required standards lvii and funding for mitigation efforts.lviii
Preparedness includes planning and training to respond to an event. The National
Response Framework details how national-level resources would be used. Additionally, federal,
state, and local responders train for explosives scenarios.
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303 CYBER-ATTACKS
Cyber-attacks are Internet based acts against the confidentiality, integrity, and accessibility of
information systems. They could or do target financial systems, power grids, the defense
manufacturing base, and critical government services. Cyber-attacks can be used to shut down
utilities leaving people without vital services and also halt financial transactions further
disrupting society. Because computer networks and information systems undergird an increasing
amount of the nation’s economic and public service activity, the risk from cyber-attacks
continues to grow. That noted, cyber-attacks come in two types. They can be used to disrupt
systems: e.g., shutting down utilities leaving people without vital services or scrambling
financial transactions. They also can be and are used every day to facilitate espionage,
electronically stealing sensitive national security information from industry and government
computers. Disruptive events, however, are extremely rare, while information theft is common,
albeit rarely detected. This circumstance makes the likelihood and severity very difficult to
quantify. Consequences primarily come from economic damage and national security
vulnerabilities
Risk Characteristics of Cyber Attacks
Public Health and Safety
Average number of deaths per year
Greatest number of deaths in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damage per year
Greatest economic damage in a single event
Duration of economic damage
Size of area affected by economic damage
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
0i
0ii
0iv-10v
0vii
0x
Lowxii
1iii
0vi
0ix
$10Mxiii
$50Mxiv
$100Mxvi-$10Bxvii
Days to weeks
Company to nation
Lowxviii
0xix
Moderate to highxx
5viii
5xi
$1Bxv
Human-induced
Low to moderatexxi
Immediatexxii
Low to moderatexxiii
Moderatexxiv
WHAT IS KNOWN ABOUT THE RISK OF CYBER ATTACKS?
As computer networks become an ever more important part of the nation’s critical
infrastructure, the disruption and exploitation of these networks becomes an ever larger threat.
Nearly everything is vulnerable to attack. Computer networks can be targeted by mischiefmakers (e.g., LulzSec), freelance criminals, organized crime, governments, or, in theory,
terrorists. The overwhelming majority of attacks are small scale events, most often acts of
304 cybercrime that scam individuals or steal money from their accounts, or steal personal or
national security information. This routine cybercrime is endemic to the Internet and is not
included in our definition of cyber-attacks. The kinds of events about which DHS is concerned
are much larger and rarer, damaging industrial machinery, disrupting critical services or sectors,
or even compromising national security. A minor cyber-attack may be an attempt to completely
disrupt the communications of a company or government agency. A more damaging event may
disrupt a critical sector, such as the electrical grid of a city or county. These events are more
likely to create large economic damage rather than significant casualties. But the latter could
happen; a computer system failure (albeit one unrelated to any cyber-attack) in the D.C. subway
system in 2009 killed nine and injured more than 70.xxv Similar disruptions of heavy machinery,
or of electrical grids, water treatment plants, or other infrastructure, could result in injuries or
deaths, but most cyber-attacks are not associated with injuries or deaths even in worst case
scenarios.
There are several ways to breach an information system. One way to gain access is by
using the username and password of a legitimate user (e.g., by trying large numbers of words
from a dictionary file, or tricking legitimate users into sharing their password). Similarly, a
legitimate user can access the system in an illegitimate way, as an insider threat – which is both
common and potentially costly.xxvi Another way is by using malicious software (“malware”),
including viruses and worms, to take control of a system. Malware is often disguised to trick the
user into accessing it, but some malware can install and run itself without users doing anything.
Once a system has been breached, the attacker can steal information or can alter the software of
the system to make it malfunction. The loss of proprietary information can cost a business sales
or market share, while the loss of private information can expose a company to legal liability. If
the breach is made public, it may also harm the reputation and stock price of the company. xxvii
The government’s costs of losing secret information are harder to quantify, but the ramifications
may be serious.
Distributed Denial-of-Service (DDOS) attacks do not breach a computer, but cripple the
ability of a computer or a computer network to communicate via the Internet.xxviii DDOS attacks
use thousands to millions of co-opted computers to contact the target network repeatedly and
overwhelm the network’s connection. Such attacks can disrupt a company’s business activities
for days, resulting in millions of dollars of lost business;xxix site owners have been known to pay
blackmail to ward off its threat. Government information systems, particularly those that support
emergency services, may also be specifically targeted for disruption. On the other hand, it is
believed that the distributed nature of the networks may lead to resilience, limiting the
consequences of an event.xxx
More recently, the disruption of industrial control systems has emerged as a potentially
large threat.xxxi Control systems for the U.S. power grid, industrial plants, and financial systems
can be breached and even destroyed. Attacks on control systems have reportedly been used for
extortion in foreign nations,xxxii and another event believed to be a cyber-attack resulted in
damage to a uranium centrifuge facility in Iran.xxxiii Similar attacks targeting domestic electrical
or financial systems could result in wide scale blackouts, damage to physical plants, or widescale business and financial disruption.xxxiv
WHAT IS THE EXPOSURE TO CYBER ATTACKS?
It is difficult to estimate the exposure to cyber-attacks conclusively. Not only are cyberattacks evolving rapidly, thereby limiting the reliability of current experience in predicting future
305 attacks, but current experience is also largely hidden. Victims of typical cyber-attacks are often
unaware of that an attack occurred. Even when a breach is evident, they may not know the
damage because the costs or lost data or system disruption are ambiguous or indirect.xxxv
Additionally, victims are often reluctant to report known breaches. xxxvi Companies may conceal
a breach to prevent exposure to liability claims or being seen as a riskier investment, while the
government may not want to reveal breaches for security reasons. xxxvii
Currently, we know that small breaches are widespread. Although exposure to large
incidents is unknown, there have been few if any incidents whose effects have been noticeable to
the public. While criminals and hackers are largely responsible for smaller attacks, foreign
governments, and, in theory, terrorists are the parties with an interest in carrying out catastrophic
attacks (it is unclear whether terrorists have sufficient sophistication to carry out a very
disruptive attack or even could carry out cyber-attacks, it is unclear whether or not they want to,
with experts believing they may prefer the greater fear associated with physical destruction.xxxviii)
Foreign governments are not only capable of cyber-attacks, but are believed to have perpetrated
cyber-attacks in the past.xxxix Russia, China, and Israel are believed to be the other countries
most capable of cyber-attacks.xl Governments may also have an interest in using cyber-attacks
covertly, allowing either anonymity or at least deniability, thereby reducing their odds of
suffering retaliation.xli
It is said to be much easier to attack than to defend in cyberspace. xlii Internet commerce
is the most vulnerable, followed by publicly networked computer systems.xliii Closed networks,
such as the U.S. government’s classified network, are less vulnerable.xliv Industrial control
systems, such as the software controlling machines in a factory, have become a known
vulnerability more recently largely because control systems have long had major vulnerabilities
that were allowed to persist because such systems were misleadingly believed to be closed.xlv
Cyber-attacks range across a spectrum of severity. Smaller events are more common that
more severe events. The CSI/FBI Computer Crime and Security Survey found that
approximately one quarter of companies reported a more serious attack targeted specifically at
them above and beyond the routine cybercrime and near constant malware.xlvi Major attacks on
businesses, such as the 2010 attacks on Google and 40 other U.S. companies, occur a few times a
year.xlvii Most IT executives expect to see a major attack in the next two years.xlviii Major attacks
have also targeted infrastructure, via the disruption of train signals and water filtering systems,
and the invasion of classified networks.xlix The extent to which these attacks have been
successful in the United States is unclear. A major breach could cost a company via financial
fraud,l leaked technology and trade secrets and operational disruption or even damage.
Catastrophic attacks that cause widespread disruption on a regional or national scale have not yet
occurred in the United States, but major DDOS attacks harassed Estonia (2007) and Georgia
(2008).li Estimating the likelihood of a catastrophic cyber-attack is challenging as such attacks
are the result of the actions of adversaries rather than any probabilistic event. The likelihood of
such attacks is unknown, believed to be low but possibly increasing.lii Using a 10% chance of a
catastrophic cyber-attack in a given year, our best estimate for average economic damage per
year is $50 million, within a range of $10M to $1B.liii
Non-economic effects are very low. Our best estimate of the health effects per year on
average are zero, as typical events have no direct deaths or injuries. We do allow for the
possibility of rare events with fatalities or injuries (following the example of the disrupted
subway, above), with an upper bound of 1 fatality, 5 more severe injuries and 5 less severe
injuries per year on average.liv Psychological damage per year is considered low relative to other
306 disasters. Displacement of households and environmental harm are negligible for most
scenarios, as there is typically little physical damage.
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF CYBER ATTACKS?
As our dependence on technology grows, so will our need for cybersecurity.lv Private
sector best practices include technological and behavioral efforts. The government adds
classified networks and active response capabilities by having defense and homeland security
analysts directly monitoring and responding to cyber events. Many of these networks are linked
through the Internet, although some networks are isolated.
Much of the IT infrastructure to be protected is in the private sector. Typical defensive
actions range from technological (including anti-virus/spyware software, firewalls, encryption,
and sensors to detect and track intrusions), to architecture (including security audits, passwords,
virtual private networks, and biometrics), and management (notably, changing user behavior
through security awareness training).lvi Technological fixes cannot be static, since software is
not static, allowing hackers to find new vulnerabilities to exploit. Even well-maintained systems
can be vulnerable to attacks exploiting completely new vulnerabilities (called “zero-day
exploits”).
Government networks, as private sector ones do, use anti-virus software, firewalls, and
intrusion sensors, supported by qualified IT professionals.lvii However, the Government takes
additional steps to secure their computers. One is to separate classified or otherwise sensitive
networks from the Internet, reducing their vulnerability to some extent but not absolutely
(incompletely air-gapped systems can still be compromised using thumb drives or other mobile
media.)lviii Another is to limit network gateways to a small number of Trusted Internet
Connections, allowing easier monitoring of intrusions in real time.lix The monitors include
DoD’s Cyber Commandlx and the DHS National Cybersecurity and Communications Integration
Center under the new National Cyber Incident Response Plan.lxi
The government also coordinates efforts to secure private networks. The Dept. of
Defense started coordinating with defense critical infrastructure. Expanded coordination for
other critical cyber infrastructure falls to DHS, with several coordination forums. lxii DHS also
exercises for cyber event response in partnership with state and local governments as well as the
private sector through the Cyberstorm exercises in 2006, 2008, and 2010.lxiii The latest
Cyberstorm included 7 cabinet-level departments, 11 states, 12 international partners, and 60
private companies, and exercised the new National Cyber Incident Response Plan.lxiv
Additionally, the government is significantly increasing sponsorship of cybersecurity research.
References
(2006). "Paller: Government cybersecurity gets an F." Retrieved Dec. 11, 2011, from http://www.infoworld.com/d/security‐central/paller‐government‐cybersecurity‐gets‐f‐679. 307 Aitoro, J. R. (2009). "DHS' Cyber Storm III to test Obama's national cyber response plan." Technology and the business of Government Retrieved Dec. 11, 2011, from http://www.nextgov.com/nextgov/ng_20090826_9168.php. Aitoro, J. R. (2010). "Successful attack on nation's infrastructure is 'when,' not 'if'." Technology and the business of Government Retrieved Dec. 11, 2011, from http://www.nextgov.com/nextgov/ng_20100806_9847.php. Baker, S., S. Waterman and G. Ivanov (2010). In the Crossfire: Critical Infrastructure in the Age of Cyber War. Santa Clara, CA, McAfee and the Center for Strategic and International Studies. BNAC (2007). Cyber Attack: A Risk Management Primer for CEOs and Directors, British‐North American Committee. Broad, W. J., J. Markoff and D. E. Sandger (2011). Israeli Test on Worm Called Crucial in Iran Nuclear Delay. New York Times. New York, NY: A1. CACI/USNI (2010). Cyber threats to national security: Countering Challenges to the Global Supply Chain, CACI International U.S. Naval Institute. Cashell, B., W. Jackson, M. Jickling and B. Webel (2004). "The economic impact of cyber‐attacks." Congressional Research Service Documents, CRS RL32331 (Washington DC). Chen, T. (2010). "Stuxnet, the real start of cyber warfare?[Editor's Note]." Network, IEEE 24(6): 2‐3. Dexter, J. (2010). "Fact Check: Cyberattack threat." CNNTech Retrieved Dec. 11, 2011, from http://www.cnn.com/2010/TECH/02/16/fact.check.cyber.threat/. 308 Dharapak, C. (2009). Computer failure may have caused D.C. subway crash. USA Today. DHS‐OIG (2010). DHS Needs to Improve the Security Posture of Its Cybersecurity Program Systems. Department of Homeland Security Office of Inspector General. Washingon, DC. DHS (2009). Information Technology Sector Baseline Risk Assessment Report. D. o. H. Security. Washington, DC, U.S. Department of Homeland Security. DHS. (2011). "Cyberstorm: Securing Cyber Space." Retrieved Dec. 11, 2011, from http://www.dhs.gov/files/training/gc_1204738275985.shtm. DHS. (2011). "Fact Sheet: Cyberstorm III: National Cyber Exercise." Retrieved Dec. 11, 2011, from http://www.dhs.gov/files/training/cyberstorm‐iii.shtm. Dilanian, K. (2011). Russia and China accused of cyber‐spying campaign to steal U.S. secrets. Los Angeles Times. Los Angeles, CA. Dynes, S., E. Andrijcic and M. Johnson (2006). Costs to the US economy of information infrastructure failures: estimates from field studies and economic data, Citeseer. Ettredge, M. and V. Richardson (2002). Assessing the risk in e‐commerce, Published by the IEEE Computer Society. Garg, A., J. Curtis and H. Halper (2003). "Quantifying the financial impact of IT security breaches." Information Management & Computer Security 11(2): 74‐83. Gorman, S. (2009). "Electricity grid in US penetrated by spies." Technology. Homeland Security News Wire. (2010, Feb. 19, 2010). "How real is the threat of cyberattack on the United States?" Retrieved Dec. 11, 2011. 309 Hoover, J. N. (2010). Feds Strengthen Cybersecurity Workforce Plans. InformationWeeks. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. Kerr, P., J. Rollins and C. Theohary (2010). The Stuxnet Computer Worm: Harbinger of an Emerging Warfare Capability. C. R. Service. Washington, DC, Congressional Research Service. Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY. 5. Polk, W., P. Malkewicz and J. Novak (2010). Industrial Cyber Security: From the Perspective of the Power Sector. DEFCON 18. Los Vegas, NV: 65. Porche, I. (2010) "Stuxnet is the world's problem." Bulletin of the Atomic Scientists. Rawlinson, K. (2011). China and Russia accused of orchestrating cyber attacks. The Independent. London, UK. 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cert.gov/federal/. Zumwalt, J. (2010) "Israel Behind Iran's Computer Worm." Human Events. 311 TOXIC INDUSTRIAL CHEMICAL ACCIDENTS
Toxic industrial chemical accidents are common at low levels, but have the potential for
catastrophe. Catastrophic toxic industrial chemical accidents occur when a large number of
people are exposed to a chemical that is fatal even at low levels of exposure, typically released in
a crash or fire. Acutely toxic chemicals can kill hundreds to tens of thousands, cost millions of
dollars, and disrupt business and government for days to weeks. Additionally, some chemicals
can remain toxic in water and on land for years, causing contamination with the potential for
disruption and illness, with economic costs potentially in the billions as the areas are either
cleaned or quarantined. The likelihood of a catastrophic toxic industrial chemical accident in the
U.S. is unclear; while events with high fatalities have happened in other countries, only near
misses have occurred in the U.S. The EPA regulates the planning and response of facilities that
use chemicals with regards to public release, and also has a role in responding to large events
along with local firefighters, police, and hospitals.
Risk Characteristics of Toxic Chemical
Accidents
Public Health and Safety
Average number of deaths per year
Greatest number of casualties in a single episode
More severe injuries/illnesses
Less severe injuries/illnesses
Psychological harms per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single episode
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
5i
8ii
600iv-20,000v
50vii
500x
Lowxii
200iii
30vi
300ix
$200Mxiii
$300Mxiv
$2Bxvi-$700Bxvii
Days to yearsxviii
Blocks to countiesxix
Moderate to highxx
5,000xxi-200,000xxii
Lowxxiii
200viii
5,000xi
$7Bxv
Human-inducedxxiv
Low to moderatexxv
Immediate to decadesxxvi
Low to moderatexxvii
Low to moderatexxviii
WHAT IS KNOWN ABOUT THE RISK OF TOXIC INDUSTRIAL CHEMICALS?
Toxic industrial chemicals are chemicals used in industrial or mining applications that are
lethal at low doses. They can exist at a fixed site (where they are produced, stored, or used) or in
transit (by road, rail, sea, or pipeline).xxix These chemicals are typically used safely, but can be
dangerous when released uncontrollably. Chemicals can be released into the air from
312 explosions, fires, evaporation from liquid spills, and gaseous releases. Liquids can spill into
waterways and groundwater. Solids can be spread in ash from a fire or from an explosion. Once
the chemicals are in the environment, people can be exposed by contact with the skin, inhalation,
and ingestion.xxx They can also be ingested indirectly, as contaminated food has also given rise
to chemical-induced disease including toxic oil syndrome and organic mercury poisoning.xxxi
For our purposes, toxic industrial chemical accidents refer to catastrophically large events where
hundreds or more of the general public are put at risk of severe injury or death. We do not
include the routine small events, such as a chemical spill in an industrial setting where a handful
of employees are exposed or exposure to gasoline fumes following a traffic accident, which are
not considered mass casualty scenarios.
Chemical risks can be described by three primary factors- the toxicity of the chemical,
factors of the exposure and the dose, and the number of people that are close enough to
potentially be exposed.xxxii
Chemicals are technically defined under the law as highly lethal if they kill 50 percent of
people exposed at a concentration of 100,000 mg-min/m3 or lower.xxxiii The Environmental
Protection Agency tracks the potential for public release of 140 toxic industrial chemicals with a
use in manufacture or mining, 21 of which are considered high risk for a hazardous chemical
event, including ammonia, chlorine, and hydrochloric acid.xxxiv
Large scale public releases can expose thousands to millions of the general public.xxxv
Considerations that affect exposure include the area over which the chemical spreads, the density
of people in that area, and actions taken by people in that area.xxxvi One important factor of the
area over which the chemical spreads is the amount of the chemical released. Other factors
include characteristics of the chemical itself- whether it is a solid, liquid, or a gas; how
flammable or explosive it is; and how far it will carry before settling to the ground, which can
include local meteorological conditions. Substances that become airborne from gas clouds or
fumes and those chemicals that are persistent in the food chain and accumulate in contaminated
food have the greatest potential to spread and contact large numbers of people.xxxvii Typically,
gas clouds and fumes are associated with greater immediate casualties but dissipate with little
contamination. xxxviii Toxic liquids or solids are more likely to have fewer immediate casualties
but are more likely to persistently remain in the water or on land as contamination. xxxix The area
of the event is typically a mile or two, but some facilities report worst case scenarios of 25
miles.xl Factories and production sites are commonly placed in urban areas,xli and chemicals are
often transported through highly populated urban areas.xlii Others factors that affect exposures
relate to how people respond to an event, including time of day, whether people are at home or at
work, and whether the release is immediately recognized or silent and unnoticed.xliii
Injury and death can occur in many ways depending on the particular chemicals involved.
Health effects generally fall into three categories- local, systemic, and psychological.xliv Local
effects occur at the site of the contact with the chemical, and include constricted airways,
irritation of the skin and eyes, and potential fatal fluid in the lungs. Systemic injuries involve the
absorption of chemicals into the body, causing cancer or organ damage such as lung or liver
failure, nerve damage, or reproductive problems.xlv While local effects usually occur within days
of exposure, systemic effects can be immediate or delayed.xlvi Additionally, toxic events can
cause psychological harms; in addition to the stressors of actual harm and disruption of the
community, the concealed nature of many toxic exposures means that even people who are not
physically harmed may feel the stress of exposure.xlvii
313 Toxic industrial chemical accidents also have economic costs. Low
contamination events are typically associated with business disruption, which can cover an area
for several days. In addition to the immediate disruption of shutting down businesses, tens to
hundreds of thousands of people may evacuate and need time to return. High contamination
events can close businesses for a longer period of time of months to years, as companies or
workers’ home will need decontamination or to be replaced in a new location.xlviii
WHAT IS THE EXPOSURE TO TOXIC INDUSTRIAL CHEMICALS?
Approximately 850,000 businesses in the U.S. produce, store, or use toxic industrial
chemicals,xlix and several million tank car loads are transported each year to supply that demand.l
There are approximately 60,000 chemical spill events per year, about evenly split between fixedsite and transportation events.li The overwhelming majority of events are small, with no
fatalities or severe injuries, and involve only employees and not the general public.lii Public
exposures are rare- accidents killing more than 20 people have occurred on average once per
decade. liii
The likelihood of a catastrophic event is unclear. The U.S. has never had a catastrophic
event on the scale of the Bhopal disaster where hundreds of thousands of people were exposed,
but there have been several close calls (such as the train derailments in Baltimore, MD in 2001
and Graniteville, SC in 2005liv) that have raised concerns that a catastrophic accident could
happen here. Additionally, there have been major industrial accidents, such as the 1989
explosion and fire at a Phillips Petroleum plant in Texas that left 23 dead.lv If a catastrophic
chemical accident were to occur, 40,000 people would be exposed at the average facility, with
some facilities reporting over 1,000,000 people exposed.lvi
Our estimate of annual fatalities is 8, reflecting the average annual deaths in the U.S.
from historical data of large scale chemical releases from spills, collisions, and explosions.lvii The
estimate of deaths per year on average ranges from a low of 5 (from historical data) to a high of
200 (from a 1% chance of worst-case Bhopal-style event). Severe injuries are about six times as
high, with 50 hospitalizations per year on average (ranging from 30 to 200 hospitalizations per
year on average), while less severe injuries are much higher, with 500 non-hospitalized
injuries/illnesses per year on average (ranging from 300 to 5,000 non-hospitalizations per year on
average).lviii The greatest number of fatalities in a single event is 600 (reflecting the largest
number of deaths in a domestic incident, in Texas in 1947) to 20,000, (reflecting the largest
number of deaths anywhere, in Bhopal, India in 1984).lix The greatest number killed in the U.S.
is around 560, in an ammonium nitrate explosion in 1947.lx
Our best estimate of the average economic damages per year is $300M (within a range of
$200M to $7B per year on average).lxi The damages from a worst case scenario are much higher,
with estimates ranging from $2B (reflecting the largest chemical accident in the U.S.) to $700B
(reflecting the largest chemical accident in the world).lxii The duration of economic damages can
range from days for a quickly dispersing gas to months for a contaminating heavy metal. lxiii
Similarly, the area of economic damages can vary depending on the type of chemical and the
type of accident, ranging from a few blocks to an entire city for a gas leak, to several counties for
a chemical spill in a waterway.lxiv
Environmental damages from toxic industrial chemical accidents range from moderate to
lxv
high. Large scale spills into waterways and groundwater can contaminate up to several
counties, and explosions or industrial fires can cover a city or county. The annual number of
displaced individuals is estimated as 8,000 per year on average (within a range of 5,000 to
314 200,000 displaced per year on average),lxvi and hundreds of thousands may flee a worst case
scenario.lxvii These accidents are unlikely to significantly disrupt emergency services.lxviii
WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF TOXIC INDUSTRIAL
CHEMICALS?
The risk of toxic industrial chemical accidents became a clear concern following the
Bhopal incident in 1984 and a near release of the same chemical in West Virginia the subsequent
year. Initial steps involved the public’s right to know of industrial risks, including the creation of
state and local emergency response commissions and access to information on local chemical
risks under the Emergency Planning and Community Right-to-Know Act.lxix Facilities must
make local and state agencies (and the public, to a limited extent) aware of the hazardous
chemicals on site (so they can be prepared for a release) and of releases of hazardous substances
(so the public can actively evacuate or take shelter).lxx In 1990, a Chemical Safety Board was
added to promote the prevention of major chemical accidents.lxxi The sharing of information on
chemical risks between companies is also important but has largely focused on day-to-day
accidents rather than on circumstances that can lead to a catastrophic accident.lxxii
In addition to overseeing the plans, the government regulates the use of specific
chemicals. lxxiii Companies that deal with hazardous industrial chemicals are required develop
plans to prevent and respond to chemical accidents. The Occupational Safety and Health
Administration sets guidelines and regulations for safer processes in order to prevent accidents.
While they are tasked with employee safety, reducing accidents also protects the public.lxxiv The
Environmental Protection Agency (EPA) has a more explicit role with regards to accidents that
could harm the public.lxxv Facilities must report accidents, perform a consequence analysis
including worst case scenarios, and create an emergency response plan.lxxvi The EPA assists
companies with modeling consequences, collects the plans, and conducts site visits to evaluate
compliance with those plans.lxxvii Local emergency planning committees associated with the
local mayor’s office or county emergency management office also make plans based on
information in these Risk Management Plans.lxxviii
Response to a typical, small-scale accident is largely local, involving firefighters, police,
and hospitals. Local emergency responders often have specialized training and equipment for
protection and decontamination.lxxix Hospitals are also involved in hazardous materials response,
adding both decontamination and medical treatment for smaller incidents.lxxx Larger events
involve the EPA as an active partner, responding to 300 incidents each year and assisting on
another 500.lxxxi Response assets include regional and national response teams.lxxxii For a
catastrophic toxic industrial chemical event, the National Response Plan would be activated.lxxxiii
If activated, the EPA coordinates the federal response (with the U.S. Coast Guard, if the incident
involves waterways), coordinating assets from 12 other departments.lxxxiv
Recovery activities may include longer-term decontamination, compensation, and the
determination of liability. EPA investigations are authorized under the Comprehensive
Environmental Response, Compensation, and Liability Act (CIRCLA).lxxxv In the case of a
catastrophic event, such as the contamination accompanying Hurricane Katrina, the EPA can
work with FEMA to establish long-term recovery plans from contaminating events.lxxxvi
The number of toxic industrial chemical accidents has decreased in recent yearslxxxvii but
the damage per event has increased substantially.lxxxviii On balance, it is unclear whether the
overall risk of catastrophic accidents is increasing or decreasing.
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Catastrophic oil spills occur when an oil well or tanker breaches, sending oil onto land or into
water. The quantity of oil, geography of the coastline and weather heavily influence the severity
of the spill. While minor oil spills occur hundreds of times a year, spills of 10,000 barrels or
more only occur a couple times a decade, and the U.S. has experienced only 3 spills of 100,000
barrels or more in the past 40 years. Spills are becoming less frequent and less severe, but
catastrophic risk remains due to continued increase in oil imports and exploration in deeper
waters. Much of this decrease is due to preventative actions placed in the 1970’s through 1990’s,
including technological improvements such as double hulls and improved navigation. While
much of the responsibility for the response and recovery for an event lies with in the private
sector, the federal government also coordinates responses through the EPA (for in-land spills) or
the Coast Guard (for off-shore spills). Specific activities include cleaning up the water and
shores and reparations for economic damages to individuals and businesses.
Risk Characteristics of Oil Spills
Public Health and Safety
Average number of deaths per year
Greatest number of deaths in a single episode
Average more severe injuries/illnesses per year
Average less severe injuries/illnesses per year
Psychological damage per year on average
Other Damage
Average economic damages per year
Greatest economic damages in a single event
Duration of economic damages
Size of area affected by economic damages
Average environmental damage per year
Average individuals displaced per year
Disruption of government operations
Other Characteristics
Natural/human-induced
Ability of individual to control their exposure
Time between exposure and health effect
Quality of scientific understanding
Combined uncertainty
Low
Best
High
0i
1ii
200iv
5vi
60ix
Moderatexi
4iii
3v
30viii
$1Bxii
$1Bxiii
$4Bxv-40Bxvi
Months to decades
Counties to states
Highxvii
5xviii
Lowxix
8vii
90x
$4Bxiv
Human-induced
Moderatexx
Immediate to yearsxxi
Lowxxii
Lowxxiii
WHAT IS KNOWN ABOUT THE RISK OF OIL SPILLS?
Catastrophic oil spills occur when an oil well or tanker breaches, spilling tens of
thousands to millions of barrels of oil or other volatile fuels into the environment. This can
occur in inland waterways, off-shore, or on land; however, the largest concern for catastrophic
releases is off-shore. Oil naturally seeps in the nation’s waters in amounts larger than even large
oil spills, but it is the concentrated nature of large oil spills that does the most damage. Spills
can spread rapidly and can cover a very large area, potentially up to tens of thousands of square
miles of water and hundreds of miles of coastline.xxiv The physical and chemical changes caused
322 by oil spills alter the ocean environment adversely affecting wildlife, shorelines and the
communities and industries that live off them. Consequences of oil spills include minor health
effects, significant environmental damage, and economic damage due to clean-up, and effects on
fisheries and tourism.
The health effects from oil and fumes are generally minor. Deaths in an accident are rare.
While some oil spills are associated with fatalities, it is typically an accident that causes both the
deaths and the oil spill, rather than the deaths being caused by the oil spill itself.xxv Health effects
are minor in the short term and generally limited to those involved in the clean-up operations.
Symptoms include swelling and itching skin, sore and watery eyes, cough and shortness of
breath, and neurological symptoms such as nausea, dizziness, confusion, and weakness of
extremities.xxvi Similar symptoms can accompany worker exposure to the dispersant chemicals
used in clean-up operations. xxvii These acute effects are temporary and diminish within six
months following exposure.xxviii
Long-term health effects are minor. Oil spills do release chemicals that are known to
increase the likelihood of nerve damage, birth defects, and cancer.xxix However, because the
chemicals are strongly diluted, the information whether or not oil spills have significant longterm health consequences is inadequate to form many conclusions.xxx Studies of the health
effects on workers and those directly exposed on the beaches finds little to no increased risk of
death in the long-term.xxxi Some persistent damage to lung functioning and the endocrine system
has been identified among workers.xxxii The effects on residents may be negligible if they take
steps to avoid areas with visible contamination; this is particularly important for children,
pregnant women, and those who have respiratory impairments.xxxiii
Oil spills are also linked to mental health consequences, both for responders and
members of the community. The uncertainty of exposure and anxiety play a role, as do job loss,
disruption of communities, and distress caused by the messages sent by authorities.xxxiv Specific
disorders can include PTSD, depression, domestic abuse, substance abuse, and other stressrelated disorders due to exposure to toxic chemicals, societal and community disruption, or
prolonged economic damage and job loss.xxxv
Harm varies across individuals. The individuals who are most exposed to risks of illness
are cleanup crews, although these can be decreased with proper protection.xxxvi People at sea or
living in adjacent coastal areas are at somewhat lower level of risk. People completely inland are
only considered to be at risk through eating contaminated foods. Additionally, harms may be
more significant for children and the elderly. Children’s development may be disrupted by the
toxic chemicals to a greater extent than adults.xxxvii Similarly, the elderly and those with
compromised immune systems may experience greater harms from petrochemical exposure.xxxviii
The most severe damage from an oil spill is to the environment. There are both short and
long term effects.xxxix Short term effects include killing sea life in the immediate aftermath of the
spill. Long term effects include the slow degradation of the ecosystem if spilling continues or is
not completely cleaned up and the potential for certain species to be eradicated from a local area.
Birds and marine mammals that live on the ocean’s surface are most likely to come in contact
with oil, sticking to the feathers and preventing them from floating and flying. Toxic elements in
the spill may adversely affect marine life’s reproduction systems.xl Oil slicks also limit the
amount of light that passes underwater, retarding the development of marine plants that fish feed
on. These effects create a chain reaction which can destroy the local underwater ecosystem. The
Exxon Valdez spill still has lingering environmental effects after 20 years.xli
323 Economic damage comes from three main sources: from degraded fisheries, decreased
tourism, and clean-up costs. xlii Damage to fisheries can be severe and persistent; some Alaskan
fisheries damaged by the 1989 Exxon Valdez spill have still not recovered.xliii Natural resources
lost include fish and other commercial seafood. Business activities like tourism and industry may
depend on clear waterways, for example, power plants needing clean water for cooling. Finally,
clean up expenses vary, depending on the size, type and location of spills. Some coastlines, like
marshlands and tidal flats, are more sensitive to spills and more expensive to cleanup.
WHAT IS THE EXPOSURE TO OIL SPILLS?
Catastrophic oil spills are rare, occurring in the U.S. less than once per decade on
average. While moderate spills are becoming less common, it is unclear if catastrophic spills are
becoming less common as well. Exposure to the consequences of oil spills varies, both
geographically and across individuals. Spills are most likely to occur at sea, in areas involved in
oil extraction or transport, specifically in the Gulf States and Alaska.
The U.S. experiences hundreds of oil spills a year; these are largely low-level events, not
rising to a catastrophic level. Only 0.05% of spills are greater than 10,000 barrels, and only a
small portion of those are truly catastrophic events.xliv Spills of over 10,000 barrels occur less
than once a year in the U.S.,xlv and the U.S. has experienced only 3 spills of 100,000 barrels or
more in the past 40 years: the Exxon Valdez in 1989, the Mega Borg in 1990, and the Deepwater
Horizon in 2010.xlvi The Deepwater Horizon spill was the worst of these, releasing an estimated
5M barrels of oil in 2010.xlvii
The exposure to spilling accidents has decreased dramatically since the late 1980s due to
new regulatory legislation, despite an increase in oil transported to the United States.xlviii
However, it is unclear whether this also includes a decrease in catastrophic events. Catastrophic
events happen too infrequently to determine whether they are increasing or decreasing.xlix While
technology and expertise continue to improve, this may be offset by the shift of drilling to deeper
and less accessible areas offshore as well as the increase in population along the at-risk areas of
the Gulf Coast.l
Consequences of an oil spill are largely due to catastrophic events. Our estimate of
greatest number of deaths associated with a catastrophic oil spill is 200, representing an event
similar to the explosion and fire on the Piper Alpha oil platform in the North Sea in 1988.li
However, most oil spills are not associated with fatalities, so our best estimate of fatalities per
year on average is 1 while our high estimate of fatalities per year on average is 4.lii Our estimate
of severe illnesses or injuries per year on average is 5 (ranging from 3 to 8) and less severe
illnesses or injuries per year on average is 60 (ranging 30 to 90), based on reported illnesses from
workers cleaning-up the 1989 Exxon-Valdez spill.liii
Costs per metric ton are smaller for larger spills, and estimated costs range from $10M
for a 10,000 barrels spill to over $1B for spills over 1,000,000 barrels, with costs much higher
on-shore than off-shore.liv The worst economic damages for a catastrophic event range from $4
to 40 billion, representing estimates of damages from the 1989 Exxon-Valdez spill and the 2010
Deepwater Horizon spill.lv Using a historically based likelihood of 3 large accidents in 40 years,
the economic damages per year on average are $1 billionlvi (ranging from $1 billionlvii to $4
billionlviii).
324 WHAT HAS ALREADY BEEN DONE ABOUT THE RISK OF OIL SPILLS?
Much of the contemporary policy on oil spills follows on previous disasters. The 1969
oil spill near Santa Barbara, California, spurred environmental regulation of oil platforms.lix
Later, the Exxon Valdez spill inspired the transportation-related policy in the Oil Pollution Act of
1990. Legislation proposed in the wake of the Deepwater Horizon spill of 2010 has not yet been
passed.lx These laws cover oil spill prevention, preparedness, and liability, including locations
acceptable for drilling, technological standards, and training. Additionally, the government can
get involved in response activities along with the private sector.
Prevention serves to limit the number and size of spills. Drilling restrictions include the
prohibition of new wells in certain areas, such off the coast of California and New Jersey.lxi A
national moratorium on new drilling and exploration offshore dating from the mid-1980’s was
opened just before the Deepwater Horizon accident 2010;lxii the timing of this event may be
likely to slow or even stop the trend to increased access for offshore drilling.
Following the Exxon Valdez accident in 1989, preventative efforts have focused on
making oil tankers safer. Tankers are now required to have double hulls to reduce the risk and
extent of spills. Newer navigation technology reduces accidents, specifically collisions with
rocks, ships and manmade objects. New laws mandate the monitoring and control of shipping
equipment to reduce accidental discharges and being able to trace who polluters are. Traffic
control is instituted in shipping channels. Many spills occur when tankers are docked and
unloading oil. Liabilities for these discharges encourage self-monitoring of this process.
Preparation leads to quicker responses that minimize damage. Contingency planning has
been mandated since 1990. Facilities and vessels are required to have their own response
plans,lxiii although these may not be sufficient for a large-scale event.lxiv For catastrophic events,
the government activates the National Response Plan, with the Environmental Protection Agency
coordinating response for inland events and the Coast Guard coordinating response at sea.lxv The
US Coast Guard, Environmental Protection Agency, and the National Oceanic and Atmospheric
Administration have developed plans to anticipate and respond to spills.lxvi Planning includes
developing the chain of command, lists of organizations and equipment needed to respond, and
knowledge of shoreline characteristics and probable movement patterns of the oil slicks.lxvii Plans
to protect sensitive areas can now be developed ahead of time and practiced in exercises.
Training is not only required for responders but for tanker crews and oil terminals employees so
they can learn how to react quickly when a spill occurs.
The owners of the leaking oil are responsible for responding to an event. The response
varies depending on the size and type of spill, the type of oil and what areas are at risk. There are
many methods to clean oil spills include: physical removal of the oil through skimming,
vacuuming or removing contaminated earth; absorbing the oil; breaking down the oil chemically;
and burning the oil off the ocean’s surface:lxviii Circumstances such as weather, type of
shoreline, and presence of people and wildlife dictate which method is best to apply.
Some avoidance of exposure is possible for individuals, through avoiding affected areas,
avoiding seafood, and staying indoors. However, individuals have little ability to control a spill
itself and its environmental and economic impacts other than to live away from coastal areas.
Recovery includes environmental restoration on a longer-scale and may also involve
paying those who have been harmed. The Oil Pollution Act increased revenue to subsidize the
Oil Spill Liability Trust Fund (OSLTF), while limiting the liability of oil companies under the
law. Within the OSLTF is a $50 million Emergency Fund that provides the Coast Guard and
325 local agencies with quick discretionary funding for spill response.lxix However, the OSLTF is
not sufficient to cover a large spill.lxx
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A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * Peek-Asa count of Northridge
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ii
Based on FEMA’s HAZUS modeling estimates of annual earthquake deaths in the U.S.
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iii
High estimate of deaths from Steinbrugge et al. * a 6.8% probability of earthquake of 6.7 or larger in populated
areas of California Steinbrugge, K. V., H. J. Degenkolb, G. L. Laverty and J. E. McCarty (1987). Earthquake
planning scenario for a magnitude 7.5 earthquake on the Hayward Fault in the San Francisco Bay area, California
Dept. of Conservation, Division of Mines and Geology. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
iv
Based on high estimates of a 7.5 near San Francisco ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
v
Field, E. H., H. A. Seligson, N. Gupta, V. Gupta, T. H. Jordan and K. W. Campbell (2005). "Loss estimates for a
Puente Hills blind-thrust earthquake in Los Angeles, California." Earthquake Spectra 21: 329. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
vi
A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * Peek-Asa count of Northridge
hospitalizations. Peek-Asa, C., J. F. Kraus, L. B. Bourque, D. Vimalachandra, J. Yu and J. Abrams (1998). "Fatal
and hospitalized injuries resulting from the 1994 Northridge earthquake." International Journal of Epidemiology
27(3): 459. All numerical estimates have been rounded to one significant figure to reduce overstating the precision
of these measures.
vii
Average number of major injuries from HAZUS-MH in FEMA 366. FEMA (2008). HAZUS MH estimated
annualized earthquake losses for the United States. Washington, D.C., National Institute of Building Sciences
FEMA. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
viii
A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * high estimate from
Steinbrugge et al. Steinbrugge, K. V., H. J. Degenkolb, G. L. Laverty and J. E. McCarty (1987). Earthquake
planning scenario for a magnitude 7.5 earthquake on the Hayward Fault in the San Francisco Bay area, California
Dept. of Conservation, Division of Mines and Geology. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
331 ix
A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * Peek-Asa count of Northridge
hospital visits but not admissions. Peek-Asa, C., J. F. Kraus, L. B. Bourque, D. Vimalachandra, J. Yu and J. Abrams
(1998). "Fatal and hospitalized injuries resulting from the 1994 Northridge earthquake." International Journal of
Epidemiology 27(3): 459. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
x
Average number of minor injuries from HAZUS-MH in FEMA (2008). HAZUS MH estimated annualized
earthquake losses for the United States. Washington, D.C., National Institute of Building Sciences
FEMA. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
xi
A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * high estimate from
Steinbrugge et al. Steinbrugge, K. V., H. J. Degenkolb, G. L. Laverty and J. E. McCarty (1987). Earthquake
planning scenario for a magnitude 7.5 earthquake on the Hayward Fault in the San Francisco Bay area, California
Dept. of Conservation, Division of Mines and Geology. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xii
High psychological damage per year on average based on the higher of high 1) PTSD and 2) high depression per
year on average. High PTSD based on crossing high lives lost per year on average and moderate severe injuries per
year on average. High depression based on crossing high combined damage per year on average and high duration
of economic damage. High combined damage (defined as a combination of lives lost, severe damage, and economic
damage per year on average of two high and one moderate or all high). Lives lost are considered low when fewer
than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are considered low if fewer
than 50, moderate 50-500, and high if over 500 per year on average. Economic damage are considered low if less
than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is considered low if
measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to years, weeks to years,
months to years, or months; high if measured in years, decades, months to decades, or years to decades.
xiii
EM-DAT reported estimate of damage per year on average from 1980-2000 http://www.emdat.be/final-resultrequest. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
xiv
FEMA estimate of average cost per year FEMA (2008). HAZUS MH estimated annualized earthquake losses for
the United States. Washington, D.C., National Institute of Building Sciences
FEMA. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
xv
Estimates from Petik and Atkisson, adjusted for inflation. Petak, W. J. and A. A. Atkisson (1985). "Natural
hazard losses in the United States: A public problem." Review of Policy Research 4(4): 662-669. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xvi
The number from Eguchi has been adjusted for inflation into 2012 dollars. Eguchi, R. T., J. D. Goltz, C. E.
Taylor, S. E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais (1998). "Direct economic losses in
the Northridge earthquake: a three-year post-event perspective." Earthquake Spectra 14(2): 245-264. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xvii
Using the direct estimates of $250B from Field et al, but multiplying that direct economic damage by 4 to
incorporate indirect fire damage and business disruption as consistent with the ShakeOut scenario. Field, E. H., H.
A. Seligson, N. Gupta, V. Gupta, T. H. Jordan and K. W. Campbell (2005). "Loss estimates for a Puente Hills blindthrust earthquake in Los Angeles, California." Ibid. 21: 329. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xviii
Eguchi, R. T., J. D. Goltz, C. E. Taylor, S. E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais
(1998). "Direct economic losses in the Northridge earthquake: a three-year post-event perspective." Ibid. 14(2): 245264.
xix
Economic damage for a large earthquake will be larger than a city, but less than a state. Direct damage will be
approximately city sized, but indirect damage such as business disruption will be larger.
xx
Based on low damage to species, but high potential aesthetic damage, across a high area, but with low likelihood.
On balance, these highs and lows reflect a moderate expected damage.
xxi
Based on EMDAT data of earthquake-displaced individuals in the US in the past 30 years.
xxii
A 6.8% probability of earthquake of 6.7 or larger in populated areas of California * National Planning Scenario
estimate of displacement. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
332 partnership with the U.S. Department of Homeland Security. Washington, DC. It is of a similar order of magnitude
as estimates derived from the Bay Area Retrofit data on numbers of displaced people for a 7.3 earthquake. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xxiii
Increased demand for and decreased capabilities for non-essential government- Perry, R. W. and M. K. Lindell
(1997). "Earthquake planning for government continuity." Environmental Management 21(1): 89-96. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xxiv
An individual’s ability to reduce their likelihood of being in an earthquake is limited to larger lifestyle choices
like relocating outside high-risk areas. However, there are ways in which an individual can mitigate their exposure,
through insurance, preparing food and water for a disaster, or through improving the structure of their buildings.
xxv
The health effects of an earthquake are immediate, with no significant mechanisms for contamination or
infection.
xxvi
The mechanisms of physical trauma and structural collapse are well known to science.
xxvii
Moderate combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of
the high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damage) which
equals 662. Values below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are
considered high. This reflects an understanding of the consequences and likelihood of an event generally, but little
understanding of the consequences and likelihood of an event specifically.
xxviii
As the damage of an earthquake depends not only on the energy, but also the distance and depth of the epicenter
and the type of soil, it is measured on a different scale, the Modified Mercalli Scale.
xxix
Cobum, A. W., R. J. S. Spence and A. Pomonis (1992). Factors determining human casualty levels in
earthquakes: mortality prediction in building collapse, Taylor & Francis.
xxx
Peek-Asa, C., J. F. Kraus, L. B. Bourque, D. Vimalachandra, J. Yu and J. Abrams (1998). "Fatal and hospitalized
injuries resulting from the 1994 Northridge earthquake." International Journal of Epidemiology 27(3): 459.
xxxi
Ibid, Nichols, J. M. and J. E. Beavers (2003). "Development and calibration of an earthquake fatality function."
Earthquake Spectra 19: 605.
xxxii
Peek-Asa, C., J. F. Kraus, L. B. Bourque, D. Vimalachandra, J. Yu and J. Abrams (1998). "Fatal and
hospitalized injuries resulting from the 1994 Northridge earthquake." International Journal of Epidemiology 27(3):
459.
xxxiii
Brookshire, D. S., S. Eeri, H. Cochrane, R. Eeri, A. R. M. Eeri and J. Steenson (1997). "Direct and indirect
economic losses from earthquake damage." Earthquake Spectra 13: 683, Eguchi, R. T., J. D. Goltz, C. E. Taylor, S.
E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais (1998). "Direct economic losses in the
Northridge earthquake: a three-year post-event perspective." Earthquake Spectra 14(2): 245-264.
xxxiv
Eguchi, R. T., J. D. Goltz, C. E. Taylor, S. E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais
(1998). "Direct economic losses in the Northridge earthquake: a three-year post-event perspective." Earthquake
Spectra 14(2): 245-264.
xxxv
Horwich, G. (2000). "Economic lessons of the Kobe earthquake." Economic Development and Cultural Change
48(3): 521-542.
xxxvi
Grossi, P. (1999). "Assessing the Benefits and Costs of Earthquake Mitigation." Center for Financial Institutions
Working Papers.
xxxvii
USGS. "Magnitude/Intensity Comparison." Retrieved Jan. 12, 2012, from
http://earthquake.usgs.gov/learn/topics/mag_vs_int.php.
xxxviii
NAHB Research Center (1994). Assessment of Damage to Residential Buildings Caused by the Northridge
Earthquake. U. S. D. o. H. a. U. D. O. o. P. D. a. Research. Washington, DC. 2012, Boarnet, M. G. (1998).
"Business losses, transportation damage and the Northridge earthquake." Journal of transportation and statistics 1(2):
49-64.
xxxix
Kroll, C. A., J. D. Landis, Q. Shen and S. Stryker (1990). "Economic impacts of the loma prieta earthquake: A
focus on small business." Berkeley Planning Journal 5(1): 3958, Gordon, P., H. Richardson, B. Davis, C. Steins and
A. Vasishth (1996). "The business interruption effects of the Northridge earthquake." Lusk Center Research
Institute, University of Southern California, Los Angeles, CA.
xl
Rose, A., J. Benavides, S. E. Chang, P. Szczesniak and D. Lim (2002). "The regional economic impact of an
earthquake: Direct and indirect effects of electricity lifeline disruptions." Journal of Regional Science 37(3): 437458, Rose, A. and D. Lim (2002). "Business interruption losses from natural hazards: conceptual and
methodological issues in the case of the Northridge earthquake." Global Environmental Change B: Environmental
Hazards 4(1): 1-14.
333 xli
Boarnet, M. G. (1998). "Business losses, transportation damage and the Northridge earthquake." Journal of
transportation and statistics 1(2): 49-64, Giuliano, G. and J. Golob (1998). "Impacts of the northridge earthquake on
transit and highway use." Journal of transportation and statistics 1(2): 1-20.
xlii
Thilenius, J. F. (1990). "Woody plant succession on earthquake-uplifted coastal wetlands of the Copper River
Delta, Alaska." Forest Ecology and Management 33: 439-462, Hancox, G., N. Perrin and G. Dellow (2002). "Recent
studies of historical earthquake-induced landsliding, ground damage, and MM intensity in New Zealand." Bulletin
of the New Zealand Society for Earthquake Engineering 35(2): 59-95.
xliii
Thilenius, J. F. (1990). "Woody plant succession on earthquake-uplifted coastal wetlands of the Copper River
Delta, Alaska." Forest Ecology and Management 33: 439-462.
xliv
Committee on Assessing the Costs of Natural Disasters (1999). The Impacts of Natural Disasters: A Framework
for Loss Estimation. National Research Council, National Academies Press, Washington, D.C., Appendix A
xlv
John, H. (1968). "Earthquake-initiated changes in the nesting habitat of the dusky Canada goose." The great
Alaska earthquake of 1964: 129.
xlvi
Committee on Assessing the Costs of Natural Disasters (1999). The Impacts of Natural Disasters: A Framework
for Loss Estimation. National Research Council, National Academies Press, Washington, D.C.., Appendix A
xlvii
Lindell, M. K. and R. W. Perry (1996). "Addressing gaps in environmental emergency planning: hazardous
materials releases during earthquakes." Journal of Environmental Planning and Management 39(4): 529-543.
xlviii
USGS. "2008 NSHM Figures." Retrieved Jan. 12, 2012, from
http://earthquake.usgs.gov/hazards/products/conterminous/2008/maps/
xlix
Parise, M. and R. W. Jibson (2000). "A seismic landslide susceptibility rating of geologic units based on analysis
of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake." Engineering
Geology 58(3-4): 251-270.
l
USGS for probability USGS. "FAQs - Probabilities, Seismic Hazard & Earthquake Engineering." Retrieved Jan.
12, 2012, from http://earthquake.usgs.gov/learn/faq/?faqID=42.
li
Science Daily. "Disaster Earthquake Scenario Unveiled For Southern California." Retrieved Jan. 12, 2012, from
http://www.sciencedaily.com/releases/2008/05/080522104754.htm, Steinbrugge, K. V., H. J. Degenkolb, G. L.
Laverty and J. E. McCarty (1987). Earthquake planning scenario for a magnitude 7.5 earthquake on the Hayward
Fault in the San Francisco Bay area, California Dept. of Conservation, Division of Mines and Geology, Elnashai, A.
S., L. J. Cleveland, T. Jefferson and J. Harrald (2008). "Impact of Earthquakes on the Central USA.", FEMA.
(2012). "Earthquake." Retrieved Jan. 12, 2012, from http://www.fema.gov/hazard/earthquake/index.shtm.
lii
FEMA. (2012). "Earthquake." Retrieved Jan. 12, 2012, from
http://www.fema.gov/hazard/earthquake/index.shtm.
liii
FEMA (2008). HAZUS MH estimated annualized earthquake losses for the United States. Washington, D.C.,
National Institute of Building Sciences
FEMA. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
liv
See endnotes i, vi, and ix. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
lv
See endnotes iii, viii, xii. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
lvi
See endnote xiv. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
lvii
The number from Eguchi has been adjusted for inflation into 2012 dollars. Eguchi, R. T., J. D. Goltz, C. E.
Taylor, S. E. Chang, P. J. Flores, L. A. Johnson, H. A. Seligson and N. C. Blais (1998). "Direct economic losses in
the Northridge earthquake: a three-year post-event perspective." Earthquake Spectra 14(2): 245-264. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
lviii
See endnotes xvi and xvii for estimates based on a Shake-Out scenario earthquake (on the low end) and a rupture
of the Puente Hills thrust fault in Los Angeles. All numerical estimates have been rounded to one significant figure
to reduce overstating the precision of these measures.
lix
See endnotes xxi and xxiiError! Bookmark not defined. for details. All numerical estimates have been rounded
to one significant figure to reduce overstating the precision of these measures.
lx
Risk Management Solutions (2009). Catastrophe Modeling and California Earthquake Ris: A 20-Year Perspective,
Risk Management Solutions.
lxi
FEMA. (2012). "Earthquake Response and Recovery." Retrieved Jan. 12, 2012, from
http://www.fema.gov/hazard/earthquake/response.shtm.
334 lxii
California Emergency Management Agency. "Infrastructure Protection." Retrieved Jan. 12, 2012, from
http://www.calema.ca.gov/InfrastructureProtection/Pages/Infrastructure-Protection.aspx, Multihazard Mitigation
Council (2005). Natural Hazard Mitigation Saves: An Independent Study to Assess the Future Savings from
Mitigation Activities, National Institute of Building Sciences. 2- Study Documentation: 126-127, FEMA. (2012).
"FEMA Earthquake Mitigation Handbook." Retrieved Jan. 12, 2012, from
http://www.conservationtech.com/FEMA-WEB/FEMA-subweb-EQ/index.htm.
lxiii
Multihazard Mitigation Council (2005). Natural Hazard Mitigation Saves: An Independent Study to Assess the
Future Savings from Mitigation Activities, National Institute of Building Sciences. 2- Study Documentation: 126127, FEMA. (2012). "Earthquake Response and Recovery." Retrieved Jan. 12, 2012, from
http://www.fema.gov/hazard/earthquake/response.shtm, FEMA. (2012). "IS-8.a Building for the Earthquakes of
Tomorrow: Complying with Executive Order 12699." Retrieved Jan. 12, 2012, from
http://training.fema.gov/EMIWeb/IS/is8a.asp.
lxiv
Roberts, J. E. (1994). Highway Bridges. Practical Lesson from the Loma Prieta Earthquake. Geotechnical Board
and National Research Council. Washington, DC, National Research Council.
lxv
Risk Management Solutions (2009). Catastrophe Modeling and California Earthquake Ris: A 20-Year
Perspective, Risk Management Solutions.
lxvi
USGS. "Magnitude/Intensity Comparison." Retrieved Jan. 12, 2012, from
http://earthquake.usgs.gov/learn/topics/mag_vs_int.php.
lxvii
Multihazard Mitigation Council (2005). Natural Hazard Mitigation Saves: An Independent Study to Assess the
Future Savings from Mitigation Activities, National Institute of Building Sciences. 2- Study Documentation: 126127.
lxviii
San Francisco Fire Department. "Neighborhood Emergency Response Team Home." Retrieved Jan. 12, 2012,
from http://www.sfgov.org/site/sfnert_index.asp.
lxix
DHS Office of Inspector General (2010). FEMA's Preparedness for the Next Catastrophic Disaster- An Update.
U.S. Department of Homeland Security Office of Inspector General. Washingon, DC.
lxx
Walter, L. (2008). "FEMA Develops Earthquake Disaster Response Initiative." Retrieved Jan. 12, 2012, from
http://ehstoday.com/fire_emergencyresponse/disaster-planning/FEMA_earthquake_disaster_response_1125/.
lxxi
The Great California Shake Out. "Who Is Participating?" Retrieved Jan. 12, 2012, from
http://www.shakeout.org/whoisparticipating/
lxxii
Laatsch, E. (2007). "FEMA's Statutory Activities." Retrieved Jan. 12, 2012, from
http://www.nehrp.gov/pdf/fema_statutory_activities_ppt.pdf.
lxxiii
American Red Cross. "Preparedness Fast Facts- Earthquakes." Retrieved Jan. 12, 2012, from
http://www.redcross.org/portal/site/en/menuitem.53fabf6cc033f17a2b1ecfbf43181aa0/?vgnextoid=6f461c99b5ccb1
10VgnVCM10000089f0870aRCRD&currPage=13cbb969ae282210VgnVCM10000089f0870aRCRD, San
Francisco Fire Department. "Neighborhood Emergency Response Team Home." Retrieved Jan. 12, 2012, from
http://www.sfgov.org/site/sfnert_index.asp, The Great California Shake Out. "The Great California Shake Out."
Retrieved Jan. 12, 2012, from www.shakeout.org.
lxxiv
California Earthquake Authority. "About Earthquake Insurance." Retrieved Jan. 12, 2012, from
http://www.earthquakeauthority.com/index.aspx?id=13.
lxxv
Ibid.
lxxvi
FEMA. (2012). "Earthquake Response and Recovery." Retrieved Jan. 12, 2012, from
http://www.fema.gov/hazard/earthquake/response.shtm.
i
Lowest 30 year average over past 70 years, using data from NOAA NOAA. (2011). "NOAAEconomics: The
Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
ii
Average of the 30 year averages over the past 70 years, using data from NOAA ibid. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
iii
Highest 30 year average over past 70 years, using data from NOAA ibid. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
iv
Official estimates of the deaths due to Hurricane Katrina, 2005, are 1,727 Shultz, J., J. Russell and Z. Espinel
(2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development." Epidemiologic
Reviews 27(1): 21.. Unofficial estimates including missing and those who may have died of indirect causes is
higher, with one estimate of 4,081 Norwalk, L. M. (2007). Post Katrina health care: continuing concerns and
immediate needs in the New Orleans region. Committee on Energy and Commerce Subcommittee on Oversight
335 Investigations,. Washington, DC, Government Printing Office. 4.. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
v
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
vi
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
vii
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21.. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
viii
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
ix
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
x
Based on the number of injuries times a fraction of injuries that are more severe. The number of injuries is
calculated by applying an injury to deaths ratio to the number of deaths per year on average. The injuries to deaths
ratio of 46.8 is the average of the injuries to deaths in Hurricanes Andrew, Hugo, and Elena and Gloria. These
336 numbers are from Shultz et al. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the
dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21. The proportion of injuries
which are more severe comes from the proportion of hurricane-related injuries presenting to the hospital which were
admitted to that hospital following Hurricane Isabella, see Gagnon et al. Gagnon, E., M. Aboutanos, A. Malhotra, D.
Dompkowski, T. Duane and R. Ivatury (2005). "In the wake of Hurricane Isabel: a prospective study of postevent
trauma and injury control strategies." The American Surgeon, 71(3): 194-197. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xi
High psychological damage per year on average based on the higher of 1) high PTSD and 2) high depression per
year on average. High PTSD based on crossing moderate deaths per year on average and high severe injuries per
year on average. High depression based on crossing high combined damages per year on average and moderate
duration of economic damages. High combined damages (defined as a combination of lives lost, severe damages,
and economic damages per year on average of both high or one moderate and one high). Lives lost are considered
low when fewer than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are considered
low if fewer than 50, moderate 50-500, and high if over 500 per year on average. Economic damages are considered
low if less than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is considered
low if measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to years, weeks to
years, months to years, or months; high if measured in years, decades, months to decades, or years to decades. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xii
Lowest damage in a decade over the past 70 years, adjusted for wealth, from Pielke et al. (2008) Pielke Jr, R., J.
Gratz, C. Landsea, D. Collins, M. Saunders and R. Musulin (2008). "Normalized hurricane damage in the United
States: 1900–2005." Natural Hazards Review 9: 29. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xiii
Average of ten year averages, adjusted for wealth ibid. This number is very similar to the EMDAT average of
damage from hurricanes from 1980-2010, being $10.95B. We preferred the average of averages estimate so that the
approach and data were consistent between the low, best, and high cases (low was the lowest 10-year average, best
was the average 10-year average, and high was the highest 10-year average). That the average of the best 10-year
averages was similar to the 30-year average from EMDAT is only presented to show that the average of averages
approach is reasonable. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
xiv
Highest ten year average of damages over the last 70 years, adjusted for wealth, ibid. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
xv
High estimate based on wealth adjusted estimates for the Miami hurricane of 1926, which is similar to the $125B
estimate of Hurricane Katrina in 2005, ibid.. Low estimate based on the next largest hurricane in the past 30 years
when adjusted for wealth, being Hurricane Andrew in 1992. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xvi
Economic damages can last for months to years as an area adjusts to the damages. While there are cases where
the economic damages may last longer than years (e.g., Hurricane Katrina), these are more reflective of underlying
economic conditions rather than the shock of the event itself.
xvii
Davidson, R. A. and K. B. Lambert (2001). "Comparing the Hurricane Disaster Risk of U. S. Coastal Counties."
Natural Hazards Review 2(3): 132-142.
xviii
Due to excessively large area that can be contaminated and the associated chemicals and oil spills that occur.
xix
Using the average ratio of people displaced from their homes to economic damage from hurricanes Andrew, Ike,
Katrina, and Hugo, times the lower bound of economic damage per year on average. Average ratio of people
displaced to economic damage calculated from Blake et al., Comerio, Gabe et al., and a FEMA report. Comerio, M.
C. (1997). "Housing issues after disasters." Journal of Contingencies and Crisis Management 5(3): 166-178, Gabe,
T., G. Falk, M. McCarty and V. W. Mason (2005). Hurricane Katrina: Social-demographic characteristics of
impacted areas. Congressional Research Service and the Library of Congress. Washington, DC, FEMA (2008).
Hurricane Ike Impact Report. Federal Emergency Management Agency. Washington, DC, Blake, E. S., C. Landsea
and E. J. Gibney (2011). The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006
(and other frequently requested hurricane facts). U. D. o. C.-T. M. N. T.-. National Oceanic & Atmospheric
Administration (NOAA)—National Hurricane Center (NHC). All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xx
Using the average ratio of people displaced from their homes to economic damage from hurricanes Andrew, Ike,
Katrina, and Hugo, times the upper bound of economic damage per year on average. Average ratio of people
337 displaced to economic damage calculated from Blake et al., Comerio, Gabe et al., and a FEMA report. Comerio, M.
C. (1997). "Housing issues after disasters." Journal of Contingencies and Crisis Management 5(3): 166-178, Gabe,
T., G. Falk, M. McCarty and V. W. Mason (2005). Hurricane Katrina: Social-demographic characteristics of
impacted areas. Congressional Research Service and the Library of Congress. Washington, DC, FEMA (2008).
Hurricane Ike Impact Report. Federal Emergency Management Agency. Washington, DC, Blake, E. S., C. Landsea
and E. J. Gibney (2011). The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006
(and other frequently requested hurricane facts). U. D. o. C.-T. M. N. T.-. National Oceanic & Atmospheric
Administration (NOAA)—National Hurricane Center (NHC). All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xxi
A catastrophic hurricane can disrupt emergency services when they are severe, and can also disrupt nonemergency services over a very large area. However, services are not necessarily disrupted long-term.
xxii
Based on advance warning of up to several days before the event to take preventative action including
evacuation.
xxiii
While most health effects of a hurricane are immediate, such as drowning or physical damage from wind (see
Schultz et al. (2005), Rappaport (2000)), health effects can also produce toxic mold which can have health effects
years later. See Brandt et al. (2006) Rappaport, E. N. (2000). "Loss of life in the United States associated with
recent Atlantic tropical cyclones." Bulletin of the American Meteorological Society 81(9): 2065-2073, Shultz, J., J.
Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease, and
development." Epidemiologic Reviews 27(1): 21, Brandt, M., C. Brown, J. Burkhart, N. Burton, J. Cox-Ganser, S.
Damon, H. Falk, S. Fridkin, P. Garbe and M. McGeehin (2006). "Mold prevention strategies and possible health
effects in the aftermath of hurricanes and major floods." Morbidity and Mortality Weekly Report 55: 1-27.
xxiv
The mechanisms of health effects, including physical trauma and toxic effects, are well understood to science.
xxv
Low combined uncertainty is based on sum of the ratios of the ranges of consequences applied on an anchored
scale. The sum of the ratio of the high estimate to the low estimate for lives lost (4.3), severe injuries (4.3), less
severe injuries (4.3), and economic damages (9.3) equals 22. Values below 100 are considered low, 100 to 1000 are
considered moderate, and above 1000 are considered high. The estimate of 22 for hurricanes is considered low.
This reflects an understanding of the consequences of an event, an ability to avoid health effects through evacuation,
some understanding of the likelihood of an event generally, and the relatively high number of events that occur in a
single year.
xxvi
Center for Climate and Energy Solutions. (2011). "Hurricanes and Global Warming FAQ." Retrieved Nov. 20,
2011, from http://www.pewclimate.org/hurricanes.cfm#when.
xxvii
NOAA. (2011). "Coastal Hazards." Retrieved Nov. 20, 2011, from
http://coastalmanagement.noaa.gov/hazards.html.
xxviii
Willoughby, H. (2011). "About Hurricanes." IHRC: Meteorology Retrieved Nov. 20, 2011, from
http://www.ihc.fiu.edu/about_us/meteorology.htm.
xxix
Blake, E. S., C. Landsea and E. J. Gibney (2011). The deadliest, costliest, and most intense United States tropical
cyclones from 1851 to 2006 (and other frequently requested hurricane facts). U. D. o. C.-T. M. N. T.-. National
Oceanic & Atmospheric Administration (NOAA)—National Hurricane Center (NHC).
xxx
Pielke Jr, R., J. Gratz, C. Landsea, D. Collins, M. Saunders and R. Musulin (2008). "Normalized hurricane
damage in the United States: 1900–2005." Natural Hazards Review 9: 29.
xxxi
Rappaport, E. N. (2000). "Loss of life in the United States associated with recent Atlantic tropical cyclones."
Bulletin of the American Meteorological Society 81(9): 2065-2073.
xxxii
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster,
disease, and development." Epidemiologic Reviews 27(1): 21, Blake, E. S., C. Landsea and E. J. Gibney (2011). The
deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006 (and other frequently
requested hurricane facts). U. D. o. C.-T. M. N. T.-. National Oceanic & Atmospheric Administration (NOAA)—
National Hurricane Center (NHC).
xxxiii
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster,
disease, and development." Epidemiologic Reviews 27(1): 21.
xxxiv
Rappaport, E. N. (2000). "Loss of life in the United States associated with recent Atlantic tropical cyclones."
Bulletin of the American Meteorological Society 81(9): 2065-2073.; Wolshon, B., E. Urbina, C. Wilmot and M.
Levitan (2005). "Review of policies and practices for hurricane evacuation. I: Transportation planning,
preparedness, and response." Natural Hazards Review 6: 129.
xxxv
Rappaport, E. N. (2000). "Loss of life in the United States associated with recent Atlantic tropical cyclones."
Bulletin of the American Meteorological Society 81(9): 2065-2073, Wolshon, B., E. Urbina, C. Wilmot and M.
338 Levitan (2005). "Review of policies and practices for hurricane evacuation. I: Transportation planning,
preparedness, and response." Natural Hazards Review 6: 129.
xxxvi
Brandt, M., C. Brown, J. Burkhart, N. Burton, J. Cox-Ganser, S. Damon, H. Falk, S. Fridkin, P. Garbe and M.
McGeehin (2006). "Mold prevention strategies and possible health effects in the aftermath of hurricanes and major
floods." Morbidity and Mortality Weekly Report 55: 1-27, CDC. (2011). "Prevent Illness and Injuries After a
Hurricane or Flood." Emergency Preparedness and Response Retrieved Nov. 20, 2011, from
http://www.bt.cdc.gov/disasters/hurricanes/illnessinjury.asp.
xxxvii
Brandt, M., C. Brown, J. Burkhart, N. Burton, J. Cox-Ganser, S. Damon, H. Falk, S. Fridkin, P. Garbe and M.
McGeehin (2006). "Mold prevention strategies and possible health effects in the aftermath of hurricanes and major
floods." Morbidity and Mortality Weekly Report 55: 1-27.
xxxviii
Ibid.
xxxix
Euripidou, E. and V. Murray (2004). "Public health impacts of floods and chemical contamination." Journal of
Public Health 26(4): 376.
xl
Sheikh, P. A. "The Impact of Hurricane Katrina on Biological Resources." Washington, DC: Congressional
Research Service, Mallin, M. and C. Corbett (2006). "How hurricane attributes determine the extent of
environmental effects: multiple hurricanes and different coastal systems." Estuaries and Coasts 29(6): 1046-1061,
NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved
May 28, 2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.
xli
Rappaport, E. N. (2000). "Loss of life in the United States associated with recent Atlantic tropical cyclones."
Bulletin of the American Meteorological Society 81(9): 2065-2073.
xlii
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease,
and development." Epidemiologic Reviews 27(1): 21.
xliii
Chang, S. E., T. L. McDaniels, J. Mikawoz and K. Peterson (2007). "Infrastructure failure interdependencies in
extreme events: power outage consequences in the 1998 Ice Storm." Natural Hazards 41(2): 337-358.et al.,
Hampson, N. B. and J. L. Zmaeff (2005). "Carbon monoxide poisoning from portable electric generators." American
Journal of Preventive Medicine 28(1): 123-125.
xliv
Noji, E. K. (1993). "Analysis of medical needs during disasters caused by tropical cyclones: anticipated injury
patterns." Journal of Tropical Medicine and Hygiene 96(6): 370-376, CDC. (2005). "Preventing Chain Saw Injuries
after a Hurricane." Retrieved Mar. 1, 2011, from http://www.urbanforestrysouth.org/resources/library/preventingchain-saw-injuries-during-tree-removal-after-a-hurricane/at_download/file, Shultz, J., J. Russell and Z. Espinel
(2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development." Epidemiologic
Reviews 27(1): 21, Sullivent III, E. E., C. A. West, R. S. Noe, K. E. Thomas, L. Wallace and R. T. Leeb (2006).
"Nonfatal injuries following Hurricane Katrina--New Orleans, Louisiana, 2005." Journal of Safety Research 37(2):
213-217, Zhang, G., R. Sneed, F. Leguen, L. Cutie, R. Borroto-Ponce and E. O'Connell (2007). "Use of Syndromic
Data for Surveillance of Hurricane-Related Injuries in Miami-Dade County, FL." Advances in Disease Surveillance
2(4): 1, CDC. (2011). "Prevent Illness and Injuries After a Hurricane or Flood." Emergency Preparedness and
Response Retrieved Nov. 20, 2011, from http://www.bt.cdc.gov/disasters/hurricanes/illnessinjury.asp.
xlv
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster, disease,
and development." Epidemiologic Reviews 27(1): 21.
xlvi
Gabe, T., G. Falk, M. McCarty and V. W. Mason (2005). Hurricane Katrina: Social-demographic characteristics
of impacted areas. Congressional Research Service and the Library of Congress. Washington, DC, Super, N. and B.
Biles (2005) "Displaced by Hurricane Katrina: issues and options for Medicare beneficiaries. ." Henry J. Kaiser
Family Foundation Medicare Policy Brief. .
xlvii
Bourque, L., J. Siegel, M. Kano and M. Wood (2006). "Weathering the storm: the impact of hurricanes on
physical and mental health." The ANNALS of the American Academy of Political and Social Science 604(1): 129.
xlviii
Ibid.
xlix
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster,
disease, and development." Epidemiologic Reviews 27(1): 21.; Blake et al. (2007)
l
Based on hurricane data from NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA
Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs. See endnotes i, ii, and iii for
details.
li
Deaths due to Hurricane Katrina, 2005. Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical
cyclones: the dynamics of disaster, disease, and development." Epidemiologic Reviews 27(1): 21.
lii
See endnotes v through x for details.
339 liii
Center for Climate and Energy Solutions. (2011). "Hurricanes and Global Warming FAQ." Retrieved Nov. 20,
2011, from http://www.pewclimate.org/hurricanes.cfm#when.
liv
Pielke Jr, R. A., C. Landsea, M. Mayfield, J. Laver and R. Pasch (2005). "Hurricanes and global warming."
Bulletin of the American Meteorological Society 86(11): 1571-1575, Shultz, J., J. Russell and Z. Espinel (2005).
"Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development." Epidemiologic Reviews
27(1): 21.
lv
Center for Climate and Energy Solutions. (2011). "Hurricanes and Global Warming FAQ." Retrieved Nov. 20,
2011, from http://www.pewclimate.org/hurricanes.cfm#when.
lvi
Pielke Jr, R. A., C. Landsea, M. Mayfield, J. Laver and R. Pasch (2005). "Hurricanes and global warming."
Bulletin of the American Meteorological Society 86(11): 1571-1575, Pielke Jr, R., J. Gratz, C. Landsea, D. Collins,
M. Saunders and R. Musulin (2008). "Normalized hurricane damage in the United States: 1900–2005." Natural
Hazards Review 9: 29.
lvii
NOAA. (2011). "Coastal Hazards." Retrieved Nov. 20, 2011, from
http://coastalmanagement.noaa.gov/hazards.html.
lviii
See endnote xi, xii, and xiii.
lix
See endnote xiv.
lx
See endnotes xix and xx.
lxi
Kahn, M. (2005). "The death toll from natural disasters: the role of income, geography, and institutions." Review
of Economics and Statistics 87(2): 271-284.; Skidmore, M. and H. Toya (2002). "Do natural disasters promote longrun growth?" Economic Inquiry 40(4): 664-687, Toya, H. and M. Skidmore (2007). "Economic development and the
impacts of natural disasters." Economics Letters 94(1): 20-25.; Anbarci, N., M. Escaleras and C. Register (2005).
"Earthquake fatalities: the interaction of nature and political economy." Journal of Public Economics 89(9-10):
1907-1933.
lxii
(2011). "Hurricane Research Division: Frequently Asked Questions." Retrieved Dec. 1, 2011, from
http://www.aoml.noaa.gov/hrd/tcfaq/F6.html
lxiii
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster,
disease, and development." Epidemiologic Reviews 27(1): 21.
lxiv
(2006). Report to Congress on Catastrophic Hurricane Evacuation Plan Evaluation. U. S. D. o. T. i. c. w. t. U. S.
D. o. H. Security. Washington, DC.
lxv
NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved
May 28, 2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs,
Willoughby, H. (2011). "About Hurricanes." IHRC: Meteorology Retrieved Nov. 20, 2011, from
http://www.ihc.fiu.edu/about_us/meteorology.htm.
lxvi
Shultz, J., J. Russell and Z. Espinel (2005). "Epidemiology of tropical cyclones: the dynamics of disaster,
disease, and development." Epidemiologic Reviews 27(1): 21.
lxvii
FEMA. (2011). "Response." Retrieved Dec. 10, 2011, from http://www.fema.gov/government/response.shtm.
lxviii
Arguez, A. and J. B. Elsner (2001). "Trends in US tropical cyclone mortality during the past century." Florida
Geographer 32: 28-37, Ryckman, L. (2007). Effects of education on risk perception: a study of Hurricane Katrina
cleanup workers, Brown University.
lxix
FEMA (2005). Home Builder’s Guide to Coastal Construction; Technical Fact Sheet Series. Washington, DC,
Federal Emergency Management Agency,, FEMA. (2011). "Mitigation Best Practices Portfolio (Hurricane
Katrina)." Retrieved Dec. 10, 2011, from http://www.fema.gov/plan/prevent/bestpractices/kat_ms.shtm.
lxx
Wahle, T. and G. Beatty (1993). Emergency management guide for business and industry. Washington, DC,
Federal Emergency Management Agency.
lxxi
(2011). "Florida Hurricane Info Watch Tips Maps Safety." Retrieved Dec. 10, 2011, from
http://www.floridahurricane.net/, FEMA. (2011). "Frequently Asked Questions." Retrieved Dec. 10, 2011, from
http://www.fema.gov/plan/prevent/fhm/fq_main.shtm.
lxxii
(2011). "Hurricane & Storm Damage Risk Reduction System (HSDRRS)." Team New Orleans Retrieved Dec.
10, 2011, from http://www.mvn.usace.army.mil/hps2/index.asp.
lxxiii
(2011). "Preparing for a Hurricane." Retrieved Dec. 10, 2011, from www.iii.org/Articles/Preparing-for-aHurricane.html.
lxxiv
Jaffee, D. M. and T. Russell (1997). "Catastrophe insurance, capital markets, and uninsurable risks." The Journal
of Risk and Insurance 64(2): 205-230, Jametti, M. and T. von Ungern-Sternberg. (2009). "Hurricane Insurance in
Florida." CESifo Working Paper Series No. 2768. Available at SSRN. Retrieved Feb. 26, 2012, from
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1469984.
340 lxxv
While Stewart et al. (2003) finds a high value for mitigation efforts, and Ganderton et al. (2006) and Rose et al.
(2007) find that FEMA mitigation grants save around $4 in damages for each dollar of mitigation, Pinelli finds
mitigation efforts at the household level to rarely be cost effective. Stewart, M., D. Rosowsky and Z. Huang (2003).
"Hurricane risks and economic viability of strengthened construction." Natural Hazards Review 4(1): 12-19,
Ganderton, P. (2006). "Mitigation generates savings of four to one and enhances community resilience: MMC
releases independent study on savings from natural hazard mitigation." Natural Hazards Observer 30(4): 1–3, Rose,
A., K. Porter, N. Dash, J. Bouabid, C. Huyck, J. Whitehead, D. Shaw, R. Eguchi, C. Taylor and T. McLane (2007).
"Benefit-cost analysis of FEMA hazard mitigation grants." Natural Hazards Review 8: 97, Pinelli, J., B. Torkian, K.
Gurley, C. Subramanian and S. Hamid (2009). Cost effectiveness of hurricane mitigation measures for residential
buildings. 11th American Conference on Wind Engineering.
lxxvi
Cutter, S. and C. Emrich (2006). "Moral hazard, social catastrophe: The changing face of vulnerability along the
hurricane coasts." The ANNALS of the American Academy of Political and Social Science 604(1): 102.
lxxvii
Hebert, P., J. Jarrell, M. Mayfield and T. Center (1993). The deadliest, costliest, and most intense United States
hurricanes of this century (and other frequently requested hurricane facts), US Dept. of Commerce, National
Oceanic and Atmospheric Administration, National Weather Service, National Hurricane Center., as cited in
Arguez, A. and J. Elsner (2001) "Trends in US tropical cyclone mortality during the past century." Florida
Geographer, 28. Viewed at
http://myweb.fsu.edu/jelsner/HTML/Research/papers/mortality/mortal.html#tthFtNtAAB on 1/10/2011. Also Rose
et al. Rose, A., K. Porter, N. Dash, J. Bouabid, C. Huyck, J. Whitehead, D. Shaw, R. Eguchi, C. Taylor and T.
McLane (2007). "Benefit-cost analysis of FEMA hazard mitigation grants." Natural Hazards Review 8: 97.
i
Based on the death rate including time trends from Brooks and Doswell (2002), particularly the lower end of the CI
of the projection which is approximately 0.045 per million, multiplied by approximately 300M. These include
consideration of the downward time trend. These numbers end in 2000, and it could be reasonable to extrapolate the
downward trend since this period; however, as there have been several high casualty tornadoes since then, we are
reluctant to extrapolate in this manner and will use the year 2000 death rate estimates. Brooks, H. E. and C. A.
Doswell III (2002). "Deaths in the 3 May 1999 Oklahoma City tornado from a historical perspective." Weather and
Forecasting 17(3): 354-361. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
ii
Based on the death rate including time trends from Brooks and Doswell (2002) particularly the best estimate of the
projection which is approximately 0.13 per million, multiplied by approximately 300M. These include
consideration of the downward time trend. These numbers end in 2000, and it could be reasonable to extrapolate the
downward trend since this period; however, as there have been several high casualty tornadoes since then, we are
reluctant to extrapolate in this manner and will use the year 2000 death rate estimates. Ibid. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
iii
Based on the death rate including time trends from Brooks and Doswell (2002) particularly the upper end of the CI
of the projection which is approximately 0.35 per million, multiplied by approximately 300M. These include
consideration of the downward time trend. These numbers end in 2000, and it could be reasonable to extrapolate the
downward trend since this period; however, as there have been several high casualty tornadoes since then, we are
reluctant to extrapolate in this manner and will use the year 2000 death rate estimates. Ibid. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
iv
Based on the largest tornado outbreak of the past 20 years, the combined tornadoes from supercell activity on
April 27, 2011. NOAA estimates 320 single day fatalities from that day. See
http://www.noaanews.noaa.gov/2011_tornado_information.html for details NOAA. (2011). "2011 Tornado
Information." Retrieved Jan. 1, 2012, from http://www.noaanews.noaa.gov/2011_tornado_information.html . This
was rounded down to the nearest hundred for reasons of bounding- rounding down allows us to present a more
conservative estimate for the lower bound and have the actual number within the bounds. Other estimates including
non-tornado related deaths and other days are higher, but still in the 300s. These numbers would also round down to
300 if we round down to the nearest hundred. All numerical estimates have been rounded to one significant figure
to reduce overstating the precision of these measures.
v
Based on largest tornado, the Tri-state tornado in 1929. Note that there are projections for a tornado in an urban
area in the thousands to tens of thousands, but this projection has been criticized as unreasonable and alarmist.
vi
Low estimate from historical numbers. We did not have data on the injuries older before 2000, so we estimated
the number of injuries from the 2000-2008 injury to death ratio from NOAA NOAA. (2011). "NOAAEconomics:
The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This injury to death ratio is
341 885 injuries per death. This is multiplied by the lowest 10 year average number of deaths from 1940-2009 to give
the low estimated average injuries per year. The percentage of those injuries that were severe is from Brown Brown,
S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to the 3
May 1999 tornadoes." Weather and Forecasting 17: 343-353. This estimate is also similar to the RMS estimates of
hospital admissions. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
vii
Best estimate from historical numbers, using estimated the number of injuries from the 2000-2008 from NOAA
NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved
May 28, 2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This is
similar to but lower than the number of injuries that would be estimated from the average of the 10 year averages
over the 70 years of death data, using an approach similar to low and high estimates. However, deaths of tornadoes
have been consistently trending lower over the past 70 years, so a more recent estimate is more appropriate than the
average over the entire period. The percentage of those injuries that were severe is from Brown Brown, S., P.
Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to the 3 May 1999
tornadoes." Weather and Forecasting 17: 343-353. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
viii
High estimate from historical numbers. We did not have data on the injuries older before 2000, so we estimated
the number of injuries from the 2000-2008 injury to death ratio from NOAA NOAA. (2011). "NOAAEconomics:
The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This injury to death ratio is
885 injuries per death. This is multiplied by the highest 10 year average number of deaths from 1940-2009 to give
the low estimated average injuries per year. The percentage of those injuries that were less severe is from Brown
Brown, S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to
the 3 May 1999 tornadoes." Weather and Forecasting 17: 343-353. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
ix
Low estimate from historical numbers. We did not have data on the injuries older before 2000, so we estimated
the number of injuries from the 2000-2008 injury to death ratio from NOAA NOAA. (2011). "NOAAEconomics:
The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This injury to death ratio is
885 injuries per death. This is multiplied by the lowest 10 year average number of deaths from 1940-2009 to give
the low estimated average injuries per year. The percentage of those injuries that were less severe is from Brown
Brown, S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to
the 3 May 1999 tornadoes." Weather and Forecasting 17: 343-353. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
x
Best estimate from historical numbers, using estimated the number of injuries from the 2000-2008 from NOAA
NOAA. (2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved
May 28, 2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This is
similar to but lower than the number of injuries that would be estimated from the average of the 10 year averages
over the 70 years of death data, using an approach similar to low and high estimates. However, deaths of tornadoes
have been consistently trending lower over the past 70 years, so a more recent estimate is more appropriate than the
average over the entire period. The percentage of those injuries that were less severe is from Brown et al. Brown,
S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to the 3
May 1999 tornadoes." Weather and Forecasting 17: 343-353. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xi
High estimate from historical numbers. We did not have data on the injuries older before 2000, so we estimated
the number of injuries from the 2000-2008 injury to death ratio from NOAA NOAA. (2011). "NOAAEconomics:
The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.. This injury to death ratio is
885 injuries per death. This is multiplied by the highest 10 year average number of deaths from 1940-2009 to give
the low estimated average injuries per year. The percentage of those injuries that were less severe is from Brown et
al. Brown, S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due
to the 3 May 1999 tornadoes." Weather and Forecasting 17: 343-353. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
xii
Moderate psychological damage per year on average based on the higher of 1) moderate PTSD and 2) moderate
depression per year on average. Moderate PTSD based on crossing moderate deaths per year on average and
342 moderate severe injuries per year on average. Moderate depression based on crossing moderate combined damages
per year on average and moderate duration of economic damages. Moderate combined damages (defined as a
combination of lives lost, severe damages, and economic damages per year on average of one low and two
moderate, all moderate, two moderate and one high, two low and one high, or two high and one low). Lives lost are
considered low when fewer than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are
considered low if fewer than 50, moderate 50-500, and high if over 500 per year on average. Economic damages are
considered low if less than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is
considered low if measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to
years, weeks to years, months to years, or months; high if measured in years, decades, months to decades, or years to
decades.
xiii
Based on NOAA estimate of average economic damages from 2000-2008 NOAA. (2011). "NOAAEconomics:
The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
xiv
Economic costs, based on Extreme Weather Sourcebook (2001). Extreme Weather Sourcebook: Economic and
other societal impacts related to hurricans, floods, tornadoes, lightning and other US weather phenomena, The
National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA),
US Weather Research Program (USWRP), National Science Foundation (NSF), and the American Meteorological
Society (AMS). Available at http://sciencepolicy. colorado. edu/sourcebook/index. html., viewed at NOAA NOAA.
(2011). "NOAAEconomics: The Economics and Social Benefits of NOAA Data & Products." Retrieved May 28,
2011, from http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xv
Based on UCAR wealth adjusted average economic damages, viewed at (2011). "Tornadoes 1950-2009." Extreme
Weather Sourcebook Retrieved Jan. 1, 2012, from http://www.sip.ucar.edu/sourcebook/tornadoes.jsp. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xvi
Average of the ten most costly tornadoes since 1950. FEMA. (2012). "Tornadoes." Ready Retrieved Jan. 1, 2012,
from http://www.ready.gov/tornadoes. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xvii
Based on damage from major events Ross, T. and N. Lott (2003). A Climatology of 1980-2003 Extreme Weather
and Climate Events. U. D. o. C. N. O. a. A. A. N. C. D. Center.. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xviii
The median tornado path width is approximately 90 ft., with the largest recorded at 2.5 miles. Tornado path
lengths average 3 miles, but paths of over 200 miles have been documented. Sanderson, L. (1989). Tornadoes. The
Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA: Centers for Disease Control and Prevention:
39–49, Schaefer, J. T., R. S. Schneider and M. P. Kay (2002). The robustness of tornado hazard estimates. Third
Symposium on Environmental Applications.
xix
Potentially severe aesthetic damage but in a limited area; little to no significant damage to species.
xx
Based on Sanderson (91,000) but given a range based on the ratio of low to best and best to high lives lost per
year on average. Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed).
Atlanta, GA: Centers for Disease Control and Prevention: 39–49.
xxi
Based on moderate longevity and low to moderate disruption. Moderate longevity represents a disruption of
government for a week to three months, while recovering. Low to moderate disruption allows the potential for
disruption for emergency services for a smaller area, if for example a hospital, police station, or fire station was
destroyed by the tornado, or debris blocks roads preventing response.
xxii
Individuals have little ability to control their exposure to tornadoes generally, as areas of heightened risk cover
much of the country. This also puts limits on the ability of an individual to control their economic damage.
However, individuals do have quite a bit of ability to limit their health damage, by taking shelter, covering or
wearing a helmet on the head, or covering with a blanket. Belville, J. (1987). "The national weather service warning
system." Annals of Emergency Medicine 16(9): 1078-1080, Duclos, P. J. and R. T. Ing (1989). "Injuries and risk
factors for injuries from the 29 May 1982 tornado, Marion, Illinois." Int J Epidemiol 18(1): 213-219, Brown, S., P.
Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to the 3 May 1999
tornadoes." Weather and Forecasting 17: 343-353.
xxiii
While many injuries happen during the tornado, many others also occur in the aftermath. These include stepping
on nails or debris, straining oneself getting out from debris, fires and carbon monoxide exposure from damaged
structures, and heat stroke, dehydration, or food poisoning from tainted meat due to lack of shelter. Carter, A., M.
343 Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes." American Journal of
Epidemiology 130(6): 1209, Duclos, P. J. and R. T. Ing (1989). "Injuries and risk factors for injuries from the 29
May 1982 tornado, Marion, Illinois." Int J Epidemiol 18(1): 213-219, Sanderson, L. (1989). Tornadoes. The Public
Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA: Centers for Disease Control and Prevention: 39–
49, Bohonos, J. and D. Hogan (1999). "The medical impact of tornadoes in north america-Comparison of black and
white victims." Journal of Emergency Medicine 17(1): 67-73, Brown, S., P. Archer, E. Kruger and S. Mallonee
(2002). "Tornado-related deaths and injuries in Oklahoma due to the 3 May 1999 tornadoes." Weather and
Forecasting 17: 343-353.
xxiv
Scientists have a good understanding of how tornadoes create their damage through wind, having multiple cases
from which to learn in actual experience the circumstances of health impacts. While scientists do not fully
understand the formation of tornadoes, they are able to identify the conditions of increased risk for hours in advance
and the actual presence of a tornado minutes in advance.
xxv
Low combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of the
high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages) which
equals 15. This reflects a low uncertainty in likelihood, a low uncertainty in consequence due to the large number of
events. Values below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are considered
high.
xxvi
National Climate Data Center. (2012). "The Enhanced Fujita Tornado Scale." Retrieved Jan. 1, 2012, from
http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html, National Severe Storms Laboratory.
(2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012, from
http://www.nssl.noaa.gov/edu/safety/tornadoguide.html, Storm Prediction Center. (2012). "The Enhanced Fujita
Scale (EF Scale)." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/, Storm Prediction Center. (2012).
"Enhanced F Scale for Tornado Damage." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/faq/tornado/efscale.html.
xxvii
National Climate Data Center. (2012). "The Enhanced Fujita Tornado Scale." Retrieved Jan. 1, 2012, from
http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html, National Severe Storms Laboratory.
(2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012, from
http://www.nssl.noaa.gov/edu/safety/tornadoguide.html, Storm Prediction Center. (2012). "The Enhanced Fujita
Scale (EF Scale)." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/, Storm Prediction Center. (2012).
"Enhanced F Scale for Tornado Damage." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/faq/tornado/efscale.html.
xxviii
National Climate Data Center. (2012). "The Enhanced Fujita Tornado Scale." Retrieved Jan. 1, 2012, from
http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html, National Severe Storms Laboratory.
(2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012, from
http://www.nssl.noaa.gov/edu/safety/tornadoguide.html, Storm Prediction Center. (2012). "The Enhanced Fujita
Scale (EF Scale)." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/, Storm Prediction Center. (2012).
"Enhanced F Scale for Tornado Damage." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/faq/tornado/efscale.html.
xxix
National Climate Data Center. (2012). "The Enhanced Fujita Tornado Scale." Retrieved Jan. 1, 2012, from
http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html, National Severe Storms Laboratory.
(2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012, from
http://www.nssl.noaa.gov/edu/safety/tornadoguide.html, Storm Prediction Center. (2012). "The Enhanced Fujita
Scale (EF Scale)." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/, Storm Prediction Center. (2012).
"Enhanced F Scale for Tornado Damage." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/faq/tornado/efscale.html.
xxx
National Climate Data Center. (2012). "The Enhanced Fujita Tornado Scale." Retrieved Jan. 1, 2012, from
http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html, National Severe Storms Laboratory.
(2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012, from
http://www.nssl.noaa.gov/edu/safety/tornadoguide.html, Storm Prediction Center. (2012). "The Enhanced Fujita
Scale (EF Scale)." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/, Storm Prediction Center. (2012).
"Enhanced F Scale for Tornado Damage." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/faq/tornado/efscale.html.
xxxi
Wurman, J., P. Robinson, C. Alexander and Y. Richardson (2007). "Low-level winds in tornadoes and potential
catastrophic tornado impacts in urban areas." Bulletin of the American Meteorological Society 88(1): 31-46, Brooks,
344 H., C. Doswell III and D. Sutter (2008). "Low-Level Winds in Tornadoes and Potential Catastrophic Tornado
Impacts in Urban Areas." Bulletin of the American Meteorological Society 89(1): 87-90.
xxxii
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Bohonos, J. and D. Hogan (1999). "The medical impact of
tornadoes in north america-Comparison of black and white victims." Journal of Emergency Medicine 17(1): 67-73,
May, A., G. McGwin Jr, L. Lancaster, W. Hardin, A. Taylor, S. Holden, G. Davis and L. Rue III (2000). "The April
8, 1998 tornado: assessment of the trauma system response and the resulting injuries." The Journal of Trauma 48(4):
666.
xxxiii
Mandelbaum, I., D. Nahrwold and D. Boyer (1966). "Management of tornado casualties." Ibid. 6(3): 353,
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Brenner, S. and E. Noji (1992). "Head and neck injuries from
1990 Illinois tornado." American Journal of Public Health 82(9): 1296, May, A., G. McGwin Jr, L. Lancaster, W.
Hardin, A. Taylor, S. Holden, G. Davis and L. Rue III (2000). "The April 8, 1998 tornado: assessment of the trauma
system response and the resulting injuries." The Journal of Trauma 48(4): 666, Millie, M., C. Senkowski, L. Stuart,
F. Davis, G. Ochsner and C. Boyd (2000). "Tornado disaster in rural Georgia: triage response, injury patterns,
lessons learned." Am Surg 66(3): 223-228.
xxxiv
Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA:
Centers for Disease Control and Prevention: 39–49, Millie, M., C. Senkowski, L. Stuart, F. Davis, G. Ochsner and
C. Boyd (2000). "Tornado disaster in rural Georgia: triage response, injury patterns, lessons learned." Am Surg
66(3): 223-228.
xxxv
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Sanderson, L. (1989). Tornadoes. The Public Health
Consequences of Disasters (Gregg MB, ed). Atlanta, GA: Centers for Disease Control and Prevention: 39–49.
xxxvi
Duclos, P. J. and R. T. Ing (1989). "Injuries and risk factors for injuries from the 29 May 1982 tornado, Marion,
Illinois." Int J Epidemiol 18(1): 213-219.
xxxvii
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Bohonos, J. and D. Hogan (1999). "The medical impact of
tornadoes in north america-Comparison of black and white victims." Journal of Emergency Medicine 17(1): 67-73,
Brown, S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to
the 3 May 1999 tornadoes." Weather and Forecasting 17: 343-353, CDC. (2012). "Reentering Your Flooded Home."
Emergency Preparedness and Response Retrieved Jan. 1, 2012, from
http://www.bt.cdc.gov/disasters/mold/reenter.asp.
xxxviii
CDC. (2012). "After a Tornado." Retrieved Jan. 1, 2012, from
http://www.bt.cdc.gov/disasters/tornadoes/after.asp.
xxxix
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Bohonos, J. and D. Hogan (1999). "The medical impact of
tornadoes in north america-Comparison of black and white victims." Journal of Emergency Medicine 17(1): 67-73,
Brown, S., P. Archer, E. Kruger and S. Mallonee (2002). "Tornado-related deaths and injuries in Oklahoma due to
the 3 May 1999 tornadoes." Weather and Forecasting 17: 343-353, CDC. (2012). "Reentering Your Flooded Home."
Emergency Preparedness and Response Retrieved Jan. 1, 2012, from
http://www.bt.cdc.gov/disasters/mold/reenter.asp.
xl
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Sanderson, L. (1989). Tornadoes. The Public Health
Consequences of Disasters (Gregg MB, ed). Atlanta, GA: Centers for Disease Control and Prevention: 39–49,
Bohonos, J. and D. Hogan (1999). "The medical impact of tornadoes in north america-Comparison of black and
white victims." Journal of Emergency Medicine 17(1): 67-73.
xli
Carter, A., M. Millson and D. Allen (1989). "Epidemiologic study of deaths and injuries due to tornadoes."
American Journal of Epidemiology 130(6): 1209, Sanderson, L. (1989). Tornadoes. The Public Health
Consequences of Disasters (Gregg MB, ed). Atlanta, GA: Centers for Disease Control and Prevention: 39–49.
xlii
Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA:
Centers for Disease Control and Prevention: 39–49, Eidson, M., J. A. Lybarger, J. E. Parsons, J. N. MacCormack
and J. I. Freeman (1990). "Risk factors for tornado injuries." Int J Epidemiol 19(4): 1051-1056.
xliii
Ablah, E., A. Tinius, K. Konda, C. Synovitz and I. Subbarao (2007). "Regional Health System Response to the
2007 Greensburg, Kansas, EF5 Tornado." Disaster Medicine and Public Health Preparedness 1(2): 90.
345 xliv
Storm Prediction Center. (2012). "The Enhanced Fujita Scale (EF Scale), 2." Retrieved Jan. 1, 2012, from
http://www.spc.noaa.gov/efscale/2.html.
xlv
Storm Prediction Center. (2012). "The Enhanced Fujita Scale (EF Scale), 3." Retrieved Jan. 1, 2012, from
http://www.spc.noaa.gov/efscale/3.html, Storm Prediction Center. (2012). "The Enhanced Fujita Scale (EF Scale),
1." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/1.html.
xlvi
Storm Prediction Center. (2012). "The Enhanced Fujita Scale (EF Scale), 20." Retrieved Jan. 1, 2012, from
http://www.spc.noaa.gov/efscale/20.html, Storm Prediction Center. (2012). "The Enhanced Fujita Scale (EF Scale),
19." Retrieved Jan. 1, 2012, from http://www.spc.noaa.gov/efscale/19.html.
xlvii
Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United States. C. R. Service. Washington, DC.
xlviii
Based on a median width of 27m in Schaefer, J. T., R. S. Schneider and M. P. Kay (2002). The robustness of
tornado hazard estimates. Third Symposium on Environmental Applications. For widest tornado see World
Meteorological Organization World Meteorological Organization. (2011). "Global Weather and Climate Extremes."
Retrieved Jan. 1, 2012, from http://wmo.asu.edu.
xlix
Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA:
Centers for Disease Control and Prevention: 39–49. See World Meteorological Organization for longest tornado
path. World Meteorological Organization. (2011). "Global Weather and Climate Extremes." Retrieved Jan. 1,
2012, from http://wmo.asu.edu.
l
National Severe Storms Laboratory. (2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1, 2012,
from http://www.nssl.noaa.gov/edu/safety/tornadoguide.html.
li
Schaefer, J. T., R. S. Schneider and M. P. Kay (2002). The robustness of tornado hazard estimates. Third
Symposium on Environmental Applications, Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United
States. C. R. Service. Washington, DC.
lii
National Climate Data Center. (2011). "State of the Climate Tornadoes Annual 2010." Retrieved Jan. 10, 2012,
from http://www.ncdc.noaa.gov/sotc/tornadoes/2010/13, National Climatic Data Center. (2011). "U.S. Tornado
Climatology." Retrieved Jan. 10, 2012, from
http://www.ncdc.noaa.gov/oa/climate/severeweather/tornadoes.html#timing.
liii
Imbornoni, A. M. "Tornadoes: Facts and figures about twisters." Retrieved Jan. 9, 2012, from
http://www.infoplease.com/spot/tornado1.html#axzz0wik6oXtX
liv
National Severe Storms Laboratory. (2012). "Tornadoes... Nature's Most Violent Storms." Retrieved Jan. 1,
2012, from http://www.nssl.noaa.gov/edu/safety/tornadoguide.html.
lv
Ashley, W. S. (2007). "Spatial and Temporal Analysis of Tornado Fatalities in the United States: 1880–2005."
Weather and Forecasting 22(6): 1214-1228.
lvi
Aguirre, B., R. Saenz, J. Edmiston, N. Yang, E. Agramonte and D. Stuart (1993). "The human ecology of
tornadoes." Demography: 623-633.
lvii
Storm Prediction Center. "Downtown Tornadoes." Retrieved Jan. 10, 2012, from
http://www.spc.noaa.gov/faq/tornado/downtown.html.
lviii
Approximately 0.036 for any given year. See NOAA (1973). Tornado. U.S. Department of Commerce National
Oceanic and Atmospheric Administration. Washington, DC, U.S. Government Printing Office. ; viewed in
Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA:
Centers for Disease Control and Prevention: 39–49.; see also Brooks et al. Brooks, H., C. Doswell III and M. Kay
(2003). "Climatological estimates of local daily tornado probability for the United States." Weather and Forecasting
18: 626-640.
lix
See endnotes i, ii, and iii
lx
See endnotes iv
lxi
See endnote iv
lxii
See endnotes vi, vii, viii, ix, x, and xi
lxiii
See endnotes xiii, xiv, and xv.
lxiv
See endnotes xvi and xvii.
lxv
Brooks, H. E. and C. A. Doswell III (2002). "Deaths in the 3 May 1999 Oklahoma City tornado from a historical
perspective." Weather and Forecasting 17(3): 354-361, Simmons, K. M. and D. Sutter (2005). "Protection from
Nature's Fury: Analysis of Fatalities and Injuries from F5 Tornadoes." Natural Hazards Review 6(2): 82-87,
National Climate Data Center. (2011). "State of the Climate Tornadoes Annual 2010." Retrieved Jan. 10, 2012,
from http://www.ncdc.noaa.gov/sotc/tornadoes/2010/13.
346 lxvi
Brooks, H. E. and C. A. Doswell III (2002). "Deaths in the 3 May 1999 Oklahoma City tornado from a historical
perspective." Weather and Forecasting 17(3): 354-361, Ashley, W. S. (2007). "Spatial and Temporal Analysis of
Tornado Fatalities in the United States: 1880–2005." Weather and Forecasting 22(6): 1214-1228.
lxvii
Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United States. C. R. Service. Washington, DC.
lxviii
Ibid.
lxix
Erickson, S. A. and H. Brooks. (2006). "Lead time and time under tornado warnings: 1986–2004." 23rd
Conference on Severe Local Storms, from http://ams.confex.com/ams/23SLS/techprogram/paper_115194.htm,
Simmons, K. and D. Sutter (2008). "Tornado warnings, lead times, and tornado casualties: An empirical
Investigation." Weather and Forecasting 23: 246-258, NOAA. (2011). "NOAAEconomics: The Economics and
Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.
lxx
Sanderson, L. (1989). Tornadoes. The Public Health Consequences of Disasters (Gregg MB, ed). Atlanta, GA:
Centers for Disease Control and Prevention: 39–49.
lxxi
Liu, S., L. Quenemoen, J. Malilay, E. Noji, T. Sinks and J. Mendlein (1996). "Assessment of a severe-weather
warning system and disaster preparedness, Calhoun County, Alabama, 1994." American Journal of Public Health
86(1): 87, Simmons, K. and D. Sutter (2008). "Tornado warnings, lead times, and tornado casualties: An empirical
Investigation." Weather and Forecasting 23: 246-258, NOAA. (2011). "NOAAEconomics: The Economics and
Social Benefits of NOAA Data & Products." Retrieved May 28, 2011, from
http://www.economics.noaa.gov/?goal=weather&file=events/hurricane&view=costs.
lxxii
Eidson, M., J. A. Lybarger, J. E. Parsons, J. N. MacCormack and J. I. Freeman (1990). "Risk factors for tornado
injuries." Int J Epidemiol 19(4): 1051-1056.
lxxiii
Belville, J. (1987). "The national weather service warning system." Annals of Emergency Medicine 16(9): 10781080, Duclos, P. J. and R. T. Ing (1989). "Injuries and risk factors for injuries from the 29 May 1982 tornado,
Marion, Illinois." Int J Epidemiol 18(1): 213-219.
lxxiv
Simmons, K. M. and D. Sutter (2007). "Tornado shelters and the housing market." Construction Management
and Economics 25(11): 1119 - 1126, Simmons, K. and D. Sutter (2008). "Tornado warnings, lead times, and tornado
casualties: An empirical Investigation." Weather and Forecasting 23: 246-258.
lxxv
FEMA (2007). Tornado Risks and Hazards in the Southeastern United States. U.S. Department of Homeland
Security Federal Emergency Management Agency. Washington, DC, Simmons, K. and D. Sutter (2008).
"Manufactured home building regulations and the February 2, 2007 Florida tornadoes." Natural Hazards 46(3): 415425.
lxxvi
Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United States. C. R. Service. Washington, DC.
lxxvii
Ibid.
lxxviii
Ibid.
lxxix
Ibid.
lxxx
FEMA (2008). Federal Response to Tornado- Struck Communities Continues. U.S. Department of Homeland
Security Federal Emergency Management Agency. Washington, DC.
lxxxi
Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United States. C. R. Service. Washington, DC.
lxxxii
Ibid.
lxxxiii
FEMA (2008). Federal Response to Tornado- Struck Communities Continues. U.S. Department of Homeland
Security Federal Emergency Management Agency. Washington, DC.
lxxxiv
Folger, P. (2009). Severe Thunderstorms and Tornadoes in the United States. C. R. Service. Washington, DC.
i
Low estimate of probability of a pandemic in a given year (1.67%) multiplied by low severity from national
planning scenarios (147,000). Low probability of 1.67% comes from pandemic occurring every 60 years as a least
frequent estimate in the National Planning Scenarios The best estimate of deaths (147,000) comes from the
midpoint of low and high deaths in a typical pandemic (87,000 and 207,000) in the National Planning Scenarios.
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
ii
Best estimate of probability of a pandemic in a given year (3%) multiplied by best estimate of deaths (147,000).
Best probability comes from three major pandemics in the past 100 years. The best estimate of deaths (147,000)
comes from the midpoint of low and high deaths in a typical pandemic (87,000 and 207,000) in the National
Planning Scenarios. Ibid.. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
347 iii
High estimate of probability of a pandemic in a given year (10%) multiplied by best estimate of deaths (147,000).
The high estimate of probability (10%) comes from pandemics occurring every 10 years at the most frequent in the
National Planning Scenarios. Ibid. The best estimate of deaths (147,000) comes from the midpoint of low and high
deaths in a typical pandemic (87,000 and 207,000) in the National Planning Scenarios. Ibid. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
iv
Based on Murray et al.’s median estimate for the number of deaths in the U.S. should there be another Spanish
Flu-style pandemic Murray, C. J. L., A. D. Lopez, B. Chin, D. Feehan and K. H. Hill (2007). "Estimation of
potential global pandemic influenza mortality on the basis of vital registry data from the 1918–20 pandemic: a
quantitative analysis." The Lancet 368(9554): 2211-2218. As the advances in medicine and public health may not
fully blunt a pandemic as great as the Spanish Flu of 1918-1920, their median estimate of 297,883 represents a lower
bound. All numerical estimates been rounded to one significant figure to reduce overstating the precision of these
measures.
v
Based on applying a death rate of 0.65% to a population of approximately 300,000,000. The death rate of 0.65%
represents the actual death rate in the U.S. from the Spanish Flu of 1918-1920 as presented in Johnson and Mueller
(2002). Johnson, N. and J. Mueller (2002). "Updating the accounts: global mortality of the 1918-1920" Spanish"
influenza pandemic." Bulletin of the History of Medicine 76(1): 105-115. As this does not reflect the impact of
advances of medicine and public health since then, this presents a reasonable upper bound. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
vi
Low estimate of probability of a pandemic in a given year (1.67%) multiplied by best estimate of hospitalizations
(516,000) HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC. Low probability comes from pandemic occurring every 60
years as a least frequent estimate equal to 1.67%. Best estimate of hospitalizations (516,900) comes from the
midpoint of low (300,000) and high (733,800) estimates for hospitalizations in a pandemic from National Planning
Scenarios. All numerical estimates have been rounded to one significant figure to reduce overstating the precision
of these measures.
vii
Best estimate of probability of a pandemic in a given year (3%) multiplied by best estimate of hospitalizations
(516,000) ibid. Best probability comes from three major pandemics in the past 100 years. Best estimate of
hospitalizations (516,900) comes from the midpoint of low (300,000) and high (733,800) estimates for
hospitalizations in a pandemic from National Planning Scenarios. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
viii
High estimate of probability of a pandemic in a given year (10%) multiplied by best estimate of hospitalizations
(516,900) The high estimate of probability (10%) comes from pandemics occurring every 10 years at the most
frequent in the National Planning Scenarios. Best estimate of hospitalizations (516,000) comes from the midpoint of
low (300,000) and high (733,800) estimates for hospitalizations in a pandemic from National Planning Scenarios.
All numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
ix
Low estimate of probability of a pandemic in a given year (1.67%) multiplied by best estimate of non-hospitalized
infections (65 million). Low probability comes from pandemic occurring every 60 years as a least frequent estimate
equal to 1.67%. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the
U.S. Department of Homeland Security. Washington, DC. Best estimate of non-hospitalized infections is 65
million, calculated as the midpoint between low and high estimates in National Planning Scenarios. Ibid. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
x
Best estimate of probability of a pandemic in a given year (3%) multiplied by best estimate of non-hospitalized
infections (65 million). Best probability comes from three major pandemics in the past 100 years. Best estimate of
non-hospitalized infections is 65 million, calculated as the midpoint between low and high estimates in National
Planning Scenarios. Ibid.
All numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xi
High estimate of probability of a pandemic in a given year (10%) multiplied by best estimate of non-hospitalized
infections (65 million). The high estimate of probability (10%) comes from pandemics occurring every 10 years at
the most frequent in the National Planning Scenarios. Best estimate of non-hospitalized infections is 65 million,
calculated as the midpoint between low and high estimates in National Planning Scenarios. Ibid. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
348 xii
High psychological damage per year on average based on the higher of 1) high PTSD and 2) high depression per
year on average. High PTSD based on crossing high expected deaths per year on average and high severe injuries
per year on average. High depression based on crossing high combined damages per year on average and moderate
duration of economic damages. High combined damages (defined as a combination of lives lost, severe damages,
and economic damages per year on average of two high and one moderate or all high). Lives lost are considered
low when fewer than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are considered
low if fewer than 50, moderate 50-500, and high if over 500 per year on average. Economic damages are considered
low if less than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is considered
low if measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to years, weeks to
years, months to years, or months; high if measured in years, decades, months to decades, or years to decades.
xiii
Low estimate of probability of a pandemic in a given year (1.67%) multiplied by best estimate for a pandemic if
one should occur ($132B). Low probability of 1.67% comes from pandemic occurring every 60 years as a least
frequent estimate in the National Planning Scenarios Best estimate for a pandemic should one occur comes from the
average of high and low from National Planning Scenarios ($87B and $203B) estimates. Global Security.
"Pandemic Influenza." Retrieved Jan. 12, 2012, from http://www.globalsecurity.org/security/ops/hsc-scen-3.htm,
(2005). National Planning Scenarios. Homeland Security Council in partnership with the Department of Homeland
Security. Washington, DC. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xiv
Best estimate of probability of a pandemic in a given year (3%) multiplied by best estimate for a pandemic if one
should occur ($132B). Best probability comes from three major pandemics in the past 100 years. Best estimate for
a pandemic should one occur comes from the average of high and low from National Planning Scenarios ($87B and
$203B) estimates. Global Security. "Pandemic Influenza." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3.htm, (2005). National Planning Scenarios. Homeland
Security Council in partnership with the Department of Homeland Security. Washington, DC. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xv
High estimate of probability of a pandemic in a given year (10%) multiplied by best estimate for a pandemic if
one should occur ($132B). The high estimate of probability (10%) comes from pandemics occurring every 10 years
at the most frequent in the National Planning Scenarios. Best estimate for a pandemic should one occur comes from
the average of high and low from National Planning Scenarios ($87B and $203B) and estimates. Global Security.
"Pandemic Influenza." Retrieved Jan. 12, 2012, from http://www.globalsecurity.org/security/ops/hsc-scen-3.htm,
(2005). National Planning Scenarios. Homeland Security Council in partnership with the Department of Homeland
Security. Washington, DC. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xvi
Low economic damages from CDC Global Security. "Pandemic Influenza." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3.htm. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xvii
High estimate from high estimate in National Planning Scenario HSC/DHS (2005). National Planning Scenarios.
Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xviii
Global Security. "Flu Pandemic Morbidity/Mortality." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3_flu-pandemic-deaths.htm, HSC/DHS (2005). National
Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security.
Washington, DC, Colizza, V., A. Barrat, M. Barthelemy, A. Valleron and A. Vespignani (2007). "Modeling the
worldwide spread of pandemic influenza: Baseline case and containment interventions." PLoS Medicine 4(1): 95.
xix
Colizza, V., A. Barrat, M. Barthelemy, A. Valleron and A. Vespignani (2007). "Modeling the worldwide spread
of pandemic influenza: Baseline case and containment interventions." PLoS Medicine 4(1): 95.
xx
While pandemics can be associated with animal deaths (such as wild birds in the avian flu), the influenza virus
often incubates in the animal population before leaping to the human population rather than starting with the human
population and then harming animals. NYS Dept. of Health. "Questions and Answers about Avian Influenza (Bird
Flu) and Animals." Retrieved Jan. 12, 2012, from
http://www.health.state.ny.us/diseases/communicable/influenza/avian/questions_and_answers/animals.htm, WHO
(2002). WHO Manual on Animal Influenza Diagnosis and Surveillance. W. H. O. D. o. C. D. S. a. Response and W.
G. I. Programme, HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with
the U.S. Department of Homeland Security. Washington, DC.
349 xxi
While short term quarantines are possible, pandemics are not associated with destruction of property or long-term
displacement. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
xxii
High severity, with disruption of health care and emergency response, as well as non-essential government
services including schools. Moderate to high length of disruption. EPA. "Pandemic Flu." Retrieved Jan. 12, 2012,
from http://www.epa.gov/pandemicflu/, HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC, Randall, T. and A.
Nussbaum. (2009). "Hospitals May Face Severe Disruption from Swine Flu (Update1)." Retrieved Jan. 12, 2012,
from http://www.bloomberg.com/apps/news?pid=20601087&sid=abCN9aBVJIkg, Robelen, E. W. (2009). "Swine
Flu Disruption Has School Officials Looking for Lessons." Retrieved Jan. 12, 2012, from
http://www.edweek.org/ew/articles/2009/05/13/31swineadmin.h28.html.
xxiii
While there are some steps that individuals can take to minimize their exposure (such as washing hands, staying
home from work, avoiding public spaces) they are not entirely effective or in the individual’s control. Also, there is
some reason to believe that even these preventative measures are not effective at stopping the spread, but merely
slow the rate at which it spreads.
xxiv
The mechanism for viruses generally and influenza specifically are very well understood. The mechanism for
pandemic influenza is somewhat less well understood, including the cytosine storm, but is still well known to
science.
xxv
Low combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of the
high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages) which
equals 24. Values below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are
considered high. This reflects some understanding of the consequences of a pandemic, some understanding of the
frequency of events generally, but little understanding of the likelihood of a specific event.
xxvi
Avian Flu Working Group (2006). The Global Economic and Financial Impact of an Avian Flu Pandemic and
the Role of the IMF. International Monetary Fund. Washington, DC.
xxvii
Blumenshine, P., A. Reingold, S. Egerter, R. Mockenhaupt, P. Braveman and J. Marks (2008). "Pandemic
influenza planning in the United States from a health disparities perspective." Emerging Infectious Diseases 14(5):
709.
xxviii
Cox, N. J. and K. Subbarao (2000). "Global epidemiology of influenza: past and present." Annual Review of
Medicine 51(1): 407-421, Kilbourne, E. (2006). "Influenza pandemics of the 20th century." Emerging Infectious
Diseases 12(1): 9.
xxix
Avian Flu Working Group (2006). The Global Economic and Financial Impact of an Avian Flu Pandemic and
the Role of the IMF. International Monetary Fund. Washington, DC.
xxx
Global Security. "Flu Pandemic Timeline/Event Dynamics." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3_flu-pandemic-timeline.htm
xxxi
Bridges, C. B., M. J. Kuehnert, C. B. Hall and R. A. Weinstein (2003). "Transmission of influenza: implications
for control in health care settings." Clinical infectious diseases 37(8): 1094-1101.
xxxii
Global Security. "Flu Pandemic Morbidity/Mortality." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3_flu-pandemic-deaths.htm.
xxxiii
. "Symptoms." Symptoms and Treatment Retrieved Jan. 12, 2012, from http://www.flu.gov/symptomstreatment/symptoms/index.html.
xxxiv
WebMD. "Flu Complications." Retrieved Jan. 12, 2012, from http://www.webmd.com/cold-and-flu/fluguide/flu-complications.
xxxv
CDC. "People at High Risk of Developing Flu-Related Complications." Retrieved Jan. 12, 2012, from
http://www.cdc.gov/flu/about/disease/high_risk.htm, Global Security. "Pandemic Influenza." Retrieved Jan. 12,
2012, from http://www.globalsecurity.org/security/ops/hsc-scen-3.htm.
xxxvi
CDC. "People at High Risk of Developing Flu-Related Complications." Retrieved Jan. 12, 2012, from
http://www.cdc.gov/flu/about/disease/high_risk.htm.
xxxvii
. "People with Health Conditions." Who's at Risk Retrieved Jan. 12, 2012, from http://www.flu.gov/atrisk/health-conditions/index.html.
xxxviii
Global Security. "Pandemic Influenza." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3.htm, Simonsen, L., T. A. Reichert, C. Viboud, W. C.
Blackwelder, R. J. Taylor and M. A. Miller (2005). "Impact of influenza vaccination on seasonal mortality in the US
elderly population." Archives of Internal Medicine 165(3): 265.
350 xxxix
Kumar, A., R. Zarychanski, R. Pinto, D. J. Cook, J. Marshall, J. Lacroix, T. Stelfox, S. Bagshaw, K. Choong
and F. Lamontagne (2009). "Critically ill patients with 2009 influenza A (H1N1) infection in Canada." JAMA: The
Journal of the American Medical Association 302(17): 1872.
xl
Balicer, R. D., S. B. Omer, D. J. Barnett and G. S. Everly (2006). "Local public health workers' perceptions
toward responding to an influenza pandemic." BMC Public Health 6(1): 99..
xli
. "School Planning." Planning and Preparedness Retrieved Jan. 12, 2012, from http://www.flu.gov/planningpreparedness/school/index.html.
xlii
. "Transportation Planning." Planning and Preparedness Retrieved Jan. 12, 2012, from
http://www.flu.gov/planning-preparedness/transportation/index.html.
xliii
EPA. "Pandemic Flu." Retrieved Jan. 12, 2012, from http://www.epa.gov/pandemicflu/.
xliv
. "Business Planning." Planning and Preparedness Retrieved Jan. 12, 2012, from http://www.flu.gov/planningpreparedness/business/index.html.
xlv
Reuters. "FACTBOX: Economic Costs of a Flu Pandemic." Retrieved Jan. 12, 2012, from
http://www.reuters.com/article/idUSTRE53O0WO20090425, Meltzer, M. I., N. J. Cox and K. Fukuda (1999).
"Modeling the economic impact of pandemic influenza in the United States: Implications for setting priorities for
intervention." Background paper: available on the Web at: http://www. cdc. gov/ncidod/eid/vol5no5/melt_back.
htm, Arnold, R., J. De Sa, T. Gronniger, A. Percy and J. Somers (2005). "A potential influenza pandemic: possible
macroeconomic effects and policy issues." US Congressional Budget Office..
xlvi
Kilbourne, E. (2006). "Influenza pandemics of the 20th century." Emerging Infectious Diseases 12(1): 9.
xlvii
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC.
xlviii
. "Pandemic Flu." Trust for America's Health Initiatives Retrieved Jan. 12, 2012, from
http://healthyamericans.org/pandemic-flu/, Global Security. "Pandemic Influenza." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3.htm.
xlix
Monto, A. S., F. M. Davenport, J. A. Napier and T. Francis Jr (1969). "Effect of vaccination of a school-age
population upon the course of an A2/Hong Kong influenza epidemic." Bull. WldlllthOrg 41: 537-542, Glezen, W. P.
(1980). "Consideration of the risk of influenza in children and indications for prophylaxis." Reviews of infectious
diseases 2(3): 408-420, Hurwitz, E. S., M. Haber, A. Chang, T. Shope, S. Teo, M. Ginsberg, N. Waecker and N. J.
Cox (2000). "Effectiveness of influenza vaccination of day care children in reducing influenza-related morbidity
among household contacts." JAMA: The Journal of the American Medical Association 284(13): 1677, Longini Jr, I.
M. and M. E. Halloran (2005). "Strategy for distribution of influenza vaccine to high-risk groups and children."
American Journal of Epidemiology 161(4): 303.
n of the Risk of Influenza in Children and Indications for Prophylaxis," Reviews of infectious diseases, Vol. 2, no. 3,
1980.
lii
CNN. (2009). "H1N1 Death Toll Estimated at 3,900 in U.S." Retrieved Jan. 12, 2012, from
http://www.cnn.com/2009/HEALTH/11/12/h1n1.flu.deaths/index.html, CDC. (2010). "Updated CDC Estimates of
2009 H1N1 Influenza Cases, Hospitalizations and Deaths in the United States, April 2009-April 10, 2010."
Retrieved Jan. 12, 2012, from http://www.cdc.gov/h1n1flu/estimates_2009_h1n1.htm.
liii
Johnson, N. and J. Mueller (2002). "Updating the accounts: global mortality of the 1918-1920" Spanish" influenza
pandemic." Bulletin of the History of Medicine 76(1): 105-115.
liv
See endnotes v.
lv
See endnote iv.
lvi
Global Security. "Pandemic Influenza." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3.htm, HSC/DHS (2005). National Planning Scenarios.
Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC.
lvii
See endnotes i, ii, and iii
lviii
See endnotes vi, vii, and viii
lix
See endnotes ix, x, and xi
lx
See endnotesxvi and xvii
lxi
See endnotes xiii, xiv, and xv
lxii
. "Global Activities." Pandemic Awareness Retrieved Jan. 12, 2012, from
http://www.flu.gov/pandemic/global/index.html.
lxiii
CDC. (2010). "Vaccine Safety." Retrieved Jan. 12, 2012, from
http://www.cdc.gov/h1n1flu/vaccination/vaccine_safety.htm.
351 lxiv
Global Security. "Flu Pandemic Mitigation- Vaccine." Retrieved Jan. 12, 2012, from
http://www.globalsecurity.org/security/ops/hsc-scen-3_flu-pandemic-vaccine.htm.
lxv
CDC. (2009). "2009 H1N1 Vaccination Recommendations." Retrieved Jan. 12, 2012, from
http://www.cdc.gov/h1n1flu/vaccination/acip.htm.
lxvi
CDC. "CDC Organizations Involved in Preparedness and Response Activities." Retrieved Jan. 12, 2012, from
http://www.bt.cdc.gov/cdc/orgs_progs.asp.
lxvii
. "Health Professional." Planning and Preparedness Retrieved Jan. 12, 2012, from http://www.flu.gov/planningpreparedness/hospital/index.html.
lxviii
HHS. "HHS Pandemic Influenza Plan Supplement 4 Infection Control." Retrieved Jan. 12, 2012, from
http://www.hhs.gov/pandemicflu/plan/sup4.html.
lxix
. "State and Local Government." Planning and Preparedness Retrieved Jan. 12, 2012, from
http://www.flu.gov/planning-preparedness/states/index.html.
lxx
Ferguson, N. M., D. A. Cummings, C. Fraser, J. C. Cajka, P. C. Cooley and D. S. Burke (2006). "Strategies for
mitigating an influenza pandemic." Nature 442(7101): 448-452.
lxxi
Ibid.
lxxii
. "Business Planning." Planning and Preparedness Retrieved Jan. 12, 2012, from http://www.flu.gov/planningpreparedness/business/index.html, CDC. (2010). "Guidance for Businesses and Employers to Plan and Respond to
the 2009-2010 Influenza Season." Retrieved Jan. 12, 2012, from http://www.cdc.gov/h1n1flu/business/guidance/
lxxiii
. "Key Planning Considerations." Retrieved Jan. 12, 2012, from
http://www.medexassist.com/pandemic/keyplanning.html, (2006). "Survey Finds Companies 'Very Concerned"
About a Possible Flu Epidemic." Retrieved Jan. 12, 2012, from http://www.prnewswire.com/news-releases/surveyfinds-companies-very-concerned-about-a-possible-flu-pandemic-56525367.html, Rosenthal, E. and K. Bradsher
(2006). Is Business Ready for a Flu Pandemic. New York Times. New York, NY.
lxxiv
Sandman, P. M. and J. Lanard. (2004). "Pandemic influenza risk communication: The teachable moment."
Retrieved Feb. 27, 2012, from http://www.psandman.com/col/pandemic.htm, Glik, D. C. (2007). "Risk
communication for public health emergencies."
i
Based on low likelihood scenario * 2690 deaths. As these are not probabilistic scenarios, expert opinions on
likelihood are used. Low likelihood is 0.07% chance for an event happening in a given year. This is based on the
low estimate of the likelihood of a nuclear attack, multiplied by 7. The multiplier of 7 comes from a modeling
estimate from Sandia Labs which estimates that an anthrax attack is 7 times as likely as a nuclear attack. That
multiplier is applied to a 0.1% chance of attack in ten years, approximately 0.01% in one year, also as estimated by
the Sandia model. Multiplying these factors gives a best estimated probability of an anthrax attack in a given year
being 0.0007=0.07%. This is applied to a typical consequence estimate. 2,690 deaths is based on the average of
three scenarios: first, a scenario of a 2,750 deaths arising from 550 Anthrax letters, similar to the hoax undertaken by
Clayton Waagner had he used B. anthracis rather than B. thuringiensis, identified as a likely scenario by Sarasin;
second, the DHS National Planning Scenario, but limited to one city expose rather than five, suggesting 1320
deaths; third, 4000 deaths suggested by Inglesby. Inglesby, T. (1999). "Anthrax: A possible case history." Emerging
Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA,
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy.
Cambridge, Mass., Harvard University Press. All numerical estimates have been rounded to one significant figure
to reduce overstating the precision of these measures.
ii
Based on a 0.7% likelihood in a given year* 2690 deaths. Low likelihood is 0.07% chance for an event happening
in a given year. This is based on the low estimate of the likelihood of a nuclear attack, multiplied by 7. The
multiplier of 7 comes from a modeling estimate from Sandia Labs which estimates that an anthrax attack is 7 times
as likely as a nuclear attack. That multiplier is applied to a 1% chance in ten years, approximately 0.1% in one year,
drawn from Jenkins (2008). Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA,
Jenkins, B. M. (2008). Will terrorists go nuclear? Amherst, NY, Prometheus Books. Multiplying these factors gives
a best estimated probability of an anthrax attack in a given year being 0.007=0.7%. 2,690 deaths is based on the
average of three scenarios: first, a scenario of a 2,750 deaths arising from 550 Anthrax letters, similar to the hoax
undertaken by Clayton Waagner had he used B. anthracis rather than B. thuringiensis, identified as a likely scenario
by Sarasin; second, the DHS National Planning Scenario, but limited to one city expose rather than five, suggesting
1320 deaths; third, 4000 deaths suggested by Inglesby. Inglesby, T. (1999). "Anthrax: A possible case history."
Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories.
Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the
352 U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy.
Cambridge, Mass., Harvard University Press. All numerical estimates have been rounded to one significant figure
to reduce overstating the precision of these measures.
iii
Based on high likelihood scenario * 2690 deaths. As these are not probabilistic scenarios, expert opinions on
likelihood are used. High likelihood is based on high estimates from U.S. expert opinion in the Luger Survey. The
high estimates are “near certainty” of a nuclear attack in the next 10 years, which we interpreted as 95% in the next
ten years or approximately 26% in a single year. This is not adjusted for a higher likelihood for anthrax as the ten
year estimate is already near certain. This is applied to a typical consequence. 2690 deaths is based on the average
of three scenarios: first, a scenario of a 550 Anthrax letters, similar to the hoax undertaken by Clayton Waagner had
he used B. anthracis rather than B. thuringiensis, identified as a likely scenario by Sarasin; second, the DHS
National Planning Scenario, but limited to one city expose rather than five, suggesting 1320 deaths.; third, 4000
deaths suggested by Inglesby. Inglesby, T. (1999). "Anthrax: A possible case history." Emerging Infectious
Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, HSC/DHS
(2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of
Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass.,
Harvard University Press. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
iv
We have bounded the expected number of deaths in our best scenario (2,690) with estimates for the largest
suggested scenario. Estimate of deaths is based on the average of three scenarios: first, a scenario of a 550 Anthrax
letters, similar to the hoax undertaken by Clayton Waagner had he used B. anthracis rather than B. thuringiensis,
identified as a likely scenario by Sarasin; second, the DHS National Planning Scenario, but limited to one city
expose rather than five, suggesting 1320 deaths.; third, 4000 deaths suggested by Inglesby. Inglesby, T. (1999).
"Anthrax: A possible case history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A
Case Study. S. N. Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006).
Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
v
The high estimate of deaths is taken as 15,000, representing the number of deaths in the full National Planning
Scenario of five cities, as well as a HHS scenario and Kellman’s estimate of tens of thousands of deaths (drawn from
Bioviolence Kellman, B. (2007). Bioviolence : preventing biological terror and crime. New York, Cambridge
University Press., p. 38). There are higher estimates of deaths, in the hundreds of thousands to millions, but these
are based on military scenarios, and are not included in this hazard. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
vi
Based on low likelihood scenario * best estimate of consequence. As these are not probabilistic scenarios, expert
opinions on likelihood are used. Low likelihood is based on a 0.07% chance of an event happening in a given year,
based on a Sandia Labs estimate that an Anthrax attack is 7 times as likely as a nuclear attack and an estimate of the
likelihood 0.01% chance of a nuclear attack in a given year also from Sandia. Best consequence more severe
injuries and illnesses for an anthrax attack under these assumptions is 8,606, representing the average of estimates of
the number of symptomatic people. The average is calculated using estimates of 8500 (from Sarasin), 1320 (from
one city exposed in the DHS Scenarios), and 16,000 (from Inglesby). Inglesby, T. (1999). "Anthrax: A possible case
history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S. N.
Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax :
bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
vii
Based on the best likelihood * best estimate of consequence. Best likelihood of 0.7%, as per endnote ii. Best
consequence more severe injuries and illnesses for an anthrax attack under these assumptions is 8,606, representing
the average of estimates of the number of symptomatic people. The average is calculated using estimates of 8500
(from Sarasin), 1320 (from one city exposed in the DHS Scenarios), and 16,000 (from Inglesby). Inglesby, T.
(1999). "Anthrax: A possible case history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin
Laden: A Case Study. S. N. Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios.
Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC,
Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
353 viii
Based on high likelihood scenario * best estimate of consequence. High likelihood of 26%, from Luger Survey,
as per endnote iii. Best consequence more severe injuries and illnesses for an anthrax attack under these
assumptions is 8,606, representing the average of estimates of the number of symptomatic people. The average is
calculated using estimates of 8500 (from Sarasin), 1320 (from one city exposed in the DHS Scenarios), and 16,000
(from Inglesby). Inglesby, T. (1999). "Anthrax: A possible case history." Emerging Infectious Diseases 5(4): 556,
Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, HSC/DHS (2005). National
Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security.
Washington, DC, Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University
Press. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
ix
Based on low likelihood as per endnote i * average exposed needing inoculation in the DHS scenario and the
Inglesby scenario. Low likelihood is based on a 0.07% chance of an event happening in a given year, based on a
Sandia Labs estimate that an Anthrax attack is 7 times as likely as a nuclear attack, and an estimate of the likelihood
0.01% chance of a nuclear attack in a given year also from Sandia as per endnote i. Best consequence for less
severe injuries and illnesses from an anthrax attack under these assumptions is 44,366, representing the average of
estimates of the number of nonsymptomatic people. The average is calculated using estimates of 20,000 (from
Sarasin), 63,097 (from one city exposed in the DHS Scenarios), and 50,000 (from Inglesby). Inglesby, T. (1999).
"Anthrax: A possible case history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A
Case Study. S. N. Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006).
Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
x
Based on best likelihood as per endnote ii * average exposed needing inoculation in the DHS scenario and the
Inglesby scenario. Best likelihood is 0.7%, as per endnote ii. Best consequence for less severe injuries and illnesses
from an anthrax attack under these assumptions is 44,366, representing the average of estimates of the number of
nonsymptomatic people. The average is calculated using estimates of 20,000 (from Sarasin), 63,097 (from one city
exposed in the DHS Scenarios), and 50,000 (from Inglesby). Inglesby, T. (1999). "Anthrax: A possible case
history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S. N.
Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax :
bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xi
Based on high likelihood as per endnote iii * average exposed needing inoculation in the DHS scenario and the
Inglesby scenario. High likelihood is 26%, as per endnote iii. Best consequence for less severe injuries and
illnesses from an anthrax attack under these assumptions is 44,366, representing the average of estimates of the
number of nonsymptomatic people. The average is calculated using estimates of 20,000 (from Sarasin), 63,097
(from one city exposed in the DHS Scenarios), and 50,000 (from Inglesby). Inglesby, T. (1999). "Anthrax: A
possible case history." Emerging Infectious Diseases 5(4): 556, Sandia (1999). Osama bin Laden: A Case Study. S.
N. Laboratories. Livermore, CA, HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC, Sarasin, P. (2006). Anthrax :
bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xii
Moderate psychological damage per year on average based on the higher of 1) moderate PTSD and 2) moderate
depression per year on average. Moderate PTSD based on crossing moderate expected deaths per year on average
by moderate severe injuries per year on average. Moderate depression based on crossing moderate combined
damages per year on average and moderate duration of economic damages. Low combined damages (defined as a
combination of lives lost, severe damages, and economic damages per year on average all low or two low and one
moderate). Lives lost are considered low when fewer than 10, moderate 10-100, and high if over 100 per year on
average. Severe injuries are considered low if fewer than 50, moderate 50-500, and high if over 500 per year on
average. Economic damages are considered low if less than $500M, moderate $500M to $5B, and high if over $5B
per year on average. Duration is considered low if measured in days, days to weeks, weeks, or weeks to months;
moderate if measured in days to years, weeks to years, months to years, or months; high if measured in years,
decades, months to decades, or years to decades.
xiii
Based on low likelihood scenario best estimate of consequence. Low likelihood is based on a 0.07% chance of an
event happening in a given year, based on a Sandia Labs estimate that an Anthrax attack is 7 times as likely as a
354 nuclear attack, and an estimate of the likelihood 0.01% chance of a nuclear attack in a given year also from Sandiaas
per endnote i. Best consequence estimates come from RAND indoor scenario for anthrax release. Carroll, S. J., T.
LaTourrette, B. G. Chow, G. S. Jones and C. Martin (2004). "Assessing the Effectiveness of the Terrorism Risk
Insurance Act." Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xiv
Based on best likelihood * best estimate of consequence. Best likelihood is 0.7%, as per endnote ii. Best
consequence estimates come from RAND indoor scenario for anthrax release. Carroll, S. J., T. LaTourrette, B. G.
Chow, G. S. Jones and C. Martin (2004). "Assessing the Effectiveness of the Terrorism Risk Insurance Act." All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xv
Based on high likelihood scenario * best estimate of consequence. High likelihood is 26%, as per endnote iii.
Best consequence estimates come from RAND indoor scenario for anthrax release. Ibid. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
xvi
Based on the estimated damage from the 2001 Anthrax case Sarasin, P. (2006). Anthrax : bioterror as fact and
fantasy. Cambridge, Mass., Harvard University Press. Similar to the CDC estimates of an anthrax attack Kaufmann,
A. F., M. I. Meltzer and G. P. Schmid (1997). "The economic impact of a bioterrorist attack: are prevention and
postattack intervention programs justifiable?" Emerging Infectious Diseases 3(2): 83. All numerical estimates have
been rounded to one significant figure to reduce overstating the precision of these measures.
xvii
Based on the RAND damage for an outdoor anthrax release. Carroll, S. J., T. LaTourrette, B. G. Chow, G. S.
Jones and C. Martin (2004). "Assessing the Effectiveness of the Terrorism Risk Insurance Act." All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xviii
Clean-up for even a concentrate indoor event can take months, a contamination of a larger, outdoor area could
take years to clean up. The disrupted economy may be resilient in the medium term, with businesses moving to new
locations markets adjusting with substitutions to other firms.
xix
A reasonable amount of anthrax could contaminate up to several square kilometers. The area contaminated
would depend on the scenario. Direct methods, such as spraying outside from a vehicle or from a point source may
contaminate the entire area while potentially having fewer fatalities. An indoor release would contaminate a smaller
area, but could have higher fatalities. Indirect methods that expose infrastructure vulnerabilities could contaminate a
spread area. For example, the Amerithrax case that contaminated several buildings across several states.
xx
As an attack is not contagious and is likely to happen in an urban area, it is unlikely to present much harm to
species in the environment. An attack will also have negligible harm to aesthetics.
xxi
Based on the low likelihood of an attack * 25,000 displaced. Low likelihood is based on a 0.07% chance of an
event happening in a given year, based on a Sandia Labs estimate that an Anthrax attack is 7 times as likely as a
nuclear attack, and an estimate of the likelihood 0.01% chance of a nuclear attack in a given year also from Sandia,
as per endnote i. Estimates of 25,000 displaced come from the National Planning Scenario estimate / 5, representing
the number displaced in an attack in one city, as compared to the estimates for five cities in the National Planning
Scenario. Sandia (1999). Osama bin Laden: A Case Study. S. N. Laboratories. Livermore, CA. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xxii
Based on the high likelihood of an attack * 25,000 displaced. Estimates of the high likelihood of an attack are
26%, as per endnote iii. Estimates of 25,000 displaced come from the National Planning Scenario estimate / 5,
representing the number displaced in an attack in one city, as compared to the estimates for five cities in the
National Planning Scenario. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xxiii
Based on potential for targeted disruption non-emergency government services to a moderate area.
xxiv
Anthrax spores are barely visible to the naked eye, and it is unlikely that individuals will know that an attack had
occurred until days later. Individual risk mitigation would involve moving out of major cities, but even this would
not be certain.
xxv
CDC. "Fact Sheet: Anthrax Information for Health Care Providers." Emergency Preparedness and Response
Retrieved Jan. 10, 2012, from http://www.bt.cdc.gov/agent/anthrax/anthrax-hcp-factsheet.asp
xxvi
High scientific understanding is based on a good scientific understanding of the mechanism of infection
generally and anthrax specifically.
xxvii
High combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of the
high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages) which
355 equals 1,479. This reflects high uncertainty as to both the scale of the attack and the likelihood of an attack. Values
below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are considered high.
xxviii
Takahashi, H., P. Keim, A. Kaufmann, C. Keys, K. Smith, K. Taniguchi, S. Inouye and T. Kurata (2004).
"Bacillus anthracis incident, Kameido, Tokyo, 1993." Emerging Infectious Diseases 10(1).
xxix
CDC. "Questions and Answers About Anthrax." Emergency Preparedness and Response Retrieved Jan. 10,
2012, from http://www.bt.cdc.gov/agent/anthrax/faq/
xxx
CDC. "Anthrax Q&A: Signs and Symptoms." Emergency Preparedness and Response Retrieved Jan. 10, 2012,
from http://www.bt.cdc.gov/agent/anthrax/faq/signs.asp, Center for Biosecurity of UPMC. "Anthrax Fact Sheet."
Retrieved Jan. 10, 2012, from http://www.upmc-biosecurity.org/website/our_work/biological-threats-andepidemics/fact_sheets/anthrax.html.
xxxi
Gerstein, D. M. (2009). Bioterror in the 21st century : emerging threats in a new global environment. Annapolis,
Md., Naval Institute Press.
xxxii
Since 1900 there have been approximately 50 attempts by non-state actors to acquire bioweapons, and most of
those did not successfully acquire a bioweapon let alone use it.
xxxiii
CIA, in Cordesman, A. H. (2005). The challenge of biological terrorism. Significant issues series v. 27, no. 10.
C. f. S. a. I. S. W. D.C.). Washington, D.C., CSIS Press: xiii, 208 p.
xxxiv
CIA, in ibid.
xxxv
Bowman, S. (2002). Weapons of mass destruction: The terrorist threat. Library of Congress Congressional
Research Service. Washington, DC.
xxxvi
Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism : containing and preventing biological threats.
Amsterdam ; Boston, Butterworth-Heinemann.
xxxvii
Divided opinion- Lugar, R. G. (2005). The Lugar Survey on Proliferation Threats and Responses. Washington,
DC, US Senate.;
Divided opinion Stern, M. (2008). "Experts Divided Over Risk of Bio-Terrorist Attack." Retrieved Jan. 10, 2012,
from http://www.propublica.org/article/experts-divided-over-risk-of-bioterrorist-attack.;
More likely than not- Graham, B., J. M. Talent and G. T. Allison (2008). World at risk: the report of the
Commission on the Prevention of WMD Proliferation and Terrorism, Vintage.
“Inevitable” Sen. Frist, quoted in Leitenberg, M. (2005). Assessing the biological weapons and bioterrorism threat,
Strategic Studies Institute, US Army War College.
xxxviii
Divided opinion- Lugar, R. G. (2005). The Lugar Survey on Proliferation Threats and Responses. Washington,
DC, US Senate.;
Divided opinion Stern, M. (2008). "Experts Divided Over Risk of Bio-Terrorist Attack." Retrieved Jan. 10, 2012,
from http://www.propublica.org/article/experts-divided-over-risk-of-bioterrorist-attack.;
More likely than not- Graham, B., J. M. Talent and G. T. Allison (2008). World at risk: the report of the
Commission on the Prevention of WMD Proliferation and Terrorism, Vintage.
“Inevitable” Sen. Frist, quoted in Leitenberg, M. (2005). Assessing the biological weapons and bioterrorism threat,
Strategic Studies Institute, US Army War College.
xxxix
Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press.
xl
WHO, OTA, CDC in Cordesman, A. H. (2005). The challenge of biological terrorism. Significant issues series v.
27, no. 10. C. f. S. a. I. S. W. D.C.). Washington, D.C., CSIS Press: xiii, 208 p, Kellman, B. (2007). Bioviolence :
preventing biological terror and crime. New York, Cambridge University Press.
xli
The domestic anthrax release in the Soviet Union in 1979 and the Amerithrax case in the US in 2001
xlii
The Aum Shinrikyo attack in Japan in 1993.
xliii
The anthrax hoax letters of Clayton Waagner in 2001.
xliv
The first was an accidental release of military-grade Anthrax from a military facility in Sverdlovsk.
Approximately 100 people were killed from a plume lasting only a few hours, with untold others infected. The
Amerithrax case was a much smaller release, infecting only 22 with 5 deaths. Spores were mailed to several
locations, with contamination resulting to both the offices receiving the letters and the postal infrastructure. The
perpetrator is believed to have been a U.S. defensive bioweapons researcher, and the motive for the attack is unclear,
potentially presenting a misguided warning of the threat. Finally, a liquid-suspension of Anthrax was aerosolized
from a building in Japan by the Aum Shinrikyo cult in 1993. This attack was intended to kill large numbers, but
several factors- including the strain used and problems with the mechanism and time of dispersion- prevented any
fatalities. Cordesman, A. H. (2005). The challenge of biological terrorism. Significant issues series v. 27, no. 10. C.
f. S. a. I. S. W. D.C.). Washington, D.C., CSIS Press: xiii, 208 p.; Bioviolence Kellman, B. (2007). Bioviolence :
preventing biological terror and crime. New York, Cambridge University Press. Clayton Waagner was a bank
356 robber and antiabortion activist who sent letters claiming to contain Anthrax to 550 abortion clinics and doctors in
2001. The letters contained B. thuringiensis, a bacteria used as an insecticide, rather than B. anthracis, but present
one scenario for an Anthrax attack. Had his letters contained Anthrax, it is estimated that 2200 people would have
been infected with 500 deaths. Sarasin, P. (2006). Anthrax : bioterror as fact and fantasy. Cambridge, Mass.,
Harvard University Press.
xlv
Kellman, B. (2007). Bioviolence : preventing biological terror and crime. New York, Cambridge University
Press., p. 38
xlvi
We have bounded the expected number of deaths in our best scenario (2,690) with estimates for the largest
suggested scenario. Estimate of deaths is based on the average of three scenarios: first, a scenario of a 550 Anthrax
letters, similar to the hoax undertaken by Clayton Waagner had he used B. anthracis rather than B. thuringiensis,
identified as a likely scenario by Sarasin; second, the DHS National Planning Scenario, but limited to one city
expose rather than five, suggesting 1320deaths.; third, 4000 deaths suggested by Inglesby. Sarasin, P. (2006).
Anthrax : bioterror as fact and fantasy. Cambridge, Mass., Harvard University Press.; Inglesby, T. (1999). "Anthrax:
A possible case history." Emerging Infectious Diseases 5(4): 556. This number has been rounded to the nearest
thousand below for purposes of bounding. The high estimate of deaths is taken as 15,000, representing the number
of deaths in the full National Planning Scenario of five cities, as well as a HHS scenario and Kellman’s estimate of
tens of thousands of deaths (drawn from Bioviolence Kellman, B. (2007). Bioviolence : preventing biological terror
and crime. New York, Cambridge University Press., p. 38). There are higher estimates of deaths, in the hundreds of
thousands to millions, but these are based on military scenarios, and are not included in this hazard.
xlvii
Inglesby, T., D. Henderson, J. Bartlett, M. Ascher, E. Eitzen, A. Friedlander, J. Hauer, J. McDade, M. Osterholm
and T. O'Toole (1999). "Anthrax as a biological weapon: medical and public health management." Jama 281(18):
1735.
xlviii
See endnotes i, ii, iii, vi, vii, viii, ix, x, and xi.
xlix
Cost estimates based on DHS scenario for number of people exposed * the CDC estimate on the cost per person
exposed.
l
Kellman, B. (2007). Bioviolence : preventing biological terror and crime. New York, Cambridge University Press.
li
Best estimate of economic damages for an event of $1.06B based on RAND indoor scenario, as per endnote xiv.
Best likelihood of 2.2% as per endnote ii. Low estimate of likelihood of 0.5% as per endnote i. High estimate of
likelihood of 26% as per iii.
lii
Cordesman, A. H. (2005). The challenge of biological terrorism. Significant issues series v. 27, no. 10. C. f. S. a. I.
S. W. D.C.). Washington, D.C., CSIS Press: xiii, 208 p.
liii
National Security Council (2009). National Strategy for Countering Biological Threats. Washington, D.C.,
Defense Technical Information Center.
liv
Graham, B., J. M. Talent and G. T. Allison (2008). World at risk: the report of the Commission on the Prevention
of WMD Proliferation and Terrorism, Vintage.
lv
Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism : containing and preventing biological threats.
Amsterdam ; Boston, Butterworth-Heinemann.
lvi
Parnell, G. S., L. L. Borio, G. G. Brown, D. Banks and A. G. Wilson (2008). "Scientists urge DHS to improve
Bioterrorism Risk Assessment." Biosecur Bioterror 6(4): 353-356.
lvii
Counterproliferation Program Review Committee (2009). Report on Activities and Programs for Countering
Proliferation and NBC Terrorism. Washington, D.C.
lviii
National Security Council (2009). National Strategy for Countering Biological Threats. Washington, D.C.,
Defense Technical Information Center.
lix
Ibid.
lx
Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism : containing and preventing biological threats.
Amsterdam ; Boston, Butterworth-Heinemann.
lxi
CDC. "CDC Emergency Operations Center (EOC)." Retrieved Jan. 10, 2012, from
http://www.bt.cdc.gov/cdcpreparedness/eoc/, Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism :
containing and preventing biological threats. Amsterdam ; Boston, Butterworth-Heinemann.
lxii
Ryan, J. R. and J. Glarum (2008). Biosecurity and bioterrorism : containing and preventing biological threats.
Amsterdam ; Boston, Butterworth-Heinemann.
lxiii
Wright, J. G., C. P. Quinn, S. Shadomy and N. Messonnier. (2009). "Use of Anthrax Vaccine in the United
States." Morbidity and Mortality Weekly Report Retrieved Jan. 10, 2012, from
http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5906a1.htm
357 lxiv
CDC. (2001). "Interim Recommendations for the Selection and Use of Protective Clothing and Respirtators
Against Biological Agents." Retrieved Jan. 10, 2012, from
http://www.bt.cdc.gov/documentsapp/Anthrax/Protective/10242001Protect.asp.
lxv
CDC (2011). Public Health Emergency Response Guide for State, Local, and Tribal Public Health DirectorsVersion 2.0. U.S. Department of Health and Human Services Centers for Disease Control and Prevention.
Washington, DC.
lxvi
CDC. "Strategic National Stockpile (SNS)." Retrieved Jan. 10, 2012, from
http://www.cdc.gov/phpr/stockpile/stockpile.htm.
lxvii
Ibid.
i
Based on low likelihood of attack (0.01% per year, based on a Sandia Labs report)* average damage from a 1015kT explosion. Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, Sandia
National Laboratories. Economic damages come from the average of several 10-15kT scenarios. HSC/DHS (2005).
National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland
Security. Washington, DC, Bunn, M. (2006). "A mathematical model of the risk of nuclear terrorism." The
ANNALS of the American Academy of Political and Social Science 607(1): 103, Meade, C. and R. Molander
(2006). Considering the effects of a catastrophic terrorist attack. Washington, DC, RAND, Jenkins, B. M. (2008).
Will terrorists go nuclear? Amherst, NY, Prometheus Books, Schanzer, D. H., J. Eyerman and V. De Rugy (2009).
Strategic Risk Management in Government: A Look at Homeland Security, IBM Center for the Business of
Government, Risk Management Solutions (2010). Modeled Losses. H. Willis. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
ii
Based on a best estimate of likelihood of a nuclear attack as 0.1% in a given year * the average damage from a 1015kT explosion. Determining the best likelihood of a nuclear attack is difficult. Our best estimate of likelihood of
0.1% in a given year is drawn from European expert opinions of risk being 1% within the next 10 years from
Jenkins (2008). Jenkins, B. M. (2008). Will terrorists go nuclear? Amherst, NY, Prometheus Books. Economic
damages come from the average of several 10-15kT scenarios. HSC/DHS (2005). National Planning Scenarios.
Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC, Bunn,
M. (2006). "A mathematical model of the risk of nuclear terrorism." The ANNALS of the American Academy of
Political and Social Science 607(1): 103, Meade, C. and R. Molander (2006). Considering the effects of a
catastrophic terrorist attack. Washington, DC, RAND, Jenkins, B. M. (2008). Will terrorists go nuclear? Amherst,
NY, Prometheus Books, Schanzer, D. H., J. Eyerman and V. De Rugy (2009). Strategic Risk Management in
Government: A Look at Homeland Security, IBM Center for the Business of Government, Risk Management
Solutions (2010). Modeled Losses. H. Willis. All numerical estimates have been rounded to one significant figure to
reduce overstating the precision of these measures.
iii
High estimate of likelihood 26% (calculated from 95% likelihood over 10 years, the high response in Lugar and in
Jenkins) * average damage from a 10-15 kt explosion. Lugar, R. G. (2005). The Lugar Survey on Proliferation
Threats and Responses. Washington, DC, US Senate. Other high estimates include the median value of American
expert opinion is 29% over ten years, the same as the probability developed by Bunn (2006). Bunn, M. (2006). "A
mathematical model of the risk of nuclear terrorism." The ANNALS of the American Academy of Political and
Social Science 607(1): 103. Economic damages come from the average of several 10-15kT scenarios. HSC/DHS
(2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of
Homeland Security. Washington, DC, Bunn, M. (2006). "A mathematical model of the risk of nuclear terrorism."
The ANNALS of the American Academy of Political and Social Science 607(1): 103, Meade, C. and R. Molander
(2006). Considering the effects of a catastrophic terrorist attack. Washington, DC, RAND, Jenkins, B. M. (2008).
Will terrorists go nuclear? Amherst, NY, Prometheus Books, Schanzer, D. H., J. Eyerman and V. De Rugy (2009).
Strategic Risk Management in Government: A Look at Homeland Security, IBM Center for the Business of
Government, Risk Management Solutions (2010). Modeled Losses. H. Willis. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
iv
Estimate of damage from a 10kt detonation based on the deaths from Hiroshima CISAC Understanding the Risks
and Realities of Nuclear Terrorism. Center for International Security and Cooperation, Stanford University,
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC. Lower estimates due to a fizzle are not being considered. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
v
Estimate of 150kT warhead Ferguson, C. and W. Potter (2004). Improvised nuclear devices and nuclear terrorism,
Weapons of Mass Destruction Commission. Larger military style warheads in the megaton range exist but are not
358 likely to be used by terrorists and are not considered a plausible upper bound. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
vi
Based on low likelihood of attack (0.01% per year, based on a Sandia Labs report)* average of estimates of
injuries in several 10-15kT scenarios * percentage of hospitalized injuries from Toon et al. Toon, O. B., R. P. Turco,
A. Robock, C. Bardeen, L. Oman and G. L. Stenchikov (2007). "Atmospheric effects and societal consequences of
regional scale nuclear conflicts and acts of individual nuclear terrorism." Atmospheric Chemistry and Physics 7(8):
1973-2002. and RMS. Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, Sandia
National Laboratories. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
vii
Middle likelihood of 0.1% as per endnote 2 * average of estimates of injuries in several 10-15kT scenarios*
percentage of hospitalized injuries from Toon et al. Toon, O. B., R. P. Turco, A. Robock, C. Bardeen, L. Oman and
G. L. Stenchikov (2007). "Atmospheric effects and societal consequences of regional scale nuclear conflicts and acts
of individual nuclear terrorism." Atmospheric Chemistry and Physics 7(8): 1973-2002. and RMS. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
viii
High likelihood of 26% as per endnote 3 * average of estimates of injuries in several 10-15kT scenarios*
percentage of hospitalized injuries from ibid. and RMS. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
ix
Based on low likelihood of attack (0.01% per year, based on a Sandia Labs report)* average of estimates of
injuries in several 10-15kT scenarios* percentage of non-hospitalized injuries from Toon et al. Ibid. and RMS.
Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories. Livermore, CA, Sandia National Laboratories.
All numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
x
Middle estimate of likelihood as per endnote 2* average of estimates of injuries in several 10-15kT scenarios*
percentage of non-hospitalized injuries from Toon et al. Toon, O. B., R. P. Turco, A. Robock, C. Bardeen, L. Oman
and G. L. Stenchikov (2007). "Atmospheric effects and societal consequences of regional scale nuclear conflicts and
acts of individual nuclear terrorism." Atmospheric Chemistry and Physics 7(8): 1973-2002. and RMS. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xi
High estimate of likelihood as per endnote 3* average of estimates of injuries in several 10-15kT scenarios*
percentage of non-hospitalized injuries from Toon et al. Ibid. and RMS. All numerical estimates have been rounded
to one significant figure to reduce overstating the precision of these measures.
xii
High psychological damage per year on average based on the higher of 1) high PTSD and 2) high depression per
year on average. High PTSD based on crossing high expected deaths per year on average by moderate severe
injuries per year on average. High depression based on crossing moderate combined damages per year on average
and high duration of economic damages. Moderate combined damages (defined as a combination of lives lost,
severe damages, and economic damages per year on average of one low and two moderate, all moderate, two
moderate and one high, two low and one high, or two high and one low). Lives lost are considered low when fewer
than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are considered low if fewer
than 50, moderate 50-500, and high if over 500 per year on average. Economic damages are considered low if less
than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is considered low if
measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to years, weeks to years,
months to years, or months; high if measured in years, decades, months to decades, or years to decades.
xiii
Based on low likelihood of attack (0.01% per year, based on a Sandia Labs report)* $3.5B based on the average
of estimates of injuries in several 10-15kT scenarios. Sandia (1999). Osama Bin Laden: A Case Study. S. N.
Laboratories. Livermore, CA, Sandia National Laboratories. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xiv
Middle estimate of likelihood as per endnote 2 * $3.5B based on the average of estimates of injuries in several
10-15kT scenarios. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
xv
High likelihood of likelihood as per endnote 3 * $3.5B based on the average of estimates of injuries in several 1015kT scenarios. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
xvi
Based on several reports, including Bunn (2006), Meade and Molander (2006), and others. HHS. "Nuclear
Explosions: Weapons, Improvised Nuclear Devices." Radiation Emergency Medical Management Retrieved Dec.
13, 2011, from http://www.remm.nlm.gov/nuclearexplosion.htm, Bunn, M. (2006). "A mathematical model of the
risk of nuclear terrorism." The ANNALS of the American Academy of Political and Social Science 607(1): 103,
359 Meade, C. and R. Molander (2006). Considering the effects of a catastrophic terrorist attack. Washington, DC,
RAND. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures.
xvii
$10T, based on high estimates from Jenkins. Jenkins, B. (2009). "5.2 The Impact of Cataclysmic Events."
Retrieved April 14, 2011, from http://www.jhuapl.edu/urw_symposium/Proceedings/2009/Authors/Jenkins.pdf
xviii
While fallout could create damage to species over an area of miles, it is also unlikely, making expected
environmental damage moderate.
xix
Based on low likelihood of attack (0.01% per year, based on a Sandia Labs report)* average of estimates of
displaced in several 10-15kT scenarios. Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories.
Livermore, CA, Sandia National Laboratories. All numerical estimates have been rounded to one significant figure
to reduce overstating the precision of these measures.
xx
High estimate of likelihood as per endnote 3 * average of estimates of displaced in several 10-15kT scenarios. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xxi
Exposure can only be limited by large lifestyle choices, such as moving out of the most likely targeted areas of
New York or DC, or limited further by moving out of urban areas entirely.
xxii
Most harm comes immediately. Some acute harm from initial radiation manifests weeks later. Relatively small
amounts but still significant chronic harm and cancers from radiation can develop years or decades later.
xxiii
The physics behind a nuclear explosion is well understood and modeled. The impact of an actual event is less
well known, with few specific examples and more models.
xxiv
High combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of the
high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages) which
equals 10,350. Values below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are
considered high. This reflects some certainty in the consequences, although the likelihood of an event is highly
uncertain.
xxv
Holdstock, D. and L. Waterston (2000). "Nuclear weapons, a continuing threat to health." The Lancet 355(9214):
1544-1547.
xxvi
Rodionov, S. (2002). Could Terrorists Produce Low-Yield Nuclear Weapons? High Impact Terrorism:
Proceesings of a Russian-American Workshop. S. S. Hecker. Washington, DC, National Academies Press.
xxvii
NCRP Report 138, cited in “Nuclear Explosions” HHS. "Nuclear Explosions: Weapons, Improvised Nuclear
Devices." Radiation Emergency Medical Management Retrieved Dec. 13, 2011, from
http://www.remm.nlm.gov/nuclearexplosion.htm.
xxviii
NCRP Report 138, cited in “Nuclear Explosions” ibid.
xxix
Glasstone and Dolan (1977), cited in HSC (2009). Homeland Security Council Interagency Policy Coordination
Subcommittee for Preparedness Response to Radiological Nuclear Threats (2009). Planning Guidance for Response
to a Nuclear Detonation. McLean, VA.
xxx
Ohkita, T. "Health effects on individuals and health services of the Hiroshima and Nagasaki bombs." Effects of
Nuclear War on Health and Health Services: Report of the International Committee of Experts in Medical Sciences
and Public Health to Implement Resolution WHA3438. Annex4.
xxxi
NCRP Report 138, cited in “Nuclear Explosions” HHS. "Nuclear Explosions: Weapons, Improvised Nuclear
Devices." Radiation Emergency Medical Management Retrieved Dec. 13, 2011, from
http://www.remm.nlm.gov/nuclearexplosion.htm, Glasstone, S. and P. J. Dolan (1977). "The Effects of Nuclear
War." Washington: Department of Defense,.; also Armed Forces Radiobiology Research Institute's Medical Effects
of Ionizing Radiation Course on CD-ROM (1999), cited in “Nuclear Explosions” HHS. "Nuclear Explosions:
Weapons, Improvised Nuclear Devices." Radiation Emergency Medical Management Retrieved Dec. 13, 2011,
from http://www.remm.nlm.gov/nuclearexplosion.htm.
xxxii
Mettler Jr, F. A. and G. L. Voelz (2002). "Major radiation exposure--what to expect and how to respond." The
New England Journal of Medicine (CME) 346(20): 1554, Barnett, D. J., C. L. Parker, D. W. Blodgett, R. K.
Wierzba and J. M. Links (2006). "Understanding radiologic and nuclear terrorism as public health threats:
preparedness and response perspectives." Journal of Nuclear Medicine 47(10): 1653, Bushberg, J. T., L. A. Kroger,
M. B. Hartman and E. M. Leidholdt (2007). "Nuclear/radiological terrorism: Emergency department management of
radiation casualties." Journal of Emergency Medicine 32(1): 71-85.
xxxiii
NCRP report 138, as cited in Meade and Molander (2006) and Barnett et al. (2006) Barnett, D. J., C. L. Parker,
D. W. Blodgett, R. K. Wierzba and J. M. Links (2006). "Understanding radiologic and nuclear terrorism as public
360 health threats: preparedness and response perspectives." Journal of Nuclear Medicine 47(10): 1653, Meade, C. and
R. Molander (2006). Considering the effects of a catastrophic terrorist attack. Washington, DC, RAND.
xxxv
Preston, D. L., S. Kusumi, M. Tomonaga, S. Izumi, E. Ron, A. Kuramoto, N. Kamada, H. Dohy, T. Matsui and
H. Nonaka (1994). "Cancer incidence in atomic bomb survivors. Part III: Leukemia, lymphoma and multiple
myeloma, 1950-1987." Radiation Research 137(2): 68-97, Preston, D. L., Y. Shimizu, D. A. Pierce, A. Suyama and
K. Mabuchi (2003). "Studies of mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease
mortality: 1950-1997." Radiation Research 160(4): 381-407, Carter, A. B., M. M. May and W. J. Perry (2007). "The
day after: action following a nuclear blast in a US city." The Washington Quarterly 30(4): 19-32.
xxxvi
Preston, D. L., Y. Shimizu, D. A. Pierce, A. Suyama and K. Mabuchi (2003). "Studies of mortality of atomic
bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950-1997." Radiation Research 160(4):
381-407.
xxxvii
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC.
xxxviii
Meade, C. and R. Molander (2006). Considering the effects of a catastrophic terrorist attack. Washington, DC,
RAND.
xxxix
Ibid.
xl
Masse, T. "Nuclear Terrorism Redux: Conventionalists, Skeptics, and the Margin of Safety." Orbis.
xli
Ibid.
xlii
Jenkins, B. M. (1975). Will terrorists go nuclear?, California Seminar on Arms Control and Foreign Policy,
Lugar, R. G. (2005). The Lugar Survey on Proliferation Threats and Responses. Washington, DC, US Senate.
xliii
Jenkins, B. (2009). "5.2 The Impact of Cataclysmic Events." Retrieved April 14, 2011, from
http://www.jhuapl.edu/urw_symposium/Proceedings/2009/Authors/Jenkins.pdf ; similar to event tree modeling from
Sandia National Laboratories Sandia (1999). Osama Bin Laden: A Case Study. S. N. Laboratories. Livermore, CA,
Sandia National Laboratories. and other expert opinions (see Jenkins, B. M. (2008). Will terrorists go nuclear?
Amherst, NY, Prometheus Books.)
xliv
See endnotes iv and v. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
xlv
See endnote ii. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
xlvi
See endnotes i and iii. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
xlvii
See endnotes vii and x. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xlviii
See endnotes vi, viii, ix and xi. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xlix
See endnotes xvi. Estimates of $1T come from an IBM estimate of damage for a 10kT explosion in NY of $1T
Schanzer, D. H., J. Eyerman and V. De Rugy (2009). Strategic Risk Management in Government: A Look at
Homeland Security, IBM Center for the Business of Government.. Estimates of $10T come from inflating the size
of the blast for a device larger than 10kT calculated using estimates from HHS Radiation Emergency Medical
Management citation of NATO. Other estimates in this range include $5T from Jenkins estimate of damage of $5T
Jenkins, B. (2009). "5.2 The Impact of Cataclysmic Events." Retrieved April 14, 2011, from
http://www.jhuapl.edu/urw_symposium/Proceedings/2009/Authors/Jenkins.pdf All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
l
See endnote xvii. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
li
See endnote xiv. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
lii
See endnote xiii. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
361 liii
See endnote xv. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
liv
Bunn, M. (2010). Securing the Bomb 2010: Securing All Nuclear Materials in Four Years, Project on Managing
the Atom, Harvard University.
lv
Wilson, S. and M. B. Sheridan (2010). Obama leads summit effort to secure nuclear materials. Washington Post.
Washington, DC.
lvi
Medalia, J. and S. Library Of Congress Washington Dc Congressional Research (2005). "Nuclear Terrorism: A
Brief Review of Threats and Responses. CRS Report for Congress."
lvii
Wilson, S. and M. B. Sheridan (2010). Obama leads summit effort to secure nuclear materials. Washington Post.
Washington, DC.
lviii
ibid.
lix
Executive Office of the President (2002). National Strategy to Combat Weapons of Mass Destruction. Executive
Office of the President. Washington, DC, Wilson, S. and M. B. Sheridan (2010). Obama leads summit effort to
secure nuclear materials. Washington Post. Washington, DC.
lx
CBP. (2005). "Inspections and Surveillance Technologies- Extended." Retrieved Jan. 11, 2012, from
http://www.cbp.gov/xp/cgov/newsroom/fact_sheets/port_security/fact_sheet_cbp_securing.xml, Gustafson, T.
(2007). "Radiological and Nuclear Detection Devices." Retrieved Jan. 11, 2012, from
http://www.nti.org/analysis/articles/radiological-nuclear-detection-devices/, GAO (2009). Nuclear Detection:
Domestic Nuclear Detection Office Should Improve Planning to Better Address Gaps and Vulnerabilities. G. A.
Office. Washington, DC.
lxi
CBP. (2005). "Inspections and Surveillance Technologies- Extended." Retrieved Jan. 11, 2012, from
http://www.cbp.gov/xp/cgov/newsroom/fact_sheets/port_security/fact_sheet_cbp_securing.xml.
lxii
Faherty, C. (2007). Police Test Technology to Safeguard City from Nuclear Attacks. The Sun. New York.
lxiii
American Physical Society/American Association for the Advancement of Science (2008). Nuclear Forensics:
Role, State of the Art, and Program Needs, Joint Working Group of the American Physical Society and the
American Association for the Advancement of Science.
lxiv
Remick, A. L., J. L. Crapo and C. R. Woodruff (2005). "US national response assets for radiological incidents."
Health Physics 89(5): 471, (2010). Planning Guidance for Response to a Nuclear Detonation. N. S. S. I. P. C. S. f. P.
a. R. t. R. a. N. Threats. Washington, DC.
lxv
Oak Ridge Associated Universities. "Radiological and Nuclear Terrorism: Medical Response to Mass Casualties."
Retrieved Jan. 11, 2012, from http://www.orau.gov/hsc/RadMassCasualties/content/text_version.htm, GAO (2010).
Combatting Nuclear Terrorism: Actions Needed to Better Prepare to Recover from Possible Attacks Using
Radiological or Nuclear Materials. G. A. Office.
i
Based on historical data from the RAND Database of Worldwide Terrorism Incidents. Low deaths per year on
average based on the lowest 10-year average of deaths from explosives in the United States from 1980-2008. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
ii
Based on historical data from the RAND Database of Worldwide Terrorism Incidents. Best deaths per year on
average based on the average 10-year average of deaths from explosives in the United States from 1980-2008. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
iii
Based on historical data from the RAND Database of Worldwide Terrorism Incidents. High deaths per year on
average based on the average 10-year average of deaths from explosives in the Israel and the West Bank from 19802008. Israel and the West Bank were used as an analogous case for the high end representing a permanent
campaign of bombings. All numerical estimates have been rounded to one significant figure to reduce overstating
the precision of these measures.
iv
Low estimate based on bombing in Oklahoma City, Oklahoma, in 1995, which is the largest bombing event in the
United States. High estimate based on the destruction of the building with the larger casualties in attacks of Sept.
11, 2001; although the events of Sept. 11th, 2001 were not a bombing, as the destruction of a large building may be
considered an upper limit of the bombing scenario. We use the estimated number killed in the tower with the
greater fatalities (tower 1, with 1466 fatalities), plus one half of the combination of first responders (421/2=210.5),
bystanders/nearby building occupants (18/2=9), and those killed with no information (17/2=8.5) as per NIST report
on WTC victims location. This sums to 1,694, which we round to 1,700. NIST. (2004). "Federal Investigators
Classify WTC Victims' Locations within Collapsed Buildings." Retrieved Feb. 8, 2012. Between these two
numbers include the train bombings in Madrid, Spain, in 2004, the aircraft bombing in Lockerbie, Scotland, U.K., in
362 1988, the coordinated bombings of U.S. embassies in Africa in 1998, and the bombing of tourist locations in Bali,
Indonesia, in 2002. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
v
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. Low
injuries per year on average based on the lowest 10-year average of injuries from explosives in the United States
from 1980-2008. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from
Arnold et al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from
the Oklahoma City bombing of 1995 and other events. Arnold, J., M. Tsai, P. Halpern, H. Smithline, E. Stok and G.
Ersoy (2003). "Mass-casualty, terrorist bombings: epidemiological outcomes, resource utilization, and time course
of emergency needs (Part I)." Prehospital and Disaster Medicine 18(3): 220-234. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
vi
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. Best
injuries per year on average based on the average 10-year average of injuries from explosives in the United States
from 1980-2008. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from
Arnold et al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from
the Oklahoma City bombing of 1995 and other events. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
vii
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. High
injuries per year on average is based on the average 10-year average of injuries from explosives in Israel and the
West Bank from 1980-2008. Israel and the West Bank were used to represent a county under a persistent campaign
of bombing. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from
Arnold et al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from
the Oklahoma City bombing of 1995 and other events. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
viii
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. Low
injuries per year on average based on the lowest 10-year average of injuries from explosives in the United States
from 1980-2008. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from
Arnold et al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from
the Oklahoma City bombing of 1995 and other events. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
ix
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. Best
injuries per year on average based on the average 10-year average of injuries from explosives in the United States
from 1980-2008. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from
Arnold et al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from
the Oklahoma City bombing of 1995 and other events. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
x
Injuries were based on historical number of injuries from the RAND Database of Worldwide Terrorism Incidents
times a factor allocation a portion of those injuries to more severe injuries and a portion to less severe injuries. Low
injuries per year on average based on the average 10-year average of injuries from explosives in Israel and the West
Bank from 1980-2008. Israel and the West Bank were used to represent a county under a persistent campaign of
bombing. Estimate of percentage of bombing injuries that result in hospitalization is 33% and comes from Arnold et
al.’s analysis of mass-casualty bombings from 1996-2002, and is supported by the hospitalizations from the
Oklahoma City bombing of 1995 and other events. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xi
Low psychological damage per year on average based on the higher of 1) low PTSD and 2) low depression per
year on average. Low PTSD based on crossing low lives lost by low severe injuries. Low depression based on
crossing low combined damages per year on average and low duration of economic damages. Low combined
damages (defined as a combination of lives lost, severe damages, and economic damages per year on average all low
or two low and one moderate). Lives lost are considered low when fewer than 10, moderate 10-100, and high if
over 100 per year on average. Severe injuries are considered low if fewer than 50, moderate 50-500, and high if
363 over 500 per year on average. Economic damages are considered low if less than $500M, moderate $500M to $5B,
and high if over $5B per year on average. Duration is considered low if measured in days, days to weeks, weeks, or
weeks to months; moderate if measured in days to years, weeks to years, months to years, or months; high if
measured in years, decades, months to decades, or years to decades.
xii
Based on the historical average by the FBI from years 1989-1998, but adjusted by a factor to scale to the low
estimate of deaths. For our low estimate of damage, we multiply the best estimate of damage by 1/9 (the low
estimate of deaths per year on average, 1, divided by the best estimate of deaths per year on average, 9) to get $10M.
All numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xiii
Based on the historical average by the FBI from years 1989-1998, adjusted for inflation. Gadson, L. O., M. L.
Michael and N. Walsh. (2002). "FBI Bomb Data Center: 1998 Bombing Incidents." Retrieved Feb. 8, 2012, from
http://library.sau.edu/bestinfo/Majors/criminal/Bomb.pdf. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xiv
Based on best estimate costs (as per endnote xv), but adjusted by a factor to scale to the high estimate of deaths.
For our high estimate of damage, we multiply the best estimate of damage by 36/9 (the high estimate of deaths per
year on average, 36, divided by the best number of deaths per year on average, 9) to get $370M. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xv
Low estimate based on the bombing in Oklahoma City, OK, in 1995. High estimate based on the attacks of Sept.
11, 2001, (which are considered by many to be similar to a bombing), divided by three. Conceptually, the events of
Sept. 11, 2001 are similar to three bombings, one for each of the three impacts and treating the economic damages of
the Shakesville, Pa. crash as negligible compared to the other impacts. All numerical estimates have been rounded
to one significant figure to reduce overstating the precision of these measures.
xvi
From the National Planning Scenarios. HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC.
xvii
Data from the Oklahoma City bombing found approximate three people made homeless for each person killed in
the Oklahoma City bombing of 1995. This ratio is applied to the average number of people expected to be killed
each year to estimate the number of people made homeless each year on average. Records of the number homeless
and the number killed in the Oklahoma City bombing from the Injury Prevention Service. Injury Prevention Service
(1996). Injury Update: Investigation of Physical Injuries Directly Associated with the Oklahoma City Bombing. A
Report to Oklahoma Injury Surveillance Participants. Oklahoma State Department of Health. Oklahoma City, Ok.
Cited in Sitterle, K. A. and R. H. Gurwitch (1999). "The terrorist bombing in Oklahoma City." When a community
weeps: Case studies in group survivorship: 160-189. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xviii
Data from the Oklahoma City bombing found approximate three people made homeless for each person killed in
the Oklahoma City bombing of 1995. This ratio is applied to the average number of people expected to be killed
each year to estimate the number of people made homeless each year on average. Records of the number homeless
and the number killed in the Oklahoma City bombing from the Injury Prevention Service. Injury Prevention Service
(1996). Injury Update: Investigation of Physical Injuries Directly Associated with the Oklahoma City Bombing. A
Report to Oklahoma Injury Surveillance Participants. Oklahoma State Department of Health. Oklahoma City, Ok.
Cited in Sitterle, K. A. and R. H. Gurwitch (1999). "The terrorist bombing in Oklahoma City." When a community
weeps: Case studies in group survivorship: 160-189. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xix
Most attacks have little governmental disruption, but given the targeted nature of the attacks, it is plausible.
xx
Exposure can be limited by moving out of the most likely targeted areas of New York or DC, or limited further by
moving out of urban areas entirely. However, these are large lifestyle choices.
xxi
Some health effects can occur later (e.g. lung injuries due to inhalation at 9/11) but these are atypical of injuries
sustained.
xxii
The scientific understanding of a bomb blast is well understood and well modeled.
xxiii
Moderate combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of
the high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages)
which sums to 278. Combined uncertainty is a qualitative estimate based on the addition of the ratio of the high
estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages. The ratio of
high to low gives a sense of the range of the consequence in terms of orders of magnitude, representing how
uncertain the estimates are for each of these variables. These ratios are then added to combine these measures of
uncertainty into a single dimensionless estimate. The additive combination for explosive bombings is 278, which
364 considered moderate. Values below 100 are considered low, 100 to 1000 are considered moderate, and above 1000
are considered high. These uncertainties reflect an uncertainty in the likelihood of the event, the consequences of an
event, and an intelligent adversary. However, they also are mitigated by being a known threat where there is
experience and modeling of scenarios.
xxiv
DePalma, R. G., D. G. Burris, H. R. Champion and M. J. Hodgson (2005). "Blast injuries." The New England
Journal of Medicine 352(13): 1335.
xxv
Ibid.
xxvi
Leibovici, D., O. N. Gofrit, M. Stein, S. C. Shapira, Y. Noga, R. J. Heruti and J. Shemer (1996). "Blast injuries:
bus versus open-air bombings--a comparative study of injuries in survivors of open-air versus confined-space
explosions." The Journal of Trauma 41(6): 1030.
xxvii
Cukier, W. and A. Chapdelaine (2003). "Small arms, explosives, and incendiaries." Terrorism and public health:
a balanced approach to strengthening systems and protecting people: 155.
xxviii
DePalma, R. G., D. G. Burris, H. R. Champion and M. J. Hodgson (2005). "Blast injuries." The New England
Journal of Medicine 352(13): 1335.
xxix
Cukier, W. and A. Chapdelaine (2003). "Small arms, explosives, and incendiaries." Terrorism and public health:
a balanced approach to strengthening systems and protecting people: 155.
xxx
DePalma, R. G., D. G. Burris, H. R. Champion and M. J. Hodgson (2005). "Blast injuries." The New England
Journal of Medicine 352(13): 1335.
xxxi
Ibid.
xxxii
Ibid.
xxxiii
Zukas, J. A., W. Walters and W. P. Walters (2002). Explosive effects and applications, Springer Verlag.
xxxiv
Hoffman, B. (2006). Inside terrorism, Columbia University Press., Ch. 7
xxxv
Drake, C. J. M. (1998). Terrorists' target selection, Palgrave Macmillan.
xxxvi
Hoffman, B. (2003). "Al Qaeda, trends in terrorism, and future potentialities: An assessment." Studies in
Conflict & Terrorism 26(6): 429-442.
xxxvii
Certain urban areas are considered higher risk than others in the risk models of risk management companies and
the federal government. Willis, H. H. (2007). Terrorism risk modeling for intelligence analysis and infrastructure
protection, RAND Corporation, FEMA. (2011). "FY 2011 Homeland Security Grant Program." Retrieved Feb. 8,
2012, from http://www.fema.gov/government/grant/hsgp/.
xxxviii
Drake, C. J. M. (1998). Terrorists' target selection, Palgrave Macmillan.
xxxix
Hoffman, B. (2003). "Al Qaeda, trends in terrorism, and future potentialities: An assessment." Studies in
Conflict & Terrorism 26(6): 429-442.
xl
Drake, C. J. M. (1998). Terrorists' target selection, Palgrave Macmillan.
xli
See RAND Database of Worldwide Terrorism Incidents, START Global Terrorism Database.
xlii
See endnote iv
xliii
The estimated annual expected damages do not include the 2001 WTC attacks as a bombing. First, it does not
use explosives per se, although one could make the case that they utilized the jet fuel as an explosive. Additionally,
because it did not use explosives, the policy implications of a 2001 WTC attack are very different than the policy
implications of a bombing. Second, there are good reasons to believe that a 2001 WTC style attack are no longer
possible, that the security hole has been patched and it is no longer within the possible range of attacks. However, a
bombing could potentially lead to building collapse, even of a large building, so it is included in the worst case
estimates for a single event. Historical data on U.S. bombing deaths from terrorists comes from the RAND
Database of Worldwide Terrorism Incidents. See endnote ii.
xliv
RAND Database of Worldwide Terrorism Incidents. See endnote i for details.
xlv
RAND Database of Worldwide Terrorism Incidents. See endnote iii for details.
xlvi
See endnotes v, vi, vii, viii, ix, and x.
xlvii
Low estimate based on the bombing in Oklahoma City, OK, in 1995, adjusted for inflation. High estimate based
on the attacks of Sept. 11, 2001, which are considered by many to be similar to a bombing.
xlviii
See endnote xv
xlix
Gadson, L. O., M. L. Michael and N. Walsh. (2002). "FBI Bomb Data Center: 1998 Bombing Incidents."
Retrieved Feb. 8, 2012, from http://library.sau.edu/bestinfo/Majors/criminal/Bomb.pdf.
l
To get the low and high estimate, we adjust the best estimate by a factor to match the low and high estimate of
deaths. For our low estimate of damage, we multiply the best estimate of damage by 1/9 (the low estimate of deaths
per year on average, 1, divided by the best estimate of deaths per year on average, 9) to get $10M. For our high
estimate of damage, we multiply the best estimate of damage by 36/9 (the high estimate of deaths per year on
365 average, 36, divided by the best number of deaths per year on average, 9) to get $370M. See endnotes xii, xiii, and
xiv.
li
ATF. "Ammonium Nitrate Security." Retrieved Jan. 11, 2012, from
http://www.atf.gov/explosives/programs/ammonium-nitrate-security/.
lii
(2010). Proposed Spending on Counterterrorism Soars. The Washington Times. Washington, DC.
liii
Hoffman, B. (2006). Inside terrorism, Columbia University Press., Ch. 7
liv
(2011). "Ten Years Later, Terrorism Exposure Remains an Issue for U.S. Insurance Companies." Retrieved Feb.
2, 2012, from
http://www.standardandpoors.com/ratings/articles/en/us/?articleType=HTML&assetID=1245325865741, Coburn,
A., M. Paul, V. Vyas, G. Woo and W. Yeo (2012). Terrorism Risk in the Post-9/11 Era: A 10-Year Retrospective.
Newark, CA, Risk Management Solutions, Inc.: 30.
lv
DOD (2003). Unified Facilities Criteria (UFC) 4-010-01 DoD Minimum Antiterrorism Standards for Buildings,
Includes Change 1. Department of Defense. Washington, DC.
lvi
Kress, J. and S. Grogger (2008). The Domestic IED Threat. Joint Forces Quarterly. Winter 2008.
lvii
DOD (2003). Unified Facilities Criteria (UFC) 4-010-01 DoD Minimum Antiterrorism Standards for Buildings,
Includes Change 1. Department of Defense. Washington, DC, FEMA (2003). FEMA 426- Reference Manual to
Mitigate Potential Terrorist Attacks Against Buildings. U.S. Department of Homeland Security Federal Emergency
Management Agency. Washington, DC. http://www.fema.gov/plan/prevent/rms/rmsp426
lviii
DHS Office of Inspector General (2010). Efficacy of DHS Grant Program. U.S. Department of Homeland
Security Office of Inspector General. Washington, DC.
i
Based on no deaths in a typical cyber event HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
ii
Based on no deaths in a typical cyber event ibid. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
iii
Based on the number of deaths in the DC subway disruption, with a 10% chance of an event. Number of deaths
from the subway disruption from Dharapak Dharapak, C. (2009). Computer failure may have caused D.C. subway
crash. USA Today.. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
iv
Based on no deaths in a typical cyber event HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
v
Based on the number of deaths in the 2003 Northeast Blackout ibid. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
vi
Based on no severe injuries in a typical cyber event ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
vii
Based on no severe injuries in a typical cyber event ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
viii
Based on several assumptions. The number of injuries in the DC subway disruption is used for the number of
injuries in a cyber-event with high morbidity. This is multiplied by 0.1, representing a 1 in 10 chance of an event in
a given year. We then assign half of those injuries to severe and half to less severe, representing a uniform prior
with no information. Number of injuries from the subway disruption from Dharapak Dharapak, C. (2009).
Computer failure may have caused D.C. subway crash. USA Today.. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
ix
Based on no less severe injuries in a typical cyber event HSC/DHS (2005). National Planning Scenarios.
Homeland Security Council in partnership with the U.S. Department of Homeland Security. Washington, DC. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
x
Based on no less severe injuries in a typical cyber event ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xi
Based on several assumptions. The number of injuries in the DC subway disruption is used for the number of
injuries in a cyber-event with high morbidity. This is multiplied by 0.1, representing a 1 in 10 chance of an event in
a given year. We then assign half of those injuries to severe and half to less severe, representing a uniform prior
with no information. Number of injuries from the subway disruption from Dharapak Dharapak, C. (2009).
366 Computer failure may have caused D.C. subway crash. USA Today.. All numerical estimates have been rounded to
one significant figure to reduce overstating the precision of these measures.
xii
Low psychological damage per year on average based on the higher of 1) low PTSD and 2) low depression per
year on average. Low PTSD based on crossing low lives lost by low severe injuries. Low depression based on
crossing low combined damage per year on average and low duration of economic damage. Low combined damage
(defined as a combination of lives lost, severe damage, and economic damage per year on average all low or two
low and one moderate). Lives lost are considered low when fewer than 10, moderate 10-100, and high if over 100
per year on average. Severe injuries are considered low if fewer than 50, moderate 50-500, and high if over 500 per
year on average. Economic damage is considered low if less than $500M, moderate $500M to $5B, and high if over
$5B per year on average. Duration is considered low if measured in days, days to weeks, weeks, or weeks to
months; moderate if measured in days to years, weeks to years, months to years, or months; high if measured in
years, decades, months to decades, or years to decades.
xiii
Based on a 10% chance of a low economic cost incident ($100M) in any given year. The low estimate of $100M
comes from the low range of the National Planning Scenarios (“hundreds of millions”). HSC/DHS (2005). National
Planning Scenarios. Homeland Security Council in partnership with the U.S. Department of Homeland Security.
Washington, DC. The likelihood estimate of 10% balances expert opinion with the historical record. Studies of IT
professionals suggest that nearly 80% expect a major attack in the U.S. within the next five years. Baker, S., S.
Waterman and G. Ivanov (2010). In the Crossfire: Critical Infrastructure in the Age of Cyber War. Santa Clara, CA,
McAfee and the Center for Strategic and International Studies. However, historical experience does not show any
true major attacks, suggesting a likelihood of 0%. If we give equal weight to these two positions, we average 10%
in a given year. Taking likelihood in this manner is problematic, as attacks are not probabilistic events but are rather
the choice of adaptive adversaries and as such are not probabilistic at all. All numerical estimates have been rounded
to one significant figure to reduce overstating the precision of these measures.
xiv
Based on a 10% chance of a moderate economic cost incident ($500M) in any given year. The moderate estimate
of $500M is based on the mid-range of the National Planning Scenarios (“hundreds of millions”), but also Dynes
estimate for a 10 day oil and gas shutdown. HSC/DHS (2005). National Planning Scenarios. Homeland Security
Council in partnership with the U.S. Department of Homeland Security. Washington, DC, Dynes, S., E. Andrijcic
and M. Johnson (2006). Costs to the US economy of information infrastructure failures: estimates from field studies
and economic data, Citeseer. The likelihood estimate of 10% balances expert opinion with the historical record.
Studies of IT professionals suggest that nearly 80% expect a major attack in the U.S. within the next five years.
Baker, S., S. Waterman and G. Ivanov (2010). In the Crossfire: Critical Infrastructure in the Age of Cyber War.
Santa Clara, CA, McAfee and the Center for Strategic and International Studies. However, historical experience
does not show any true major attacks, suggesting a likelihood of 0%. If we give equal weight to these two positions,
we average 10% in a given year. Taking likelihood in this manner is problematic, as attacks are not probabilistic
events but are rather the choice of adaptive adversaries and as such are not probabilistic at all. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
xv
Based on a 10% chance of a large economic costs incident ($10B) in any given year. The large estimate of $10B
comes from large estimates of the costs of the blackout in the 2003 northeastern United States from ICF Consulting.
Saha, B. and B. Moody (2004). The Economic Cost of the Blackout. Fairfax, VA, ICF Consulting. The likelihood
estimate of 10% balances expert opinion with the historical record. Studies of IT professionals suggest that nearly
80% expect a major attack in the U.S. within the next five years. Baker, S., S. Waterman and G. Ivanov (2010). In
the Crossfire: Critical Infrastructure in the Age of Cyber War. Santa Clara, CA, McAfee and the Center for Strategic
and International Studies. However, historical experience does not show any true major attacks, suggesting a
likelihood of 0%. If we give equal weight to these two positions, we average 10% in a given year. Taking
likelihood in this manner is problematic, as attacks are not probabilistic events but are rather the choice of adaptive
adversaries and as such are not probabilistic at all. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xvi
Based on the low estimate from HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xvii
Based on high estimates of the economic damage due to the 2003 Northeast Blackout. High estimates of
economic damage from IFC. Cited in Cashell, B., W. Jackson, M. Jickling and B. Webel (2004). "The economic
impact of cyber-attacks." Congressional Research Service Documents, CRS RL32331 (Washington DC). All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
367 xviii
There is no known mechanism by which cyber-terrorism would have environmental consequences. For
example, an event leading to a blackout could actually have a slight positive effect for the environment, with
decreased pollution and energy use for that span, and no additional contamination. There are plausible scenarios
that could lead to environmental consequences following a cyber-event, but these are not typical. See also
HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC.
xix
It is unclear how a cyber-event would displace households. It is possible that a cyber-event could lead to a
chemical or radioactivity event, or that they would lead to a long term power disruption that could cause people to
move. However, these effects are not clear and there is no good estimate for these values. Our best understanding
of typical events suggests no displacements. Ibid. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xx
Moderate to high impact on government operations based on moderate longevity and moderate to high severity.
Moderate to high severity based on non-emergency/small disruption to emergency to large disruption to
government, as described in the direct and indirect scenarios in CACI CACI/USNI (2010). Cyber threats to national
security: Countering Challenges to the Global Supply Chain, CACI International
U.S. Naval Institute.
xxi
To a large extent, the ability of an individual to control their exposure to a cyber-event is low, as a disastrous
cyber event would impact infrastructures outside an individual’s control. However, it ranges to moderate, in that
people can take some steps to limit their exposure, by limiting personal information online, by preparing for a
disaster, and through maintaining personal computing hygiene such as anti-virus software, firewalls, and strong
password selection.
xxii
While there is little understanding of the health consequences of a cyber-event, there is even less reason to
believe that a cyber-event would be linked with contaminants that would have delayed health effects. While there
are plausible scenarios that could lead to long-term contamination following a cyber-event, these are not typical.
See also HSC/DHS HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with
the U.S. Department of Homeland Security. Washington, DC.
xxiii
Cyber events are an emerging threat, and can cover a range of scenarios. They may disrupt infrastructure
leading to a power outage, chemical release, transportation collision, the loss of information, or many others. While
there are plausible scenarios that could lead to health consequences, they are not well understood by scientists.
xxiv
Moderate combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the ratio of
the high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damage) which
equals 210. Cyber events have moderate combined uncertainty due to both the range of expected damage and to the
difficulty quantifying certain estimates. On the one hand, the lives lost due to a cyber-attack are low in any scenario,
making those components relatively well known. However, the range of the economic damage for a cyber-attack is
very wide due to the uncertainty over both the likelihood and consequence of a cyber-event. Conceptually, the low
uncertainty in health and the high uncertainty in economic damage combine to a moderate combined uncertainty.
More technically, we adding the range of high to low for average lives lost (a ratio of high to low of 10, using 0.1
rather than 0 for the low end of the range to prevent dividing by 0), severe injuries (50), less severe injuries (50), and
economic damage (100) gives our combined uncertainty rating of 210. Values below 100 are considered low, 100 to
1000 are considered moderate, and above 1000 are considered high.
xxv
Dharapak, C. (2009). Computer failure may have caused D.C. subway crash. USA Today.
xxvi
Richardson, R. (2008). CSI/FBI Computer Crime and Security Survey. San Francisco, CA, Computer Security
Institute.
xxvii
Based on the 3% estimate for DDOS from Garg et al. Garg, A., J. Curtis and H. Halper (2003). "Quantifying the
financial impact of IT security breaches." Information Management & Computer Security 11(2): 74-83., and the 5%
plus 5% for e-commerce companies in Ettrredge and Richardson Ettredge, M. and V. Richardson (2002). Assessing
the risk in e-commerce, Published by the IEEE Computer Society., both cited in Cashell et al. Cashell, B., W.
Jackson, M. Jickling and B. Webel (2004). "The economic impact of cyber-attacks." Congressional Research
Service Documents, CRS RL32331 (Washington DC).
xxviii
BNAC (2007). Cyber Attack: A Risk Management Primer for CEOs and Directors, British-North American
Committee.
xxix
Robb, J. (2007). When bots attack, Wired.
xxx
DHS (2009). Information Technology Sector Baseline Risk Assessment Report. D. o. H. Security. Washington,
DC, U.S. Department of Homeland Security.
368 xxxi
Polk, W., P. Malkewicz and J. Novak (2010). Industrial Cyber Security: From the Perspective of the Power
Sector. DEFCON 18. Los Vegas, NV: 65.
xxxii
Ibid.
xxxiii
Chen, T. (2010). "Stuxnet, the real start of cyber warfare?[Editor's Note]." Network, IEEE 24(6): 2-3, Kerr, P.,
J. Rollins and C. Theohary (2010). The Stuxnet Computer Worm: Harbinger of an Emerging Warfare Capability. C.
R. Service. Washington, DC, Congressional Research Service, Porche, I. (2010) "Stuxnet is the world's problem."
Bulletin of the Atomic Scientists, Zumwalt, J. (2010) "Israel Behind Iran's Computer Worm." Human Events.
xxxiv
Homeland Security News Wire. (2010, Feb. 19, 2010). "How real is the threat of cyberattack on the United
States?" Retrieved Dec. 11, 2011.
xxxv
Cashell, B., W. Jackson, M. Jickling and B. Webel (2004). "The economic impact of cyber-attacks."
Congressional Research Service Documents, CRS RL32331 (Washington DC).
xxxvi
Ibid.
xxxvii
Ibid.
xxxviii
Dexter, J. (2010). "Fact Check: Cyberattack threat." CNNTech Retrieved Dec. 11, 2011, from
http://www.cnn.com/2010/TECH/02/16/fact.check.cyber.threat/.
xxxix
Dilanian, K. (2011). Russia and China accused of cyber-spying campaign to steal U.S. secrets. Los Angeles
Times. Los Angeles, CA, Rawlinson, K. (2011). China and Russia accused of orchestrating cyber attacks. The
Independent. London, UK.
xl
Broad, W. J., J. Markoff and D. E. Sandger (2011). Israeli Test on Worm Called Crucial in Iran Nuclear Delay.
New York Times. New York, NY: A1, Dilanian, K. (2011). Russia and China accused of cyber-spying campaign to
steal U.S. secrets. Los Angeles Times. Los Angeles, CA, Rawlinson, K. (2011). China and Russia accused of
orchestrating cyber attacks. The Independent. London, UK.
xli
Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY. 5.
xlii
BNAC (2007). Cyber Attack: A Risk Management Primer for CEOs and Directors, British-North American
Committee.
xliii
Rollins, J. and C. Wilson (2007). Terrorist Capabilities for Cyberattack: Overview and Policy Issues. C. R.
Service. Washington, DC, Congressional Research Service: 28.
xliv
Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY. 5.
xlv
Gorman, S. (2009). "Electricity grid in US penetrated by spies." Technology, Polk, W., P. Malkewicz and J.
Novak (2010). Industrial Cyber Security: From the Perspective of the Power Sector. DEFCON 18. Los Vegas, NV:
65.
xlvi
Richardson, R. (2008). CSI/FBI Computer Crime and Security Survey. San Francisco, CA, Computer Security
Institute.
xlvii
Shiels, M. (2010). "Security experts say Google cyber-attack was routine." Retrieved Dec. 11, 2011, from
http://news.bbc.co.uk/2/hi/8458150.stm.
xlviii
Homeland Security News Wire. (2010, Feb. 19, 2010). "How real is the threat of cyberattack on the United
States?" Retrieved Dec. 11, 2011.
xlix
(2006). "Paller: Government cybersecurity gets an F." Retrieved Dec. 11, 2011, from
http://www.infoworld.com/d/security-central/paller-government-cybersecurity-gets-f-679, United States.
Government Accountability Office. (2007). Critical Infrastructure Protection: Multiple Efforts to Secure Control
Systems Are Underway, but Challenges Remain. Washington, DC, Goverment Printing Office: 58 p, Lynn, W. J.
(2010). Defending a New Domain. Foreign Affairs. New York, NY. 5.
l
Richardson, R. (2008). CSI/FBI Computer Crime and Security Survey. San Francisco, CA, Computer Security
Institute.
li
Dexter, J. (2010). "Fact Check: Cyberattack threat." CNNTech Retrieved Dec. 11, 2011, from
http://www.cnn.com/2010/TECH/02/16/fact.check.cyber.threat/.
lii
Aitoro, J. R. (2010). "Successful attack on nation's infrastructure is 'when,' not 'if'." Technology and the business
of Government
Retrieved Dec. 11, 2011, from http://www.nextgov.com/nextgov/ng_20100806_9847.php, Baker, S., S. Waterman
and G. Ivanov (2010). In the Crossfire: Critical Infrastructure in the Age of Cyber War. Santa Clara, CA, McAfee
and the Center for Strategic and International Studies.
liii
Based on a 10% chance of a low ($100M), moderate ($500M), or high ($10B) economic cost incident in any
given year. The low estimate of $100M comes from the low range of the National Planning Scenarios (“hundreds of
millions”). HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in partnership with the U.S.
Department of Homeland Security. Washington, DC. The moderate estimate of $500M is based on the mid-range of
369 the National Planning Scenarios (“hundreds of millions”), but also Dynes estimate for a 10 day oil and gas
shutdown. ibid, Dynes, S., E. Andrijcic and M. Johnson (2006). Costs to the US economy of information
infrastructure failures: estimates from field studies and economic data, Citeseer. The large estimate of $10B comes
from large estimates of the costs of the blackout in the 2003 northeastern United States from ICF Consulting. Saha,
B. and B. Moody (2004). The Economic Cost of the Blackout. Fairfax, VA, ICF Consulting. The likelihood
estimate of 10% balances expert opinion with the historical record. Studies of IT professionals suggest that nearly
80% expect a major attack in the U.S. within the next five years. Baker, S., S. Waterman and G. Ivanov (2010). In
the Crossfire: Critical Infrastructure in the Age of Cyber War. Santa Clara, CA, McAfee and the Center for Strategic
and International Studies. However, historical experience does not show any true major attacks, suggesting a
likelihood of 0%. If we give equal weight to these two positions, we average 10% in a given year. Taking
likelihood in this manner is problematic, as attacks are not probabilistic events but are rather the choice of adaptive
adversaries and as such are not probabilistic at all. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
liv
See endnotes iii, vii, and xi.
lv
Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY. 5.
lvi
Richardson, R. (2008). CSI/FBI Computer Crime and Security Survey. San Francisco, CA, Computer Security
Institute.
lvii
l; Hoover, J. N. (2010). Feds Strengthen Cybersecurity Workforce Plans. InformationWeeks, Lynn, W. J. (2010).
Defending a New Domain. Foreign Affairs. New York, NY. 5.
lviii
Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY. 5.
lix
DHS-OIG (2010). DHS Needs to Improve the Security Posture of Its Cybersecurity Program Systems. D. o. H. S.
O. o. I. General. Washingon, DC, Lynn, W. J. (2010). Defending a New Domain. Foreign Affairs. New York, NY.
5.
lx
US-CERT. "Related Resources." Retrieved Dec. 11, 2011, from http://www.us-cert.gov/resources.html, USCERT National Cyber Security Division US-CERT Overview. U.S. Department of Homeland Security National
Cyber Security Division US-CERT. Washington, DC, DHS-OIG (2010). DHS Needs to Improve the Security
Posture of Its Cybersecurity Program Systems. D. o. H. S. O. o. I. General. Washingon, DC. ;
lxi
Aitoro, J. R. (2009). "DHS' Cyber Storm III to test Obama's national cyber response plan." Technology and the
business of Government Retrieved Dec. 11, 2011, from http://www.nextgov.com/nextgov/ng_20090826_9168.php,
DHS. (2011). "Fact Sheet: Cyberstorm III: National Cyber Exercise." Retrieved Dec. 11, 2011, from
http://www.dhs.gov/files/training/cyberstorm-iii.shtm.
lxii
US-CERT. (2011). "Government Users." Retrieved Dec. 11, 2011, from http://www.us-cert.gov/federal/.
lxiii
DHS. (2011). "Cyberstorm: Securing Cyber Space." Retrieved Dec. 11, 2011, from
http://www.dhs.gov/files/training/gc_1204738275985.shtm.
lxiv
DHS. (2011). "Fact Sheet: Cyberstorm III: National Cyber Exercise." Retrieved Dec. 11, 2011, from
http://www.dhs.gov/files/training/cyberstorm-iii.shtm.
i
Based on the lowest 10 year average of deaths from industrial chemicals leaks from disasters in the U.S. from
1980-2010 in EM-DAT. EM-DAT’s methodology includes an event as a disaster if a) over 10 people are killed, b)
over 1,000 people are affected, c) a state of emergency was declared, or d) there was a call for international
assistance. Technological events were examined, including those where there was an industrial plant explosion,
chemical release, or transportation accident centering on the release of chemicals (as compared to a transportation
accident centering on passengers, such as a collision of trains on a subway or a bus crash on a highway). EM-DAT.
(2011). "Database|EM-DAT." Retrieved March 1, 2011, from http://www.emdat.be/database. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures.
ii
Based on the average of the 10 year averages of deaths from industrial chemicals leaks from disasters in the U.S.
from 1980-2010 in EM-DAT. EM-DAT’s methodology includes an event as a disaster if a) over 10 people are
killed, b) over 1,000 people are affected, c) a state of emergency was declared, or d) there was a call for international
assistance. Technological events were examined, including those where there was an industrial plant explosion,
chemical release, or transportation accident centering on the release of chemicals (as compared to a transportation
accident centering on passengers, such as a collision of trains on a subway or a bus crash on a highway). Ibid. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
iii
Using 1% possibility of a Bhopal-style event and a high estimate of the number killed in the largest chemical
accident (20,000). One percent probability is an ad hoc assumption, viewed to be a high estimate to bound the
range. The estimate of 20,000 killed is based upon the high estimate of the number injured in Bhopal, India in 1984.
370 National Research Council (2006). Terrorism and the chemical infrastructure : protecting people and reducing
vulnerabilities. Washington, DC, National Academies Press.. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
iv
Low estimate of greatest consequence is based on event with greatest number of casualties in the U.S., an
explosion in Texas in 1947. Ibid. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
v
High estimate of greatest consequence based on high estimate of deaths in Bhopal. Ibid.. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
vi
Based on the low average deaths per year as per endnote i, times 6 representing the number of serious injuries per
death in safety accidents in the United States in 1998. Mannan, M. S., H. H. West, K. Krishna, A. A. Aldeeb, N.
Keren, S. R. Saraf, Y. S. Liu and M. Gentile (2005). "The legacy of Bhopal: The impact over the last 20 years and
future direction." Journal of Loss Prevention in the Process Industries 18(4-6): 218-224. All numerical estimates
have been rounded to one significant figure to reduce overstating the precision of these measures.
vii
Based on the best average deaths per year as per endnote ii, times 6 representing the number of serious injuries per
death in safety accidents in the United States in 1998. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
viii
Using 1% possibility of a Bhopal-style event and a high estimate of the number severely injured in a chemical
accident (16,667). One percent probability is an ad hoc assumption, viewed to be a high estimate to bound the
range. The estimate of 16,667 severely injured is based upon the high estimate of injured in the chemical leak in
Bhopal, India in 1984 (based on National Research Council), where 1 in 30 injured are treated as severe (based on
Mannan et al, and Heinrich). Heinrich, H. W. and E. Granniss (1959). Industrial accident prevention, McGraw-Hill
New York, Mannan, M. S., H. H. West, K. Krishna, A. A. Aldeeb, N. Keren, S. R. Saraf, Y. S. Liu and M. Gentile
(2005). "The legacy of Bhopal: The impact over the last 20 years and future direction." Journal of Loss Prevention
in the Process Industries 18(4-6): 218-224, National Research Council (2006). Terrorism and the chemical
infrastructure : protecting people and reducing vulnerabilities. Washington, DC, National Academies Press. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
ix
Based on the low average deaths per year as per endnote i, times 61.9 representing the number of injuries per death
in safety accidents in the United States in 1998. Mannan, M. S., H. H. West, K. Krishna, A. A. Aldeeb, N. Keren, S.
R. Saraf, Y. S. Liu and M. Gentile (2005). "The legacy of Bhopal: The impact over the last 20 years and future
direction." Journal of Loss Prevention in the Process Industries 18(4-6): 218-224. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
x
Based on the best average deaths per year as per endnote ii, times 61.9 representing the number of injuries per
death in safety accidents in the United States in 1998. Ibid. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
xi
Using 1% possibility of a Bhopal-style event and a high estimate of the number severely injured in a chemical
accident (483,333). One percent probability is an ad hoc assumption, viewed to be a high estimate to bound the
range. The estimate of 483,333 severely injured is based upon the high estimate of injured in the chemical leak in
Bhopal, India in 1984 (based on National Research Council), where 1 in 30 injured are treated as severe (based on
Mannan et al, and Heinrich). Heinrich, H. W. and E. Granniss (1959). Industrial accident prevention, McGraw-Hill
New York, Mannan, M. S., H. H. West, K. Krishna, A. A. Aldeeb, N. Keren, S. R. Saraf, Y. S. Liu and M. Gentile
(2005). "The legacy of Bhopal: The impact over the last 20 years and future direction." Journal of Loss Prevention
in the Process Industries 18(4-6): 218-224, National Research Council (2006). Terrorism and the chemical
infrastructure : protecting people and reducing vulnerabilities. Washington, DC, National Academies Press. All
numerical estimates have been rounded to one significant figure to reduce overstating the precision of these
measures.
xii
Low psychological damage per year on average based on the higher of 1) low PTSD and 2) low depression per
year on average. Low PTSD based on crossing low lives lost by moderate severe injuries. Low depression based on
crossing low combined damages per year on average and moderate duration of economic damages. Low combined
damages (defined as a combination of lives lost, severe damages, and economic damages per year on average all low
or two low and one moderate). Lives lost are considered low when fewer than 10, moderate 10-100, and high if
over 100 per year on average. Severe injuries are considered low if fewer than 50, moderate 50-500, and high if
over 500 per year on average. Economic damages are considered low if less than $500M, moderate $500M to $5B,
and high if over $5B per year on average. Duration is considered low if measured in days, days to weeks, weeks, or
371 weeks to months; moderate if measured in days to years, weeks to years, months to years, or months; high if
measured in years, decades, months to decades, or years to decades.
xiii
Based on the low average deaths per year as per endnote i, times $36.29M, representing the number of damage
per death in the National Planning Scenarios. The NPS estimates 350 lives lost and “billions” in physical damage
and business interruption. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. We estimate “billions” as $12.7B,
representing the largest economic damages in a chemical release (a 2002 chemical release in Spain, adjusted for
inflation), from EM-DAT. EM-DAT. (2011). "Database|EM-DAT." Retrieved March 1, 2011, from
http://www.emdat.be/database. This is consistent with the ratio of damages to lives lost in the U.S. from 1994 to
2001 from Kleindorfer et al. (2003). Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe and H. Feldman (2003).
"Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP* Info."
Risk Analysis 23(5): 865-881. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xiv
Based on the best average deaths per year as per endnote ii, times $36.29M, representing the number of damage
per death in the National Planning Scenarios. The NPS estimates 350 lives lost and “billions” in physical damage
and business interruption. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. We estimate “billions” as $12.7B,
representing the largest economic damages in a chemical release (a 2002 chemical release in Spain, adjusted for
inflation), from EM-DAT. EM-DAT. (2011). "Database|EM-DAT." Retrieved March 1, 2011, from
http://www.emdat.be/database. This is consistent with the ratio of damages to lives lost in the U.S. from 1994 to
2001 from Kleindorfer et al. (2003). Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe and H. Feldman (2003).
"Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP* Info."
Risk Analysis 23(5): 865-881. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xv
Based on the high average deaths per year as per endnote iii, times $36.29M, representing the number of damage
per death in the National Planning Scenarios. The NPS estimates 350 lives lost and “billions” in physical damage
and business interruption. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. We estimate “billions” as $12.7B,
representing the largest economic damages in a chemical release (a 2002 chemical release in Spain, adjusted for
inflation), from EM-DAT. EM-DAT. (2011). "Database|EM-DAT." Retrieved March 1, 2011, from
http://www.emdat.be/database. This is consistent with the ratio of damages to lives lost in the U.S. from 1994 to
2001 from Kleindorfer et al. (2003). Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe and H. Feldman (2003).
"Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP* Info."
Risk Analysis 23(5): 865-881. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xvi
Based on the economic consequences of the largest U.S. release (a Texas processing plant fire in 1989) from
EMDAT, adjusted for inflation to 2011 dollars. EM-DAT. (2011). "Database|EM-DAT." Retrieved March 1, 2011,
from http://www.emdat.be/database. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xvii
Based on the high average deaths per year as per endnote v, times $36.7M/life lost representing the number of
damage per death in the National Planning Scenarios. The NPS estimates 350 lives lost and “billions” in physical
damage and business interruption. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC. We estimate “billions” as $12.7B,
representing the largest economic damages in a chemical release (a 2002 chemical release in Spain, adjusted for
inflation), from EM-DAT. EM-DAT. (2011). "Database|EM-DAT." Retrieved March 1, 2011, from
http://www.emdat.be/database. This is consistent with the ratio of damages to lives lost in the U.S. from 1994 to
2001 from Kleindorfer et al. (2003). Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe and H. Feldman (2003).
"Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP* Info."
Risk Analysis 23(5): 865-881. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xviii
Some scenarios (e.g. chlorine gas leak) will have limited contamination, and disruption will only last days. For
example, it took only days for the chemicals of Bhopal to be neutralized. Pastel, R. H. (2007). "What We Have
Learned About Mass Chemical Disasters." Psychiatric Annals 37(11): 754. Other chemicals will leave contaminants
that can disrupt businesses long-term. In many cases, the economy will adapt to the disruption in months, by
372 moving to different facilities. However, some infrastructure (e.g. ports) may take years to replace. Additionally,
clean-up of contaminants may take years.
xix
Most scenarios have a limited range for a vapor cloud or smoke plume, covering several blocks to an entire city.
The median facility’s worst case scenario distance for a toxic event is 1.6 miles, with the overwhelming majority of
worst case scenario distances less than 5 miles. However, a few hundred facilities do report worst case distances of
25 miles. A typical chemical, chlorine, has a modeled end-point distance of 14 miles for the release of 90 tons of
chlorine, a typical amount from a railroad tank car. Belke, J. C. (2000). Chemical accident risks in US industry: A
preliminary analysis of accident risk data from US hazardous chemical facilities. United States. Environmental
Protection Agency. Chemical Emergency Preparedness Prevention Office. Some scenarios can cover several states
(e.g. chemical release into waterways), but only limited areas of those states (e.g. adjacent to waterways).
xx
Based on limited range of damage. Some scenarios are associated with limited contamination over a limited area.
Some scenarios are associated with severe contamination over a limited to moderate area (e.g. Bhopal, chemical
release into waterways).
xxi
Based on the low average deaths per year as per endnote i, times 995 representing the number of evacuees per
death in safety accidents in the United States in 1994-2001. Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe
and H. Feldman (2003). "Accident epidemiology and the US chemical industry: Accident history and worst-case
data from RMP* Info." Risk Analysis 23(5): 865-881.
xxii
Based on the high average deaths per year as per endnote iii, times 995 representing the number of evacuees per
death in safety accidents in the United States in 1994-2001. Ibid.
xxiii
Based on DHS scenarios. HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC.
xxiv
While a natural accident may be the proximate cause of a TIC accident, the creation and transportation of the
chemicals are the ultimate cause, and are human-induced.
xxv
The consequences of typical low-level accidents are almost entirely born by workers and contractors on site. (see
Kleindorfer, 2003; Elliott, 2003). In such cases, risk may be managed by avoiding certain jobs or careers. However,
high-level accidents are less avoidable. While exposure from chemical plants may be limited by living away from
physical plants, exposure from transportation is limited. Transportation of chemicals occurs near residential,
commercial and recreational centers, limiting risk avoidance. Elliott, M., P. Keindorfer and R. Lowe (2003). "The
role of hazardousness and regulatory practice in the accidental release of chemicals at US industrial facilities." Risk
Analysis 23(5): 883-896, Kleindorfer, P., J. Belke, M. Elliott, K. Lee, R. Lowe and H. Feldman (2003). "Accident
epidemiology and the US chemical industry: Accident history and worst-case data from RMP* Info." Risk Analysis
23(5): 865-881.
xxvi
Some chemicals have immediate impact (e.g. chlorine, with acute poisoning). Other chemicals include toxic
chemicals with accumulation as well as known carcinogens, which can have their impact decades from exposure.
Atkins, R. (2004). Chemical Attack: Warfare Agents, Industial Chemicals, and Toxins. National Acadamies and the
U.S. Department of Homeland Security. Washington, DC, National Academies Press: 4.
xxvii
Low to moderate quality of scientific understanding based on good understanding of the lethality of toxic
chemicals, but limited understanding of exposures in an actual catastrophic event and the lethality of toxic chemicals
in the long-term.
xxviii
Low to moderate combined uncertainty is based on additive ratios of the ranges of consequences (the sum of the
ratio of the high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic
damages) which equals 100.59. Values below 100 are considered low, 100 to 1000 are considered moderate, and
above 1000 are considered high. We consider this value as straddling our dividing line between low and moderate,
accounting for the imprecision of this measure and the arbitrary nature of the cut-points.
xxix
Khan, F. I. and S. Abbasi (1999). "Major accidents in process industries and an analysis of causes and
consequences." Journal of Loss Prevention in the Process Industries 12(5): 361-378.
xxx
Baxter, P. (1991). "Major chemical disasters." British Medical Journal 302(6768): 61.
xxxi
Ackermann-Liebrich, U., P. Baxter, P. A. Bertazzi, D. Campbell and M. Krzyzanowski (1997). Assessing the
health consequences of major chemical incidents: epidemiological approaches, WHO Regional Office Europe.
xxxii
Ibid.
xxxiii
Sun, Y. and K. Y. Ong (2005). Detection technologies for chemical warfare agents and toxic vapors. Boca
Raton, CRC Press.
xxxiv
Baxter, P. (1991). "Major chemical disasters." British Medical Journal 302(6768): 61, EPA (2004). General
Risk Management Program Guidance, Introduction. U. S. E. P. A. (EPA). Washington, DC, Fatah, A. A., R. D.
Arcilesi, J. C. Peterson, C. H. Lattin, C. Y. Wells, National Institute of Standards and Technology (U.S.). Office of
373 Law Enforcement Standards. and National Institute of Justice (U.S.). Office of Science and Technology. (2005).
Guide for the selection of chemical agent and toxic industrial material detection equipment for emergency first
responders. Washington, D.C., U.S. Department of Justice, Office of Justice Program, National Institute of Justice,
Office of Science and Technology.
xxxv
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from
US hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency
Preparedness Prevention Office.
xxxvi
Drogaris, G. (1993). "Learning from major accidents involving dangerous substances." Safety Science 16(2):
89-113, Sun, Y. and K. Y. Ong (2005). Detection technologies for chemical warfare agents and toxic vapors. Boca
Raton, CRC Press.
xxxvii
Ackermann-Liebrich, U., P. Baxter, P. A. Bertazzi, D. Campbell and M. Krzyzanowski (1997). Assessing the
health consequences of major chemical incidents: epidemiological approaches, WHO Regional Office Europe,
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from US
hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency Preparedness
Prevention Office, Atkins, R. (2004). Chemical Attack: Warfare Agents, Industial Chemicals, and Toxins. National
Acadamies and the U.S. Department of Homeland Security. Washington, DC, National Academies Press: 4.
xxxviii
Deployment Health and Family Readiness Library (2007). Toxic Industrial Chemicals/Toxic Industrial
Materials (TICS/TIMS)- Awareness and Preventative Measures. A. F. I. f. O. Health, t. D. H. C. Center, F. H. P. a.
Readinesset al.
xxxix
OSHA. "Toxic Industrial Chemicals." Emergency Preparedness and REsponse: Safety and Health Guides
Retrieved Dec. 12, 2011, from http://www.osha.gov/SLTC/emergencypreparedness/guides/chemical.html As most
incidents do not have immediate casualties, the greatest health impacts come from these chronic effects. See the
Toxics Release Inventory. . "Environmental Releases for Entire United States." Scorecard- the Pollution
Information Site Retrieved Dec. 12, 2011, from http://www.scorecard.org/envreleases/us.tcl#pollution_rank_health_impact.
xl
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from US
hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency Preparedness
Prevention Office.
xli
Ringquist, E. J. (1997). "Equity and the distribution of environmental risk: The case of TRI facilities." Social
Science Quarterly 78(4): 811-829.
xlii
Khan, F. I. and S. Abbasi (1999). "Major accidents in process industries and an analysis of causes and
consequences." Journal of Loss Prevention in the Process Industries 12(5): 361-378, Karasik, T. W. (2002). Toxic
warfare. Santa Monica, CA, Rand.
xliii
Glickman, T. S. (1986). "A methodology for estimating time-of-day variations in the size of a population
exposed to risk." Risk Analysis 6(3): 317-324, WHO. (2011). "Chemical and Radiological Incidents- Technical
Hazard Sheet." Retrieved Dec. 12, 2011, from
http://www.who.int/hac/techguidance/ems/chemical_insidents/en/index.html.
xliv
Ackermann-Liebrich, U., P. Baxter, P. A. Bertazzi, D. Campbell and M. Krzyzanowski (1997). Assessing the
health consequences of major chemical incidents: epidemiological approaches, WHO Regional Office Europe.
xlv
Ibid, Bertazzi, P. A., C. Zocchetti, S. Guercilena, D. Consonni, A. Tironi, M. T. Landi and A. C. Pesatori (1997).
"Dioxin exposure and cancer risk: A 15-year mortality study after the" Seveso accident"." Epidemiology 8(6): 646652, Pastel, R. H. (2007). "What We Have Learned About Mass Chemical Disasters." Psychiatric Annals 37(11):
754.
xlvi
Ackermann-Liebrich, U., P. Baxter, P. A. Bertazzi, D. Campbell and M. Krzyzanowski (1997). Assessing the
health consequences of major chemical incidents: epidemiological approaches, WHO Regional Office Europe,
Bertazzi, P. A., C. Zocchetti, S. Guercilena, D. Consonni, A. Tironi, M. T. Landi and A. C. Pesatori (1997). "Dioxin
exposure and cancer risk: A 15-year mortality study after the" Seveso accident"." Epidemiology 8(6): 646-652,
Pastel, R. H. (2007). "What We Have Learned About Mass Chemical Disasters." Psychiatric Annals 37(11): 754.
xlvii
Green, B. L. (1998). "Psychological responses to disasters: Conceptualization and identification of high‐risk
survivors." Psychiatry and Clinical Neurosciences 52(S5): S67-S73.
xlviii
DHS chlorine and TIC scenarios HSC/DHS (2005). National Planning Scenarios. Homeland Security Council in
partnership with the U.S. Department of Homeland Security. Washington, DC.
xlix
Bennett, M. (2003). "TICs, TIMs, and Terrorists." Today’s Chemist at Work.
l
Clarke, L. B. (2006). Worst cases : terror and catastrophe in the popular imagination. Chicago, University of
Chicago Press., p.119
374 li
(1999). The 600k Report: Commercial Chemical Incidents in the United States 1987-1996. Baseline Study,
Chemical Safety and Hazard Investgation Board., viewed in EPA (1999). New Ways to Prevent Chemical Incidents.
U. S. E. P. Agency. EPA 550-B-99-012.
lii
(1999). The 600k Report: Commercial Chemical Incidents in the United States 1987-1996. Baseline Study,
Chemical Safety and Hazard Investgation Board., viewed in EPA (1999). New Ways to Prevent Chemical Incidents.
U. S. E. P. Agency. EPA 550-B-99-012.
liii
Glickman, T., D. Golding and K. Terry (1993). "Fatal hazardous materials accidents in industry-domestic and
foreign experience from 1945 to 1991." Washington: Center for Risk Management. cited in Souza Porto, M. and C.
Freitas (1996). "Major Chemical Accidents in Industrializing Countries: The Socio Political Amplification of Risk."
Risk Analysis 16(1): 19-29.
liv
Office of Emergency Management (2005). 2005 Year in Review: Emergency Management- Prevention,
Preparedness and Response. U. S. E. P. A. (EPA). Washington, DC.
lv
Gupta, J. (2002). "The Bhopal gas tragedy: could it have happened in a developed country?" Journal of Loss
Prevention in the Process Industries 15(1): 1-4.
lvi
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from US
hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency Preparedness
Prevention Office.
lvii
See endnotes i, ii, iii
lviii
See endnotes vi, vii, viii, ix, x, xi
lix
See endnotes iv, v
lx
National Research Council (2006). Terrorism and the chemical infrastructure : protecting people and reducing
vulnerabilities. Washington, DC, National Academies Press.
lxi
See endnotes xiii, xiv, xv
lxii
See endnotes xvi, xvii
lxiii
See endnote xviii
lxiv
See endnote xix
lxv
See endnote xx
lxvi
See endnotes xxi, xxii, xxiii
lxvii
Pastel, R. H. (2007). "What We Have Learned About Mass Chemical Disasters." Psychiatric Annals 37(11): 754.
lxviii
See endnote xxiii
lxix
EPA. "Emergency Planning and Community Right-To-Know Act (EPCRA)." Retrieved Jan. 11, 2012, from
http://www.epa.gov/oecaagct/lcra.html#Summary%20of%20Emergency%20Planning%20And%20Community%20
Right-To-Know%20Act.
lxx
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from US
hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency Preparedness
Prevention Office.
lxxi
Rosenthal, I. (2004). OSHA, EPA and Other Stakeholder Responses to the Conclusions and Recommendations of
the Chemical Safety Board’s Report on “Improving Reactive Hazard Management” and Some Approaches Towards
Resolving Remaining Recommendation Issues, ASME.
lxxii
Kletz, T. A. (2001). Learning from accidents, Gulf Professional Publishing, Rosenthal, I. (2004). OSHA, EPA
and Other Stakeholder Responses to the Conclusions and Recommendations of the Chemical Safety Board’s Report
on “Improving Reactive Hazard Management” and Some Approaches Towards Resolving Remaining
Recommendation Issues, ASME, Belke, J. C. and D. Y. Dietrich (2005). "The post-Bhopal and post-9/11
transformations in chemical emergency prevention and response policy in the United States." Journal of Loss
Prevention in the Process Industries 18(4-6): 375-379.
lxxiii
Belke, J. C. (2000). Chemical accident risks in US industry: A preliminary analysis of accident risk data from
US hazardous chemical facilities. United States. Environmental Protection Agency. Chemical Emergency
Preparedness Prevention Office.
lxxiv
Ibid.
lxxv
Ibid.
lxxvi
Ibid.
lxxvii
EPA. "Risk Management Plan (RMP) Rule." Retrieved Jan. 11, 2012, from
http://www.epa.gov/oem/docs/chem/Intro_final.pdf
lxxviii
EPA (2004). General Risk Management Program Guidance, Introduction. U. S. E. P. A. (EPA). Washington,
DC.
375 lxxix
FEMA (2003). Guidelines for Hazmat/WMD Response, Planning, and Prevention Training. U. S. D. o. H. S. F.
E. M. Agency, (2007). EPA Emergency Response: Keeping Up With Tomorrow's Challenges. The Federal
Manager. Alexandria, VA, Federal Managers Association. 2007: 3-7.
lxxx
Keim, M. E., N. Pesik and N. Twum-Danso (2003). "Lack of hospital preparedness for chemical terrorism in a
major US city: 1996-2000." Prehospital and Disaster Medicine 18(3): 193-199, Barbera, J. A., D. J. Yeatts and A. G.
Macintyre (2009). "Challenge of hospital emergency preparedness: Analysis and recommendations." Disaster
Medicine and Public Health Preparedness 3(Supplement 1): S74.
lxxxi
(2007). EPA Emergency Response: Keeping Up With Tomorrow's Challenges. The Federal Manager.
Alexandria, VA, Federal Managers Association. 2007: 3-7.
lxxxii
EPA. "National Oil and Hazardous Substances Pollution Contingency Plan Overview." Retrieved Jan. 11,
2012, from http://www.epa.gov/emergencies/content/lawsregs/ncpover.htm, (2007). EPA Emergency Response:
Keeping Up With Tomorrow's Challenges. The Federal Manager. Alexandria, VA, Federal Managers Association.
2007: 3-7.
lxxxiii
(2007). EPA Emergency Response: Keeping Up With Tomorrow's Challenges. The Federal Manager.
Alexandria, VA, Federal Managers Association. 2007: 3-7.
lxxxiv
DHS (2008). National Response Framework- Emergency Support Function #10 Annex. U.S. Department of
Homeland Security Federal Emergency Management Agency. Washington, DC.
lxxxv
EPA/OSHA (1996). Chemical Accident Investigation. U. S. E. P. Agency and D. o. L. O. S. a. H.
Administration.
lxxxvi
Office of Emergency Management (2005). 2005 Year in Review: Emergency Management- Prevention,
Preparedness and Response. U. S. E. P. A. (EPA). Washington, DC.
lxxxvii
Elliott, M., P. Keindorfer and R. Lowe (2003). "The role of hazardousness and regulatory practice in the
accidental release of chemicals at US industrial facilities." Risk Analysis 23(5): 883-896.
lxxxviii
Khan, F. I. and S. Abbasi (1999). "Major accidents in process industries and an analysis of causes and
consequences." Journal of Loss Prevention in the Process Industries 12(5): 361-378.
i
Most oil spills are associated with no fatalities. We do not add any deaths due to toxicity or cancer risk. Baars
notes that levels of exposure to carcinogens are very low. Others note acute but not chronic health effects. Baars, B.
J. (2002). "The wreckage of the oil tanker 'Erika'--human health risk assessment of beach cleaning, sunbathing and
swimming." Toxicology Letters 128(1-3): 55-68, Rodríguez-Trigo, G., J. P. Zock and I. I. Montes (2007). "Health
effects of exposure to oil spills." Archivos de Bronconeumología 43(11): 628-635, Aguilera, F., J. Méndez, E.
Pásaro and B. Laffon (2010). "Review on the effects of exposure to spilled oils on human health." Journal of
Applied Toxicology 30(4): 291-301. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
ii
Most oil spills are associated with no fatalities from the spilled oil, but there may be fatalities associated with the
incident that leads to the spill. For example, the Deepwater Horizon spill of 2010 was associated with an explosion
that killed 11 workers. Using the 3 major spills in the past 40 years in the U.S. as a measure of frequency, we
estimate 11*(3/40) = 0.8 fatalities per year on average, which we round to 1. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
iii
Most events are associated with no fatalities, but some oil spills have fatalities associated with the incident that
leads to the explosion. The largest number of people killed in an oil platform occurred at the Piper Alpha oil
platform fire in 1988 which resulted in 167 deaths. There were no accidents of that severity in the U.S. during the
40 years used to calculate the previous frequency, suggesting 1 incident in 40 years as an upper bound for the
frequency. If we were to have an accident of that degree of fatalities once every 40 years, then we would have 4
deaths per year on average. This is our higher bound. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
iv
Most oil spills are associated with no fatalities. The long-term health risks are low, and while the health risks to
clean-up workers can be severe they are typically fleeting and as such do not result in fatalities. However, oil spills
from production platforms may come about with an explosion, which can kill. The Deepwater Horizon accident of
2010 had 11 workers killed in the explosion associated with the release of the oil. As the largest number for oil spill
fatalities in the U.S., this number is our lower bound of the greatest number killed in a single event. However, the
Piper Alpha disaster of 1988 had greater fatalities internationally. The explosion and fire aboard the Piper Alpha, an
oil platform in the North Sea, killed 167 of the 228 aboard (Woolfson and Beck, 2000 ). To include this number
within our greatest number estimate, we round this upwards to 200 and include it as our upper bound of the greatest
number killed in a single event. Woolfson, C. and M. Beck (2000). "The British offshore oil industry after Piper
376 Alpha." New Solutions 10(1): 11-65. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
v
Based on low probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
Low probability is based on the best estimate of a catastrophic event (3/40, representing 3 major spills in the past 40
years) multiplied by a factor of .5, a term that represents the trend of decreasing oil spilled, as 1990-1999 had half
the barrels spilled as the period 1980-1999. PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill
Prevention, Preparedness, and Response Program (OSPRR), Potomac Management Group, Environmental Research
Consulting. Best estimate of severe illness in a catastrophic event comes from reports of Deepwater Horizon to the
CDC although numbers for this event cover a wide range. CDC. (2010). "Health Surveillance- State of Louisiana."
Retrieved Feb. 9, 2012, from http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC.
(2010). "Health Surveillance- State of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of injured
or ill that are severe come from Deepwater Horizon McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects
of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies. Washington,
DC, The National Academies Press. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
vi
Based on best probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
Best estimate of a catastrophic event is 3/40, representing 3 major spills in the past 40 years. PMG/ERC (2002).
Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR),
Potomac Management Group, Environmental Research Consulting. Best estimate of severe illness in a catastrophic
event comes from reports of Deepwater Horizon to the CDC although numbers for this event cover a wide range.
CDC. (2010). "Health Surveillance- State of Louisiana." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC. (2010). "Health SurveillanceState of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of injured
or ill that are severe come from Deepwater Horizon, McCoy All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
vii
Based on high probability times the consequences of a catastrophic event, High probability of a catastrophic
event is based on best probability of a severe event (3/40, as above) multiplied by a scaling factor of 1.5,
representing the highest 10-year average of barrels spilled in the available PMG data. PMG/ERC (2002). Risk
Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR), Potomac
Management Group, Environmental Research Consulting. Best estimate of severe illness in a catastrophic event
comes from reports of Deepwater Horizon to the CDC although numbers for this event cover a wide range. (2010).
Gulf oil spill workers, like Exxon Valdez workers in 1989, complain of flulike symptoms. Syracuse Post-Standard.
Syracuse, NY, Associated Press, CDC. (2010). "Health Surveillance- State of Louisiana." Retrieved Feb. 9, 2012,
from http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC. (2010). "Health
Surveillance- State of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of injured
or ill that are severe come from Deepwater Horizon, McCoy All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures.
viii
Based on low probability of a catastrophic event times best estimate of less severe illnesses in a catastrophic
event. Low probability is based on the best estimate of a catastrophic event (3/40, representing 3 major spills in the
past 40 years) multiplied by a factor of .5, which represents the decreasing average barrels spilled. 1990-1999 had
377 half the barrels spilled as the period 1980-1999. PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill
Prevention, Preparedness, and Response Program (OSPRR), Potomac Management Group, Environmental Research
Consulting. Best estimate of less severe illness in a catastrophic event comes from reports of Deepwater Horizon to
the CDC although numbers for this event cover a wide range. CDC. (2010). "Health Surveillance- State of
Louisiana." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC. (2010). "Health SurveillanceState of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of
injured or ill that are severe come from Deepwater Horizon, McCoy, M. A. and J. A. Salerno (2010). Assessing the
Effects of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies.
Washington, DC, The National Academies Press. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
ix
Based on best probability of a catastrophic event times best estimate of less severe illnesses in a catastrophic
event. Best estimate of a catastrophic event is 3/40, representing 3 major spills in the past 40 years. PMG/ERC
(2002). Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR),
Potomac Management Group, Environmental Research Consulting. Best estimate of less severe illness in a
catastrophic event comes from reports of Deepwater Horizon to the CDC although numbers for this event cover a
wide range. CDC. (2010). "Health Surveillance- State of Louisiana." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC. (2010). "Health SurveillanceState of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of injured
or ill that are severe come from Deepwater Horizon, McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects
of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies. Washington,
DC, The National Academies Press. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
x
Based on high probability of a catastrophic event times the best estimate of less severe illnesses in a catastrophic
event. High probability of a catastrophic event is based on best probability of a severe event (3/40, as above)
multiplied by a scaling factor of 1.5, representing the highest 10-year average of barrels spilled in the available data
from PMG. PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and
Response Program (OSPRR), Potomac Management Group, Environmental Research Consulting. Best estimate of
severe illness in a catastrophic event comes from reports of Deepwater Horizon to the CDC although numbers for
this event cover a wide range. CDC. (2010). "Health Surveillance- State of Louisiana." Retrieved Feb. 9, 2012,
from http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_LA.asp, CDC. (2010). "Health
Surveillance- State of Mississippi." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_MS.asp, CDC. (2010). "Health SurveillanceState of Alabama." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_AL.asp, CDC. (2010). "Health SurveillanceState of Florida." Retrieved Feb. 9, 2012, from
http://www.bt.cdc.gov/gulfoilspill2010/2010gulfoilspill/surveillance_FL.asp. Estimates of the percentage of injured
or ill that are severe come from Deepwater Horizon, McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects
of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies. Washington,
DC, The National Academies Press. All numerical estimates have been rounded to one significant figure to reduce
overstating the precision of these measures.
xi
Moderate psychological damage per year on average based on the higher of 1) low PTSD and 2) moderate
depression per year on average. Low PTSD based on crossing low lives lost by low severe injuries. Moderate
depression based on crossing low combined damages (low lives lost, low severe injuries, and low economic
damages) by high duration. Low combined damages (defined as a combination of lives lost, severe damages, and
378 economic damages per year on average all low or two low and one moderate). Lives lost are considered low when
fewer than 10, moderate 10-100, and high if over 100 per year on average. Severe injuries are considered low if
fewer than 50, moderate 50-500, and high if over 500 per year on average. Economic damages are considered low if
less than $500M, moderate $500M to $5B, and high if over $5B per year on average. Duration is considered low if
measured in days, days to weeks, weeks, or weeks to months; moderate if measured in days to years, weeks to years,
months to years, or months; high if measured in years, decades, months to decades, or years to decades.
xii
Based on damage per gallon spilled times smaller estimated number of gallons spilled per year 2000-2010 (preDeepwater Horizon). Cost per gallon spilled from and estimated numbers of gallons spilled from PMG/ERC (2002).
Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR),
Potomac Management Group, Environmental Research Consulting, Tebeau, P. (2003). U.S. Coast Guard- Oil Spill
Response Research and Development Program- A Decade of Achievement. Washington, DC, Potomac Management
Group, U.S. Coast Guard..
xiii
Based on damage per gallon spilled times middle estimated number of gallons spilled per year post 1990. Cost
per gallon spilled from Tebeau and estimated numbers of gallons spilled from PMG/ERC PMG/ERC (2002). Risk
Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR), Potomac
Management Group, Environmental Research Consulting, Tebeau, P. (2003). U.S. Coast Guard- Oil Spill Response
Research and Development Program- A Decade of Achievement. Washington, DC, Potomac Management Group,
U.S. Coast Guard.. All numerical estimates have been rounded to one significant figure to reduce overstating the
precision of these measures.
xiv
Based on damage per gallon spilled times smaller estimated number of gallons spilled per year pre-1990 and prior
to additional oil spill legislation. Cost per gallon spilled from and estimated numbers of gallons spilled from
PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response
Program (OSPRR), Potomac Management Group, Environmental Research Consulting, Tebeau, P. (2003). U.S.
Coast Guard- Oil Spill Response Research and Development Program- A Decade of Achievement. Washington, DC,
Potomac Management Group, U.S. Coast Guard.. All numerical estimates have been rounded to one significant
figure to reduce overstating the precision of these measures.
xv
Based on lower estimates of Exxon Valdez (not CV), cited in Carson et al. Carson, R., R. Mitchell, M. Hanemann,
R. Kopp, S. Presser and P. Ruud (2003). "Contingent valuation and lost passive use: damages from the Exxon
Valdez oil spill." Environmental and Resource Economics 25(3): 257-286. All numerical estimates have been
rounded to one significant figure to reduce overstating the precision of these measures.
xvi
Based on recent high estimates of Deepwater Horizon from BP. News, B. (2011). "Gulf of Mexico Oil Spill: BP
Sues Transocean for $40bn." Retrieved April 20, 2011, Walsh, B. (2011). The BP oil spill, one year on: forgetting
the lessons of drilling in the Gulf, Time.. All numerical estimates have been rounded to one significant figure to
reduce overstating the precision of these measures.
xvii
Based on high damage to species and moderate aesthetic damage, across a wide area up to states.
xviii
Approximately 120 people were evacuated for a short-period due to the Port Arthur spill, while no people were
evacuated due to much larger spills of the Exxon Valdez, the Mega Borg or Deepwater Horizon Hewitt, P. and D.
Schiller (2010). Vessels' Collission Sparks Massive Oil Spill. Houston Chronicle. Houston, TX.. These 120 people
represent an average of 3 per year. Precautionary evacuations are more common for pipeline ruptures on land EPA
(1993). Understanding oil spills and oil spill response, U.S. Environmental Protection Agency Emergency Response
Division..
xix
Based on little disruption to non-emergency services.
xx
Individuals have an ability to limit much of their health exposure by avoiding coastal areas during the event.
However, home health effects are more difficult to avoid, including exposure to chemicals accumulated in seafood
from contaminated fisheries and from persistent chemicals remaining in the area and drinking water.
xxi
Most physical harm occurs at the time of touching oil or breathing volatile fumes. Additionally, oil spills contain
known carcinogens, which can lead to illnesses and death decades later. While the extent to which the additional
carcinogens from a spill contribute to cancer and other illnesses over the long term is unclear, there is not only the
potential for illness from long-term contamination, but also the perception of that long-term contamination. To the
extent that this category also reflects perception of risk through whether the harm is known or unknown, the
perception of long-term harms is relevant even if the long-term harms are minimal.
xxii
While scientific understanding of acute harms of oil spills are well known, chronic harms are largely unknown.
Additionally, harms to the environment reflect a complex ecosystem, and specific effects are difficult to trace
McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health.
Institute of Medicine of the National Academies. Washington, DC, The National Academies Press..
379 xxiii
Low combined uncertainty is based on the sum of the ratios of the ranges of consequences (the sum of the ratio
of the high estimate to the low estimate for lives lost, severe injuries, less severe injuries, and economic damages)
which equals 11.6. Combined uncertainty is a qualitative level based on the addition of the ratio of the high estimate
to the low estimate for deaths, severe injuries, less severe injuries, and economic damages per year on average. As
the low estimate for deaths is 0, we add 1 to the low estimate and to the high estimate, then take the ratio of high to
low to get a range of 5.2. The other three ranges are 3, 3, and 3.6 respectively. Adding these gives 14.7. Values
below 100 are considered low, 100 to 1000 are considered moderate, and above 1000 are considered high.
xxiv
(2010). "Deepwater Horizon oil spill." Retrieved Oct. 20, 2011, from
http://www.eoearth.org/article/Deepwater_Horizon_oil_spill, Lyon, S. and D. J. Weiss. (2010). "Oil Spills by the
Numbers." Retrieved Sept. 10,, 2010, from http://www.americanprogress.org/issues/2010/04/oil_numbers.html,
NOAA. (2010). "NOAA Expands Fishing Closed Area in Gulf of Mexico." Retrieved Sept. 20, 2010, from
http://www.noaanews.noaa.gov/stories2010/20100531_closure.html.
xxv
For example, the Deepwater Horizon accident of 2010, Winter, D. C. (2010). Interim Report on Causes of the
Deepwater Horizon Oil Rig Blowout and Ways to Prevent Such Events. Washington, DC, National Academies.
xxvi
Aguilera, I., M. Guxens, R. Garcia-Esteban, T. Corbella, M. Nieuwenhuijsen, C. Foradada and J. Sunyer (2009).
"Association between GIS-based exposure to urban air pollution during pregnancy and birth weight in the INMA
Sabadell cohort." Environmental Health Perspectives 117(9): 1322-1327, McCoy, M. A. and J. A. Salerno (2010).
Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National
Academies. Washington, DC, The National Academies Press.
xxvii
McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil Spill on Human
Health. Institute of Medicine of the National Academies. Washington, DC, The National Academies Press.
xxviii
Aguilera, I., M. Guxens, R. Garcia-Esteban, T. Corbella, M. Nieuwenhuijsen, C. Foradada and J. Sunyer (2009).
"Association between GIS-based exposure to urban air pollution during pregnancy and birth weight in the INMA
Sabadell cohort." Environmental Health Perspectives 117(9): 1322-1327, McCoy, M. A. and J. A. Salerno (2010).
Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National
Academies. Washington, DC, The National Academies Press.
xxix
McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health.
Institute of Medicine of the National Academies. Washington, DC, The National Academies Press.
xxx
Goldstein, B. D., H. J. Osofsky and M. Y. Lichtveld (2011). "The Gulf oil spill." New England Journal of
Medicine 364(14): 1334-1348.
xxxi
Baars, B. J. (2002). "The wreckage of the oil tanker 'Erika'--human health risk assessment of beach cleaning,
sunbathing and swimming." Toxicology Letters 128(1-3): 55-68, Rodríguez-Trigo, G., J. P. Zock and I. I. Montes
(2007). "Health effects of exposure to oil spills." Archivos de Bronconeumología 43(11): 628-635, Aguilera, F., J.
Méndez, E. Pásaro and B. Laffon (2010). "Review on the effects of exposure to spilled oils on human health."
Journal of Applied Toxicology 30(4): 291-301.
xxxii
Pérez-Cadahía, B., B. Laffon, M. Porta, A. Lafuente, T. Cabaleiro, T. López, A. Caride, J. Pumarega, A.
Romero and E. Pásaro (2008). "Relationship between blood concentrations of heavy metals and cytogenetic and
endocrine parameters among subjects involved in cleaning coastal areas affected by the 'Prestige' tanker oil spill."
Chemosphere 71(3): 447-455, Meo, S. A., A. M. Al-Drees, S. Rasheed, I. M. Meo, M. M. Khan, M. M. Al-Saadi
and J. R. Alkandari (2009). "Effect of duration of exposure to polluted air environment on lung function in subjects
exposed to crude oil spill into sea water." International Journal of Occupational Medicine and Environmental Health
22(1): 35-41, Aguilera, F., J. Méndez, E. Pásaro and B. Laffon (2010). "Review on the effects of exposure to spilled
oils on human health." Journal of Applied Toxicology 30(4): 291-301.
xxxiii
Goldstein, B. D., H. J. Osofsky and M. Y. Lichtveld (2011). "The Gulf oil spill." New England Journal of
Medicine 364(14): 1334-1348.
xxxiv
Ibid.
xxxv
Ibid.
xxxvi
McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil Spill on Human
Health. Institute of Medicine of the National Academies. Washington, DC, The National Academies Press.
xxxvii
; (2010). "Oil and Seafood: Evaluating the Risks for People Who Eat Fish and Shellfish." Oil Spill in the Gulf
of Mexico Retrieved Sept. 20, 2010, from http://gulfseagrant.tamu.edu/oilspill/oil_and_seafood.htm, Kim, J.
(2010). "What Are the Potential Physical Health Effects from the Gulf Oil Spill?" Retrieved Sept. 20, 2010, from
http://www.medscape.com/viewarticle/725593_3, McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of
the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies. Washington, DC,
380 The National Academies Press, O'Hanlon, L. (2010). "Children at Greatest Risk from Oil Spill." Retrieved Sept.
20,, 2010, from http://news.discovery.com/human/children-gulf-oil-hazards.html.
xxxviii
(2010). "Oil and Seafood: Evaluating the Risks for People Who Eat Fish and Shellfish." Oil Spill in the Gulf
of Mexico Retrieved Sept. 20, 2010, from http://gulfseagrant.tamu.edu/oilspill/oil_and_seafood.htm, Kim, J.
(2010). "What Are the Potential Physical Health Effects from the Gulf Oil Spill?" Retrieved Sept. 20, 2010, from
http://www.medscape.com/viewarticle/725593_3, McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of
the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National Academies. Washington, DC,
The National Academies Press, O'Hanlon, L. (2010). "Children at Greatest Risk from Oil Spill." Retrieved Sept.
20,, 2010, from http://news.discovery.com/human/children-gulf-oil-hazards.html.
xxxix
Ramseur, J. (2010). Oil Spills in US Coastal Waters: Background Governance, and Issues for Congress. C. R.
Service. Washington, DC, Library of Congress.
xl
US Fish and Wildlife Service (1998). Oil and Nature. U.S. Fish and Wildlife Service.
xli
Monson, D. H., D. F. Doak, B. E. Ballachey, A. Johnson and J. L. Bodkin (2000). Long-term impacts of the
Exxon Valdez oil spill on sea otters, assessed through age-dependent mortality patterns, National Academies Press.
97: 6562, Golet, G. H., P. E. Seiser, A. D. McGuire, D. D. Roby, J. B. Fischer, K. J. Kuletz, D. B. Irons, T. A. Dean,
S. C. Jewett and S. H. Newman (2002). "Long-term direct and indirect effects of the'Exxon Valdez' oil spill on
pigeon guillemots in Prince William Sound, Alaska." Marine Ecology Progress Series 241: 287-304, Peterson, C.
H., S. D. Rice, J. W. Short, D. Esler, J. L. Bodkin, B. E. Ballachey and D. B. Irons (2003). "Long-term ecosystem
response to the Exxon Valdez oil spill." Science 302(5653): 2082.
xlii
Ramseur, J. (2010). Oil Spills in US Coastal Waters: Background Governance, and Issues for Congress. C. R.
Service. Washington, DC, Library of Congress.
xliii
(2010). "Commercial Fishing- Exxon Valdez Oil Spill Trustee Council." Retrieved Oct. 19, 2010, from
http://www.evostc.state.ak.us/recovery/status_human_fishing.cfm.
xliv
Anderson, C. and R. LaBelle (2000). "Update of comparative occurrence rates for offshore oil spills." Spill
Science & Technology Bulletin 6(5-6): 303-321.
xlv
Ibid.
xlvi
\(2011). "Oil Spills and Disasters." Retrieved Dec. 12, 2010, from
http://www.infoplease.com/ipa/A0001451.html. Additional oil releases over 1 million barrels related to Hurricanes
Katrina and Rita in 2005 are considered as consequences of hurricane hazards rather than as consequences of oil
spills.
xlvii
(2010). "Oil Disaster by the Numbers." Retrieved Dec. 11, 2011, from
http://www.cnn.com/SPECIALS/2010/gulf.coast.oil.spill/interactive/numbers.interactive/index.html.
xlviii
Ramseur, J. (2010). Oil Spills in US Coastal Waters: Background Governance, and Issues for Congress. C. R.
Service. Washington, DC, Library of Congress.
xlix
Anderson and LaBelle are unable to update the rate for large events. Anderson, C. and R. LaBelle (2000).
"Update of comparative occurrence rates for offshore oil spills." Spill Science & Technology Bulletin 6(5-6): 303321. Additionally, the ability to establish trends based on only 3 U.S. incidents of 3 million barrels or more is even
more limited.
l
(2010). A Brief History of Offshore Oil Drilling. National Commission on the BP Deepwater Horizon Oil Spill and
Offshore Drilling. Washington, DC, Levin, A. (2010). Oil Spills Escalated in this Decade. USA Today.
li
The explosion and fire aboard the Piper Alpha, an oil platform in the North Sea, killed 167 of the 228 aboard.
Woolfson, C. and M. Beck (2000). "The British offshore oil industry after Piper Alpha." New Solutions 10(1): 1165. To include this number within our greatest number estimate, we round this upwards to 200.
lii
Most events are associated with no fatalities. In the U.S., 11 workers were killed in the explosion associated with
the Deepwater Horizon accident of 2010. Given 3 large spills in the past 40 years as the frequency, then we have
one fatality per year, as per endnote ii. If we use the larger number of fatalities from the Piper Alpha explosion with
a frequency of 1 event in 40 years then we have 4 fatalities per year, as per endnote iii. We do not add any deaths
due to toxicity or cancer risk. Baars notes that levels of exposure to carcinogens are very low. Others note acute but
not chronic health effects. Baars, B. J. (2002). "The wreckage of the oil tanker 'Erika'--human health risk
assessment of beach cleaning, sunbathing and swimming." Toxicology Letters 128(1-3): 55-68, Rodríguez-Trigo,
G., J. P. Zock and I. I. Montes (2007). "Health effects of exposure to oil spills." Archivos de Bronconeumología
43(11): 628-635, Aguilera, F., J. Méndez, E. Pásaro and B. Laffon (2010). "Review on the effects of exposure to
spilled oils on human health." Journal of Applied Toxicology 30(4): 291-301.
liii
Based on best probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
Best estimate of a catastrophic event is 3/40, representing 3 major spills in the past 40 years. PMG/ERC (2002).
381 Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR),
Potomac Management Group, Environmental Research Consulting. Best estimate of severe illness in a catastrophic
event comes from reports of Exxon Valdez, although numbers for this event cover a wide range. (2010). Gulf oil
spill workers, like Exxon Valdez workers in 1989, complain of flulike symptoms. Syracuse Post-Standard. Syracuse,
NY, Associated Press. See endnotes v, vi, and vii
liv
Etkin, D. (1999). Estimating cleanup costs for oil spills. 1999 International Oil Spill Conference, Washington,
DC, American Petroleum Institute.
lv
Low based on lower estimates of Exxon Valdez (not CV), cited in Carson, R., R. Mitchell, M. Hanemann, R.
Kopp, S. Presser and P. Ruud (2003). "Contingent valuation and lost passive use: damages from the Exxon Valdez
oil spill." Environmental and Resource Economics 25(3): 257-286. High based on recent high estimates of
Deepwater Horizon from BP. News, B. (2011). "Gulf of Mexico Oil Spill: BP Sues Transocean for $40bn."
Retrieved April 20, 2011, Walsh, B. (2011). The BP oil spill, one year on: forgetting the lessons of drilling in the
Gulf, Time. See endnotes xv and xvi.
lvi
Based on low probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
Low probability is based on the best estimate of a catastrophic event (3/40, representing 3 major spills in the past 40
years) multiplied by a factor of .5, which represents the decreasing average barrels spilled. 1990-1999 had half the
barrels spilled as the period 1980-1999. PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill
Prevention, Preparedness, and Response Program (OSPRR), Potomac Management Group, Environmental Research
Consulting. Best estimate of severe illness in a catastrophic event comes from reports of Exxon Valdez, although
numbers for this event cover a wide range. (2010). Gulf oil spill workers, like Exxon Valdez workers in 1989,
complain of flulike symptoms. Syracuse Post-Standard. Syracuse, NY, Associated Press. Estimates of the
percentage of injured or ill that are severe come from Deepwater Horizon, McCoy, M. A. and J. A. Salerno (2010).
Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health. Institute of Medicine of the National
Academies. Washington, DC, The National Academies Press. All numerical estimates have been rounded to one
significant figure to reduce overstating the precision of these measures. See endnote xii.
lvii
Based on best probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
Best estimate of a catastrophic event is 3/40, representing 3 major spills in the past 40 years. PMG/ERC (2002).
Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response Program (OSPRR),
Potomac Management Group, Environmental Research Consulting. Best estimate of severe illness in a catastrophic
event comes from reports of Exxon Valdez, although numbers for this event cover a wide range. (2010). Gulf oil
spill workers, like Exxon Valdez workers in 1989, complain of flulike symptoms. Syracuse Post-Standard. Syracuse,
NY, Associated Press. Estimates of the percentage of injured or ill that are severe come from Deepwater Horizon,
McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil Spill on Human Health.
Institute of Medicine of the National Academies. Washington, DC, The National Academies Press. All numerical
estimates have been rounded to one significant figure to reduce overstating the precision of these measures. See
endnote xiii.
lviii
Based on high probability of a catastrophic event times best estimate of severe illnesses in a catastrophic event.
High probability of a catastrophic event is based on best probability of a severe event (3/40, as above) multiplied by
a scaling factor of 1.5, representing the higher average barrels spilled in 1980-1989 rather than 1980-1999.
PMG/ERC (2002). Risk Assessment for the Coast Guard's Oil Spill Prevention, Preparedness, and Response
Program (OSPRR), Potomac Management Group, Environmental Research Consulting. Best estimate of severe
illness in a catastrophic event comes from reports of Exxon Valdez, although numbers for this event cover a wide
range. (2010). Gulf oil spill workers, like Exxon Valdez workers in 1989, complain of flulike symptoms. Syracuse
Post-Standard. Syracuse, NY, Associated Press. Estimates of the percentage of injured or ill that are severe come
from Deepwater Horizon, McCoy, M. A. and J. A. Salerno (2010). Assessing the Effects of the Gulf of Mexico Oil
Spill on Human Health. Institute of Medicine of the National Academies. Washington, DC, The National Academies
Press. All numerical estimates have been rounded to one significant figure to reduce overstating the precision of
these measures. See endnote xiv.
lix
(2010). A Brief History of Offshore Oil Drilling. National Commission on the BP Deepwater Horizon Oil Spill
and Offshore Drilling. Washington, DC, Smith, E. (2010). Offshore oil drilling might make environmental sense.
Washington Post. Washington, DC.
lx
(2010). A Brief History of Offshore Oil Drilling. National Commission on the BP Deepwater Horizon Oil Spill
and Offshore Drilling. Washington, DC, Chazan, G. (2010). New drilling rules imperil some rig operators. Wall
Street Journal. New York, NY.
lxi
Smith, E. (2010). Offshore oil drilling might make environmental sense. Washington Post. Washington, DC.
382 lxii
(2010). A Brief History of Offshore Oil Drilling. National Commission on the BP Deepwater Horizon Oil Spill
and Offshore Drilling. Washington, DC.
lxiii
EPA (1993). Understanding oil spills and oil spill response, U.S. Environmental Protection Agency Emergency
Response Division., Ch. 6
lxiv
(2010). "BP's Gulf oil spill response plans severely flawed." Retrieved Dec. 11, 2011, from
http://www.nola.com/news/gulf-oil-spill/index.ssf/2010/06/bps_gulf_oil_spill_response_pl.html.
lxv
EPA. (2010). "Oil Spills." Retrieved Dec. 11, 2011, from http://www.epa.gov/oilspill/.
lxvi
(2010). "What's the story on oil spills?" Retrieved Dec. 11, 2011, from
http://response.restoration.noaa.gov/audience_subtopic_entry.php?entry_id=184&subtopic_id=8&audience_id=2.
lxvii
Ibid.
lxviii
Ibid.
lxix
(2010). "The Oil Spill Liability Trust Fund." Retrieved Dec. 11, 2011, from
http://www.uscg.mil/npfc/About_NPFC/osltf.asp.
lxx
Ramseur, J. (2010). Oil Spills in US Coastal Waters: Background Governance, and Issues for Congress. C. R.
Service. Washington, DC, Library of Congress.
383 C O R P O R AT I O N
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