Assessing the impact of the president's emergency plan for AIDS relief on all-cause mortality.

This study estimated the impacts of PEPFAR on all-cause mortality (ACM) rates (deaths per 1,000 population) across PEPFAR recipient countries from 2004-2018. As PEPFAR moves into its 3rd decade, this study supplements the existing literature on PEPFAR 's overall effectiveness in saving lives by focusing impact estimates on the important subgroups of countries that received different intensities of aid, and provides estimates of impact for different phases of this 15-year period study. The study uses a country-level panel data set of 157 low- and middle-income countries (LMICs) from 1990-2018, including 90 PEPFAR recipient countries receiving bilateral aid from the U.S. government, employing difference-in-differences (DID) econometric models with several model specifications, including models with differing baseline covariates, and models with yearly covariates including other donor spending and domestic health spending. Using five different model specifications, a 10-21% decline in ACM rates from 2004 to 2018 is attributed to PEPFAR presence in the group of 90 recipient countries. Declines are somewhat larger (15-25%) in those countries that are subject to PEPFAR's country operational planning (COP) process, and where PEPFAR per capita aid amounts are largest (17-27%). Across the 90 recipient countries we study, the average impact across models is estimated to be a 7.6% reduction in ACM in the first 5-year period (2004-2008), somewhat smaller in the second 5-year period (5.5%) and in the third 5-year period (4.7%). In COP countries the impacts show decreases in ACM of 7.4% in the first period attributed to PEPFAR, 7.7% reductions in the second, and 6.6% reductions in the third. PEPFAR presence is correlated with large declines in the ACM rate, and the overall life-saving results persisted over time. The effects of PEFAR on ACM have been large, suggesting the possibility of spillover life-saving impacts of PEPFAR programming beyond HIV disease alone

Analysis of maternal and child health spillover effects in PEPFAR countries

Objectives This study examined whether the US President’s Emergency Plan for AIDS Relief (PEPFAR) funding had effects beyond HIV, specifically on several measures of maternal and child health in low-income and middle-income countries (LMICs). The results of previous research on the question of PEPFAR health spillovers have been inconsistent. This study, using a large, multicountry panel data set of 157 LMICs including 90 recipient countries, adds to the literature.Design Seven indicators including child and maternal mortality, several child vaccination rates and anaemia among childbearing-age women are important population health indicators. Panel data and difference-in-differences estimators (DID) were used to estimate the impact of the PEPFAR programme from inception in 2004 to 2018 using a comparison group of 67 LMICs. Several different models of baseline (2004) covariates were used to help balance the comparison and treatment groups. Staggered DID was used to estimate impacts since all countries did not start receiving aid at PEPFAR’s inception.Setting All 157 LMICs from 1990 to 2018.Participants 90 LMICs receiving PEPFAR aid and cohorts of those countries, including those required to submit annual country operational plans (COP), other recipient countries (non-COP), and three groupings of countries based on cumulative amount of per capita aid received (high, medium, low).Interventions PEPFAR aid to combat the HIV epidemic.Primary outcome measures Maternal mortality and child mortality rates, vaccination rates to protect children for diphtheria, whooping cough and tetanus, measles, HepB3, and tetanus, and prevalence of anaemia in women of childbearing age.Results Across PEPFAR recipient countries, large, favourable PEPFAR health effects were found for rates of childhood immunisation, child mortality and maternal mortality. These beneficial health effects were large and significant in all segments of PEPFAR recipient countries studied. We also found significant and favourable programme effects on the prevalence of anaemia in women of childbearing age in PEPFAR recipient countries receiving the most intensive financial support from the PEPFAR programme. Other recipient countries did not demonstrate significant effects on anaemia.Conclusions This study demonstrated that important health indicators, beyond HIV, have been consistently and favourably influenced by PEPFAR presence. Child and maternal mortality have been substantially reduced, and childhood immunisation rates increased. We also found no evidence of ‘crowding out’ or negative spillovers in these resource-poor countries. These findings add to the body of evidence that PEPFAR has had favourable health effects beyond HIV. The implications of these findings are that foreign aid for health in one area may have favourable health effects in other areas in recipient countries. More research is needed on the influence of the mechanisms at work that create these spillover health effects of PEPFAR

Estimates of PEPFAR impact on all-cause mortality (deaths per 1,000), cumulative 5-year estimates.

Estimates of PEPFAR impact on all-cause mortality (deaths per 1,000), cumulative 5-year estimates.</p

Missingness.

This study estimated the impacts of PEPFAR on all-cause mortality (ACM) rates (deaths per 1,000 population) across PEPFAR recipient countries from 2004–2018. As PEPFAR moves into its 3rd decade, this study supplements the existing literature on PEPFAR ‘s overall effectiveness in saving lives by focusing impact estimates on the important subgroups of countries that received different intensities of aid, and provides estimates of impact for different phases of this 15-year period study. The study uses a country-level panel data set of 157 low- and middle-income countries (LMICs) from 1990–2018, including 90 PEPFAR recipient countries receiving bilateral aid from the U.S. government, employing difference-in-differences (DID) econometric models with several model specifications, including models with differing baseline covariates, and models with yearly covariates including other donor spending and domestic health spending. Using five different model specifications, a 10–21% decline in ACM rates from 2004 to 2018 is attributed to PEPFAR presence in the group of 90 recipient countries. Declines are somewhat larger (15–25%) in those countries that are subject to PEPFAR’s country operational planning (COP) process, and where PEPFAR per capita aid amounts are largest (17–27%). Across the 90 recipient countries we study, the average impact across models is estimated to be a 7.6% reduction in ACM in the first 5-year period (2004–2008), somewhat smaller in the second 5-year period (5.5%) and in the third 5-year period (4.7%). In COP countries the impacts show decreases in ACM of 7.4% in the first period attributed to PEPFAR, 7.7% reductions in the second, and 6.6% reductions in the third. PEPFAR presence is correlated with large declines in the ACM rate, and the overall life-saving results persisted over time. The effects of PEFAR on ACM have been large, suggesting the possibility of spillover life-saving impacts of PEPFAR programming beyond HIV disease alone.</div

Cohorts of PEPFAR countries created for analysis.

This study estimated the impacts of PEPFAR on all-cause mortality (ACM) rates (deaths per 1,000 population) across PEPFAR recipient countries from 2004–2018. As PEPFAR moves into its 3rd decade, this study supplements the existing literature on PEPFAR ‘s overall effectiveness in saving lives by focusing impact estimates on the important subgroups of countries that received different intensities of aid, and provides estimates of impact for different phases of this 15-year period study. The study uses a country-level panel data set of 157 low- and middle-income countries (LMICs) from 1990–2018, including 90 PEPFAR recipient countries receiving bilateral aid from the U.S. government, employing difference-in-differences (DID) econometric models with several model specifications, including models with differing baseline covariates, and models with yearly covariates including other donor spending and domestic health spending. Using five different model specifications, a 10–21% decline in ACM rates from 2004 to 2018 is attributed to PEPFAR presence in the group of 90 recipient countries. Declines are somewhat larger (15–25%) in those countries that are subject to PEPFAR’s country operational planning (COP) process, and where PEPFAR per capita aid amounts are largest (17–27%). Across the 90 recipient countries we study, the average impact across models is estimated to be a 7.6% reduction in ACM in the first 5-year period (2004–2008), somewhat smaller in the second 5-year period (5.5%) and in the third 5-year period (4.7%). In COP countries the impacts show decreases in ACM of 7.4% in the first period attributed to PEPFAR, 7.7% reductions in the second, and 6.6% reductions in the third. PEPFAR presence is correlated with large declines in the ACM rate, and the overall life-saving results persisted over time. The effects of PEFAR on ACM have been large, suggesting the possibility of spillover life-saving impacts of PEPFAR programming beyond HIV disease alone.</div

Difference-in-difference (DID) methodology.

This study estimated the impacts of PEPFAR on all-cause mortality (ACM) rates (deaths per 1,000 population) across PEPFAR recipient countries from 2004–2018. As PEPFAR moves into its 3rd decade, this study supplements the existing literature on PEPFAR ‘s overall effectiveness in saving lives by focusing impact estimates on the important subgroups of countries that received different intensities of aid, and provides estimates of impact for different phases of this 15-year period study. The study uses a country-level panel data set of 157 low- and middle-income countries (LMICs) from 1990–2018, including 90 PEPFAR recipient countries receiving bilateral aid from the U.S. government, employing difference-in-differences (DID) econometric models with several model specifications, including models with differing baseline covariates, and models with yearly covariates including other donor spending and domestic health spending. Using five different model specifications, a 10–21% decline in ACM rates from 2004 to 2018 is attributed to PEPFAR presence in the group of 90 recipient countries. Declines are somewhat larger (15–25%) in those countries that are subject to PEPFAR’s country operational planning (COP) process, and where PEPFAR per capita aid amounts are largest (17–27%). Across the 90 recipient countries we study, the average impact across models is estimated to be a 7.6% reduction in ACM in the first 5-year period (2004–2008), somewhat smaller in the second 5-year period (5.5%) and in the third 5-year period (4.7%). In COP countries the impacts show decreases in ACM of 7.4% in the first period attributed to PEPFAR, 7.7% reductions in the second, and 6.6% reductions in the third. PEPFAR presence is correlated with large declines in the ACM rate, and the overall life-saving results persisted over time. The effects of PEFAR on ACM have been large, suggesting the possibility of spillover life-saving impacts of PEPFAR programming beyond HIV disease alone.</div

Test the normal distribution of residuals derived from logged and unlogged Model 4 and Model 5—Figs A—H.

Fig A in S1 Fig. Residuals from unlogged Model 4 on all PEPFAR countries. Fig B in S1 Fig. Residuals from logged Model 4 on all PEPFAR countries. Fig C in S1 Fig. Residuals from unlogged Model 4 on COP countries. Fig D in S1 Fig. Residuals from logged Model 4 on COP countries. Fig E in S1 Fig. Residuals from unlogged Model 5 on all PEPFAR countries. Fig F in S1 Fig. Residuals from logged Model 5 on all PEPFAR countries. Fig G in S1 Fig. Residuals from unlogged Model 5 on COP countries. Fig H in S1 Fig. Residuals from logged Model 5 on COP countries. (DOCX)</p

Test the parallel assumption of ACM by country cohort.

Fig A in S2 Fig. All PEPFAR countries versus control countries. Fig B in S2 Fig. COP countries versus control countries. Fig C in S2 Fig. Non-COP PEPFAR countries versus control countries. Fig D in S2 Fig. High intensity countries versus control countries. Fig E in S2 Fig. Medium intensity countries versus control countries. Fig F in S2 Fig. Low intensity countries versus control countries. (DOCX)</p

Regression results–Tables A—O.

Table A in S1 Table. Summary of PEPFAR impact by country cohort from estimation of five logged models. Table B in S1 Table. Summary of adjusted R-squares of five logged and unlogged models (level) on PEPFAR impact by country cohort. Table C in S1 Table. Summary of PEPFAR impact over three periods from estimation of five logged models. Table D in S1 Table. Full model results for "All PEPFAR" group vs control: unlogged models. Table E in S1 Table. Full model results for "All PEPFAR" group vs control: logged models. Table F in S1 Table. Full model results for "COP-PEPFAR" group vs control: unlogged models. Table G in S1 Table. Full model results for "COP-PEPFAR" group vs control: logged models. Table H in S1 Table. Full model results for "Other PEPFAR" group vs control: unlogged models. Table I in S1 Table. Full model results for "Other PEPFAR" group vs control: logged models. Table J in S1 Table. Full model results for "High intensity PEPFAR" group vs control: unlogged models. Table K in S1 Table. Full model results for "High intensity PEPFAR" group vs control: logged models. Table L in S1 Table. Full model results for "Medium intensity PEPFAR" group vs control: unlogged models. Table M in S1 Table. Full model results for "Medium intensity PEPFAR" group vs control: logged models. Table N in S1 Table. Full model results for "Low intensity PEPFAR" group vs control: unlogged models. Table O in S1 Table. Full model results for “Low intensity PEPFAR” group vs control: logged models. (DOCX)</p

DID estimates of PEPFAR impact on all-cause mortality (ACM) rates in PEPFAR-recipient countries, 2004–2018.

DID estimates of PEPFAR impact on all-cause mortality (ACM) rates in PEPFAR-recipient countries, 2004–2018.</p