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i. Background & Surveillance
(1) Understand the Clinical Approach (health of individuals) and Public Health Approach (health
of populations)
- Clinical Approach – primary role is diagnosis and treatment of illness in individuals, preventive medicine (e.g
immunizations, smoking cessation, obesity counseling and other behavioral/lifestyle concerns) has only been addressed
recently – focus remains on the individuals.
- Public Health Approach – primary role is in control and prevention of disease in populations or groups of individuals,
some activities (e.g., diagnosing cases associated with outbreaks and treating persons with communicable diseases such
as tuberculosis or syphilis) may overlap with those in clinical medicine.
(2) Understand the roles of epidemiology in public health and the steps in solving health problems
-
Epidemiology is the study of disease in populations.
Epidemiological methods are used for disease surveillance, outbreak investigation, and observational studies to identify risk
factors of disease in both humans and animals.
Knowledge of these risk factors is used to direct further research investigation and to implement disease control measures.
The use of hazard analysis critical control point (HACCP) systems depends greatly on information produced by epidemiological
studies.
Epidemiological methods are used for disease surveillance to identify which hazards are the most important.
Epidemiological studies are also used to identify risk factors which may represent critical control points in the food production
system.
(3) Understand the Natural History and Spectrum of Disease and the Chain of Infection
Natural history of disease refers to the progression of a disease process in an individual over time, in
the absence of treatment.
Spectrum of disease
The onset of symptoms marks the transition from subclinical to clinical disease. Most diagnoses are
made during the stage of clinical disease. In some people, however, the disease process may never
progress to clinically apparent illness. In others, the disease process may result in illness that ranges
from mild to severe or fatal. This range is called the spectrum of disease. Ultimately, the disease
process ends either in recovery, disability or death.
(4) Understand basic epidemiological and public health terms (e.g., outbreak, epidemic, pandemic,
surveillance, risk, vector, etc.)
Basic Epidemiology Terms
Classical Epidemiology - population oriented, studies community origins of health problems related to nutrition, environment, human
behavior, and the psychological, social, and spiritual state of a population. The event is more aimed towards this type of epidemiology.
Clinical Epidemiology - studies patients in health care settings in order to improve the diagnosis and treatment of various diseases and the
prognosis for patients already affected by a disease. These can be further divided into:
Infectious Disease Epidemiology - heavily dependent on laboratory support
Chronic Disease Epidemiology - dependent on complex sampling and statistical methods
Cluster - An aggregation of cases over a particular period closely grouped in time and space, regardless of whether the number is more than
the expected number
Endemic Disease - Present at a continuous level throughout a population/geographic area; constant presence of an agent/health condition
within a given geographic area/population; refers to the usual prevalence of an agent/condition.
Epidemic - Large numbers of people over a wide geographical area are affected.
Etiology - Study of the cause of a disease.
Fomite - A physical object that serves to transmit an infectious agent from person to person. An example of this is lice on a comb. The comb
is the fomite and the lice are the agent that can make your hair itch.
Iatrogenic- An illness that is caused by a medication or physician.
Incubation Period - Time in between when a person comes into contact with a pathogen and when they first show symptoms or signs of
disease.
Index Case - First patient in an epidemiological study (also known as patient zero).
Morbidity - Rate of disease in a population.
Mortality - Rate of death in a population.
Outbreak - More cases of a particular disease than expected in a given area or among a specialized group of people over a particular period
of time.
Pandemic - An epidemic occurring over several countries or continents and affecting a large proportion of the population.
Plague - A serious, potentially life-threatening infectious disease that is usually transmitted to humans by the bites of rodent fleas. It was one
of the scourges of our early history. There are three major forms of the disease: bubonic, septicemic, and pneumonic.
Nosocomial Disease - An infection that is acquired in a hospital.
Risk - The probability that an individual will be affected by, or die from, an illness or injury within a stated time or age span. Risk of illness
is generally considered to be the same as the Incidence (see below) and the terms are used interchangeably. Age-span is not usually a
consideration in this usage. Risk of death from a particular illness is expressed as the Case Fatality Rate (Number deaths due to a
disease/Number with the disease) or the Cause-specific Mortality Rate (Number deaths due to a disease/Number in population). Age span is
a more common consideration in this last usage.
Surveillance - The systematic and ongoing collection, analysis, interpretation, and dissemination of health data. The purpose of public health
surveillance is to gain knowledge of the patterns of disease, injury, and other health problems in a community so that we can work towards
their prevention and control.
Vector - An animal that transmits disease but is not the cause of the disease itself. For example, a mosquito is a vector for malaria.
Zoonosis - An infectious disease that is transmissible from animals to humans.
Symptomatic - Showing symptoms or signs of injury.
Asymptomatic - Showing no signs or symptoms, although can be carrier of disease
Convalescent - Humans are also capable of spreading disease following a period of illness, typically thinking themselves cured of the
disease
Incubatory - When an individual transmits pathogens immediately following infection but prior to developing symptoms
Chronic - Someone who can transmit a disease for a long period of time
Genetic - has inherited a disease trait but shows no symptoms
Transient/Temporary - Someone who can transmit an infectious disease for a short amount of time
(5) Understand the role of Surveillance in identifying health problems, the 5-Step Process for
Surveillance and the types of surveillance
Five Step Process for Surveillance
1.
2.
3.
4.
5.
Identify, define, and measure the health problem of interest
Collect and compile data about the problem (and if possible, factors that influence it)
Analyze and interpret these data
Provide these data and their interpretation to those responsible for controlling the health problem
Monitor and periodically evaluate the usefulness and quality of surveillance to improve it for future use. (Surveillance of a
problem often does not include actions to control the problem
Another alternate version of five steps is often used as well. Either may appear in events; if unsure, go with the one above because people tend
not include action as part of surveillance.
1.
2.
3.
4.
5.
Data Collection - reports, electronic and vital records, registries, and surveys.
Data Analysis - ideally analyzed by location to find illness’s location so resources are sent there.
Data Interpretation - identifying person, place, time to find how and why health event happened.
Data Dissemination (Distribution) - announcements, reports, articles and media → to important people and public.
Link to Action - without action, no real purpose. (This version includes taking action)
Surveillance is the continuous gathering of health data needed to monitor the population's health status in order
to provide or revise needed services.
An effective disease surveillance system is essential to detecting disease outbreaks quickly before they spread, cost lives
and become difficult to control. Effective surveillance can improve disease outbreak detection in emergency settings,
such as in countries in conflict or following a natural disaster.
ii. Outbreak Investigation
(1) Analyze an actual or hypothetical outbreak
(2) Understand the Types of Epidemiological Studies – Experimental and Observational
Epidemiological Studies
Basic Studies
Ecological - comparisons of geographical locations
Cross Sectional - a survey, health questionnaire, "snapshot in time"
Case-Control - compare people with and without disease to find common exposures
Cohort - compare people with and without exposures to see what happens to each
Randomized Controlled Trial - human experiment
Quasi Experiments - research similarities with traditional experimental design or RCT, but lack element of random assignment to
treatment/control
Advantages and Disadvantages to Study Designs
(3) Identify the Steps in an Outbreak Investigation
Thirteen Steps to Investigating an Outbreak
Remember that this is a conceptual order, so steps have to be done simultaneously!
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Prepare for field work - Research and Supplies, Official Arrangements, Safety Protocols, and Contacts
Establish the Existence of an Outbreak - Consider Severity, Potential for Spread, Public Concern, and Availability of Resources
Verify the Diagnosis - Verify Procedures and Eliminate Experimental Error (and Other Errors/Biases, for That Matter)
Construct a Working Case Definition
Find Cases Systematically and Record Information - Time: Tables, Epi Curves; Place: Geographical Extent of Disease & Spot
Map; Identify By Demographic Information or Exposures to Risk Factor
Describe and Orient the Data in Terms of Person, Place, and Time - Descriptive Epidemiology
Develop Hypotheses (Agent/Host/Environment Triad) = Chain of Transmission
Evaluate Hypotheses - Analytical Studies (MUST Have a Control Group)
Refine Hypotheses if Necessary
Compare and Reconcile with Laboratory and/or Environmental Studies
Implement Control and Prevention Measures (ASAP!)
Initiate or Maintain Surveillance - Monitor Implementation: Track New Cases, Check the Outbreak’s Spread Outside Targeted
Area, Control and Change if Needed
Communicate Findings - Reports, To Important People and Public
(4) Identify the problem using person, place, and time triad – formulate case definition
Epidemiological Triads
Epidemiologists use two triads. The first is the foundation for descriptive epidemiology - person, place and time. The second is described in the
next section.
Chain of Transmission Triad
This is another common triad, which is an altered form of the Chain of Infection described below. It is a companion to the Epidemiological Triad.
It also has three components:
1. An external agent
2. A vector or fomite that transmits the disease
3. A susceptible host for the disease
This is used to define the major points of a disease case.
(5) Interpret epi curves, line listings, cluster maps, and subdivided tables
Using Epi-Curves
An epi-curve is a histogram that shows the course of an outbreak by plotting the number of cases of a condition according to the time of onset.
Epi-Curves fall into three classifications:
Point source epidemics occur when people are exposed to the same exposure over a limited, well defined period of time. The shape of the curve
commonly rises rapidly and contains a definite peak, followed by a gradual decline.
Continuous common source epidemics occur when the exposure to the source is prolonged over an extended period of time and may occur over
more than one incubation period. The down slope of the curve may be very sharp if the common source is removed or gradual if the outbreak is
allowed to exhaust itself.
Propagated (progressive source) epidemics occur when a case of disease serves later as a source of infection for subsequent cases and those
subsequent cases, in turn, serve as sources for later cases. The shape of this curve usually contains a series of successively larger peaks, reflective
of the increasing number of cases caused by person-to-person contact, until the pool of those susceptible is exhausted or control measures are
implemented. The distance between these peaks may be a rough indication of the incubation period of the disease. As the outbreak progresses,
the peaks flatten out (think of the variance around a mean over multiple generations).
(6) Generate hypotheses using agent, host, environment triad
(7) Recognize various fundamental study designs and which is appropriate for this outbreak
Study Designs
Trial
Advantages
Disadvantages
Most Scientifically Sound
Time Consuming
Best Measure of Exposure
Unethical for Harmful
Exposures
Most Expensive
Cohort Study
Most Accurate Observational Study
Time Consuming
Good Measure of Exposure
Expensive
Correct Time Sequence
Bad for Rare Diseases
Good for Rare Exposures
Possible Loss of Follow-up
Easy Risk Calculation
Case-Control Study
Can Study Rare Diseases
Possible Time-Order
Confusion
Relatively Less Expensive and
Relatively Fast
Error in Recalling Exposure
Good for Rare Diseases
Only 1 outcome
Good for Long Latency Periods
Cross-Sectional Study
Fastest
Possible Time-Order
Confusion
Least Expensive
Least Confidence in Findings
Good for more than 1 Outcome
(8) Evaluate the data by calculating and comparing simple rates and proportions as attack rate,
relative risk, odds-ratio and explaining their meaning
2x2 Table
A table which has two columns and rows for people with or without exposure and with or without disease; shows the number of people with each
characteristic.
Disease
No Disease
Exposure
a
b
No Exposure
c
d
Using the 2*2 Table, we can calculate the odds ratio and relative risk. These calculations allow comparisons between the case (group of people
with disease) and control (group of people with very similar characteristics to case but with no disease). One is the neutral value and means that
there is no difference between the groups compared; when the value is greater than one it means that there has been some difference between the
two groups, whether it was caused by bias, chance, or an actual relationship between the exposure and outcome is yet to be seen. The P-value is
the measure of how confident you are that your findings are correct. You can only trust your findings to be correct if the P-value is less than .05.
Odds Ratio - used in case-control study,
a⋅db⋅c
Relative Risk - used in cohort study,
a/(a+b)c/(c+d)
Attack Rate - the rate that a group experienced an outcome or illness equal to the number sick divided by the total in that group. (There should
be a high attack rate in those exposed and a low attack rate in those unexposed.)
For the exposed:
aa+b
For the unexposed:
cc+d
Chi-Square - used to determine the statistical significance of the difference indicated by the relative risk or odds ratio. Chi-Square compares
your observed values (a, b, c, and d) with the expected values for those same groups.
The expected value for a group is calculated by multiplying the column and row total, and then dividing by the overall total. Take group a
(disease and exposed) for example:
(a+b)(b+c)a+b+c+d
. After obtaining the expected values, you can use the equation
∑(observed−expected)2expected
to find the Chi-Square value. To determine the significance of this number, you must find the P-value using a table. The P-value is the measure
of how confident you are that your findings are not due to chance; for example, a P-value of 0.01 means there is a 10% chance your results were
a result of random fluctuations as opposed to a significant effect. The alpha number is a predetermined cutoff for the P-value, usually 0.05 (5%).
If the P-value is less than alpha, the data is significant
(9) Apply the Bradford Hill Criteria for Verifying the Cause of this outbreak
How to prove x caused y, or Causation
Hill's Criteria for Causation
Nine criteria must be met to establish a cause-and-effect relationship. This is commonly known as Hill's Criteria for Causation:
1.
2.
3.
4.
Strength of Association - relationship is clear and risk estimate is high
Consistency - observation of association must be repeatable in different populations at different times
Specificity - a single cause produces a specific effect
Alternative Explanations - consideration of multiple hypotheses before making conclusions about whether an association is
causal or not
5. Temporality - cause/exposure must precede the effect/outcome
6. Dose-Response Relationship - an increasing amount of exposure increases the risk
7. Biological Plausibility - the association agrees with currently accepted understanding of biological and pathological processes
8. Experimental Evidence - the condition can be altered, either prevented or accelerated, by an appropriate experimental process
9. Coherence - the association should be compatible with existing theory and knowledge, including knowledge of past cases and
epidemiological studies
(10) Recognize factors such as study design/biases, errors, confounding that influence results
Validity of Study Results: Error and Bias
Random error is the result of fluctuations around a true value because of the sample population. As the term implies, it is random, so it is
impossible to correct. However, random error can be reduced; some ways include increasing the sample size and making measurements more
precise, either by using a more accurate measurement device or by taking more trials. While these techniques would decrease random error,
they can also be expensive. Better measurement devices will cost more, and more trials and a larger sample size will mean more work.
Precision is a measure of random error that is inversely related, so increasing random error decreases precision.
Systematic error is any error other than random error. It is usually consistent and repeatable and often occurs from flawed equipment or
experiment design. For example, systematic error can occur if the markings on your ruler are wider. This would make the numeric
measurements less than what they actually are, making all data collected inaccurate. However, trends observed may still be preserved
(shifting a line vertically preserves a line, as it is a rigid motion).
Selection bias occurs when selection of participants for a study is affected by an unknown variable that is associated with the exposure and
outcome being measured.
Information bias occurs when bias is introduced through an error in measurement or observation
An example of information bias is recall bias. When studied, some subjects may more easily recall specific habits related to a disease or
condition than subjects not affected with the disease or condition.
Confounding bias is bias resulting from mixing effects of several factors. Unlike selection and information bias, confounding bias deals
with causation and not variations in study results.
iii. Patterns, Control, and Prevention
(1) Identify patterns, trends of epidemiologic data in charts, tables and graphs.
(2) Using given data, calculate disease risk and frequency ratio, proportion, incidence proportion
(attack rate), incidence rate, prevalence and mortality rate
(3) Understand the Strategies of Disease Control
(4) Understand Strategies for Prevention-the Scope and Levels of Prevention
(5) Propose a reasonable set of prevention strategies for a public health problem once the cause has been determined
Disease Prevention
For prevention strategies relating to the yearly topics, please see Disease Detectives#Yearly Topics.
Primordial prevention - intervention at the very beginning to avoid the development of risk factors the population may be exposed to. Often
deals with changing physical and social environments.
Primary prevention - early intervention to avoid initial exposure to agent of disease preventing the process from starting.
Secondary prevention - during the latent stage (when the disease has just begun), process of screening and instituting treatment may prevent
progression to symptomatic disease.
Tertiary prevention - during the symptomatic stage (when the patient shows symptoms), intervention may arrest, slow, or reverse the
progression of disease.
Quaternary prevention - set of health activities to mitigate or avoid consequences of unnecessary/excessive intervention of the health
system. Social credit that legitimizes medical intervention may be damaged if doctors don't prevent unnecessary medical activity and its
consequences.
Immunity
Active Immunity- occurs when the person is exposed to a live pathogen, develops the disease, and becomes immune as a result of the
primary immune response
Passive Immunity-short-term immunization by the injection of antibodies, such as gamma globulin, that are not produced by the recipient's
cells. Naturally acquired passive immunity occurs during pregnancy, in which certain antibodies are passed from the maternal into the fetal
bloodstream.
Herd Immunity- protecting a whole community from disease by immunizing a critical mass of its populace. Vaccination protects more than
just the vaccinated person. By breaking the chain of an infection’s transmission, vaccination can also protect people who haven’t been
immunized. But to work, this protection requires that a certain percentage of people in a community be vaccinated.
The Basics: Epidemiology - Epidemiology is the study of distribution and determinants of health-related states in specified populations, and the application of this to control health problems. There are four basic reasons for why
disease detectives study and research outbreaks and epidemics. These reasons are: Control and Prevention, Research Opportunities, Training, and Legal Concerns. Classical Epidemiology - population oriented, studies community
origins of health problems related to nutrition, environment, human behavior, and the psychological, social, and spiritual state of a population. The event is more aimed towards this type of epidemiology. Clinical Epidemiology studies patients in health care settings in order to improve the diagnosis and treatment of various diseases and the prognosis for patients already affected by a disease. Infectious Disease Epidemiology - heavily dependent on
laboratory support Chronic Disease Epidemiology - dependent on complex sampling and statistical methods. There are all sorts of classification systems for epi and the most fundamental and common system is Descriptive epi (e.g.
person, place and time) vs Analytic epi (hypothesis testing - study design). Basic Epidemiology Terms Cluster - An aggregation of cases over a particular period closely grouped in time and space, regardless of whether the number is
more than the expected number Endemic Disease - Present at a continuous level throughout a population/geographic area; constant presence of an agent/health condition within a given geographic area/population; refers to the
usual prevalence of an agent/condition. Epidemic - Large numbers of people over a wide geographical area are affected. Etiology - Study of the cause of a disease. Fomite - A physical object that serves to transmit an infectious
agent from person to person. An example of this is lice on a comb. The comb is the fomite and the lice are the agent that can make your hair itch. Iatrogenic- An illness that is caused by a medication or physician. Incubation Period Time in between when a person comes into contact with a pathogen and when they first show symptoms or signs of disease. Index Case - First patient in an epidemiological study (also known as patient zero). Morbidity - Rate of
disease in a population. Mortality - Rate of death in a population. Outbreak - More cases of a particular disease than expected in a given area or among a specialized group of people over a particular period of time. Pandemic - An
epidemic occurring over several countries or continents and affecting a large proportion of the population. Plague - A serious, potentially life-threatening infectious disease that is usually transmitted to humans by the bites of rodent
fleas. It was one of the scourges of our early history. There are three major forms of the disease: bubonic, septicemic, and pneumonic. Nosocomial Disease - An infection that is acquired in a hospital. Risk - The probability that an
individual will be affected by, or die from, an illness or injury within a stated time or age span. Risk of illness is generally considered to be the same as the Incidence (see below) and the terms are used interchangeably. Age-span is
not usually a consideration in this usage. Risk of death from a particular illness is expressed as the Case Fatality Rate (Number deaths due to a disease/Number with the disease) or the Cause-specific Mortality Rate (Number
deaths due to a disease/Number in population). Age span is a more common consideration in this last usage. Surveillance - The systematic and ongoing collection, analysis, interpretation, and dissemination of health data. The
purpose of public health surveillance is to gain knowledge of the patterns of disease, injury, and other health problems in a community so that we can work towards their prevention and control. Vector - An animal that transmits
disease but is not the cause of the disease itself. For example, a mosquito is a vector for malaria. Zoonosis - An infectious disease that is transmissible from animals to humans. Symptomatic - Showing symptoms or signs of injury.
Asymptomatic - Showing no signs or symptoms, although can be a carrier of disease. Incidence, Prevalence, and Duration The incidence of an illness is the number of new instances of disease in a population over a given time
period. It is expressed as "X cases/Y population/ Z time". The prevalence of an illness is the number of affected persons in the population at any given point in time. It is expressed as "X cases/Y population". There are two major
ways in which prevalence is measured: period prevalence and point prevalence. Think of point prevalence as a snapshot of the population and its rate of a certain disease at a point in time while period prevalence tracks the
prevalence over a certain duration. Note the only difference is that incidence (I) includes time while prevalence (P) does not. Time (D) reflects the duration of the illness or condition. If two conditions have the same incidence in a
population, the one with the longer duration will have the greater prevalence. Importantly, , so with two of the variables, it is possible to solve for the third. Thirteen Steps to Investigating an Outbreak Prepare for field work - Research
and Supplies, Official Arrangements, Safety Protocols, and Contacts Establish the Existence of an Outbreak - Consider Severity, Potential for Spread, Public Concern, and Availability of Resources Verify the Diagnosis - Verify
Procedures and Eliminate Experimental Error and Other Biases Construct a Working Case Definition I Find Cases Systematically and Record Information - Time: Tables, Epi Curves; Place: Geographical Extent of Disease & Spot Map;
Identify By Demographic Information or Exposures to Risk Factor Describe and Orient the Data in Terms of Person, Place, and Time - Descriptive Epidemiology Develop Hypotheses (Agent/Host/Environment Triad) = Chain of
Transmission Evaluate Hypotheses - Analytical Studies (MUST Have a Control Group) Refine Hypotheses if Necessary Compare and Reconcile with Laboratory and/or Environmental Studies I Implement Control and Prevention
Measures (ASAP!) Initiate or Maintain Surveillance - Monitor Implementation: Track New Cases, Check the Outbreak’s Spread Outside Targeted Area, Control and Change if Needed Communicate Findings - Reports, To Important
People and Public Five Step Process for Surveillance Identify, define, and measure the health problem of interest I Collect and compile data about the problem (and if possible, factors that influence it) I Analyze and interpret these
data I Provide these data and their interpretation to those responsible for controlling the health problem I Monitor and periodically evaluate the usefulness and quality of surveillance to improve it for future use. (Surveillance of a
problem often does not include actions to control the problem OR Data Collection - reports, electronic and vital records, registries, and surveys. Data Analysis - ideally analyzed by location to find illness’s location so resources are
sent there. Data Interpretation - identifying person, place, time to find how and why health events happened. Data Dissemination (Distribution) - announcements, reports, articles and media → to important people and the public. Link
to Action - without action, no real purpose. (This version includes taking action) Hill's Criteria for Causation Nine criteria must be met to establish a cause-and-effect relationship. Strength of Association - relationship is clear and
risk estimate is high Consistency - observation of association must be repeatable in different populations at different times Specificity - a single cause produces a specific effect Alternative Explanations - consideration of multiple
hypotheses before making conclusions about whether an association is causal or not Temporality - cause/exposure must precede the effect/outcome Dose-Response Relationship - an increasing amount of exposure increases the
risk Biological Plausibility - the association agrees with currently accepted understanding of biological and pathological processes Experimental Evidence - the condition can be altered, either prevented or accelerated, by an
appropriate experimental process Coherence - the association should be compatible with existing theory and knowledge, including knowledge of past cases and epidemiological studies Types of Carriers/Vectors Convalescent Humans are also capable of spreading disease following a period of illness, typically thinking themselves cured of the disease Incubatory - When an individual transmits pathogens immediately following infection but prior to
developing symptoms Chronic - Someone who can transmit a disease for a long period of time Genetic - has inherited a disease trait but shows no symptoms Transient/Temporary - Someone who can transmit an infectious disease
for a short amount of time Epidemiological Triads Epidemiologists use two triads. The first is the foundation for descriptive epidemiology - person, place and time. The second is the Chain of Transmission Triad It has three
components, An external agent, A vector or fomite that transmits the disease, and A susceptible host for the disease. This is used to define the major points of a disease case. Epidemiological Studies Ecological - comparisons of
geographical locations Cross Sectional - a survey, health questionnaire, "snapshot in time" Case-Control - compare people with and without disease to find common exposures Cohort - compare people with and without exposures to
see what happens to each Randomized Controlled Trial - human experiment Quasi Experiments - research similarities with traditional experimental design or RCT, but lack element of random assignment to treatment/control.
Advantages and Disadvantages to Study Designs Trial Most Scientifically Sound Best Measure of Exposure Time Consuming Unethical for Harmful Exposures Most Expensive Cohort Study Most Accurate Observational Study Good
Measure of Exposure Correct Time Sequence Good for Rare Exposures Easy Risk Calculation Time Consuming Expensive Bad for Rare Diseases Possible Loss of Follow-up Case-Control Study Can Study Rare Diseases Relatively
Less Expensive and Relatively Fast Good for Rare Diseases Good for Long Latency Periods Possible Time-Order Confusion Error in Recalling Exposure Only 1 outcome Cross-Sectional Study Fastest Least Expensive Good for more
than 1 Outcome Possible Time-Order Confusion Least Confidence in Findings 2x2 Table A table which has two columns and rows for people with or without exposure and with or without disease; shows the number of people with
each characteristic. Disease and No Disease on top, Exposureand no No Exposure on side, abcd in boxes. Using the 2*2 Table, we can calculate the odds ratio and relative risk. These calculations allow comparisons between the
case (group of people with disease) and control (group of people with very similar characteristics to case but with no disease). One is the neutral value and means that there is no difference between the groups compared; when the
value is greater than one it means that there has been some difference between the two groups, whether it was caused by bias, chance, or an actual relationship between the exposure and outcome is yet to be seen. The P-value is
the measure of how confident you are that your findings are correct. You can only trust your findings to be correct if the P-value is less than .05. Odds Ratio - used in case-control study (ad/bc) Relative Risk - used in cohort study
[a/(a+b)]/[c/(c/d)] Attack Rate - the rate that a group experienced an outcome or illness equal to the number sick divided by the total in that group. (There should be a high attack rate in those exposed and a low attack rate in those
unexposed.) For the exposed: a/a+b For the unexposed: c/c+d Chi-Square - used to determine the statistical significance of the difference indicated by the relative risk or odds ratio. Chi-Square compares your observed values (a, b,
c, and d) with the expected values for those same groups. The expected value for a group is calculated by multiplying the column and row total, and then dividing by the overall total. (a+b)(b+c)/(a+b+c+d) Take group a (disease and
exposed) for example: After obtaining the expected values, you can use the equation to find the Chi-Square value. To determine the significance of this number, you must find the P-value using a table. The P-value is the measure of
how confident you are that your findings are not due to chance; for example, a P-value of 0.01 means there is a 10% chance your results were a result of random fluctuations as opposed to a significant effect. The alpha number is a
predetermined cutoff for the P-value, usually 0.05 (5%). If the P-value is less than alpha, the data is significant Using Epi-Curves An epi-curve is a histogram that shows the course of an outbreak by plotting the number of cases of a
condition according to the time of onset. Epi-Curves fall into three classifications: Point source epidemics occur when people are exposed to the same exposure over a limited, well defined period of time. The shape of the curve
commonly rises rapidly and contains a definite peak, followed by a gradual decline. Continuous common source epidemics occur when the exposure to the source is prolonged over an extended period of time and may occur over
more than one incubation period. The down slope of the curve may be very sharp if the common source is removed or gradual if the outbreak is allowed to exhaust itself. Propagated (progressive source) epidemics occur when a
case of disease serves later as a source of infection for subsequent cases and those subsequent cases, in turn, serve as sources for later cases. The shape of this curve usually contains a series of successively larger peaks,
reflective of the increasing number of cases caused by person-to-person contact, until the pool of those susceptible is exhausted or control measures are implemented. The distance between these peaks may be a rough indication
of the incubation period of the disease. As the outbreak progresses, the peaks flatten out (think of the variance around a mean over multiple generations). Validity of Study Results: Error and Bias Random error is the result of
fluctuations around a true value because of the sample population. As the term implies, it is random, so it is impossible to correct. However, random error can be reduced; some ways include increasing the sample size and making
measurements more precise, either by using a more accurate measurement device or by taking more trials. While these techniques would decrease random error, they can also be expensive. Better measurement devices will cost
more, and more trials and a larger sample size will mean more work. Precision is a measure of random error that is inversely related, so increasing random error decreases precision. Systematic error is any error other than random
error. It is usually consistent and repeatable and often occurs from flawed equipment or experiment design. For example, systematic error can occur if the markings on your ruler are wider. This would make the numeric
measurements less than what they actually are, making all data collected inaccurate. However, trends observed may still be preserved (shifting a line vertically preserves a line, as it is a rigid motion). Selection bias occurs when
selection of participants for a study is affected by an unknown variable that is associated with the exposure and outcome being measured. Information bias occurs when bias is introduced through an error in measurement or
observation An example of information bias is recall bias. When studied, some subjects may more easily recall specific habits related to a disease or condition than subjects not affected with the disease or condition. Confounding
bias is bias resulting from mixing effects of several factors. Unlike selection and information bias, confounding bias deals with causation and not variations in study results. Disease and Disease Transmission The process begins
with the appropriate exposure to or accumulation of factors sufficient for the disease process to begin in a susceptible host. For an infectious disease, the exposure is a microorganism. For cancer, the exposure may be a factor that
initiates the process, such as asbestos fibers or components in tobacco smoke (for lung cancer), or one that promotes the process, such as estrogen (for endometrial cancer). After the disease process has been triggered,
pathological changes then occur without the individual being aware of them. This stage of subclinical disease, extending from the time of exposure to onset of disease symptoms, is usually called the incubation period for infectious
diseases, and the latency period for chronic diseases. During this stage, disease is said to be asymptomatic (no symptoms) or inapparent. This period may be as brief as seconds for hypersensitivity and toxic reactions to as long as
decades for certain chronic diseases. Even for a single disease, the characteristic incubation period has a range. For example, the typical incubation period for hepatitis A is as long as 7 weeks. The latency period for leukemia to
become evident among survivors of the atomic bomb blast in Hiroshima ranged from 2 to 12 years, peaking at 6–7 years. The onset of symptoms marks the transition from subclinical to clinical disease. Most diagnoses are made
during the stage of clinical disease. In some people, however, the disease process may never progress to clinically apparent illness. In others, the disease process may result in illness that ranges from mild to severe or fatal. This
range is called the spectrum of disease. Ultimately, the disease process ends either in recovery, disability or death. Chain of Infection Agent leaves reservoir through portal of exit, and is conveyed by some mode of transmission, and
enters the appropriate portal of entry to infect a susceptible host. Agent - A microbial organism with the ability to cause disease. Reservoir - A place where agents can thrive and reproduce. Portal of Exit - A place of exit providing a
way for an agent to leave the reservoir; the route a pathogen takes out of an infected host. Portals of exit tend to be fairly well defined. What serve as portals of exit are often not terribly surprising, at least, once something is known
of how and where a pathogen replicates and enters new hosts. Respiratory infections tend to utilize the mouth and nose as portals of exit. Gastrointestinal diseases tend to exit in feces or saliva, depending on the site of replication.
Sexually transmitted diseases tend to have portals of exit at the urethra or genital region. Blood-borne diseases tend to exit via arthropods, needles, bleeding, or hyperdermic syringes. A more general portal of exit occurs when an
infected animal is butchered or an infected person undergoes surgery. The three most common portals of exit are the skin, gastrointestinal tract, and respiratory tract. Mode of Transmission - Method of transfer by which the
organism moves or is carried from one place to another; the transfer of disease-causing microorganisms from one environment to another, particularly from an external environment to a susceptible individual. There are three
general categories of transmission: contact, vehicle, and vector. Portal of Entry - An opening allowing the microorganism to enter the host; the route a pathogen takes to enter a host. Just as with the portals of exit, many pathogens
have preferred portals of entry. Many pathogens are not able to cause disease if their usual portal of entry is artificially bypassed. The most common portal of entry is the mucous membrane of the respiratory tract. Susceptible Host
- A person who cannot resist a microorganism invading the body, multiplying, and resulting in infection. Chain of Infection: Diagram and Explanation Characteristics of Agents Infectivity - capacity to cause infection in a susceptible
host Pathogenicity - capacity to cause disease in a host Virulence - severity of disease that the agent causes to host Modes of Disease Transmission Contact Transmission - sub-categories include direct (person-to-person), indirect
(fomite), or droplet. Direct Contact - occurs through touching, kissing, dancing, etc . To prevent direct contact transmission, wear gloves and masks, etc. Indirect Contact - occurs from a reservoir via inanimate objects called fomites.
Fomites are basically almost anything an infected individual or reservoir can touch, upon which can be left a residue of contagious pathogen. Exceptions include the various inanimates referred to as vehicles: food, air, and liquids.
Typically, it is more difficult to avoid indirect contact transmission than it is to avoid direct contact transmission. A certain degree of organismal durability may be necessary to survive passage on a fomite. The best way to prevent
indirect contact transmission is by avoiding contact with fomites, avoiding contact of hands with mucous membranes, especially when handling or potentially handling fomites, the use of barriers when handling fomites, and
disinfecting fomites before handling. Droplet Transmission - consequence of being coughed, sneezed, or spit on. To be considered droplet transmission, mucous droplets must still be traveling with the velocity imparted on it leaving
the mouth. As a rule of thumb, this is up to one meter after exiting the mouth. Any further and this is considered airborne transmission. Given interaction within one meter of people is certainly more difficult to avoid droplet
transmission than it is to avoid either direct or indirect transmission. Not surprisingly, it is especially respiratory diseases that are transmitted by droplets. Vehicle Transmission - transmission via a medium such as food, air, and
liquid, which are all routinely taken into the body, and thus serve as vehicles into the body. Airborne Transmission - occurs via droplets (typically mucous droplets) where droplets are liquids that remain airborne whether as aerosols
(very small droplets) or associated with dust particles. An example is within airliners where economizing measures reduces the turnover of cabin air and consequently increases air recycling. Organisms which can find their way into
the air and remain viable thus have repeated opportunities to infect passengers. It requires greater organismal durability that droplet transmission simply because of the length of time the microorganism is exposed to the air, before
infecting a new host, is longer. Increased durability is to the effects of desiccation, exposure to sunlight, etc. This is why breathing does not typically result in the acquisition of disease. Food-borne Transmission - any number of
pathogens are found in food and not killed during processing may be transmitted via food product. Salmonella especially tends to be part of the normal flora of chickens and consequently associated with chicken products.
Water-borne Transmission - fecal contaminated water. Generally, this is via sewage contaminated water supplies. It is especially gastrointestinal pathogens that are present in feces and therefore which rely on this type of
transmission. Vector Transmission - no entry. Portals of Entry to the Nervous System - the brain is typically fairly resistant to bacterial infection. There are four common portals of entry to the nervous system. For an organism to take
advantage of these routes, they must display increasingly specialized adaptations as read from first to last: parenteral, via the blood, via the lymphatic systems, and up the peripheral nerve axons. Ordering of blood and lymphatic
system was arbitrary and not intended to imply that one serves as a significantly more difficult portal to take advantage of than the other. The 2 main categories are Direct and Indirect Transmission (not Contact). Direct
Transmission includes Direct Contact and Droplet Spread. Examples of direct contact includes things like kissing, biting, and contact with soil containing infectious agents that penetrate the skin or enter wounds. Droplet spread is
essentially an "in your face sneeze or cough". The idea is up close and immediate. Indirect Transmission includes Airborne, Vehicles and Vectors. Airborne transmission involves dust or droplet nuclei (the latter are essentially little
(<5 micron) particles that remain suspended in the air. Time and distance are both greater than for droplet spread (distance >6-8 ft). Vehicles include things like food, water, or fomites. Vehicles may passively carry pathogens or
may promote growth or toxin production. Vectors are arthropods (e.g. mosquitoes, flies, lice, ticks) that spread infectious agents. If the agent multiplies or undergoes a change in life stage (as with malaria) within the vector, the
vector is said to be a Biologic Vector. If the agent is simply carried from one place to another (think of a fly landing on feces and then a bowl of potato salad) it is a Mechanical Vector. Generally vector-borne diseases are thought of
only in the context of biologic vectors. Rabies from a dog bite would be direct contact, not a vector. Note that while terms like food-borne, waterborne and zoonotic are not really included in this system - they are still valid. Disease
Prevention Primordial prevention - intervention at the very beginning to avoid the development of risk factors the population may be exposed to. Often deals with changing physical and social environments. Primary prevention early intervention to avoid initial exposure to agent of disease preventing the process from starting. Secondary prevention - during the latent stage (when the disease has just begun), process of screening and instituting treatment
may prevent progression to symptomatic disease. Tertiary prevention - during the symptomatic stage (when the patient shows symptoms), intervention may arrest, slow, or reverse the progression of disease. Quaternary prevention set of health activities to mitigate or avoid consequences of unnecessary/excessive intervention of the health system. Social credit that legitimizes medical intervention may be damaged if doctors don't prevent unnecessary
medical activity and its consequences. Even if you know very little about the disease, you can brainstorm ideas from the chain of infection for the disease. For example, if the chain of infection describes that a disease is comes in
contact with humans through sand at the beach and enters the body through any openings (mouth, nose, etc.), a prevention method could be putting up signs at beaches reminding the public to wash their hands before consuming any
food. Immunity Active Immunity- occurs when the person is exposed to a live pathogen, develops the disease, and becomes immune as a result of the primary immune response Passive Immunity-short-term immunization by the
injection of antibodies, such as gamma globulin, that are not produced by the recipient's cells. Naturally acquired passive immunity occurs during pregnancy, in which certain antibodies are passed from the maternal into the fetal
bloodstream. Herd Immunity- protecting a whole community from disease by immunizing a critical mass of its populace. Vaccination protects more than just the vaccinated person. By breaking the chain of an infection’s
transmission, vaccination can also protect people who haven’t been immunized. But to work, this protection requires that a certain percentage of people in a community be vaccinated.Populations and Samples The population is the
entire set under study. For example, the length of dung beetles. Because it is impossible to measure the length of every single dung beetle on planet earth, statistics use sampling. They take a subset of the dung beetles called a
sample and use measurements from the sample to make inferences about the population as a whole. A population parameter is a characteristic of a population; for example, suppose 84% of Philadelphians preferred chocolate ice
cream over vanilla ice cream. A sample statistic is an attribute of a sample; for example, we randomly sampled 10 Philadelphians and found that 70% preferred chocolate ice cream over vanilla ice cream. Distribution
Characteristics Distributions are characterized by center, shape, and spread. Central Tendency A central tendency is a "typical" or "middle" value for a distribution. Mean - Average of all of the values. Means should not be used if the
population is very skewed, as means are easily affected by extreme values. Median - The middle value that separates the data into two halves. Medians are not as affected by extreme values, e.g. the mean number of arms per
person in the world is less than 2, but the median is exactly 2. Mode - The most frequently occurring value in the data set. Modes are useful for describing "peaks" in a
distribution. Shape Skewedness - Distributions that have a few extreme values on the higher side are skewed to the right. Distributions that have a few extreme values
on
the lower side are skewed to the left. Peaks - If a distribution has no peaks, it is uniform. If it has one peak, it is unimodal. If it has two peaks, it is bimodal. Normal
distributions - A set of data that is unimodal, symmetrical, and continues off to infinity on both tails. Also known as a Gaussian distribution. In the normal distribution,
the mean, median, and mode are all the same. Technically, the normal distribution is continuous and infinite but can be approximated with discrete values. Variability
Variability, scatter, and spread all have the same meaning: the extent to which a set of data is dispersed. Range - The difference between the largest and smallest
values in a set. It is not very useful except to get a sense of the possible spread of a distribution. Interquartile Range (IQR) - The difference between the 75th (third
quartile, or ) and 25th (first quartile or ) percentiles of a data set. To find and , find the median of the data set, then divide the data set into two new sets, one with the
data from the median up to the maximum and the other with the data from the median down to the minimum. The median values of the two new sets are and . The
IQR is used with the median and is the most robust measure of variability, i.e. outliers do not affect the IQR as much. Variance - Average of the squared differences
from the mean. The variance gives a very vague sense of how far apart the values in a data set are compared to the mean. Standard Deviation (SD) - The square root of
the variance. Quantifies the spread in a data set in the same units as the original data. Standard deviation is, in a sense, the average distance away from the mean. A
low SD indicates that the data tends to be close to the mean and a high SD indicates the data is far away from the mean. SD and variance are used with the mean.
Unlike IQR, SD is not resistant to outliers.68-95-99.7 Rule - This rule states that 68% of the values in a normally distributed data fall within 1 SD of the mean, 95% fall
within 2 SD of the mean, and 99.7% fall within 3 SD of the mean. Standard Error of the Mean (SEM) - The SEM measures the variability of the mean of different
samples around the population mean. Therefore, as a general rule, the SEM decreases as sample size increases. Correlation When two variables are revealed to have
a relationship using statistical measures, the variables have a correlation. This correlation can be positive, negative, or zero. Without doing an experiment or trial, it is
impossible to conclude that one variable causes another variable to act in some way. There is always the possibility of a third lurking or confounding variable that the
original data does not account for. In this case, wording is extremely important. Correlation causation. The correlation coefficient is a measure of the scatter around a
linear relationship. It does NOT apply when a relationship is non-linear. Because the correlation coefficient is difficult to calculate by hand, exam writers will typically
give the value and ask for the interpretation of the value. The correlation coefficient is always and a value of 1 indicates a perfectly positively linear relationship.
Conversely, a value of 0 indicates no relationship. Typically, is termed strong. Standardization The standard score or z score rescales the standard deviation of a
normally distributed data set to 1 and mean to 0. Thus, we can model all normally distributed data using a single normal distribution with mean 0 and SD 1. Infant
Mortality Rate The infant mortality rate is the ratio of deaths to births. Rates in epidemiology are often expressed as a per-1000 or per-1 million, so if the infant
mortality rate were 0.05, we could write that as 50 deaths per 1000 births. Inference Statistical inference is the process of inferring something about a population
given a sample.Confidence Intervals Confidence intervals are used to estimate population attributes given statistics from a sample. However, confidence intervals do
not take into account confounding or biases. The confidence level determines how wide the interval is. A common confidence level is 95%: "I am 95% certain that the
interval captures the true population proportion/mean. This means that if the process used to obtain the interval were repeated many, many times, the interval
generated would capture the true population proportion/mean 95% of the time." Confidence Intervals for Proportions - Used to define a range of values within which a
proportion may lie. Confidence Intervals of Means - Used to define a range of values within which a mean may lie. Inference Tests In an inference test, we use
statistical inference to determine if a statement is likely or unlikely. We first create a null hypothesis ("the default"). For example, suppose that you were investigating
whether drinking the punch at the party is associated with developing salmonellosis symptoms. The null hypothesis would be that eating cabbage is not associated
with developing salmonellosis symptoms. The alternative hypothesis would be that eating cabbage is associated with developing salmonellosis symptoms. You
would then look at your sample (people who were at the party and did/did not drink the punch and did/did not develop salmonellosis symptoms) and ask, How likely is
it that this result occurred by chance, i.e. if the null hypothesis were true? This probability is called the p-value. Statisticians generally use a threshold of 0.05. If the
p-value is below 0.05, the result is significant, and you reject the null hypothesis. Otherwise, you fail to reject the null hypothesis. Error A Type I error occurs if you
reject (the null hypothesis) when it is true. The probability of a Type I error is , the significance level. A Type II error occurs if you fail to reject when it is false ( is
true). The probability of a Type II error is represented by the letter . The power of the test is the probability that the null hypothesis is rejected if it is false. The power
of the test is equal to . Chi Square A chi-square is a statistical measure used to determine the difference between an expected value and an observed value. In
epidemiology, it can be used to compare information from different groups (i.e. age) to a local or national average. Z-Test Used to compare two means when the
population variances are known and the sample size is greater than 30. When the student's T distribution becomes sufficiently similar (by the Central Limit
Theorem) to the Z distribution that we are able to use the Z distribution to compute the test. T-Test Used to compare two means when sample size is less than 30.
The T test statistic is computed the same way as the Z test statistic is computed; however, the test statistic is compared to a table for the T distribution. Paired
T-Test Used to compare multiple sets of data. Fischer's Exact Test Fischer's test searches for non-random associations between two categorical variables.
McNemar's Test The McNemar Test is similar to a Chi-Square, except that it uses matched paired data. Maentel Haenszel Test The Cochran-Mantel-Haenszel Test
aims to find the association between variables while controlling for confounding. ANOVA The analysis of variance test, or ANOVA, is a statistical measure used to
compare variances of two or more samples. Identifying patterns, trends of epidemiologic data in charts, tables and graphs: Bar graphs and line graphs can show
the trends in disease incidence or prevalence over time. Pie charts can show the distribution of disease by demographic factors such as age, sex, race, and
geography. Scatter plots can show the relationship between two variables, such as age and disease incidence. Heat maps can show the geographic distribution of
disease incidence or prevalence. Maps can also show disease incidence or prevalence by geographic area. Formulas to calculate disease risk and frequency: Risk:
The ratio of the number of events (cases of disease) to the number of opportunities (people at risk) expressed as a proportion or a percentage. Risk = Number of
cases / Number of opportunities Frequency Ratio: The ratio of the number of events (cases of disease) in one group to the number of events in another group.
Frequency Ratio = Number of cases in group 1 / Number of cases in group 2 Proportion: The ratio of the number of events (cases of disease) to the total number of
events in a population, expressed as a fraction or a percentage. Proportion = Number of cases / Total number of events Incidence Proportion (Attack Rate): The
ratio of the number of new cases of disease in a population over a specified period of time to the size of the population at risk, expressed as a fraction or a
percentage. Incidence Proportion = Number of new cases / Population at risk Incidence Rate: The number of new cases of disease in a population over a specified
period of time, expressed as a rate per unit of time. Incidence Rate = Number of new cases / Total person-time at risk Prevalence: The number of cases of disease in
a population at a specified point in time, expressed as a proportion or a percentage. Prevalence = Number of cases / Total population Mortality Rate: The number of deaths due to a disease in a population over a specified period of
time, expressed as a rate per unit of time. Mortality Rate = Number of deaths / Total person-time at risk. Strategies for Prevention: Primary Prevention: Interventions aimed at preventing the occurrence of disease in people who have
not yet been exposed to it. Secondary Prevention: Interventions aimed at early detection and treatment of disease to prevent progression or complications. Tertiary Prevention: Interventions aimed at reducing the impact of disease
and improving the quality of life for people with chronic disease. Universal Prevention: Interventions aimed at the entire population regardless of risk status. Selective Prevention: Interventions aimed at populations at higher risk for
disease. Indicated Prevention: Interventions aimed at individuals or populations who have already shown signs of developing disease.