The President's Emergency Plan for AIDS Relief in Africa: An Evaluation of Outcomes

Context The effects of the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) are unknown. Contribution Using data obtained from the United Nations, the authors detected a decrease in the number of HIV-related deaths in African countries receiving U.S. assistance after PEPFAR was introduced. Caution Many factors other than introduction of PEPFAR could have contributed to the observed findings. Implication The PEPFAR program seems to be having some favorable effects in African countries receiving AIDS assistance. The Editors Join the dialogue on health care reform. Comment on the perspectives published in Annals and offer ideas of your own. All thoughtful voices should be heard. Alleviating the burden of HIV in sub-Saharan Africa is one of the great challenges of our time (1). Infection with HIV is the leading cause of death in Africa, and it is responsible for a reduction in life expectancy in many countries (2). Available resources to prevent and treat HIV in less developed countries have expanded more than 5-fold since the Declaration of Commitment on HIV/AIDS, adopted by the United Nations General Assembly in 2001 (3, 4), but the expansion has not been evenly distributed. Since 2003, the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) provided the majority of its initial $18.8 billion budget to 15 focus countries12 of them in Africafor HIV and AIDS prevention, treatment, and care (5, 6). Although the criteria for selecting focus countries were not explicit, they were related to the burden of disease, the focus countries' governmental commitment to fighting HIV, administrative capacity, and a willingness to partner with the U.S. government. Nearly half of PEPFAR's resources were spent on antiretroviral drugs and treatment infrastructure, and about one fifth was spent on prevention programs, of which one third was earmarked for abstinence-only programs. Through its various activities, PEPFAR aimed to support the provision of life-saving antiretrovirals to 2 million people and prevent 7 million HIV infections in the focus countries within 5 years. Of the 3 major funders of HIV and AIDS programs (PEPFAR; The Global Fund to Fight AIDS, Tuberculosis, and Malaria; and the World Bank), PEPFAR is unique in its distinctive approaches and disproportionate funding of a few countries (710). In July 2008, the U.S. Senate reauthorized PEPFAR with a $48 billion budget for the next 5 years, including a broader emphasis on strengthening health systems (11). Leadership in the battle against HIV has been one of the United States' most important legacies in Africa (12, 13). However, despite the substantial financial commitment and the important role PEPFAR plays abroad, no quantitative evaluation of the program's outcomes has been completed. The original legislation mandated a short-term evaluation that the Institute of Medicine completed in 2007 (14). The report scrutinized the ability of PEPFAR to meet its targets for delivery of prevention, treatment, and care services in the focus countries, and it found that within 2 years, PEPFAR supported expansion of HIV and AIDS activities in the focus countries; however, it did not evaluate health-related outcomes, such as HIV mortality, incidence, or prevalence. The number of human lives affected and the financial stakes make it essential to assess the impact of PEPFAR's investment in Africa. Although the full impact of PEPFAR may not be felt for years, an ongoing evaluation of programmatic outcomes is central to the direction of future policies. Therefore, we quantitatively evaluated HIV-related outcomes in PEPFAR focus countries compared with other countries in sub-Saharan Africa with a generalized HIV epidemic. Methods Country Selection All countries of central, east, west, and south Africa and the island nations of Cape Verde, Comoros, Madagascar, Mauritius, So Tom and Prncipe, and Seychelles were eligible for this analysis. We excluded countries without epidemiologic data and those in which the HIV epidemic was not generalized. Generalized epidemic was defined as HIV prevalence of more than 1% in antenatal clinics and a predominantly heterosexual mode of transmission (9, 15). We examined the countries that PEPFAR selected as focus countries as the intervention group and designated all other sub-Saharan African countries with a generalized HIV epidemic as the control group. Data Sources The joint United Nations Programme on HIV/AIDS (UNAIDS) has monitored the Declaration of Commitment on HIV/AIDS by working with individual countries to measure the global epidemiology of HIV and AIDS (16). We used country and year epidemiologic data obtained through UNAIDS from 1997 to 2007 as the outcome variables for this study. The UNAIDS determines the prevalence trends by using sentinel and population-based surveys. The prevalence estimates, vital registry data, and model-based calculations are then used to determine mortality and the number of people living with HIV. All the estimates are given uncertainty bounds that depend on the quality of the primary data and the strength of the assumptions used in the estimation process (17). We obtained data on Global Fund allocation of resources, population structure, governance indicators, life expectancy, and per capita gross domestic product from publicly available databases (10, 1820). Study Periods and Outcomes We chose 2 study periods for this analysis: an early period, from 1997 to 2002, before PEPFAR began, and a late period, from 2004 to 2007, during PEPFAR's activities. We excluded 2003, the watershed year when PEPFAR's operations were getting organized. Three outcomes were examined: HIV prevalence among adults 15 to 49 years of age, the number of deaths due to HIV or AIDS, and the number of adults living with HIV. We used these indicators as outcomes because they reflect the most consistent measures for cross-country comparisons available over time. The indicators are publicly available (21). Statistical Analysis We compared trends of the epidemic outcomes between the focus and control countries during the early and late periods. We tested the hypothesis that the epidemic would show greater improvement (or less worsening) in the focus countries than in the control countries during the late period, after the onset of PEPFAR's activities. We included the early period to separate the effects of PEPFAR from the natural course of the epidemic and any between-country differences; that is, we wanted to compare not only the relative trends after PEPFAR began but also any changes in the relative trends before and during PEPFAR. We analyzed the trends by using a fixed-effects model for longitudinal data with fixed time and country effects (22). We clustered by country and calculated robust standard errors because of the inherent bias in serially correlated data (23). Year and PEPFAR designation as a focus country, and an interaction term between them, were included as predictors in the unadjusted model. Except for prevalence rates, the outcomes we examined depend on the size of the population. To account for that, we examined the percentage change in the outcomes of interest by using log transformations of the outcomes (24). For prevalence rates, we report the difference between the focus and control countries of the change in prevalence rather than the difference in percentage change. We report the interaction term coefficient, which estimates the difference in the trends between the focus and control countries. For example, a coefficient of 2% in the number of deaths from 2004 to 2007 suggests that during that period, the percentage change in the number of deaths was 2% less in the focus countries than in the control countries (which means that deaths increased more slowly or decreased more rapidly, depending on the overall trend). We used Stata software, version 9.2 (StataCorp, College Station, Texas), for all analyses and Stata's xtreg command for the main regression. In adjusted analyses, we accounted for baseline HIV prevalence in 1997, population size, life expectancy, per capita gross domestic product, the amount of funding from the Global Fund for HIV per capita, and 4 measures of governance from the World Bank's Worldwide Governance Indicators: control of corruption, political stability, rule of law, and government effectiveness (20). The indicators measure the quality of governance in more than 200 countries on the basis of 35 data sources from 32 organizations. They have been available since 1996 and are widely used in development research (25, 26). We also changed the model structure to allow for random year effects. Because none of these additional analyses changed the direction or significance of the relative effect of PEPFAR, we report the results of the unadjusted analyses. To account for uncertainty in the primary data and model estimates of the UNAIDS data, we did 2 sensitivity analyses (27). First, we performed a Monte Carlo process where the outcome variable was drawn randomly from a uniform distribution of values within the uncertainty bounds for each country-year observation, and the analysis was repeated 1000 times. We also performed a sensitivity analysis where we randomly selected groups of focus countries and ran the analysis 1000 times to verify our analyses were not biased in favor of finding significant differences (23). Role of the Funding Source This work was supported in part by a training grant from the Agency for Healthcare Research and Quality. The funding agency had no part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. Results Sample Characteristics We examined 12 PEPFAR focus countries and 29 control countries from sub-Saharan Africa for the analysis. Tables 1 and 2 show the selected countries and their baseline characteristics, respectively. The focus countries had a significantly hi