PSI Health Impact Estimation Model: Behavior Change Communications for Prevention of Overdose Deaths in Injecting Drug Users

Transcription

1 PSI Health Impact Estimation Model: Behavior Change Communications for Prevention of Overdose Deaths in Injecting Drug Users Hongmei Yang Research & Metrics, Population Services International December 2010

2 This document may be freely reviewed, quoted, reproduced or translated, in part or in full, provided the source is acknowledged: Yang H (2010). PSI Health Impact Estimation Model: Behavior Change Communications for Prevention of Overdose Deaths in Injecting Drug Users. Washington, DC: Population Services International. Available at: PSI shares its models with all interested individuals or organizations. Please note that the models are updated periodically based on the latest available epidemiological, demographic, intervention effectiveness, and utilization data. As a result, numbers used in this document should be considered illustrative only. They show how the model works, but they are likely to have changed since the time of writing. For more information or the latest model updates, contact Hongmei Yang at hyang@psi.org. Population Services International, 2010 P S I 2

3 Contents Development of the DALY Model for Estimating the Health Impact of PSI Products and Services... 4 PSI DALY Calculator... 5 Background... 6 Section 1: Principles and Structure of the Model Principles of the math model Structure of the model... 7 Section 2: Estimating the Burden of Overdose Deaths... 7 Section 3: Estimating the Impact of Interventions... 8 Section 4: Parameters and Data Sources Biological information Efficacy of products/intervention Epidemic data... 9 Section 5: Suggested Questions for IDU Overdose BCC Program References P S I 3

4 Introduction Development of the DALY Model for Estimating the Health Impact of PSI Products and Services International public health nongovernmental organizations (NGOs) and other implementers and practitioners of health policy in developing countries find themselves at a crossroads. The issue of monitoring and evaluation has grown beyond its traditional role as an internal management tool and has become a tool for accountability for agencies charged with responsibility for funding and monitoring health interventions. This change occurred partly because new, large scale funders such as the Global Fund and the Gates Foundation themselves need more effective measures to be accountable for the use of their resources. Additionally, many organizations were drawn together by their focus on the Millennium Development Goals (MDG). Along with these changes, there has been movement toward a consensus regarding the method of measuring health impact across developing countries. The aim of this effort is to create a more objective component to use in prioritizing program activities and allocating resources. The disability adjusted life year (DALY) is the result of this effort. The primary purpose of the DALY is to act as an umbrella measure to compare both the true burden of different diseases across different regions of the developing world, and to better estimate the cost effectiveness of various interventions aimed at reducing that burden. The first phase of the DALY creation process was undertaken by the Disease Control Priorities Project (DCPP), which developed a series of documents designed to both estimate and explain the full burden of various diseases around the world ( The second phase of the DALY creation process involved estimation of the cost effectiveness of the interventions available to address the diseases. This phase has been more difficult to accomplish because of the conceptual distance between the people charged with evaluating health interventions and the people charged with implementing them. The number of studies estimating the relative cost effectiveness of public health and clinical interventions in developing countries that incorporate measurement of health outputs using DALYS has increased substantially since the first DCPP report in However, publication of welldesigned field trials is only half the story. The belief that cost effectiveness of public health interventions can be influenced by the methods and mechanisms employed, as well as scale, is a large part of the reasoning behind this work. The overall intent is to develop models that will better enable programmers to estimate the cost effectiveness of public health interventions that are undertaken in practice on a large scale. P S I 4

5 PSI DALY Calculator PSI is developing a series of tools to evaluate the health impact of its interventions and services around the world. The model at the core of this toolkit will estimate: a) the burden of each disease or condition addressed in the countries in which PSI works, and b) the health impact and incremental costs associated with implementation of PSI s interventions. The first stage in this process is the development of a functioning (and evolving) mathematical model that estimates the burden of disease and the relative impact of PSI projects in specific countries, for six disease groups or health areas. They are: HIV, family planning, malaria, diarrheal disease, nutrition, and maternal health. The models are currently linear and deterministic, but future intention is to incorporate stochastic data inputs/outputs and non deterministic relationships between variables. Shown below is a graphical representation of the working components of the model. Each disease area is treated slightly differently because of the nature of the infection or its symptoms, variations in the availability of data, and the complexity of outcomes. P S I 5

6 PSI Health Impact Estimation Model: Behavior Change Communications for Prevention of Overdose Deaths in Injecting Drug Users Background Population Services International (PSI) is a social marketing organization that promotes healthy behaviors in low income and vulnerable populations. PSI has programs in 65 countries ( and covers a wide range of health areas. PSI uses the disability adjusted life year (DALY) as the metric for measuring the health impact of interventions. A DALY model has been developed for each of PSI s products/services and behavior change communication (BCC) interventions. The DALY model presented here is the Injecting Drug User (IDU) Behavior Change Communication (BCC) DALY Model for Prevention of Overdose Deaths. It uses a binomial probability model to determine the impact of PSI s BCC program to prevent overdose deaths among IDUs. The BCC program is designed to do the following: Increase awareness of and demand for naloxone treatment among injection drug users (IDUs) while overdosing, and Promote the use of naloxone treatment among licensed doctors to prevent IDU overdose death. Section 1: Principles and Structure of the Model 1.1 Principles of the math model The math model is based on Bernoulli probability theory (Gray et al., 2001; Mastro et al., 1994; Satten et al., 1994). For each injecting drug user (IDU) who has overdosed, the probability of dying from heroin overdose during the study period (one year) is modeled as follows: P S I 6 P = 1 [(1 CFR) ]*[(1 CFR*(1 PE)) A* B*(1 C) A* B* C where CFR refers to the case fatality rate (i.e., the likelihood of dying of an overdose from injection drug use), A refers to the percentage of IDUs (living and dead) who overdosed in the past year, B refers to the number of overdoses per IDU per year, C refers to percentage of overdoses for which naloxone treatment was received (among living and dead IDUs who overdosed), and PE refers to the protective effect of naloxone treatment in preventing heroin overdose death. The data on drug use and overdose experience come from PSI TRaC surveys of IDUs. Because some overdose IDUs died prior to the survey and were not interviewed, the data include a healthy worker selection bias. Therefore, A, B, and C in the above formula do not come directly from the TRaC surveys; instead, they are functions of drug use and overdose data from living IDUs. In this part of the model, it is assumed that: a refers to the percentage of living IDUs who overdosed in the past year; A refers to the percentage of living and dead IDUs who overdosed in the past year; N is the number of IDUs at the beginning of the past year; and, ],

7 n is the number of living IDUs at the end of the past year. Because n*a = N*A N*A*CFR, and n = N N*A*CFR, the following formula is obtained estimating A in terms of a: Similarly, it is assumed that: a A = 1+ CFR * a CFR T refers to the total number of overdose IDUs (living and dead) in the past year; c refers to the percentage of last overdoses that received naloxone treatment among living IDUs; and C refers to the percentage of last overdoses that received naloxone treatment among living and dead IDUs. Therefore, C * T *[1 (1 PE) * CFR] c = C * T *[1 (1 PE) * CFR] + (1 C) * T *(1 CFR), which gives (1 CFR) * c C = (1 CFR) * c + (1 c) *[1 (1 PE) * CFR]. It is assumed that the number of overdoses per IDU per year is the same for both living and dead IDUs. 1.2 Structure of the model The DALY model is designed to estimate both the burden of disease (BOD) and the health impact of PSI s behavior change communication (BCC) program to prevent overdose deaths among IDUs. Calculating the burden of disease (i.e., injecting drug overdose deaths) tests the reliability of the model by comparing the BOD obtained with those reported by other organizations (e.g., UNAIDS). The burden of disease is measured by the number of deaths and DALYs (disability adjusted life years). The health impact of the intervention is measured by the number of deaths averted and DALYs averted. To measure the burden of disease and the health impact of the intervention, the model is designed with two components: baseline (i.e., a scenario where there is no PSI BCC overdose program) and follow up (i.e., a scenario where the PSI BCC overdose program has been implemented among IDUs). The baseline component, which assumes no incremental interventions, provides an estimate of the potential disease burden of injecting drug overdose that would accrue in a given year from deaths and DALYs due to heroin overdose. The followup component estimates the reduction in risk of death (in terms of population coverage and other features) resulting from implementation of the PSI BCC overdose program. Section 2: Estimating the Burden of Overdose Deaths In the baseline component, the model runs a scenario where the IDU is x percent likely to overdose during the period of the study (one year), has y number of overdoses per year, and is z percent likely to receive naloxone treatment while experiencing an overdose. With the case fatality rate and the protective efficacy of naloxone P S I 7

8 in preventing heroin overdose death, the resulting likelihood of death can be estimated. This figure is then multiplied by the size of the IDU population to get an estimate of the total number of new overdose deaths during the one year period. The final stage of the baseline modeling process is the translation of estimated new overdose deaths into a total equivalent figure for the burden of disease measured in DALYs. Section 3: Estimating the Impact of Interventions In the follow up component (post intervention), the model estimates the health impact of the PSI s BCC program to prevent overdose death among IDUs. A time period of one year is assumed because PSI measures health impact in terms of one full year. The outputs of the model are new overdose deaths averted and DALYs averted per intervention. Currently, PSI only has overdose BCC intervention in Russia. The PSI/Russia overdose BCC program is designed to increase demand for naloxone treatment among IDUs when they overdose (or when they see others overdose) by increasing awareness of naloxone treatment and the resources available to help IDUs (e.g., hotlines, ambulance services, etc.). The program is also designed to advocate naloxone treatment among licensed doctors to prevent overdose death. The PSI/Russia overdose BCC program has an impact on the following risk factor: Percentage of overdoses that received naloxone treatment among IDUs All the behavioral data in the IDU overdose BCC model come from PSI TRaC surveys (i.e., pre and postintervention surveys). Two rounds of TRaC surveys are needed in the target population for monitoring and evaluation analysis. The model estimates the number of deaths averted and DALYs averted only when there has been significant change in behavior resulting from the PSI intervention. Section 4: Parameters and Data Sources This section presents information on the determinants (i.e., data points) for the parameters used in the DALY model and their sources. 4.1 Biological information Case fatality rate: 10% (Sporer KA, 2003). 1. EMCDDA Annual report 2008: there is one fatal OD per ODs, which means CFR is 5% or less. report/ Sporer, 2003: Death rate of IDUs at home when overdosed is about 10%. Life span: years (i.e., ). A life expectancy of was obtained following the method developed by the WHO Disease Control Priorities Project (DCPP), assuming everybody dies of natural causes. Using a fixed life expectancy across countries allows comparison of the burden of disease more reasonably and ethically. But, Dr. Vickerman (PSI s P S I 8

9 external reviewer) was right that IDUs differed from non IDUs in terms of life expectancy because of their drug abuse behaviors. According to Michael Kazatchkine (Executive Director of the Global Fund to Fight AIDS, Tuberculosis and Malaria), IDUs live 12 years less than non IDUs who become infected with HIV in their 20s and have access to antiretroviral drugs for the rest of their life ( Therefore, 12 years were subtracted from the years life expectancy when calculating life expectancy for IDUs. 4.2 Efficacy of products/intervention Effectiveness of naloxone in preventing heroin overdose death: 95% The clinical efficacy of naloxone should be very high, although we were unableto locate a number or range from the existing published data. Several pilot studies about the effectiveness of take home naloxone and naloxone distribution programs reported the effectiveness to be close to 100% (Seal et al., 2005; Galea et al., 2005), but none of these are randomized controlled studies. This parameter will be updated when relevant data are available. To be conservative, the model uses 95%. selling 50,000,000 male condoms in Tanzania gives B=0.47 months. Assuming that birth spacing is evenly distributed, 0.47 * number of births in the most recent birth spacing groups: <18 months, months, months, and months, will move to a new group with a longer interval, and as seen in Figure 2.2, have a lower mortality rate. Table 2.1 (below) shows the number of births affected in this example 4.3 Epidemic data Table 1: Epidemiologic information Country (region) Number of injecting drug users (IDUs) China (Guangxi) 1,928,000 (nationwide) India 1,294,000 Kazakhstan 174,000 Kyrgyzstan 21,000 Mexico 53,000 Myanmar 195,000 Russia 1,977,000 Tajikistan 53,000 Thailand 48,000 Uzbekistan 87,000 Vietnam 258,000 Note: The source for all data presented in Table 1 (except Vietnam) is: Aceijas C, Stimson GV, Hickman M, Rhodes T. AIDS 2004, 18: The source for Vietnam is: Lam NT. Culture, Health & Sexuality 2008; 10 Supplement: S123 S137. P S I 9

10 Section 5: Suggested Questions for IDU Overdose BCC Program The following are suggested questions to include in TRaC surveys collecting data on overdose rate among IDUs, number of times of overdose and percentage of overdose that received naloxone treatment to measure the impact of PSI behavior change communication on reducing heroin overdose death. Please adapt them for individual interventions and contexts. In the following questions, overdose/overdosed refers to someone who collapses, has bluish skin color, has convulsions, has difficulty breathing, loses consciousness, cannot be awakened, has a heart attack, or dies while using drugs. Q1. Did you overdose in the past 12 months? 0=No 1=Yes If the response to Q1 is YES, ask the following questions: Q2. How many times did you overdose last year? Number of times: Q3. The last time you overdosed, what did you or other people around do? (Indicate all that apply.) a. Did nothing b. Called OD trained ambulance services c. Called the Life Line Hotline d. Called HARM REDUCTION BUS PROJECT e. I was taken to a hospital or a clinic f. Naloxone was injected g. Physical stimulation (e.g., cold water, physical pain, applying ice to wake me, etc.) h. Talked to me to prevent me from falling asleep i. Mouth to mouth/heart massage, CPR j. Did something else: [SPECIFY] k. Don t remember Note: Make sure that the highlighted/boldface items in Q3 reflect the activities of the PSI overdose BCC intervention in your platform. Feel free to add or delete activities listed under Q3 as needed. If the response to Q3 is b, c, or d, ask Q4 and Q5: Q4. You said you b, c, or d ; did medical workers come on request? 0=No 1=Yes Q5. What did the medical workers do to help you when they arrived? a. Injected naloxone (or a medication) upon arrival b. Transported me to a hospital and treated me there c. Refused to provide assistance (or did nothing and left) d. Called for police e. Don t remember f. Did something else [specify]:. P S I 10

11 If the response to Q3 is e, ask Q6: Q6. You said you were taken to a hospital or a clinic. Did medical workers there treat you? 0=No 1=Yes If the response to Q3 is f, ask Q7: Q7. You said naloxone was injected; did you get the naloxone free of charge from a pharmacy with a doctor s prescription? 0=No 1=Yes Note: Be sure to develop BCC exposure questions that are specific to your intervention/prevention programs. P S I 11

12 References Aceijas C, Stimson GV, Hickman M, Rhodes T. Global overview of injecting drug use and HIV infection among injecting drug users. AIDS 2004; 18: Dettmer K, Saunders B, Strang J. Take home naloxone and the prevention of deaths from opiate overdose: two pilot schemes. BMJ 2001; 322: Galea S, Worthington N, Piper TM, Nandi VV, Curtis M, Rosenthal DM. Provision of naloxone to injection drug users as an overdose prevention strategy: Early evidence from a pilot study in New York City. Addict Behav. 2005; 31: Gray RH, Wawer MJ, Brookmeyer R, Sewankambo NK, Serwadda D, Wabwire Mangen F, Lutalo T, Li C, vancott T, Quinn TC, the Rakai Project Team. Probability of HIV 1 transmission per coital act in monogamous, heterosexual, HIV 1 discordant couples in Rakai, Uganda. Lancet 2001; 357: Lam NT. Drugs, Sex and AIDS: Sexual Relationships among injecting drug users and their sexual partners in Vietnam. Culture, Health & Sexuality 2008; 10 Supplement: S123 S137. Mastro TD, Satten GA, Nopkesorn T, Sangkharomya S, Longini IM. Probability of female to male transmission of HIV 1 in Thailand. Lancet 1994; 343: Satten GA, Mastro TD, Longini IM. Modelling the female to male per act HIV transmission probability in an emerging epidemic in Asia. Statistics in Medicine 1994; 13: Seal KH, Thawley R, Gee L, Bamberger J, Kral AH, Ciccarone D, Downing M, Edlin BR. Naloxone distribution and cardiopulmonary resuscitation training for injection drug users to prevent heroin overdose death: a pilot intervention study. J Urban Health 2005 Jun; 82(2): Sporer KA. Strategies for preventing heroin overdose. BMJ 2003; 326: P S I th Street, NW Suite 600 Washington, DC psi.org blog: psihealthylives.com