“SHAKE-SHAKE IT OFF”:
IMPLEMENTING SBIRT IN MWEMBESHI AND
CENTRAL PROVINCE, ZAMBIA
Prepared for the Lutheran Health Alliance
By:
Kate Austin
Michelle Duren
Ben Emmel
Diana Rosales
Alex Straka
Public Affairs 881:
Benefit-Cost Analysis
December 19, 2014
ACKNOWLEDGEMENTS
We would like to thank Dr. Jason Paltzer for his assistance and persistent resourcefulness
throughout the project. Additionally, we would like to thank both Kingdom Workers and the
Lutheran Health Alliance for their cost-benefit analysis request. The project provided us with a
wealth of knowledge and an opportunity to apply our cost-benefit analysis skills to a complex
and engaging real-world scenario. Finally, we would like to thank Professor David Weimer for
his counsel, support, and direction throughout this project.
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TABLE OF CONTENTS Acknowledgements ................................................................................................................ ii Table of Contents .................................................................................................................... iii List of Tables ............................................................................................................................ iv Executive Summary ............................................................................................................... vi Introduction .............................................................................................................................. 1 Translating SBIRT ................................................................................................................... 3 SBIRT in the United States ............................................................................................................. 4 SBIRT Modifications for Zambia .................................................................................................. 5 Analysis ....................................................................................................................................... 6 Objective .............................................................................................................................................. 6 Standing ............................................................................................................................................... 8 Contribution to the field ................................................................................................................. 9 Parameter Estimates .............................................................................................................. 9 Implementation costs ..................................................................................................................... 9 Patient costs ....................................................................................................................................... 9 Productivity Benefits ..................................................................................................................... 10 Health benefits ................................................................................................................................ 11 Avoided Cost of Crime ................................................................................................................... 12 Social Discount Rate ...................................................................................................................... 13 Figure 1: SBIRT Model ............................................................................................................................... 13 Drinking Categories ....................................................................................................................... 14 Participation and Screening Acceptance Rates .................................................................... 14 Acceptance of In-­‐Patient Treatment ........................................................................................ 15 Success Rate ..................................................................................................................................... 15 Model ......................................................................................................................................... 16 Results ................................................................................................................................................ 17 Monte Carlo Results and Analysis ......................................................................................................... 17 Figure 2: Histogram of Net Present Value, Village ......................................................................... 18 Figure 3: Histogram of Net Present Value, Province ..................................................................... 19 Worst-­‐Case Scenario ................................................................................................................................... 20 Costs for Lutheran Health Alliance ........................................................................................... 21 Costs and Benefits for Each Screened and Treated Individual ....................................... 21 Costs and Benefits for Each Successfully Treated Individuals ........................................ 22 Average Net Present Value for Population Screened by Sex ............................................ 22 Return on Investment ................................................................................................................................ 23 Conclusion ............................................................................................................................... 23 References ............................................................................................................................... 25 Appendices .............................................................................................................................. 30 Appendix A: Zambian Demographics ....................................................................................... 30 Figure 4: Demographic Characteristics .............................................................................................. 30 Appendix B: Adjusting for the Exchange Rate ....................................................................... 34 Appendix C: Adjusting for Inflation .......................................................................................... 35 iii
Appendix D: Calculation of Average Wages in Zambia ...................................................... 36 Appendix E: Calculation of Absenteeism Costs ..................................................................... 38 Appendix F: Calculation of Presenteeism Costs ................................................................... 45 Appendix G: Calculation of Patient Opportunity Cost ........................................................ 52 Appendix H: Calculation of Social network costs ................................................................. 55 Appendix I: Calculation of health care system cost ............................................................. 56 Appendix J: Calculation of avoided costs of crime and road traffic accidents ........... 57 Avoided costs of crime ............................................................................................................................... 57 Avoided costs of road traffic accidents ............................................................................................... 58 Family Cohesion and Domestic Violence ........................................................................................... 60 Appendix K: Implementation Costs .......................................................................................... 61 Salary of Community Health Worker .................................................................................................. 61 Supplies ............................................................................................................................................................ 62 Screening and Brief Intervention .......................................................................................................... 62 Initial and Follow-­‐up Training Costs ................................................................................................... 65 Recurrent Training and Support Costs ............................................................................................... 66 Appendix L: Value of a Statistical Life (VSL) .......................................................................... 68 Appendix M: Quality Adjusted Life Years (QALY) ................................................................ 72 Appendix N: Human Immunodeficiency Virus (HIV) .......................................................... 74 Appendix O: Histograms ............................................................................................................... 76 Figure 7: NPV, Province, 2% Discount ................................................................................................ 76 Figure 8: NPV, Village, 2% Discount .................................................................................................... 77 Figure 9: NPV, Province, 6% Discount ................................................................................................ 78 Figure 10: NPV, Village, 6% Discount .................................................................................................. 79 Figure 11: NPV, Province, 10% Success ............................................................................................. 80 Figure 12: NPV, Village, 10% ................................................................................................................... 81 Appendix P: Parameters Summary ........................................................................................... 82 LIST OF TABLES
Table 1: Net Present Value, Discount Rate 3.5 percent, Success Rate 20 percent ....................... 17 Table 2: Net Present Value, Discount Rate 3.5 percent, Success Rate 10 percent ....................... 19 Table 3: Net Present Value, Discount Rate 2 percent, Success Rate 20 percent .......................... 20 Table 4: Net Present Value, Discount Rate 6 percent, Success Rate 20 percent .......................... 20 Table 5: Costs Per Program to Lutheran Health Alliance............................................................. 21 Table 6: Costs and Benefits for All Screened ............................................................................... 21 Table 7: Costs and Benefits for Successfully Treated Individuals ............................................... 22 Table 8: Average NPG by Sex ...................................................................................................... 22 Table 9: Demographic Characteristics .......................................................................................... 31 Table 10a: Sex............................................................................................................................... 31 Table 10b: Geographic Location .................................................................................................. 31 Table 10c: Economic Status: Economically Active (includes employed and unemployed) ........ 31 iv
Table 10d: Employment Status: Unemployed .............................................................................. 32 Table 10e: Mwembeshi Sector of Employment, Rural ................................................................. 32 Table 11a: Sex............................................................................................................................... 32 Table 11b: Geographic Location .................................................................................................. 32 Table 11c: Economic Status: Economically Active...................................................................... 32 Table 11d: Employment Status: Unemployed .............................................................................. 33 Table 11e: Sector of Employment, Rural ..................................................................................... 33 Table 11f: Sector of Employment, Urban ..................................................................................... 33 Table 12: Zambia Inflation Rates ................................................................................................. 35 Table 13: Female Wage Rates ...................................................................................................... 37 Table 14: Male Wage Rates .......................................................................................................... 37 Table 15: Means days absent attributed to sickness by drinking category in Australia ............... 38 Table 16: Means days absent attributed to sickness by drinking category in Zambia (transferred
from South Africa context) ................................................................................................... 40 Table 17: Days absent associated with alcohol consumption per year ......................................... 40 Table 18: Percent of working year absent attributed to alcohol consumption .............................. 41 Table 19: Female Absenteeism Cost Estimates (USD) ................................................................ 43 Table 20: Male Absenteeism Cost Estimates (USD) .................................................................... 44 Table 21: Sick-related Absenteeism Rate ..................................................................................... 46 Table 22: Adjusting US Presenteeism Days to Zambia the SBIRT Likely Dependent drinking . 46 Table 23: Ratio of days absent between drinking states ............................................................... 47 Table 24: Days presenteeism occurs associated with alcohol consumption per year ................... 47 Table 25: Percent of working year presenteeism occurs attributed to alcohol consumption........ 48 Table 26: Female Presenteeism Cost Estimates (USD) ................................................................ 49 Table 27: Male Presenteeism Cost Estimates (USD) ................................................................... 50 Table 28: Cost of Crime................................................................................................................ 57 Table 29: Cost of road traffic accidents ........................................................................................ 59 Table 30: SBIRT Costs per Drinker.............................................................................................. 64 Table 31: Training Costs............................................................................................................... 66 Table 32: Values Used in VSL Calculation .................................................................................. 69 Table 33: Values Used in VSLY Calculation ............................................................................... 70 Table 34: Estimates of change in QALY after SBIRT treatment ................................................. 73 Table 35: HIV Acquisition Odds by Drinking State ..................................................................... 74 Table 36: Parameters Treated as Uncertain .................................................................................. 82 Table 37: Parameters Treated as Certain ...................................................................................... 85 v
EXECUTIVE SUMMARY
This cost-benefit analysis was prepared at the request of the Lutheran Health Alliance
(LHA) to examine the value of implementing a Screen, Brief Intervention and Referral to
Treatment (SBIRT) program in Zambia. The purpose of SBIRT is to prevent and reduce harmful
alcohol consumption patterns. The SBIRT program analysis first details the implementation of
the pilot program in Mwembeshi, a rural community in Central Province, Zambia. We also
consider the costs and benefits associated with scaling the program up to the level of the Central
Province. Based on our modeling we recommend the implementation of the pilot program.
However, because the results are sensitive to the success rate of the program, we also
recommend that LHA monitor the success rate of the pilot program before scaling up to the
provincial level.
We incorporate various costs and benefits that accrue to LHA, the individual, or society.
Cost categories include the following: implementation costs; social network costs; and patient
opportunity costs. Benefit categories encompass health benefits; social benefits; productivity
benefits; and reduction in health care system benefits. Translating these costs and benefits into a
Zambian context required researching Zambian statistics, and when no appropriate statistics were
available, making reasonable assumptions for predicting the impacts of SBIRT in Zambia.
This framework yielded positive net benefits for both the village and province level
programs. In Mwembeshi, the net present value for a successfully treated individual is $2,530
For the Central Province, these figures is $2640. Taking a broader perspective, the estimated net
present value of the program overall for Mwembeshi was $202,200 and $35,947,000 for Central
Province. Net benefits remained positive using varying discount rates and a lower success rate.
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Based on these findings we recommend that the pilot program be implemented. The
evidence suggests SBIRT is an effective intervention for changing alcohol consumption patterns.
Moreover, with the pilot program using, in large part, existing resources, little costs are incurred
in comparison to the benefits gained. In this respect our results show that for every dollar of
investment, there is at least a ten-dollar return in benefits. Before scaling up to the provincial
level, however, we suggest monitoring the results of the pilot program. In particular, we suggest
reviewing the participation rates, the success rate, and the length of time the change in alcohol
consumption is sustained. By doing a field experiment, the accuracy of the model for the
province can be significantly improved, and thus, the appropriate level of investment will be
better informed
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INTRODUCTION
In Zambia, high alcohol consumption, both in terms of quantity and frequency imposes
harm on people. Several sources estimate the prevalence of alcohol consumption in Zambia. The
World Health Organization’s Global Status on Health and Alcohol Report finds that 2.2 percent
of the total Zambian population engages in heavy episodic drinking (WHO 2014). The
prevalence is heavily weighted toward men, 4.4 percent of men and less than .01 percent of
women are classified as heavy episodic drinkers. However, the WHO estimates do not include
unrecorded consumption, resulting in an underestimated prevalence of heavy drinking. Based on
Dr. Jason Paltzer’s qualitative research, alcohol substance abuse is prevalent in 20 to 50 percent
of the population. To put this statistic in a more local perspective, the estimated drinking
prevalence in Zambia is twice that in Wisconsin. Other studies state that around 15 percent of the
total population can be classified as heavy drinkers (Haworth 2004). We estimate a distribution
of the various drinking states by gender to account for different drinking patterns among men
and women.
Cultural attitudes support a heavy drinking culture in Zambia. Heavy drinking episodes
are common practice with cultural and traditional ties accompanied by a high degree of social
acceptance in Zambian society. On one end of the spectrum there are those who abstain from
alcohol consumption, on the other end, are those who consume alcohol in excess. Consistent
with Dr. Paltzer’s research, Allan Haworth reports, “The close correlation between responses
about drinking and those about drunkenness suggests to us that the norm among Zambian
drinkers is that one goes drinking in order to get drunk” (Haworth 2004). Moreover, there is a
negative perception of those who abstain from drinking or those who moderately drink. For
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example, abstainers or moderate drinkers may be labeled a “womanizer,” a “cheat,” or “a thief”
(Paltzer 2009). Being regarded as such is considered for many Zambians worse than being
labeled a drunkard (Paltzer 2009). Additionally, the drinking culture appears to be largely
uniform, with little difference based on educational attainment, type of job, or occupational
status relative to the community as a whole (Haworth 2004).
Zambia faces challenges in the capacity of its health care infrastructure for combating
heavy episodic drinking, especially when confronted with a high HIV prevalence rate. The
country has a high burden of disease especially in terms of communicable diseases such as
malaria, tuberculosis (TB), human immunodeficiency virus (HIV), and sexually transmitted
infections (STIs). The HIV prevalence rate is 12.5 percent, which is considered at a national
epidemic level (AVERT 2012). Further compromising the effectiveness of the health care system
are limited financial resources, “weak logistics management in the supply of drugs and medical
supplies” (Zambia’s Ministry of Health 2011), outdated medical equipment, and a “brain drain”
of medical professionals. The government has only about half of the health care workers it seeks.
For example, according to a report by Joseph Schatz, Zambia has fewer than 646 doctors and
6096 nurses, representing only 28 percent and 36 percent of the government’s target (Schatz
2008). To put this in relative terms, in Zambia there is one doctor and seven nursing personnel
per 10,000 people compared to 27 doctors and 98 nurses per 10,000 people in the United States
(Global Health Workforce Alliance 2014).
Non-governmental organizations are working to improve the health status of Zambians
by piloting health interventions at the community level. One such organization is the Lutheran
Health Alliance (LHA), a subsidiary of the Kingdom Workers, which serves churches and
communities by providing support for volunteer programs internationally. LHA has sent
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professional medical trainers to work with communities in Zambia to attend to public health
needs. In addition, LHA maintains a partnership with the Lutheran Health and Development
Program located in Zambia, which prepares volunteers for service by training them in health
education, disease prevention, and responding to basic health care needs. The Lutheran Health
and Development Program consists of a manager and a program assistant who are responsible for
the training of volunteers and the coordination of the program in communities. Specific to
Mwembeshi, Zambia, LHA works with 25 lay community health workers (CHWs) to provide
health education, treat malaria and other common ailments, and refer patients to professional
medical care as needed.
CHWs are becoming increasingly vital in addressing the scarcity of healthcare services
while bridging the divide between the professional workforce and the surrounding communities.
The WHO defines CHWs as “community health aides selected, trained and working in the
communities from which they come” (Ashraf and Kindred 2011). CHWs are utilized in
communities around the world to meet health needs; however, the role of CHWs and the training
they receive varies widely. Generally, CHWs in less formalized programs work in health
promotion and educational activities and may receive non-monetary and in-kind donations from
the community. Programs formalized through government agencies often provide wages and
more extensive training to full-time CHWs (Ashraf and Kindred 2011).
TRANSLATING SBIRT
One intervention for addressing the costs stemming from alcohol abuse is Screening, Brief
Intervention, and Referral to Treatment (SBIRT). SBIRT programs seek to identify people with
harmful or hazardous alcohol consumption before the health and social consequences of their
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drinking become pronounced and motivate them to address their real or potential problem (WHO
2014). In the subsequent sections, we discuss SBIRT in the context of the United States and
modifications needed for it to be effective in the Zambian context.
SBIRT IN THE UNITED STATES
In the United States, Wisconsin has the highest rate of binge drinking and persistent rates
of alcohol abuse and misuse (WDHS 2012). Economic and health consequences to the state are
numerous, yet state residents have one of the lowest risk perceptions for binge drinking in the
country (WIPHL 2012). To address this challenge, the Substance Abuse and Mental Health
Services Administration (SAMHSA) awarded the Wisconsin Department of Health Services a
five-year grant to implement a SBIRT program in the state. This initiative, entitled Wisconsin
Initiative to Promote Healthy Lifestyles (WIPHL), sought to improve the health and lives of
residents by including SBIRT in the routine care given by primary providers.
The SBIRT protocol begins with a brief screen, typically administered as a written
questionnaire at patient check-in. The screen consists of four items to rapidly determine patient
experiences with alcohol or drugs within the last three months. Medical assistants score the
screen and those considered “positive” are guided through a full screen to determine their level
of alcohol use and risk (low risk use, risky use, harmful use, or likely dependent use). The four
potential risk levels elicit to distinct interventions. Patients determined to have risky use are led
through a brief intervention using motivational interviewing techniques. Those thought to have
harmful use are given a brief treatment and those who show likely dependent use are referred to
more extensive treatment. Clinical staff often have no more than 10 to 15 minutes with a patient
to complete the full screen and planned service (WIPHL 2012).
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The WIPHL initiative showed a 20 percent reduction of risky alcohol use among patients
who participated in the outcome study, which when viewed from a population level, translates to
substantial reductions in emergency department visits, hospitalizations, arrests, and vehicular
crashes. The results in Wisconsin were similar to those in other states, which also found SBIRT
can be both effective and efficient, and that it likely saves money and lives (WIPHL 2012).
SBIRT MODIFICATIONS FOR ZAMBIA
The primary points of departure between the Zambian and Wisconsin models are the
health care setting, the level of expertise of the workforce, and the representativeness of the
population reached. In the WIPHL model, the screening occurs during check-in at hospitals and
clinics for those seeking care. The subsequent steps are implemented by doctors and nurses at the
same, or in a similar, health care facility. This model is not feasible to replicate in Zambia
because of the critical shortage of health care professionals. To overcome this low density CHWs
provide a significant amount of health care services in Zambia. Adapting SBIRT from a clinical
to community setting would shift the screening and intervention stages to the patients’ homes.
CHWs can be trained to offer the screening and brief intervention components, and then make
referrals in cases of dependency to nearby health centers. This reduces the burden on clinics and
broadens the scope by allowing the substance abuse intervention to reach more people. In the
case of Mwembeshi, individuals would be referred to the Serenity Harm Reduction Programme
Zambia clinic in Lusaka, Zambia (SHARPZ).
In a feasibility study of a cognitive behavioral treatment to reduce alcohol consumption
among HIV-infected outpatients of a clinic in Kenya, content changes improved the cultural
applicability of the program (Papas et al. 2010). Similar to qualitative reports on Zambia, this
study reported little prior knowledge regarding the risks and harmful effects associated with
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alcohol, misinformation about traditional brews, and the importance of drinking in social circles.
In response to these observations, a risk education module was added to the first counseling
session, as well as an emphasis on alcohol refusal skills and a discussion of the financial costs of
drinking in the interventions. We assume the tailoring of the program to these motivations for
change would be able to increase the effectiveness of SBIRT for Zambians.
Several studies point to CHWs being effective at increasing access, promoting knowledge
of health maintenance and disease preventive, and encouraging behavioral change (Swider
2002). Such evidence influenced our assumptions regarding the participation and success rates,
as well as our calculations of the implementation and patient costs. Monetization of costs and
benefits was another significant difference. All measures were calculated using Zambian wage,
inflation, and cost data. Other potential differences were considered and are discussed in terms of
each cost and benefit category affected.
ANALYSIS
OBJECTIVE
The primary purpose of the following cost-benefit analysis is to determine the value of
expanding an existing CHW program to include SBIRT. This expansion includes training an
existing cohort of 25 CHWs to have the skills necessary to implement SBIRT in the community
where they serve. This augments the set of health interventions and services they currently
provide, instead of displacing these activities. All potential costs and benefits resulting from this
change were then considered and evaluated for a rural community in Central Province.
Our secondary objective is to determine the value of scaling up the current program, with
the inclusion of SBIRT services, to all of Central Province. In making this determination, we
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considered differences in wages for the CHWs, differences in administrative costs, and
differences in the population served. With the shortage of health care professionals and
observations of unsafe alcohol consumption patterns, there exists opportunities for large
economic and health benefits resulting from successfully implementing substance abuse
interventions throughout Zambia. Despite this potential, research in this area stresses that
addressing the administrative and capacity requirements for scaling up a health intervention
should be done alongside consensus building initiatives (Nyonator et al. 2005). Thus, dialogue
with community leaders and the inclusion of residents in the decision-making process is vital
when seeking to scale up such a program.
LHA is interested in including a mobile-based platform to SBIRT in Zambia. A mobile
phone platform could reduce the face-to-face interaction with a health worker and could receive
information via short message services. The rapid growth of mobile phone use in low-income
countries has opened the possibility to implementing this technology in health care. In Zambia,
between 2004 and 2009, the number of mobile phone subscribers rose from just 464,000 to some
4.4 million (Montez 2010). The use of mobile phones in health-related fields has been applied in
other countries already and research shows positive results from their use such as improving
health workers’ adherence to malaria guidelines in Kenya (Zurovac et al. 2012) and delivering
mental health interventions in South Africa (Norris et al. 2013).
Notwithstanding, the country faces several drawbacks to implementing such technology.
Zambia does not consistently meet electricity demand and many parts of the country lack grid
electricity. Consequently, those who own a cell phone often must travel to other towns to pay to
charge their cell phones. In addition, phone lending is a common practice in Zambia, especially
in rural areas. Results from a survey show that “26 percent of mobile phone owners said they
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lend their phone to someone else at least once a month” (Montez 2010). Besides the resource
constraint, the implementation of this type of intervention is fairly new in the country and the
region, which makes it difficult to determine how feasible and effective such technology would
be to encourage participation and improve adherence to SBIRT from patients. We anticipate that
adding a mobile phone component would add a fixed cost for each community health worker and
add to the training costs, with the potential to increase the effectiveness rate. However, due to
uncertainty of the change in effectiveness to substance abuse intervention in the specific
communities, we concluded that the results would be more informative without this added
complexity, and omitted analysis of a mobile-based alternative.
STANDING
We assign standing to LHA, and all individuals 15 or older within the geographic area of
interest. LHA would incur the implementation costs. These costs would change as the program is
expanded from the village to provincial level. Society would incur both costs and benefits. All
individuals in society may incur benefits and costs within the treated geographic area. At the
village level and province level we assign costs and benefits to every individual aged 15 and
older. Costs and benefits vary across drinking states, gender, income status, and geographic area.
Although evidence points to the occurrence of drinking among adolescents in Zambia, our
analysis did not incorporate this age group because a significant portion of our data was based on
the population 15 years and older. Additionally, for those 15 years and younger, benefits would
have to include the monetization of uncertain educational and mental health benefits. Therefore,
we believe we conservatively estimate overall benefits. Additionally, those under the age of 15
are outside the scope of the SBIRT implementation and will not incur any of the direct costs
associated with the screening, brief intervention, and referral to treatment.
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CONTRIBUTION TO THE FIELD
Our cost-benefit analysis presents a new perspective on estimating the effects of the
SBIRT model in a developing context. To date almost all cost-benefit analyses of SBIRT remain
in high-income industrialized countries. Both Maltzopoulos et al. (2014) and Thavorncharoensap
et al. (2010) attempt to monetize the costs of excessive alcohol consumption in South Africa and
Thailand, respectively. However, the developing context lacks a comparative comprehensive
cost-benefit analysis that examines the costs and benefits associated with reductions in excessive
problem drinking. We believe our report will assist in bridging the information gap and provide a
foundation for future cost-benefit analysis in the low-income and sub-Saharan African context.
PARAMETER ESTIMATES
IMPLEMENTATION COSTS
The costs of implementing SBIRT include the initial training of CHWs, their wages for
implementing the screenings and interventions, as well as the supplies needed for these activities.
CHWs receive an initial training of approximately two weeks immediately and receive a multiday, follow-up training after two years. In the pilot phase, employees of the Lutheran Health and
Development Program serve as the primary trainers. An additional implementation cost is the
clinical support costs when CHWs consult with local clinical staff. More elaboration on the
calculation of these implementation parameters can be found in Appendix K.
PATIENT COSTS
This category includes both costs and avoided costs (benefits) directly resulting from the
patient having completed the alcohol abuse screening and intervention process. The most direct
of these cost measures is the opportunity cost for the time an individual spends receiving a brief
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screen, brief intervention, or treatment, as applicable for the designated risk classification.
Further explanation of this calculation can be found in Appendix G. Another cost borne by the
individual receiving the health service is social network costs. This is included in the analysis
given the high importance referrals have in attaining employment and the connectedness of
drinking and peer groups. Further explanation of this cost can be found in Appendix H. The final
measure of this cost category examines changes in health care utilization resulting from SBIRT.
Based on research in this area, the resulting change is expected to be two-fold. First, the
education that goes alongside SBIRT increases awareness of health care needs, which in turn
increases the utilization of outpatient services (Paltzer 2014). At the same time, anticipated
decreases in alcohol-attributable diseases would decrease the amount of inpatient and ER
services used (Paltzer 2014). The exact calculations for this category of patient costs can be
found in Appendix I.
PRODUCTIVITY BENEFITS
Productivity benefits include the benefits derived from increased worker productivity
resulting from the reduction in alcohol consumption and frequency. We articulate two types of
productivity benefits that are realized by the individual drinker, the employer, and the greater
community.
First, we calculate absenteeism benefits, which are associated with reduced workdays
attributed to alcohol missed for the employed individual. We follow the methodology used by
Quanbeck et al. (2010) that calculates absenteeism by multiplying the daily wage by the days
absent associated with alcohol consumption (Quanbeck et al. 2010; Baumberg 2010). We
translate days absent into a percentage of one’s working year. Our final calculation is the
percentage of the working year absent attributed to alcohol multiplied by the annual wage and a
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spillover factor. Dollar value estimates vary based on drinking categories and wages. Wages are
conditional on geography, sector of employment, and gender. Appendix E shows the calculation
of absenteeism costs under current policy.
Second, we calculate presenteeism benefits, which are associated with reduced impaired
productivity at work. Impaired productivity may take the form of arriving drunk to work or being
hung over at work (Maltozpoulos et al. 2014). We use data the Arora et al. (2011) estimates of
the range of days that a problem drinker experiences presenteeism. Using the likely dependent
drinker as the base case, we estimate presenteeism for each drinking state by applying the same
rate at which absenteeism changes between drinking states. Similar to absenteeism we translate
the number of days presenteeism occurs into a percentage of one’s working year. Our final
calculation is the percentage of the working year presenteeism occurs that is attributed to alcohol
multiplied by the annual wage and an impairment factor (Quanbeck et al. 2010;
Thavorncharoensap et al. 2010). Estimates vary based on drinking categories and wages. Wages
are conditional on geography, sector of employment, and gender. Appendix F shows the
calculation of presenteeism costs under current policy.
HEALTH BENEFITS
Heavy alcohol consumption is associated with increased risk of several diseases and
negative health conditions. Drinking directly causes some conditions, such as fetal alcohol
syndrome and alcohol dependence. Others, such as slips, falls, and accidental injuries are caused
by alcoholic impairment (Rehm et al. 2003). Other conditions are partially caused by, or
correlated with alcohol consumption, such as esophageal cancer, liver cirrhosis, heart disease,
hypertension, and diabetes. Reducing drinking therefore also reduces the risk of acquiring one or
more of these conditions. For detailed calculations see Appendices L,M, and N.
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Increases in the quality of life are measured in quality-adjusted life years, where a year of
perfect health is rated as 1.0 and reduced proportionate to the reduction in quality of life.
Successful treatment of SBIRT is found to enhance a patient's life by .091 to .405 QALYs per
year, depending on sex, drinking level, and course of treatment.
AVOIDED COST OF CRIME
We measure the costs of crime that are attributed for each drinker as the avoided costs per
individual who is successfully treated. This measure includes the cost to all of society, but is only
attributed to those who drink. This is because alcohol-attributable crime will only be reduced for
those who presently consume alcohol. We also consider that by reducing alcohol consumption an
individual will reduce the probability of dying in an alcohol-related crime incident. Based on
studies of the costs of crime in the United States (Boucher et al. 2006) and South Africa
(Matzopoulos et al. 2014) and adjusting the estimates for Zambia, a lower income level country
with lower crime rates than in these studies, we estimate that 50 percent of costs of crime are
alcohol-related using an alcohol-attributable fraction (AAF) of 0.25. However, given that not all
crimes are reported and we lack official data from Zambia, our numbers for crime are
underestimated.
AVOIDED COSTS OF ROAD TRAFFIC ACCIDENTS
Another benefit category associated with the implementation of SBIRT includes the
avoided costs for victims of traffic accidents. These costs as well as the reduction in the
probability of dying in a road traffic accident are attributable to drinkers. Using estimates from
the United States, South Africa, and Thailand as points of reference, alcohol-related traffic
accidents in Zambia cost between 0.25 and 1 percent of GDP considering that Zambia is a lowermiddle income country and has lower number of motor vehicles registered than the reference
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countries (see Appendix J). These costs include damages in crash, lost of productivity, lost of
lives, and correction costs associated with alcohol.
SOCIAL DISCOUNT RATE
We discount all costs and benefits according to the following specified social discount
rates. First, we assume a real discount rate of 3.5 percent. Second, we check the robustness of our
results with lower bound real discount rate of 2 percent and an upper bound real discount rate of
6 percent. We use these three discount rates according to the justifications and recommendations
set forth by Moore et al. (2004).
FIGURE 1: SBIRT MODEL
Source: Authors
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Figure 1 maps the SBIRT flowchart and provides assumptions about probabilities of
participation.
DRINKING CATEGORIES
The drinking categories as designated by SBIRT include low-risk, at-risk, harmful, and
likely dependent. To estimate the distribution of drinkers for both the pilot and province level
projects, we use a 2008 study conducted by the Zambian Ministry of Health in Lusaka
prevalence rates of risk factors for noncommunicable diseases. Survey responses provided
estimates for males and females who abstain from drinking (56.5 percent and 82.4 percent,
respectively), and the frequency and quantity of drinking for those that consume alcohol. Among
drinkers, 9 of 10 men responded that they consume more than three to four drinks on average per
day, and 7 out of 10 women reported drinking more than three to four drinks per day. This
consumption pattern alone would most likely qualify them for the at-risk or harmful drinking
categories. Our drinking categories are based on the frequency of drinking considering that the
drinking culture is dichotomous; with a high rate of abstainers but high rates of excessive
drinking among those who do consume alcohol. The percentage of drinkers in each category in
our model are as follows: for men, 12.5 percent low-risk, 31.4 percent at risk, 39.9 percent
harmful, and 19.2 percent likely dependent; for women, 34.7 percent low-risk, 36.9 percent at
risk, 20.9 percent harmful, and 7.5 percent likely dependent.
PARTICIPATION AND SCREENING ACCEPTANCE RATES
We expect that not all adults aged 15 and older will participate with the CHWs, and of
those who participate not all will accept the initial screen. Dr. Paltzer indicated that anywhere
from 50 to 80 percent of families currently participate with CHWs in Mwembeshi. We assume
this participation rate would be similar for the SBIRT implementation and we take the midpoint
14
of 65 percent participation into account when estimating the target population. In addition, from
a study on home-based voluntary HIV counseling and testing in Zambia, 85 percent of HIV
positive individuals working with CHWs accepted the intervention (Fylkesnes et al. 2013). We
use this number to estimate the number of adults who will work with CHWs and accept the
initial screen.
ACCEPTANCE OF IN-PATIENT TREATMENT
We included an additional probability to account for the anticipated lower participation of
the likely dependent due to greater opportunity costs. All of those who are classified as likely
dependent receive the brief and full screens, and so, all in this category have the opportunity
costs associated with those two stages. However, those who accept the referral and go to a
nearby clinic to receive treatment face the further costs associated with traveling, waiting, and
the nontrivial amount of time spent in treatment. The acceptance rate we used to differentiate
those who accept treatment and those who go no further than the full screen comes from a
systematic review of patient retention in antiretroviral therapy programs throughout sub-Saharan
Africa. This study finds a 70 percent follow-up retention rate after 24 months of first receiving
treatment (Fox and Rosen 2010). While not directly tied to Zambia or alcohol interventions, this
provides the closest statistic available, which enables us to factor in this crucial probability. To
improve the likelihood of having a high acceptance rate, LHA could reimburse individuals in this
drinking category for the travel costs, to reduce the individual burden for treatment.
SUCCESS RATE
We incorporate a success rate of 20 percent, meaning of all the individuals who are
screened, receive brief interventions, or are referred to treatment; one in five will successfully
change their drinking behavior. This is supported in evaluations of SBIRT in Wisconsin
15
(SCAODA 2013) among others. Because of the differences in the drinking culture between
Zambia and the United States, we do not know the actual success rate that will be seen in the
pilot program, and so we use this relatively conservative estimate for our analysis.
MODEL
Many of the costs and benefits that we have included in our analysis are uncertain. For
example, it is not known exactly how many individuals abstain from drinking. To account for
uncertainty, we chose to create and implement a Monte Carlo simulation, a computer model that
integrates uncertain or unknown values over a large number of independent simulations. First,
we have selected point estimates for parameters needed to calculate costs and benefits from
existing research and data sources. For all uncertain parameters, we also specify a distribution or
range of values. For the majority of our parameters, we have chosen a triangle distribution
centered on our point estimates, which become the most likely value used in calculations.
Second, we use a computer model to select a random value from each distribution, and
then calculate the costs and benefits that accrue to an individual receiving screening and
treatment, LHA, and society at large. We then repeat this process 10,000 times to obtain an
average value of net benefits.
Initially, our model simulates a random sample of 10,000 individuals. Certain
characteristics, including gender; drinking category; labor sector; urban-rural classification; and
treatment participation, acceptance, and success are independently calculated based on
demographic estimates of the Zambian population. The model then assigns values to remaining
parameters based on these characteristics and the specified distribution. See Appendix Q for
parameter values and range distributions.
16
The majority of costs and benefits are calculated per individual, then multiplied by the
number of participants in both the village and province. We calculate the costs and benefits of
this project over a four-year time horizon. At year zero, initial CHW training costs and the costs
of implementing the screenings and brief interventions are incurred. Social network costs are
calculated at the midpoint of year one, and follow-up CHW training costs are incurred at the
midpoint of year two. The remaining costs and benefits are dependent upon successful SBIRT
treatment. Past SBIRT evaluations have shown varying duration of program effects. We have
taken a conservative approach and have built a linear decline of effects over the time horizon of
the program and assume effects return to baseline at the end of year four.
RESULTS
MONTE CARLO RESULTS AND ANALYSIS
After executing the Monte Carlo analysis and using population estimates to calculate the
net present values (NPVs) of the costs and benefits of the overall program, we find that the
average NPV for the pilot is $200,200 and the average NPV for the program scaled to the
province level is $42,297,000. All simulations resulted in positive NPVs on both the pilot and
province levels.
Table 1: Net Present Value, Discount Rate 3.5 percent, Success Rate 20 percent
Program
Average NPV
Lower Bound
Upper Bound
Pilot
$202,200
$171,000
$231,500
Province
$35,947,000
$30,869,000
$42,297,000
Source: Authors
17
FIGURE 2: HISTOGRAM OF NET PRESENT VALUE, VILLAGE
Source: Authors
18
FIGURE 3: HISTOGRAM OF NET PRESENT VALUE, PROVINCE
Source: Authors
Next, we calculated the NPV for both the pilot and province level programs assuming a
success rate of SBIRT of 10 percent. While the average NPV of the benefits was reduced, all
simulations resulted in positive net benefits.
Table 2: Net Present Value, Discount Rate 3.5 percent, Success Rate 10 percent
Program
Average NPV
Lower Bound
Upper Bound
Pilot
$115,500
$93,400
$141,600
Province
$21,112,000
$17,357,000
$25,033,000
Source: Authors
19
Additionally, we varied the discount rate from 3.5 percent to a lower bound of 2 percent
and an upper bound of 6 percent. Overall estimates were sensitive to discount rate, primarily due
to increased value of a statistical life year (see Appendix L.)
Table 3: Net Present Value, Discount Rate 2 percent, Success Rate 20 percent
Program
Average NPV
Lower Bound
Upper Bound
Pilot
$163,300
$137,900
$186,600
Province
$29,076,000
$285,244,000
$33,897,000
Source: Authors
Table 4: Net Present Value, Discount Rate 6 percent, Success Rate 20 percent
Program
Average NPV
Lower Bound
Upper Bound
Pilot
$273,600
$232,000
$314,500
Province
$48,621,000
$41,248,000
$57,795,000
Source: Authors
WORST-CASE SCENARIO
To further verify the sensitivity of our results to assumptions made in our model we
performed an analysis of a worse case scenario. This worse case scenario used statistics far
below any suggested by previous research. This modeling used a six percent discount rate, a five
percent success rate, and a 50 percent participation rate. Even with these very low parameters,
the outcomes of the programs are still positive in 100 percent of the trials of the Monte Carlo
simulation. The resulting average net present value for Mwembeshi is $51,100 and is
$12,474,000 for the Central Province.
20
COSTS FOR LUTHERAN HEALTH ALLIANCE
Costs to implement SBIRT for LHA are modest, particularly for the pilot in Mwembeshi.
The changes between the pilot and province levels result primarily from the formalization of the
CHW workforce, and administrative overhead costs.
Table 5: Costs Per Program to Lutheran Health Alliance
Cost/Benefit Category
Mwembeshi
Central Province
Training Costs
$6,000
$1,039,000
Clinic Support Costs
$11,500
$185,000
Program Costs
$2,800
$733,000
Overhead Costs
0
$450,000
Total Costs
$20,300
$2,407,000
Source: Authors
COSTS AND BENEFITS FOR EACH SCREENED AND TREATED INDIVIDUAL
Patient opportunity costs increase from the pilot to province level based on the change in
the make-up of the workforce, with a higher percentage of individuals living in urban areas and
working in the formal sector in Central province
Table 6: Costs and Benefits for All Screened
Cost/Benefit Category
Mwembeshi
Central Province
Patient Opportunity Costs
$1.10
$1.50
Health System Benefits
$39.70
$39.70
Source: Authors
21
COSTS AND BENEFITS FOR EACH SUCCESSFULLY TREATED INDIVIDUALS
For successfully treated individuals, the only cost apart from the opportunity and travel
costs is the loss of their social network, a one-time cost calculated at the midpoint of year one.
The net benefits at the pilot level are $1005 and $907 at the province level. The primary driver of
the positive benefits is accumulated health benefits and in particular the benefits associated with
reduction in risk of HIV infection with reduced drinking.
Table 7: Costs and Benefits for Successfully Treated Individuals
Cost/Benefit Category
Mwembeshi
Central Province
Social Network Costs
$90
$190
Health Benefits
$700
$700
HIV Reduction Benefits
$1,610
$1610
Absenteeism
$20
$25
Presenteeism
$1
$5
Crime
$65
$65
Traffic Accidents
$45
$45
Overall Net Present Value
$2,530
$2640
Source: Authors
AVERAGE NET PRESENT VALUE FOR POPULATION SCREENED BY SEX
We found the average net benefits for both men and women to be higher in Mwembeshi
than in Central Province. This difference can be attributed to the lower implementation costs at
the village level.
Table 8: Average NPG by Sex
Individual
Average Net Present Value
22
Men (Mwembeshi)
$237
Women (Mwembeshi)
$112
Men (Central Province)
$231
Women (Central Province)
$107
Source: Authors
RETURN ON INVESTMENT
When SBIRT has been systematically implemented with fidelity to the model, it has been
shown to produce a return on investment of four to one within the first year of implementation
(SCAODA 2013). The primary costs of implementing SBIRT in the United States are the wages
and benefits of the health worker or clinician giving the screenings and interventions. Modifying
the model to use CHWs dramatically reduces implementation costs. Correspondingly, our model
estimates a return on investment of approximately 10 to one over the course of four years. We
did total benefits by total costs to calculate return on investment.
CONCLUSION
We recommend LHA implement the SBIRT pilot program in Mwembeshi, Zambia.
Finding positive net benefits occurring in 100 percent of our trials, considering both a 20 percent
and a 10 percent success rate, we believe implementing SBIRT would be an efficient use of
resources. With an initial investment in alcohol screening and intervention, numerous benefits
result, extending into other aspects of society, including benefits in income, safety, health,
societal cohesion. Despite overwhelming positive results, there are several notable limitations
and assumptions that call for prudence when interpreting and implementing our findings.
23
One of these limitations is that our model relies heavily on data taken from a handful of
studies and
various assumptions stated throughout our analysis. Thus, the validity of our
model depends greatly on the reliability and validity of these studies. In many cases this means
data from the Zambian government and the WHO. For other statistics, including the acceptance
of screening rate and acceptance of referral to treatment, there is additional uncertainty because
our model uses data for HIV treatment and assumes alcohol abuse treatment will be comparable.
To gauge the appropriate level of the investment we also recommend that the LHA
monitor the pilot program before scaling up to all of Central Province. The response of the
Zambian communities to the SBIRT program is entirely unknown; particularly the success rate
and the time horizon of benefits. This program is seeking to change Zambia’s drinking culture by
encouraging moderate alcohol consumption by reducing harmful alcohol use. Any intervention
seeking to instigate such a behavior change with an addictive substance is highly uncertain. As
such, a reevaluation will be needed to assess the appropriateness of our assumptions and
probabilities used. With that said, all of our results point to SBIRT being a high-yield
investment, leading to substantial benefits to individuals and society overall.
24
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29
APPENDICES
APPENDIX A: ZAMBIAN DEMOGRAPHICS
The Central Statistics Office’s 2008 Labor Force Survey Report offers the most recent
demographic data. Our demographic parameters are directly taken or calculated from this report.
It is our assumption that these data are still relevant in 2014 with only minor changes. This
assumption is confirmed by the CSO’s preliminary 2012 labor force findings. The preliminary
2012 Labor Force Survey Report is available; however, it lacks the detail of the report in 2008.
Based on the time of release of the 2008 report, the full 2012 report may be released in the 2014
to 2016 timeframe. The following demographic information for the pilot village (Mwembeshi)
and Central Province is incorporated into our Monte Carlo simulation.
FIGURE 4: DEMOGRAPHIC CHARACTERISTICS
Source: Authors
30
Table 9: Demographic Characteristics
Sex
Male
Female
Geographic Location
Rural
Urban
Economic Status
Economically Active
Economically Inactive
Employment Status
Employed
Unemployed
Sector of Employment
Informal
Formal
Source: CSO 2008
Economic Status is divided into two categories (1) economically active and (2)
economically inactive. Economically active denotes an individual that is either employed or
unemployed (CSO 2008). Economically inactive denotes an individual that is outside the labor
force. The CSO includes full-time students, full-time homemakers, retired individuals, beggars,
etc. in the economically inactive population (CSO 2008).
Table 10: Demographic Data Specific to Pilot Village
Table 10a: Sex
Male (%)
49
Female (%)
51
Source: CSO 2008
Table 10b: Geographic Location
Rural (%)
100
Source: CSO 2008
Table 10c: Economic Status: Economically Active (includes employed and unemployed)
Rural
Male (%)
Female (%)
82.3
80
31
Table 10d: Employment Status: Unemployed
Rural
Male (%)
Female (%)
4.0
3.4
Source: CSO 2008
Table 10e: Mwembeshi Sector of Employment, Rural
Male (%)
Female (%)
Formal
6.24
2.19
Informal
93.76
97.81
Source: CSO 2008
Table 11: Demographic Data Specific to Central Province
Table 11a: Sex
Male (%)
49
Female (%)
51
Source: CSO 2008, 2013
Table 11b: Geographic Location
Rural (%)
76
Urban (%)
24
Source: CSO 2008, 2013
Table 11c: Economic Status: Economically Active
Male (%)
Female (%)
Rural
82.3
80
Urban
73
52.9
Source: CSO 2008, 2013
32
Table 11d: Employment Status: Unemployed
Male (%)
Female (%)
Rural
4.0
3.4
Urban
10.8
11.7
Source: CSO 2008, 2013
Table 11e: Sector of Employment, Rural
Male (%)
Female (%)
Formal
6.24
2.19
Informal
93.76
97.81
Source: CSO 2008, 2013
Table 11f: Sector of Employment, Urban
Male (%)
Female (%)
Formal
35.77
20.26
Informal
64.23
79.74
Source: CSO 2008, 2013
33
APPENDIX B: ADJUSTING FOR THE EXCHANGE RATE
Converting our estimates to United States Dollars (USD) required one to two calculations
depending on the estimate.
If the estimate was in ZWK, the Zambian currency prior to the 2012 debasement, we
divided the estimate by 1,000 to equate the ZWK with the new currency ZWM. One thousand
(1,000) ZWK is equal to one (1) ZWM.
To convert ZWM to USD, multiply the ZWM estimate by .1577. This is the official
exchange as of November 19, 2014. We acquire this exchange rate from the company XE. 1
1
XE can be accessed at the following link:
http://www.xe.com/currencyconverter/convert/?Amount=1&From=ZMW&To=USD
34
APPENDIX C: ADJUSTING FOR INFLATION
Throughout the cost-benefit analysis it was necessary to translate estimates across time to
2014 kwachas and dollars. The World Bank offers two measures of inflation, which include: the
Gross Domestic Product (GDP) deflator and the Consumer Price Index (CPI). To be consistent,
all estimates of costs and benefits that need be adjusted for inflation are translated using inflation
in consumer prices (CPI). Both measures are reported as the annualized increase in prices over
one calendar year. If able to determine the wage inflation rate within each sector of the economy
(i.e. rural informal, rural formal, urban informal, urban formal) a more accurate wage figure can
be incorporated into the model.
Below is a table representing the various inflation rates for the years 2009 to 2013.
Table 12: Zambia Inflation Rates
Consumer Prices
Year
Rate (%)
2009
13.4
2010
8.5
2011
6.4
2012
6.6
2013
7.0
Source: World Bank 2014
35
APPENDIX D: CALCULATION OF AVERAGE WAGES IN ZAMBIA
Using the 2008 labor report issued by the CSO, we obtained various wage rates based on
gender, geographic location, and sector of employment. We adjusted the 2008 average monthly
wage rates to 2014 average monthly wage rates by adjusting for inflation for years 2009-2013
(See Appendix E). Using the consumer price index, we obtained values in 2014 dollars. The
2008 inflation rate was not included in our adjustment because the CSO conducted the 2008
labor survey in the November/ December timeframe of year 2008. We use these eight wage
rates in the calculation of our social network costs, patient opportunity costs, and productivity
benefits.
Following our inflation adjustment, the ZMK was debased in 2012 with the introduction
of a new currency ZMW where 1,000 ZMK is equal to one (1) ZMW. We converted ZMW to
USD using the exchange rate as of November 19, 2014; with .1577 USD equal to 1 ZMW. To
conclude with our final wage estimates, we multiplied the average monthly 2014 USD wage by
12 to calculate the average annual 2014 USD wage. The following wage calculation is applied to
all eight wages displayed in the two tables below.
Inflation adjusted average 2014 ZMK wage = (average monthly 2008 wage ZMK) * (respective
inflation rates for the years 2009 to 2013)
Adjusted for new currency 2014 ZMW wage = (inflation adjusted 2014 ZMK wage) / (1000)
Average monthly 2014 wage = (adjusted for new currency 2014 ZMW wage) * (.15577)
(USD/ZMW)
Average annual 2014 wage (USD) = (average monthly 2014 wage) * (12 months)
These eight wages are displayed in the tables below.
36
Table 13: Female Wage Rates
Female
Wage Rate
Category
Average
monthly
2008 wage
(ZMK)
Inflation
adjusted
average
monthly
2014 wage
(ZMK)
Adjusted for
new
currency
ZMW
Average
monthly
2014 wage
(USD)
(1000 ZKW
= 1 ZWM)
(.1577 USD =
1 ZMW)
Average
annual 2014
wage (USD)
Formal &
Rural
923,481
1,270,937
1,271
200
2405
Formal &
Urban
1,866,801
2,787,555
2,788
440
5275
Informal &
Rural
232,814
347,644
348
55
658
Informal &
Urban
766,338
1,144,315
1,144
180
2166
Inflation
adjusted
average
monthly
2014 wage
(ZMK)
Adjusted for
new
currency
ZMW
Average
monthly
2014 wage
(USD)
Average
annual 2014
wage (USD)
(1000 ZKW
= 1 ZWM)
(.1577 USD =
1 ZMW)
Source: CSO (2008); Authors
Table 14: Male Wage Rates
Male Wage
Rate
Category
Average
monthly
2008 wage
(ZMK)
Formal &
Rural
1,052,770
1,572,023
1,572
247
2,974
Formal &
Urban
2,638,043
3,939,193
3,939
621
7,454
Informal &
Rural
358,475
535,284
535
84
1,012
Informal &
Urban
1,129,320
1,686,329
1,686
265
3,191
Source: CSO (2008); Authors
37
APPENDIX E: CALCULATION OF ABSENTEEISM COSTS
Absenteeism costs under current policy are accrued as benefits following the successful
treatment of a drinker. Absenteeism is defined as the days absent from work and in our case the
days absent from work attributed to alcohol consumption (Baumberg 2010). These costs affect
the individual drinker, the employer of the individual drinker, and the colleagues of the
individual drinker. Costs vary primarily by the drinking state and wage of each worker. Our
estimates are calculated per individual. We estimate benefits in terms of an individual moving
from a drinking state to an abstainer state with a gradual reduction in benefits overtime.
Pidd et al. (2006) estimates that there is a positive correlation in sick-absenteeism in the
workplace and alcoholic consumption and frequency. Pidd et al. (2006) uses a comprehensive
survey in Australia on sick-related absenteeism that divides drinkers into various categories
similar to SBIRT. The advantage of using the Pidd et al. (2006) analysis is the ability to assign
absenteeism based on drinking state. Adjusting these values for SBIRT we were able to obtain an
average number of days per drinking category. These values are displayed in the table below.
Table 15: Means days absent attributed to sickness by drinking category in Australia
Drinking Category
Average Days Off
Abstainers
5.51
Low Risk
5.84
At-risk
7.04
Harmful
7.05
Likely dependent
9.95
Source: Pidd et al. (2006); Authors
38
The weighted average based on the population statistics provided in Pidd et al. (2006) is
6.6 days absent attributed to sickness. Under the assumption of 250 working days, the Australian
sick-absenteeism rate is estimated at approximately 2.6 percent.
To adjust the overall absenteeism days by drinking category to Zambia, we use a 2011
South African sick-related absenteeism rate of 3.40 percent (Alexander Forbes Health
Management Solutions 2013). We assume that the South African rate is a reasonable
approximate estimate for Zambia. Absenteeism can depend upon cultural elements and given the
high prevalence of problem drinking in Zambia relative to Australia and the United States, it is
plausible that these estimates are conservative. We recommend that if a Zambian work-related
absenteeism rate is surveyed and discovered for both the formal and informal sector that our
estimates be revised accordingly.
We adjusted days absent for each drinking state by a ratio factor of 1.292 (.0340/.0263)
to account for the difference in overall absenteeism between South Africa and Australia. Upper
and lower bounds are obtained by applying an absenteeism range specific to South Africa
provided by Johnson (2009). The overall absenteeism range of 3.5 percent and 6.0 percent is
translated into an overall sick-absenteeism range of 2.51 percent and 4.29 percent using ratios
between upper and lower bounds from the average value. Average, upper bounds, and lower
bounds are provided in the table below.
39
Table 16: Means days absent attributed to sickness by drinking category in Zambia
(transferred from South Africa context)
Drinking State
Lower Bound
Average
Upper Bound
Abstainers
5.24
7.12
8.99
Low Risk
5.56
7.54
9.52
At-risk
6.70
9.10
11.49
Harmful
6.71
9.10
11.50
Likely dependent
9.47
12.85
16.22
Source: Authors
Consequently, to determine absenteeism-related to alcohol, we subtract the mean sickrelated absenteeism days for the abstainers from each drinking class to obtain the following
estimates of alcohol-related absenteeism days. Pidd et al. (2006) employs this methodology.
Table 17: Days absent associated with alcohol consumption per year
Drinking State
Lower Bound
Average
Upper Bound
Abstainers
0.00
0.00
0.00
Low Risk
0.31
0.43
0.54
At-risk
1.46
1.98
2.50
Harmful
1.46
1.98
2.51
Likely dependent
4.22
5.73
7.24
Source: Authors; Pidd et. al (2006) methodology
We convert days missed into “percent of working year absent attributed to alcohol
consumption,” to account for the wide discrepancies in work hours by sector, geographical
location and sex rather than using a “number of days absent” measure. We assume 250 working
days (assumed in both Pidd et al. 2006 and Johnson 2009) to calculate the percent lost and
40
calculate the following percentages. Our Monte Carlo simulation assumes a triangular
distribution.
Table 18: Percent of working year absent attributed to alcohol consumption
Drinking State
Lower Bound (%)
Average (%)
Upper Bound (%)
Abstainers
0.00
0.00
0.00
Low Risk
0.12
0.17
0.22
At-risk
0.58
0.79
1.00
Harmful
0.59
0.79
1.00
Likely dependent
1.69
2.29
2.90
Source: Authors
The utility of using a “percentage of the year absent,” under the assumption that these
percentages remain constant across working classes, is that we are able to apply the percentage to
different types of workers that may vary in days and hours worked. If a comprehensive survey in
Zambia collected the days absent missed by geographic location, sex, and sector of work would
greatly advance our ability to provide a more detailed Zambian-specific estimate. Furthermore,
Yende discusses absenteeism among South African males citing that each male alcoholic
interviewed missed 86 working days a year due to absence (Yende 2005). However, because of a
lack of data, differences between male and female, and other concerns we deployed only the
conservative estimates based off the Australian survey data.
Absenteeism costs will vary on the drinking state, wage, and sector of employment for a
given individual. Each baseline absenteeism cost under current policy is calculated the
following:
Baseline Absenteeism Cost = (wage) * (percent of working year absent attributed to alcohol)
41
If the individual works in the formal sector economy, we assume there is an average
spillover multiplier of 1.1 with a uniform distribution with lower and upper bounds of 1.0 to 1.2,
respectively. This spillover multiplier accounts for the increased costs to the employer or fellow
colleagues due to the absence of the drinker. A spillover of 1.1 is used for Zambia due to the
high concentration of low-wage, low-skilled and easily replaceable labor (Quanbeck et al. 2010;
Nicholson et al. 2006).
We estimated total absenteeism cost savings by sector as follows (Quanbeck et al. 2010;
Baumberg 2010):
If formal sector:
Absenteeism Cost = (absenteeism multiplier) * (wage) * (percent of working year absent
attributed to alcohol)
If informal sector:
Absenteeism Cost = (1.0) * (wage) * (percent of working year absent attributed to alcohol)
The overall range for women varies between $0.83 and $183.30. Estimates vary with
drinking state, wage that is conditional on location and sector of employment, as well as, the
absenteeism multiplier. As stated, these costs are incorporated into our model accrued as benefits
following successful treatment. The table below summarizes the range of potential estimates
within categories.
42
Table 19: Female Absenteeism Cost Estimates (USD)
Drinking State
Lower
Bound ($)
Average ($)
Upper Bound
($)
Low Risk
3.02
4.51
6.22
At-risk
14.03
20.94
28.86
Harmful
14.06
20.99
28.93
Likely Dependent
40.63
60.65
83.57
Low Risk
6.63
9.89
13.63
At-risk
30.77
45.93
63.29
Harmful
30.85
46.05
63.45
Likely Dependent
89.10
133.02
183.30
Low Risk
0.83
1.12
1.42
At-risk
3.84
5.21
6.58
Harmful
3.85
5.22
6.59
Likely Dependent
11.11
15.08
19.05
Low Risk
2.72
3.69
4.66
At-risk
12.63
17.14
21.65
Harmful
12.66
17.18
21.71
Likely Dependent
36.58
49.64
62.70
Formal & Rural
Formal & Urban
Informal Rural
Informal Urban
Source: Authors
The overall range for men varies between $1.27 and $259.03. The table below
summarizes the range of potential estimates within categories.
43
Table 20: Male Absenteeism Cost Estimates (USD)
Drinking State
Lower
Bound ($)
Average ($)
Upper Bound
($)
Low Risk
3.74
5.58
7.69
At-risk
17.35
25.90
35.69
Harmful
17.40
25.97
35.78
Likely Dependent
50.25
75.01
103.37
Low Risk
9.36
13.98
19.26
At-risk
43.48
64.90
89.44
Harmful
43.59
65.07
89.67
Likely Dependent
125.91
187.97
259.03
Low Risk
1.27
1.73
2.18
At-risk
5.91
8.02
10.13
Harmful
5.92
8.04
10.15
Likely Dependent
17.11
23.22
29.33
Low Risk
4.01
5.44
6.87
At-risk
18.61
25.26
31.91
Harmful
18.66
25.32
31.99
Likely Dependent
53.90
73.15
92.41
Formal & Rural
Formal & Urban
Informal Rural
Informal Urban
Source: Authors
The distribution of benefits will vary with the different demographic parameters specified
for the pilot phase and Central Province.
44
APPENDIX F: CALCULATION OF PRESENTEEISM COSTS
Presenteeism costs under currency policy are accrued as benefits following the successful
treatment of a drinker. Presenteeism measures the reduced productivity at work for a given
drinker, which may be attributed to hangovers, on-the-job drinking, reduced work effectiveness
resulting from alcohol-induced health disabilities, etc. (Baumberg 2010). Costs depend on the
drinking state of each respective patient. Our estimates are calculated per individual. We
estimate benefits in terms of an individual moving from a drinking state to an abstainer state with
a gradual reduction in benefits overtime. Because of lack of data, cultural sensitivities, and
existing methodological controversies, it is necessary to state several assumptions.
We assume that absenteeism rates and presenteeism rates are correlated. More
specifically, we assume that absenteeism and presenteeism occur at comparable levels and
increase jointly as an individual increases their level of drinking (Anderson 2010; Pidd et al.
2006). Additionally, we assume that the rate of absenteeism and presenteeism between drinking
states remain constant across countries and time allowing the use of western data to adjust in a
Zambian context. Furthermore, we assume the rates of change (for absenteeism and
presenteeism) between drinking classes do not change.
We calculate presenteeism days attributed to alcohol based on a cost-benefit analysis in
the United States and adjust for Zambia. Arora et al. (2011) estimates a range of days that a
likely alcohol dependent experiences presenteeism: 8.7 to 22.51 days. Based on the assumption
of a positive correlation between absenteeism and presenteeism, we multiply this range by a
factor equal to the ratio of the 2011 US sick-related absenteeism rate and the South African sickrelated absenteeism rate. This factor equals 1.545 calculated by dividing the 2011 South African
sick-related absenteeism rate (3.4 percent) by the 2011 US sick-related absenteeism rate (2.2
45
percent) (Alexander Forbes Health Management Solutions 2011; BLS 2011). Adjusting to South
Africa, which we assume to be a plausible proxy to the Zambian context, we estimate a range of
13.45 to 34.79. The range is adjusted and held constant from the US context.
Table 21: Sick-related Absenteeism Rate
Country
Sick-related absenteeism rate (2011) (%)
United States
2.2
South Africa (Zambia)
3.4
Source: BLS (2013); Alexander Forbes Health Management Solutions (2011)
Table 22: Adjusting US Presenteeism Days to Zambia the SBIRT Likely Dependent
drinking
Country
Lower Bound
Average
Upper Bound
US Likely
Dependent
8.70
15.61
22.51
Zambia Likely
Dependent
13.44
24.12
34.79
Source: Authors
To adjust presenteeism days to the other SBIRT drinking states, we use the likely
dependent drinker as the base case and adjust the estimate downward using the ratio that
absenteeism varies between drinking categories. We assume that absenteeism and presenteeism
ratios do not vary across countries and offers a plausible adjustment factor relating absenteeism
and presenteeism across different drinking states. We estimate the following:
46
Table 23: Ratio of days absent between drinking states
Drinking State
Average days absent
attributed to alcohol
Ratio between
drinking states*
Calculation
Abstainers
0.000
0.000
Abstainer / Low Risk
Low Risk
0.426
0.215
Low Risk / At-risk
At-risk
1.979
0.997
At-risk / Harmful
Harmful
1.984
0.346
Harmful / Likely
Dependent
Likely dependent
5.731
*Assumption: Ratios hold true when applied to absenteeism and presenteeism across countries.
Source: Authors
Table 24: Days presenteeism occurs associated with alcohol consumption per year
Drinking State
Lower Bound Average
Upper Bound
Abstainers
0.00
0.00
0.00
Low Risk
1.00
1.79
2.58
At-risk
4.64
8.33
12.01
Harmful
4.66
8.35
12.04
Likely Dependent
13.45
24.12
34.79
Source: Authors
Similar to absenteeism, we state presenteeism as a percentage of working days in order to
apply across large wage differentials. Our Monte Carlo simulation assumes a triangular
distribution. The following table exhibits the percentages calculated.
47
Table 25: Percent of working year presenteeism occurs attributed to alcohol consumption
Drinking State
Lower Bound (%) Average (%)
Upper Bound (%)
Abstainers
0.00
0.00
0.00
Low Risk
0.40
0.72
1.04
At-risk
1.86
3.33
4.81
Harmful
1.86
3.34
4.82
Likely Dependent
5.38
9.65
13.92
Source: Authors
Presenteeism costs depend on the drinking state, wage, and sector of employment for a
given individual. Furthermore, wage is further divided into sex, geographic location, and sector
of employment. Regarding the sector of employment, we assume that only individuals in the
formal sector of the economy will accrue presenteeism cost savings that depend on an
impairment factor. We use an impairment factor of 10 percent with a uniform distribution with
lower and upper bounds of zero and twenty respectively. We justify using a low impairment
factor for the overall low skill and relatively easy replaceable labor market characteristics (Pauly
et al. 2008; Quanbeck et al. 2010). An impairment factor measures the degree of reduced
productivity that will occur in the workplace when an individual comes hung over, drunk, etc.
Each presenteeism cost under currency policy is estimated the following (Quanbeck et al.
2010; Thavorncharoensap et al. 2010):
If formal sector:
Presenteeism Costs = (impairment factor) * (wage) * (percent of working year presenteeism
occurs attributed to alcohol)
If informal sector:
48
Presenteeism Cost = zero (0)
The overall range for women varies between $0.00 and $146.81. Estimates vary with
drinking state, wage that is conditional on location and sector of employment, as well as, the
presenteeism impairment factor. As stated, these costs are incorporated into our model as
benefits following successful treatment. The table below summarizes the range of potential
estimates within categories.
Table 26: Female Presenteeism Cost Estimates (USD)
Drinking State
Lower Bound Average ($)
($)
Upper Bound
($)
Low Risk
0.00
1.73
4.98
At-risk
0.00
8.01
23.11
Harmful
0.00
8.03
23.17
Likely Dependent
0.00
23.20
66.94
Low Risk
0.00
3.78
10.92
At-risk
0.00
17.57
50.69
Harmful
0.00
17.62
50.82
Likely Dependent
0.00
50.89
146.81
Low Risk
0.00
0.00
0.00
At-risk
0.00
0.00
0.00
Harmful
0.00
0.00
0.00
Likely Dependent
0.00
0.00
0.00
Formal & Rural
Formal & Urban
Informal Rural
49
Low Risk
0.00
0.00
0.00
At-risk
0.00
0.00
0.00
Harmful
0.00
0.00
0.00
Likely Dependent
0.00
0.00
0.00
Informal Urban
Source: Authors
The overall range for men varies between $0.00 and $207.46. Estimates vary with
drinking state, wage that is conditional on location and sector of employment, as well as, the
presenteeism impairment factor. The table below summarizes the range of potential estimates
within categories.
Table 27: Male Presenteeism Cost Estimates (USD)
Drinking State
Lower Bound Average ($)
($)
Upper Bound
($)
Low Risk
0.00
2.13
6.16
At-risk
0.00
9.91
28.59
Harmful
0.00
9.93
28.66
Likely Dependent
0.00
28.70
82.79
Low Risk
0.00
5.35
15.43
At-risk
0.00
24.83
71.63
Harmful
0.00
24.89
71.82
Likely Dependent
0.00
71.91
207.46
Formal & Rural
Formal & Urban
50
Low Risk
0.00
0.00
0.00
At-risk
0.00
0.00
0.00
Harmful
0.00
0.00
0.00
Likely Dependent
0.00
0.00
0.00
Low Risk
0.00
0.00
0.00
At-risk
0.00
0.00
0.00
Harmful
0.00
0.00
0.00
Likely Dependent
0.00
0.00
0.00
Informal Rural
Informal Urban
Source: Authors
The distribution of benefits will vary with the different demographic parameters specified
for the pilot phase and Central Province.
51
APPENDIX G: CALCULATION OF PATIENT OPPORTUNITY COST
The incorporation of patient costs into cost-benefit analyses of health interventions
appears mixed in the literature. Some analyses do not include any costs that accrue only to the
patient, but instead focus on provider costs (Bray et al. 2012). Another set of studies argue that
ignoring these costs underestimates disease burden and biases results, stating further that even if
the amount of time appears negligible, when aggregated it could significantly impact findings
(Russell 2009). In an effort to guard against the possibility of overstating the benefits of SBIRT
in Zambia, especially in view of the significant uncertainty in the acceptance and success rates of
the SBIRT program, we have chosen the more conservative approach of including patient costs
in our analysis.
The opportunity costs for patients takes into account alternative uses of the time spent
receiving health care services. The time spent receiving services is a summation of expected time
allocated to the brief screen, brief intervention (in cases of positive screens), and time spent
receiving treatment as well as time waiting for and traveling to and from treatment at the nearest
referral center (in cases of likely dependency).
Other studies that have incorporated patient time costs have used the average wage rate as
a proxy for the patients’ opportunity cost (Fleming et al. 2000). This is in agreement with
economic literature on opportunity cost valuations, which posits that the wage rate is the price of
labor as well as the price of leisure since an hour’s wage is the price of consuming an additional
hour of leisure (Russell 2009). The wage rate used to determine the patient opportunity cost in
this cost-benefit analysis is the same as that used in the absenteeism and presenteeism
calculations. This accounts for whether the patient is economically active or inactive, employed
or unemployed, in a rural or urban area, in a formal or informal sector of the economy, and
52
whether the patient is male or female. Based on distributions of these characteristics in the
population, an observation in the Monte Carlo simulation is assigned a corresponding wage.
One distinction is made, however, with respect to the likely dependent drinking category.
For this classification of patients, to take account of the additional burden of traveling to a clinic
to receive care and the significant time commitment, we factored in a travel cost and a retention
rate. The travel cost used is an estimate of the fuel cost, while the retention rate accounts for the
fact that not all who are referred to the clinic actually go to receive treatment. This rate is based
on a systematic review of adult patient attrition from ART programs in service delivery settings
in sub-Saharan Africa, which found that on average 70 percent remain in treatment after 24
months (Fox and Rosen 2010). A travel cost is not employed for those in the other drinking
categories. All screening and treatment takes place in the patient’s home for these other cases,
and therefore, the patient requires no travel.
The simulated wage is then converted into the proper time unit and multiplied by the
estimated range of time spent receiving treatment. For all in the sample, this was 15 minutes for
the brief screen, with a range of 10 to 20 minutes, and moderated by the participation rate. For
those with at-risk drinking, the estimate is 45 to 65 minutes, with an estimated 15 minutes for the
brief screen, 20 minutes for the full screen, and 1 to 3 counseling sessions at 15 minutes each.
For harmful drinkers, this time allotment becomes 50 to 80 minutes, with 15 minutes for the brief
screen, 20 minutes for the full screen, and a range of 3 to 6 intervention sessions at 15 minutes
each. Lastly, for those with likely dependency, in view of high uncertainty in the travel costs,
time spent waiting to receive treatment, and the duration of treatment, a large range was used. In
the simulation, however, those who were classified as likely dependent but did not accept
treatment were only assigned the cost for the brief and full screens.
53
One nearby clinic (SHARPZ clinic) offers a five-day intensive alcohol treatment. With
this in mind, we used five days as our point estimate for those likely dependent, again, using the
daily wage as the proxy for opportunity cost. A range of plus or minus two days was then
incorporated into our sensitivity analysis to account for the large degree of uncertainty with this
aspect of the opportunity cost estimate. The travel cost ranged from 0, if the travel could be
incorporated into another trip to Lusaka, to a high of 25 dollars, if the travel was undertaken by
an individual in a personal vehicle. Carpooling or use of public transportation would decrease the
cost, but would remain in the above range. A simulated acceptance of treatment was then used
based on a distribution using the retention rate mentioned above, with two levels of 0 and 1.
Thus, if 0, no opportunity cost apart from the brief and full screens were accounted for, while for
a 1, the complete list of costs were included.
54
APPENDIX H: CALCULATION OF SOCIAL NETWORK COSTS
The social network costs are calculated as a loss in employment opportunities from no
longer drinking with a cohort of friends. This measure is included in response to evaluations of
the Zambian drinking culture. These observations stress the centrality of drinking in some social
circles, and how changing an individual’s drinking habits would likely result in a disruption of
his or her existing social network (Haworth 2004). Thus, with this estimation we are assuming
that the individual will be excluded from his or her previous social network as a result of the
changed alcohol consumption and that no new network is formed during the time period of the
program.
To estimate this impact, we use information gathered from an International Labour Office
study on the Zambian labor market (ILO 2013). In this research it was found that roughly 31
percent of youth found their employment through friends, relatives, and acquaintances. As some
members of this social network would not change as a result of decreased drinking, we use a
range from zero to 31 percent. This percentage of employment gained through referrals was then
multiplied by a composite of the annual wage and the average length of transition to new
employment. The transition statistic used in the calculations showed an average length of
transition of 1.6 months for men and 2.9 months for women (ILO 2013). The composite wage
was separate for men and women, and was weighted by the distribution of rural and urban areas
and the distribution of formal and informal employment specific to the pilot area and Central
Province. This is only included in the model for successfully treated drinkers that are
unemployed.
55
APPENDIX I: CALCULATION OF HEALTH CARE SYSTEM COST
The health care system costs are the change in the burden on nearby health care centers as
a result of the SBIRT program. A Wisconsin study found that implementing SBIRT annually
increases outpatient days by 4.5 per person, while decreasing in-patient days by 2 (Paltzer 2014).
This is consistent with other studies that find SBIRT reduces health costs by reducing the
number of emergency department visits and hospitalizations (Agerwala and McCance-Katz
2012; Gentilello et al. 2005). Moreover, the increase in outpatient days found in the Wisconsin
study can also be used to factor in the increased outpatient days resulting from the referrals to
treatment, and other possible visits resulting from increased awareness of health consequences of
drinking.
To then translate this into the Zambian context, we accounted for differences in the
propensity to use health care services and differences in health care costs. Based on survey data
from the two countries we calculated a percent difference in use of health care facilities when ill:
Zambians are 35 percent less likely to seek care (Schoen et al. 2013; Hjortsberg 2003). This
percent difference was then used to adjust the expected changes in inpatient and outpatient
service utilization resulting from SBIRT. The second translation mechanism used average cost
estimates of outpatient and inpatient days from a Zambian study. The study found inpatient costs
to be $18 per bed day and outpatient costs to be $3 per visit (Chola and Robberstad 2009). These
statistics were then converted to 2014 USD, which resulted in estimates of $19.97 per bed day
for inpatient services and $3.33 per visit for outpatient services. Using these cost estimates and
the adjusted expected utilization changes gave an estimated $16.30 in annual cost savings per
person who participates in SBIRT.
56
APPENDIX J: CALCULATION OF AVOIDED COSTS OF CRIME AND ROAD TRAFFIC
ACCIDENTS
AVOIDED COSTS OF CRIME
To approximate the cost of alcohol-related crime, we used estimates of the cost of
harmful alcohol use in South Africa; the aggregate cost of crime was $3.27 billion or 7.8 percent
of GDP. Evidence shows that in low- to middle-income countries in Latin America, with high
crime levels, crime costs range from 5 to 15 percent of GDP (Matzopoulos et al. 2014).
Matzopoulos et al. (2014) apply an alcohol-attributable fraction (AAF) of crime of 0.25
and assume that 75 percent of costs of crime are relevant to alcohol use, which include response
of crime, consequences of crime such as damages to victims, anticipation of crime, and nonfinancial welfare costs which includes the emotional costs associated with alcohol-related crime.
Under these assumptions, alcohol-attributable crime costs totaled $4.14 billion (1.45 percent of
GDP) in South Africa. In another study Boucher et al. (2006) estimate the economic costs of
alcohol consumption in the United States. Victim costs, criminal justice system costs, and
productivity loss represented $73.327 billion in 2006 (0.53 percent of GDP).
Using Zambia’s 2013 GDP of $22.38 billion, crime costs represent between $112 and
$217 million (0.50 to 0.97 percent of GDP), an average cost of $20.99 per drinker.
Table 28: Cost of Crime
Cost
Percentage of GDP (%)
2014 US$ (million)
Per drinker* US$
Upper Bound
0.97
217
$27.70
Average
0.74
164
$20.99
Lower Bound
0.50
112
$14.28
*Estimate using target population of drinkers 7,837,039 at the national level.
Source: Authors
57
Using WHO data we apply an AAF of 0.37 for men and 0.07 for women to determine
that completing SBIRT will reduce 0.0138 percentage-points in men’s (from 0.038 to 0.024
percent) and 0.0026 percentage-points (from 0.038 to 0.035 percent) in women’s probabilities of
dying in a crime incident.
Reductions in alcohol attributable mortality risk for crime calculations:
0.038*0.37=0.0001382
0.0138 percentage-point (men)
0.038*0.07=0.000026
0.0026 percentage-point (women)
AVOIDED COSTS OF ROAD TRAFFIC ACCIDENTS
Drinking and driving increases the risk of a crash and the probability of death or serious
injury. This risk increases with higher levels of blood alcohol concentration (BAC). For a person
with a BAC level of 0.01g/100ml the crash risk is almost five times higher than for someone
with a BAC level of zero (WHO 2013). In 2010, the police recorded 1,388 road traffic fatalities
across Zambia (Zambian Road Safety Trust 2014). Death tolls indicate that 46 percent of
fatalities were pedestrians, 30 percent passengers, 8 percent drivers, 13 percent cyclists, and 3
percent riders of 2- or 3-wheelers motorized vehicles. However, evidence for relating alcohol
and traffic accidents is limited because authorities do not enforce alcohol testing. Non-fatal crash
injuries are usually not reported or documented. This results in a lack of reliable data regarding
Zambia’s traffic accidents involving impaired drivers with BAC levels over the national limit of
0.08g/100ml, with very low enforcement of the national drink-driving law (WHO 2013).
According to Bouchery et al. (2011), the costs of alcohol-related road traffic accidents in
the United States represent 0.23 percent of GDP in 2006. These costs include damages in crash,
lost of productivity, and correction costs associated with alcohol. In another study,
58
Thavorncharoensap et al. (2010) estimate that the economic cost of alcohol consumption in
Thailand represented 0.023 percent of GDP in 2006. Matzopoulos et al. (2014) calculated the
cost of alcohol-related traffic accidents in South Africa as 0.98 percent of GDP in 2007.
According to the WHO, Zambia had 337,513 vehicles registered in 2010. South Africa has seven
times the number of vehicles per 100,000 people than Zambia. Using these estimates as
reference, alcohol-related traffic accidents in Zambia cost between $56 and $224 million, an
average of $17.85 per drinker.
Table 29: Cost of road traffic accidents
Drinking State
Percentage of GDP
(%)
2014 US$
(million)
Per drinker US$
Upper Bound
1.00
224
28.56
Average
0.63
140
17.85
Lower Bound
0.25
56
7.14
*Estimate using target population of drinkers 7,837,039 at the national level.
Source: Authors
We also predict that reducing alcohol consumption will decrease the probability of dying
in a road traffic accident. According to the WHO, 41.7 per 100,000 men and 20.4 per 100,000
women die in road traffic accidents every year and the AAF are 0.05 for men and 0.02 for
women. Based on these estimates, a successfully treated patient will reduce his or her probability
of dying by 0.002 percentage-points, from 0.042 to 0.040 percent for men and by 0.0005
percentage-points from 0.020 to 0.015 percent for women.
Reductions in alcohol attributable mortality risk for traffic accidents calculations:
0.00042*0.05 = 0.00002
0.002 percentage-point (men)
0.000204*0.02 = 0.000005
0.0005 percentage-point (women)
59
FAMILY COHESION AND DOMESTIC VIOLENCE
For the purpose of our analysis we decided not to take into consideration domestic
violence costs. First, many experts argue it is difficult putting a “cost” on the physical and
psychological damage of violence against women and children. Additionally, most of the studies
estimating costs of domestic violence have been conducted in industrialized countries, including
costs in health care, social services, criminal justice, and businesses. For example, costs of
domestic violence in the United States range between $3.5 and $12.6 billion a year. In the United
Kingdom these costs added to $30.6 million (Duvvury et al. 2004).
Estimating costs in developing countries is a challenging task because social norms
enable domestic violence, creating a “culture of silence” among victims. Therefore, incidents are
not reported and there is no utilization of services. In Zambia, 98 percent of women working in
rural areas and 80 percent in urban areas are in the informal sector. Women perform most of the
unpaid household labor, which makes it difficult to determine lost or reduced income attributable
to domestic violence. According to a 2007 national survey, women whose husbands drink
frequently are more likely to have experienced spousal violence. In this study, 77 percent of
married women whose husbands get drunk frequently reported to have experienced physical,
sexual, or emotional violence (CSO et al. 2009). Given the uncertainty to determine the actual
number of domestic violence cases that can be attributed to alcohol consumption we did not
monetize the avoided costs that SBIRT could provide if successful treating patients. We assume
that omitting these costs will not substantially underestimate the net benefits of the program.
60
APPENDIX K: IMPLEMENTATION COSTS
SALARY OF COMMUNITY HEALTH WORKER
While the salary and benefits of the health educator or clinician implementing SBIRT
typically comprises the majority of the implementation cost estimate, the proposed model in
Zambia would use CHWs to implement the screenings and brief interventions, which would
change the cost distribution. CHWs are compensated very differently across the country,
depending on the health area and the organization for which they are working. The CHWs that
would participate in the SBIRT pilot have worked with the Lutheran Health and Development
Program in Mwembeshi since 2003. They have been compensated by in-kind donations from the
community, bags of rice, and most recently a bicycle to facilitate home visits with an estimated
value of $137 (Fretz 2011). On the national level, the Ministry of Health has proposed using
CHWs to compensate for the shortage in public health workers the country faces. CHWs
working through the Zambian Ministry of Health would receive roughly $295 per month (2010
dollars), for full-time, formalized work through the Ministry of Health program (Ashraf and
Kindred 2011).
To determine the costs associated with using CHWs to implement diagnostic screening
and brief interventions, a minute wage was calculated for both the CHWs in the pilot program
and the CHWs that would be used in the scaled-up program in Central Province. As previously
mentioned, CHWs in the pilot are not formal workers, so there is the assumption that their work
will vary through the year, based on farming season demands. A survey of CHWs in Zambia
conducted by Ashraf and Kindred found that CHWs work an average of 18 hours per week and a
third of the respondents reported periods of inactivity or extended leaves during their time as a
CHW. To account for hours the CHWs are inactive or may be dedicating to other health needs,
61
we estimate that they would each be dedicating roughly 9.5 hours of their time per week to
SBIRT related activities. The CHWs working for the province level project are assumed to be
working full-time and dedicating all 40 hours to SBIRT activities.
CHW minute wage calculations:
Pilot: $137 yearly salary / 52 weeks / 9.5 hours / 60 minutes = $0.0037 minute wage
Province: $3746 yearly salary/ 52 weeks / 40 hours / 60 minutes = $0.03 minute wage
SUPPLIES
The screening and brief interventions supply costs are calculated as per patient costs
using estimates from Fleming et al. (2000). The supply costs are the costs incurred for one
screening or one brief intervention. Dollar estimates from the study were converted to 2014
dollars and calculated as follows:
Initial screening supply costs $3107/ 8962 patients = $0.35 per patient
Full screening supply costs $514/ 1481 patients = $0.35 per patient
Intervention supply costs $1086/ 392 patients = $2.77 per patient
SCREENING AND BRIEF INTERVENTION
The amount of time needed to execute SBIRT components will vary in program
implementation. To account for this variation, we calculate upper and lower bounds for the
screening and brief intervention costs. The standard time for the initial screen is set at 15
minutes, with an additional 7.5 to 15 minutes of preparation and transition between patients. The
standard time for the comprehensive screen is 20 minutes, with an additional 10 to 20 minutes of
preparation and wrap-up per patient. The following calculations take into consideration the CHW
minute wage, and the time and necessary supplies per screening.
62
Initial Screening-Only Cost Calculations:
Upper Bound: ($0.0037 wage) * (15 min of screening + 15 min of prep) + ($0.35 supplies) =
$.461
Lower Bound: ($0.0037 wage) * (15 min of screening + 7.5 min of prep) + $0.35 supplies = $
0.433
Comprehensive Screen Cost Calculations:
Upper Bound: $0.0037 wage * (20 min of screening + 20 min of prep) + $0.35 supplies =
$0.498
Lower Bound: $0.0037 wage * (20 min of screening + 10 min of prep) + $0.35 supplies =
$0.461
Summation of Initial and Comprehensive Screen:
Upper Bound of Both Screenings: $0.461 + $0.498= $0.959
Lower Bound of Both Screenings: $0.433+ $0.461 = $0.894
Brief Interventions Cost Calculations:
Only those drinkers identified as “at risk” or “harmful” would receive brief interventions
from the CHWs. One intervention lasts 15 minutes, with preparation time varying from 30 to 60
minutes. In previous SBIRT implementations, “at-risk” drinkers receive 1 to 3 interventions and
“harmful drinkers” receive 3 to 6 interventions. To account for the variation of potential
treatment, upper and lower bounds were calculated, again taking into consideration the CHW
minute wage, and the time and necessary supplies per intervention. Costs are calculated per
drinker, assuming 100 percent completion of the outlined interventions.
63
“At-Risk” Calculations:
Upper Bound: (($0.0037 wage) * (15 min + 60 min prep) * 3 treatments) + ($2.77 supplies * 3
treatments) = $9.14
Lower Bound: (($0.0037 wage) * (15 min + 30 min prep)) * 1 treatment) + ($2.77 supplies * 1
treatment) = $2.94
“Harmful” Calculations:
Upper Bound: (($0.0037 wage * (15 min + 60 min prep)) * 6 treatments) + ($2.77 supplies * 6
treatments) = $18.26
Lower Bound: (($0.0037 wage * (15 min + 30 min prep)) * 3 treatments) + ($2.77 supplies * 3
treatments) = $8.81
Screening and Intervention Costs by Drinking Category:
Following the SBIRT programmatic flowchart, the average costs per drinker their
corresponding screenings and interventions are as follows:
Table 30: SBIRT Costs per Drinker
Drinking State
Average
Range
Low Risk
$0.9265
$0.0325
At Risk
$6.965
$3.135
Harmful
$14.46
$4.76
Dependent
$0.9265
$0.0325
Pilot
64
Low Risk
$2.538
$0.2625
At Risk
$9.59
$5.47
Harmful
$21.24
$8.88
Dependent
$2.538
$0.2625
Province
Source: Authors
INITIAL AND FOLLOW-UP TRAINING COSTS
SBIRT has never been implemented in Zambia, meaning the CHWs expected to screen
and treat community members would have to be trained. Training costs are calculated for the
initial training, and also for a follow-up training course at year two. CHW unit cost estimates for
training were provided by Ashraf and Kindred (2011). They calculated the initial and follow-up
training costs for full-time, Ministry of Health CHWs. The ratio of the pilot CHW wage to the
Ministry of Health CHW wage was used to calculate a per CHW cost of training for the pilot
program:
Province CHW minute wage / Pilot CHW minute wage = 0.03/0.0037 = 8.108
Unit cost of one day of initial training for Province level project = $218.75
Unit cost of one day of initial training for Pilot level project = $27.00
Unit cost of one day of follow-up training for Province level project = $132.90
Unit cost of one day of follow-up training for Pilot level project = $16.39
In consultations with Dr. Paltzer, we estimated that the initial training of CHW for the
pilot would last 7 days and transportation costs for the LHA Trainers traveling in from Lusaka
should be included. Using WIPHL reports on training full-time health educators dedicated to
SBIRT, we are assuming at the province level, the initial training will last 10 days, with all
65
relevant costs included in the CHW unit cost estimate. We are estimating that an official followup training at year two would last 3 days for both the pilot and province level projects.
Transportation costs: 140mi/(17mi/gallon) = 4.12 gallons*$6.09 = $25.01/round trip
Table 31: Training Costs
Unit training Number of
cost ($ per
CHW
CHW, per
day)
Length of
training
(Days)
Transportation Total ($)
costs ($)
Initial
27.00
25
7
25.01
4,750.01
Follow-up
16.39
25
25.01
1,254.26
Initial
218.70
402
10
n/a
879,735
Follow-up
132.90
402
3
n/a
160,277
Pilot
Province
Source: Authors
RECURRENT TRAINING AND SUPPORT COSTS
Costs are incurred in the provision of regular technical and social support to the
community health workers. Technical and social supports have been widely acknowledged as
being integral to the success of community health worker programs. In the context of the SBIRT
program currently being considered, this support would include costs for the Lutheran Health
and Development Program, as the primary trainer, as well as in the local clinic staff, which will
be providing additional technical support. To approximate these costs, we rely on data provided
in a technical report prepared by the Earth Institute at Columbia University. This report looks
66
specifically on the establishment of CHW systems in developing countries as a means to work
toward achieving the millennium development goals.
The aforementioned report provides a breakdown of the average yearly expenditures for
community health worker programs for Zambia. Adding the annual training and management
costs from this accounting and dividing them by the number of CHWs, we calculated a value for
the expected support costs per health worker. For the Central Province estimate, there is an
additional administrative support cost. This takes into account the administrative overhead for
running a larger program, including the additional burden of managing salaries. This results in an
estimated cost of $123.38 per CHW for the pilot program and $298.98 per CHW for the Central
Province program. As this is based on national level government data, rather than data specific to
the Lutheran Health and Development Program and clinics anticipated to provide support, we
factored in a high degree of uncertainty into the Monte Carlo simulations. Specifically, we
included 50 percent uncertainty on either side of the point estimate.
67
APPENDIX L: VALUE OF A STATISTICAL LIFE (VSL)
In order to monetize certain costs and benefits, including those associated with health,
crime, and individual welfare, analyses routinely use figures known as the value of a statistical
life (VSL). A VSL is the hypothetical amount an individual would be willing to pay in order to
decrease the aggregate risk equal to a single reduction in fatalities. It is not a monetary value of
actual life, but rather the reduction in risk associated equal to one life (Viscusi & Aldy 2003).
Studies estimating the willingness to pay for VSL have used a variety of methods. Recent
federal guidance on the use of statistical life in cost-benefit analyses has encouraged the use of
studies of employment decisions based on risk (Department of Transportation 2012). This
research, known as “hedonic wage studies” generally use measures of income, risk of death,
morbidity, and other factors associated with wage to estimate the change in salary due to
increases of occupational risk. A 2012 meta-analysis of recent studies, suggested by a federal
panel of cost-benefit experts, reveals a VSL of $9.23 million for workers in the United States, in
2013 USD.
In addition to the variables mentioned above, estimated a VSL for developing countries
must also take into account the different use of financial resources associated with lower incomes
and shortened life expectancy. Recent research for the World Bank suggests that individuals in
low-income countries will prefer present consumption of income in favor of future consumption,
given greater uncertainty about life expectancy and quality of life (ICF International, Robinson,
& Hammitt 2009). While US regulations prefer an income elasticity of 1.0 in transferring
estimates of VSL, estimates greater than 1.0 are more appropriate for low-income countries
given slower growth. Because VSL studies are routinely developed for high-income countries,
68
we have adopted the following methodology for transferring current VSL estimates for the
United States to a Zambia-appropriate rate.
Viscusi & Aldy (2003) provide the following ordinary-least-squares regression for mean
VSL
𝑉 = 𝑎 + 𝛽i + γr + δ𝑟 !
where V is ln(VSL), i is ln(income), r is mean risk, β γ δ are coefficients, and α is a constant.
Income is measured by median household income, as estimated by the Federal Reserve of St.
Louis, and the World Bank. Mean risk is estimated by danger or risk in the workplace, as
measured by occupational injuries and fatalities (Hämäläinen et al. 2006). We utilized the values
as in Table 32.
Table 32: Values Used in VSL Calculation
Value
Value
VSL (ln)
$9,300,000 (16.05)
α, constant
11.22
β, coefficient for ln(income)
1.50
γ, coefficient for mean risk
-0.053
δ, coefficient for mean risk squared
0.00016
Gross national income, US (ln)
$53,960 (10.896)
Gross national income, Zambia (ln)
$3,070 (8.029)
Occupational fatality rate, US
5.20 deaths per 10,000 workers per year
Occupational fatality rate, Zambia
19.80 deaths per 10,0000 workers per year
Source: Authors
Using the value and equations above, we calculate the difference between ln(VSL) for the United
States and Zambia:
United States:
𝑉(𝑈𝑆) = 11.22 + 1.50 ∗ 10.896 + −0.053 ∗ 5.20 + 0.00016 ∗ 5.20! 𝑉(𝑈𝑆) = 27.29
69
Zambia:
𝑉(𝑍) = 11.22 + 1.50 ∗ 8.029 + −0.053 ∗ 19.80 + 0.00015 ∗ 19.80! 𝑉(𝑍) = 22.30
𝑉 𝑈𝑆 − 𝑉 𝑍 = 4.99
This represents the change in ln(VSL) transferred from the United States to Zambia. To
find the figure in current dollars, we subtract 4.99 from our adopted VSL, and calculate.
ln 𝑉𝑆𝐿! = 16.06 − 4.99
𝑉𝑆𝐿! = 63081.12 We round this figure to attain $63,000 for the value of a statistical life in Zambia.
Value of a statistical life year (VSLY) is calculated by dividing the VSL by an annuity
factor, based on the estimated remaining years of life for the average citizen, and an appropriate
discount rate (Boardman et al. 2010). Parameters used in calculating the annuity factor are
described in Table 33 (WHO 2014; CSO 2013).
Table 33: Values Used in VSLY Calculation
Value
Value
VSL
$63,000
Discount rate
3.5 percent (standard model)
Average age, male
17 (rounded from 17.1)
Average age, female
17 (rounded from 17.3)
Years of life remaining, male, age 17
48
Years of life remaining, female, age 17
50
Source: Authors
1
𝐴𝐹 = × [1 − 1 + 𝑖
𝑖
70
! !"!!"#
]
where AF is the annuity factor, i is the discount rate, LE is life expectancy, and age is average
age. At the standard 3.5 percent discount rate used in our primary model and calculations, we
complete the following calculations:
𝐴𝐹!"#$%" =
1
× 1 − 1 + 0.035
0.035
𝐴𝐹!"#$%" =
1
× 1 − 1.035
0.035
! !"!!"
! !"
𝐴𝐹!"#$%" = 23.46
𝐴𝐹!"#$ =
1
× 1 − 1 + 0.035
0.035
𝐴𝐹!"#$ =
1
× 1 − 1.035
0.035
! !"
! !"!!"
𝐴𝐹!"#$ = 23.09
Dividing the VSL by the annuity factor for each gender provides a VSLY value of $2,695 for
women, and $2,798 for men within the standard model.
71
APPENDIX M: QUALITY ADJUSTED LIFE YEARS (QALY)
Implementation of the SBIRT program may result in quality-of-life improvements for the
population that successfully reduces its levels of drinking. The improved life quality stems from
reduction in diseases and conditions related to high levels of alcohol consumption, such as heart
disease, stroke, liver cirrhosis, and cancer.
Several studies have attempted to calculate the improvement in quality of life due to
reduction in drinking. The appropriate measurement for this change is quality adjusted life years
(QALY). This measure captures the intangible benefits of improved health (Broome 1993),
rating quality of health in intervals from 0 to 1. Therefore a year of perfect health and two years
of health rated at .5 equal 1 QALY. It is difficult to transfer estimates across studies, as the
figures are calculated for certain populations, diseases, and estimates of quality of life. For
example, Solberg et al. (2008) completed a meta-analysis of randomized control trials to
calculate the increased benefits from alcohol interventions in primary care settings estimated at
0.052 QALYs per person and completed screening. However, this figure does not account for
referral to more intensive treatment, as called for in the SBIRT model.
We have adopted figures from Mortimer & Segal, who used computer simulations to
estimate QALY gains due to different alcohol treatment protocols, as given to a population of
“problem drinkers” in Australia (2005). Treatments included brief interventions, a series of brief
interventions, intensive counseling utilizing psychotherapeutic methods such as cue exposure
and reflective listening. In the absence of a complete protocol, we assume that inpatient
treatments, such as the five-day SHARPZ residential treatment in Lusaka, utilize similar
psychotherapy during the course of treatment.
72
We adopted the QALY estimates from the Mortimer & Segal study for use in the Monte
Carlo Simulation, as specified in Table 34. All QALY estimates were applied using a triangle
distribution centered on the mean QALY gain, and ranging from 0 to the maximum figure.
Table 34: Estimates of change in QALY after SBIRT treatment
Treatment
Population
Average change in QALY
(minimum – maximum)
No treatment
Abstainers and low drinking
0
level
Brief intervention
At-risk male drinkers
.091 (0-.225)
Brief intervention
At-risk female drinkers
.125 (0-.225)
Series of brief interventions
Harmful male drinkers
.091 (0-.405)
Series of brief interventions
Harmful female drinkers
.125 (0-.405)
Series of psychotherapeutic
treatments
Likely dependent male and
female drinkers
.115 (0-.232)
Source: Authors
73
APPENDIX N: HUMAN IMMUNODEFICIENCY VIRUS (HIV)
Current literature on brief interventions and SBIRT do not quantify any potential
improvements to health arising from reduction attainment of human immunodeficiency virus
infection and acquired immune deficiency syndrome (HIV/AIDS). Given the high prevalence of
HIV in Zambia (12.5. percent) and the relationship between risky alcohol consumption and HIV
transmission, we include this as a separate benefit to successfully treated drinkers (UNAIDS
2014; Kalichman 2007).
Benefits will accrue only to individuals who have not yet acquired HIV at the time of
screening and treatment. To account of this, we utilize the incidence rate of HIV acquisition,
currently .8 percent (National Council on AIDS 2014). This figure is the base probability of a
new case of HIV within our screened population. Drinkers have an increased probability of
acquiring HIV, for example due to decreased adherence to safe sexual practices, and less
utilization of pre-exposure prophylaxis and other measures. Using data collected in Kenya, we
estimate the increase probability by multiplying the incidence rate by the odds in Table 34
(L’Engle et al. 2014).
Table 35: HIV Acquisition Odds by Drinking State
Drinking State
Increased
Zambia HIV
Probability Rate
Incidence
Abstainers
1
.008
Low-Risk
1
.008
At-Risk
2.66
.008
Harmful
9.64
.008
Likely Dependent
4.34
.008
Source: Authors
Odds * Incidence
.008
.008
.02128
.07712
.03472
A successfully treated individual will reduce their risk of acquiring HIV to the incidence
rate of abstainers and low-risk drinkers, that is, the incidence rate of Zambia. We monetize this
reduction in risk as the avoided cost of living with HIV, as estimated in QALYs. As quality of
74
life measures are sensitive to life expectancy, population characteristics, and available medical
treatment, figures vary across HIV treatment types and countries where studies are performed
(Farnham et al. 2013). Recent studies show that the reduced life expectancy for a male infected
with HIV is around 80 percent of average age expectancy: 58 for women, 55 for men (CSO
2013). Based on this figure, we adopt Hutchinson et al. (2010) for avoiding HIV infection, which
found a QALY gain 6.43 of based on computer simulations of expected years-of-life-lost of 12.6
with a discount rate of 3 percent.
75
APPENDIX O: HISTOGRAMS
FIGURE 7: NPV, PROVINCE, 2% DISCOUNT
SBIRT in Central Province
10,000 Trials
Mean: $29,076,000
500
0
260
280
300
320
340
Net Present Value, in Hundred Thousands of US Dollars
Discount rate 2%, success rate 20%. 100% of trials were positive.
Source: Authors
76
Frequency
1000
FIGURE 8: NPV, VILLAGE, 2% DISCOUNT
SBIRT in Mwembeshi
10,000 Trials
Mean: $163,300
1000
600
400
200
140
150
160
170
180
Net Present Value, in Thousands of US Dollars
Discount rate 2%, success rate 20%. 100% of trials were positive.
Source: Authors
77
0
190
Frequency
800
FIGURE 9: NPV, PROVINCE, 6% DISCOUNT
SBIRT in Central Province
10,000 Trials
Mean: $48,621,000
500
0
400
450
500
550
600
Net Present Value, in Hundred Thousands of US Dollars
Discount rate 6%, success rate 20%. 100% of trials were positive.
Source: Authors
78
Frequency
1000
FIGURE 10: NPV, VILLAGE, 6% DISCOUNT
SBIRT in Mwembeshi
10,000 Trials
Mean: $273,600
1000
600
400
200
240
260
280
300
Net Present Value, in Thousands of US Dollars
Discount rate 6%, success rate 20%. 100% of trials were positive.
Source: Authors
79
0
320
Frequency
800
FIGURE 11: NPV, PROVINCE, 10% SUCCESS
SBIRT in Central Province
10,000 Trials
Mean: $21,112,000
500
0
180
200
220
240
260
Net Present Value, in Hundred Thousands of US Dollars
Discount rate 3.5%, success rate 10%. 100% of trials were positive.
Source: Authors
80
Frequency
1000
FIGURE 12: NPV, VILLAGE, 10%
SBIRT in Mwembeshi
10,000 Trials
Mean: $115,500
500
90
100
110
120
130
140
Net Present Value, in Thousands of US Dollars
Discount rate 3.5%, success rate 10%. 100% of trials were positive.
Source: Authors
81
0
Frequency
1000
APPENDIX P: PARAMETERS SUMMARY
Table 36: Parameters Treated as Uncertain
Source: Authors
Parameter
Point
Estimate
($)
Range
Mwembeshi
[Central Province]
Mwembeshi
[Central Province]
Low risk
0.93 [2.54]
0.06 [0.53]
At Risk
6.70 [9.59]
6.26 [10.94]
14.50 [21.24]
9.52 [17.76]
0.93 [2.54]
0.06 [0.53]
Health Care System
Cost
16.30
1.64
Uniform
Clinic Support Costs
$3,085
[49,600]
[120,200]
$3,085
[49,600]
[120,200]
Triangular
Female
568
1,137
Uniform
Male
455
911
20.99
13.42
Triangular
17.85
21.42
Triangular
Abstainer
0.26
0.18
Triangular
Low risk
0.61
0.53
At Risk
1.10
0.40
Harmful
1.80
1.30
Dependent
61.20
64.00
Abstainer
0.57
0.38
Low risk
1.30
1.10
At Risk
2.50
3.40
Harmful
3.90
2.80
119.30
110.5
Abstainer
0.07
0.05
Low risk
0.17
0.14
At Risk
0.31
0.29
Harmful
0.49
0.36
Dependent
26.00
35.80
Abstainer
0.24
0.15
Low risk
0.55
0.46
Screening and Brief
Intervention Costs
(per drinker)
Harmful
Dependent
Administrative
overhead
Social Network Costs
(per drinker)
Avoided Costs of crime
(Per drinker)
Avoided Costs of road
traffic accidents
(Per drinker)
Patient Opportunity
Cost
Female
Formal
Rural
Urban
Dependent
Informal
Rural
Urban
82
Distribution
($)
Triangular
Triangular
Male
Formal
Rural
Urban
At Risk
1.00
0.79
Harmful
1.60
1.20
Dependent
56.40
60.10
Abstainer
0.32
0.21
Low risk
0.75
-0.44
At Risk
1.40
1.24
Harmful
2.20
1.60
Dependent
72.70
73.30
Abstainer
0.81
0.56
Low risk
1.90
1.60
At Risk
3.50
3.20
Harmful
5.50
4.10
165.10
148.3
Abstainer
0.11
0.08
Low risk
0.26
0.23
At Risk
0.48
0.45
Harmful
0.75
0.53
Dependent
33.00
41.4
Abstainer
0.35
0.23
Low risk
0.46
0.69
At Risk
1.50
1.42
Harmful
2.40
1.70
76.80
76.74
Low risk
4.51
3.20
At Risk
20.94
14.83
Harmful
20.99
14.87
Dependent
60.65
42.94
Low risk
9.89
7.00
At Risk
45.93
32.52
Harmful
46.05
32.60
133.02
94.2
Low risk
1.12
0.59
At Risk
5.21
2.74
Harmful
5.22
2.74
15.08
7.94
Low risk
3.69
1.94
At Risk
17.14
9.02
Harmful
17.18
9.05
Dependent
Informal
Rural
Urban
Dependent
Absenteeism Avoided
Costs
Female
Formal
Rural
(per drinker)
Urban
Dependent
Informal
Rural
Dependent
Urban
83
Triangular
Dependent
Male
Formal
Rural
Urban
49.64
26.12
Low risk
5.58
3.95
At Risk
25.90
18.34
Harmful
25.97
18.38
Dependent
75.01
53.12
Low risk
13.98
9.90
At Risk
64.90
45.96
Harmful
65.07
46.08
187.97
133.12
Low risk
1.73
0.91
At Risk
8.02
4.22
Harmful
8.04
4.23
23.22
12.22
Low risk
5.44
2.86
At Risk
25.26
13.30
Harmful
25.32
13.33
Dependent
73.15
38.51
Low risk
1.73
4.98
At Risk
8.01
23.11
Harmful
8.03
23.17
23.20
66.94
Low risk
3.78
10.92
At Risk
17.57
50.69
Harmful
17.62
50.82
Dependent
50.89
146.81
Low risk
0.00
0.00
At Risk
0.00
0.00
Harmful
0.00
0.00
Dependent
0.00
0.00
Low risk
0.00
0.00
At Risk
0.00
0.00
Harmful
0.00
0.00
Dependent
0.00
0.00
Low risk
2.13
4.98
At Risk
9.91
23.11
Harmful
9.93
23.17
28.70
66.94
5.35
10.92
Dependent
Informal
Rural
Dependent
Urban
Presenteeism Avoided
Costs
Female
Formal
Rural
Dependent
Urban
Informal
Rural
Urban
Male
Formal
Rural
Dependent
Urban
Low risk
84
Triangular
Informal
Rural
Urban
Health benefits
(*measured in change
in quality adjusted life
years per person)
Female
Male
At Risk
24.83
50.69
Harmful
24.89
50.82
Dependent
71.91
146.81
Low risk
0.00
0.00
At Risk
0.00
0.00
Harmful
0.00
0.00
Dependent
0.00
0.00
Low risk
0.00
0.00
At Risk
0.00
0.00
Harmful
0.00
0.00
Dependent
0.00
0.00
Abstainer
0.00
0.00
Low risk
0.00
0.00
At Risk
0.13
0.23
Harmful
0.13
0.23
Dependent
0.13
0.23
Abstainer
0.00
0.00
Low risk
0.00
0.00
At Risk
0.91
0.41
Harmful
0.91
0.41
Dependent
0.12
0.23
Table 37: Parameters Treated as Certain
Source: Authors
Parameter
Training Costs
Initial
Follow-up
Point Estimate Mwembeshi [Central
Province]
$4,750 [$879,735]
Sensitivity
Analysis
Performed
No
$1,254 [$160,280]
CHWs
25 [402]
No
Participation Rate
65%
Yes
HIV Incidence
(Zambia)
Relative Risk of
HIV infection
0.8%
No
Abstainers
1
No
Low-risk
1
Discount Rate
At-risk
2.66
Harmful
9.64
Likely dependent
4.34
3.5%
85
Yes
Triangular
Success Rate
20%
Yes
VSL
$63,000
No
Female
$2,695
No
Male
$2,798
VSLY
Population
(over age 15)
Wage
Female
Formal
Informal
Male
Formal
Informal
3,600 [412,645]
No
Rural
$2,405
No
Urban
$5,275
Rural
$657
Urban
$2,165
Rural
$2,975
Urban
$7,455
Rural
$1,013
Urban
$3,191
86