Potential Clinical and Economic Impacts of Cutbacks in the President’s Emergency Plan for AIDS Relief Program in South Africa: A Modeling AnalysisFREE
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Visual Abstract. Potential Clinical and Economic Impacts of Cutbacks in the President’s Emergency Plan for AIDS Relief Program in South Africa
The United States President’s Emergency Plan for AIDS Relief (PEPFAR) has been widely hailed as a highly successful global health initiative. This modeling study examines possible clinical and economic impacts of decreases in PEPFAR funding in South Africa.
Abstract
Background:
Future U.S. congressional funding for the President’s Emergency Plan for AIDS Relief (PEPFAR) program is uncertain.
Objective:
To evaluate the clinical and economic impacts of abruptly scaling back PEPFAR funding ($460 million) from South Africa’s total HIV budget ($2.56 billion) in 2024.
Design:
Model-based analysis of 100%, 50%, and 0% PEPFAR funding with proportional decreases in HIV diagnosis rates (26.0, 24.3, 22.6 per 100 person-years [PY]), 1-year treatment engagement (people with HIV [PWH] receiving/initiating antiretroviral therapy: 92.2%/80.4%, 87.1%/76.0%, 82.0%/71.5%), and primary prevention (4.0%, 2.2%, 0.5% reduction in incidence with no programming [1.24 per 100 PY]).
Data Sources:
Published HIV care continuum; PEPFAR funding estimates.
Target Population:
South African adults (HIV prevalence, 16.2%; incidence, 0.32 per 100 PY).
Time Horizon:
Lifetime.
Perspective:
Health care sector.
Intervention:
PEPFAR funded 100% (PEPFAR_100%), 50% (PEPFAR_50%), or 0% (PEPFAR_0%).
Outcome Measures:
HIV infections, life expectancy, and lifetime costs (2023 U.S. dollars).
Results of Base-Case Analysis:
With current HIV programming (PEPFAR_100%), 1 190 000 new infections are projected over 10 years; life expectancy would be 61.42 years for PWH, with lifetime costs of $11 180 per PWH. Reduced PEPFAR funding (PEPFAR_50% and PEPFAR_0%) would add 286 000 and 565 000 new infections, respectively. PWH would lose 2.02 and 3.71 life-years with nominal lifetime cost reductions of $620 per PWH and $1140 per PWH that would be offset at the population level by more PWH requiring treatment for infection.
Results of Sensitivity Analysis:
Countries with similar HIV prevalence and greater reliance on PEPFAR funding could experience disproportionately higher incremental infections and survival losses.
Limitation:
Budget fungibility and exact programmatic implications of reducing PEPFAR funding are unknown.
Conclusion:
Abrupt PEPFAR cutbacks would have immediate and long-term detrimental effects on epidemiologic and clinical HIV outcomes in South Africa.
Primary Funding Source:
National Institutes of Health.
South Africa has the world’s largest HIV epidemic, with 7.4 million adults with HIV and 152 000 new infections in 2023 (1). Recent strides have brought the nation close to meeting the Joint United Nations Programme on HIV/AIDS (UNAIDS)’s 95-95-95 targets: 94% of people with HIV (PWH) are aware of their status, 80% of those who are aware are receiving antiretroviral therapy (ART), and 89% of PWH receiving ART are virally suppressed (1).
Although this progress is largely attributable to domestic investments, international partners have played an important role, including the United States President’s Emergency Plan for AIDS Relief (PEPFAR) program (2). With more than $8 billion invested in South Africa and $110 billion worldwide since its inception in 2003 (3), PEPFAR supports more than 20 million PWH in 55 countries (4). In 2022, $460 million from PEPFAR represented 18% of South Africa’s $2.56 billion HIV budget (5).
PEPFAR is a highly successful global health initiative and is recognized for its role in strengthening sustainable country-led systems, an important component of U.S. foreign policy (6–8). However, PEPFAR’s future is uncertain, as false claims about misuse of PEPFAR funds and political shifts have led to waning bipartisan congressional support (9–11). Although the program secured short-term funding through March 2025 (12), long-term support remains unclear. Our objective was to assess the potential clinical and economic ramifications of curtailed PEPFAR support in South Africa to inform policy decisions by Congress and global health leaders and provide an evidence base to clinicians and the American public.
Methods
Analytic Overview
We used the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)-International mathematical model to simulate 2024 HIV programming in South Africa with no cutbacks (PEPFAR_100%), partial cutbacks (PEPFAR_50%), and complete cutbacks (PEPFAR_0%) in PEPFAR funding. We modeled all South African adults (n = 45 680 760) in 2024 over a lifetime: people with prevalent HIV (n = 7 400 000) and people without HIV (n = 38 280 760), including those who acquire HIV after 2024 (1, 13). We assumed that funding cutbacks were permanent in the base case and limited cutbacks to 5 years in a secondary analysis. We estimated proportional decreases in HIV testing capacity, treatment engagement, and primary prevention with preexposure prophylaxis (PrEP) and voluntary medical male circumcision (VMMC), assuming fixed returns on PEPFAR investments (Supplement Methods and Supplement Table 1) (1, 5). We assumed non-PEPFAR funding would remain constant (4, 14) and the availability of acute care for PWH would remain unchanged.
We report the HIV care continuum at 5 years and new HIV infections and HIV-related deaths at 10 years to demonstrate short-term epidemiologic effects of PEPFAR cutbacks. We report lifetime outcomes for people with prevalent HIV (n = 7 400 000) and the South African population (n = 45 680 760) (1, 13), including life expectancy and HIV-related costs in 2023 U.S. dollars from a health care sector perspective (that is, excluding nonmedical resource consumption, time, and productivity [Supplement Methods]) (15). We calculated decremental cost-effectiveness ratios (DCERs; ratios of the differences in 3%-per-year discounted lifetime costs and life-years) to quantify savings per year of life lost among people with prevalent HIV (16). Although there are no generally agreed-upon thresholds by which to evaluate a DCER, empirical studies consistently find that decision makers’ minimum acceptable savings per year of life lost exceeds the maximum they are willing to pay per year of life gained (16, 17).
Model Structure
CEPAC-International Model
The CEPAC-International model is a validated mathematical model of HIV diagnosis, treatment, and transmission (https://mpec.massgeneral.org/cepac-model) (18, 19). Simulated PWH receive user-specified characteristics (such as initial age, sex, CD4 count, and HIV RNA viral load) and experience CD4-dependent risks for opportunistic infections and HIV-related mortality. People with HIV have an initial probability of viral suppression while using ART. Without suppressive ART, CD4 count decreases each month. With suppressive ART, PWH experience monthly increases in CD4 count and risks for virologic failure, care disengagement, and reengagement. They face monthly risks for age- and sex-stratified non–HIV-related mortality and HIV RNA–dependent HIV transmission. The model generates HIV transmission rates that are representative of total community HIV RNA, which are used to project outcomes for 1 generation of incident HIV infection (Supplement Methods).
Model Inputs
Modeled Population
We modeled 7 400 000 adults with prevalent HIV by using 2024 HIV care continuum data: 410 398 (5.6%) undiagnosed, 1 414 662 (19.1%) diagnosed but not receiving ART, and 5 574 940 (75.3%) diagnosed and receiving ART (Table 1) (1). We also modeled all 38 280 760 South African adults without HIV (1, 13). Mean initial age was 30.3 years for undiagnosed PWH and people without HIV and 39.2 years for diagnosed PWH (Table 1; Supplement Methods) (20). People with HIV receiving ART have a higher mean initial CD4 count (0.553 × 109 cells/L), reflecting prior HIV treatment, compared with those who are undiagnosed (0.484 ×109 cells/L) or are diagnosed but not receiving ART (0.481 ×109 cells/L) (Supplement Methods) (20). Among PWH receiving ART at the start of the model, 88.5% were virally suppressed (20).
| Variable | Base-Case Value | Range | Reference |
|---|---|---|---|
| 2024 HIV care continuum, n (%) | |||
| Undiagnosed | 410 398 (5.6) | —* | 1 |
| Diagnosed, not receiving ART | 1 414 662 (19.1) | —* | 1 |
| Diagnosed, receiving ART | 5 574 940 (75.3) | —* | 1 |
| Cohort characteristics | |||
| PWH | |||
| Mean age (SD), y | Undiagnosed: 30.3 (10.0)† Diagnosed: 39.2 (11.0) | 20–45 | 20 |
| Female sex, % | Undiagnosed: 63.3 Diagnosed: 68.6 | —* | 20 |
| Mean CD4 count (SD), × 109 cells/L | Undiagnosed: 0.484 (0.134)† Diagnosed, not receiving ART: 0.481 (0.319) Diagnosed, receiving ART: 0.553 (0.312) | ±1 SD | 20, 21 |
| Virologic suppression, % | Undiagnosed: 0.0 Diagnosed, not receiving ART: 0.0 Diagnosed, receiving ART: 88.5 | — | 20, 22, 23 |
| People at risk of acquiring HIV at model start | |||
| Mean age (SD), y | 30.3 (10.0) | 25–45 | 20 |
| Female sex, % | 63.3 | —* | 20 |
| HIV natural history | |||
| Range of monthly probability of HIV mortality, by OI, CD4, and ART status | 0.0001–0.0953 | 0.5–2.0 times | 24, 25 |
| HIV treatment | |||
| Mean ART adherence (SD), % | |||
| Receiving ART | 85.1 (19.5) | — | 26 |
| Initiating or reinitiating ART | 66.8 (28.8) | — | 27 |
| Range of 48-wk viral suppression, by ART adherence, %‡ | |||
| First-line: TDF/3TC/DTG | 0.0–93.8 | 90–100 | 28–31 |
| Second-line: ABC/FTC/ATV–r | 0.0–84.2 | 75–90 | 28–31 |
| HIV transmission and prevention | |||
| Range of HIV transmission rate per 100 PY, by HIV RNA load | 0.0–9.0 | 0.5–2.0 times | 32 |
| Status quo HIV incidence per 100 PY | 0.32 | 0.2–0.5 | 1 |
| Costs, 2023 U.S. $ | |||
| HIV diagnosis, per result | |||
| Negative | 3.80 | 0.5–2.0 times | 33 |
| Positive | 5.90 | 0.5–2.0 times | 33 |
| ART, monthly | |||
| TDF/3TC/DTG | 3.60 | 0.5–2.0 times | 34 |
| ABC/FTC/ATV–r | 13.70 | 0.5–2.0 times | 34 |
| OI prophylaxis, monthly | 5.10 | 0.5–2.0 times | 35 |
| CD4 count test | 8.20 | 0.5–2.0 times | 35 |
| HIV RNA test | 31.10 | 0.5–2.0 times | 35 |
| OI treatment, range by OI | 208.00–725.00 | 0.5–2.0 times | 36–38 |
| HIV care, monthly, range by CD4 count | 14.00–137.00 | 0.5–2.0 times | 36–38 |
3TC = lamivudine; ABC = abacavir; ART = antiretroviral therapy; ATV–r = ritonavir-boosted atazanavir; DTG = dolutegravir; FTC = emtricitabine; OI = opportunistic infection; PEPFAR = President’s Emergency Plan for AIDS Relief; PY = person-years; TDF = tenofovir disoproxil fumarate.
* Subgroup stratified outcomes are reported in sensitivity analyses in the Supplement.
† Calibrated to published data at ART initiation: mean age of 32.9 years and CD4 count of 0.481 × 109 cells/L. See the HIV testing section in the Supplement Methods.
‡ 48-week viral suppression rates are linearly interpolated between 10% and 85% adherence.
HIV Natural History
HIV-related mortality is dependent on CD4 count, ART status, and present or prior opportunistic infection (Table 1). People with HIV receiving ART have lower risks for opportunistic infection and mortality than PWH who are not receiving ART. Non–HIV-related mortality is derived from age- and sex-stratified HIV-deleted life tables (Supplement Table 2).
HIV Testing
With PEPFAR_100%, we calculated an HIV diagnosis rate of 26.0 per 100 person-years (PY) based on 417 000 diagnoses estimated in 2023 (Table 2; Supplement Methods) (39, 40). We calculated a cost of $102 per diagnosis based on $42.4 million allocated for HIV testing, of which PEPFAR funds $4.8 million (Supplement Table 1) (5). Assuming a fixed $102 per diagnosis, we estimated 394 000 diagnoses (24.3 per 100 PY) with PEPFAR_50% (−$2.4 million) and 370 000 diagnoses (22.6 per 100 PY) with PEPFAR_0% (−$4.8 million) (5).
| Program | Model Input | Base-Case Value* | Sensitivity Analyses† | Reference | ||||
|---|---|---|---|---|---|---|---|---|
| PEPFAR_100% | PEPFAR_50% | PEPFAR_0% | Scenario | PEPFAR_50% | PEPFAR_0% | |||
| HIV testing | HIV diagnosis rate per 100 PY | 26.0 | 24.3 | 22.6 | Resilient | 25.3 | 23.5 | 5, 39, 40 |
| Susceptible | 21.5 | 18.5 | ||||||
| Care and treatment | Care engagement at 1 y, % (PWH already receiving ART/PWH initiating or reinitiating ART) | 92.2/80.4 | 87.1/76.0 | 82.0/71.5 | Resilient | 90.1/78.6 | 84.8/74.0 | 5, 40, 41 |
| Susceptible | 78.6/68.5 | 68.7/59.9 | ||||||
| Prevention | HIV incidence reduction with primary prevention services, %‡ | 4.0 | 2.2 | 0.5 | Resilient | 3.3 | 1.6 | 5, 42 |
| Susceptible | 1.8 | 0.2 | ||||||
ART = antiretroviral therapy; PEPFAR = President’s Emergency Plan for AIDS Relief; PWH = people with HIV; PY = person-years.
* Base-case inputs were estimated using a proportional (linear) relationship in cutbacks in PEPFAR funding and HIV programming.
† Resilient scenario reflects ability of existing HIV programming to withstand cutbacks in PEPFAR funding of up to 30%. Susceptible scenario reflects sensitivity of existing HIV programming to cutbacks with diminishing returns on investments (Supplement Methods).
‡ Relative to a projected HIV incidence of 1.24 per 100 PY in the absence of any HIV programming (Supplement Methods).
HIV Treatment
We modeled current South African HIV treatment guidelines: 2 ART regimens, CD4 and HIV RNA monitoring, and opportunistic infection prophylaxis (Supplement Methods) (43). We assumed all PWH receiving or initiating ART were prescribed a dolutegravir-based regimen (20). Viral suppression probabilities were pooled from ART efficacy trials (Table 1).
Care Engagement
We varied care engagement to represent decreased treatment capacity resulting from an abrupt $217 million PEPFAR cutback for HIV treatment (Supplement Table 1) (5). Of the $1.962 billion HIV treatment budget in South Africa (5), we calculated $307 million to be allocated for PWH who initiate or reinitiate ART based on 1 031 000 ART initiations or reinitiations in 2023, 80.4% care engagement at 1 year (829 000 PWH), and $371 per person for the first year of treatment (Table 2; Supplement Methods) (35, 40, 41, 44). We calculated the remaining $1.655 billion to be allocated for the 5 574 940 PWH already receiving ART based on 92.2% care engagement at 1 year (5 140 000 PWH) and $322 per person per subsequent year of treatment (5, 35, 41, 45). We assumed that PEPFAR funding cutbacks for HIV treatment would be proportional to existing allocations for PWH initiating or reinitiating ART and PWH already receiving ART and that costs for the first and subsequent years of treatment would not change. With PEPFAR_50% (−$109 million) and PEPFAR_0% (−$217 million), we estimated that care engagement at 1 year would decrease from 829 000 (80.4%) to 783 000 (76.0%) and 737 000 (71.5%) PWH initiating or reinitiating ART and from 5 140 000 (92.2%) to 4 856 000 (87.1%) and 4 572 000 (82.0%) PWH already receiving ART. We used International epidemiology Databases to Evaluate AIDS (IeDEA) cohort data to derive adherence-stratified probabilities of care disengagement (Supplement Table 2) (41).
HIV Transmissions and Prevention
We projected transmissions over 5 years from 7 400 000 people with prevalent HIV using HIV RNA–dependent transmission rates (0.0 to 9.0 per 100 PY; Table 1) (32). From these transmissions, we estimated an HIV incidence of 0.37 per 100 PY reflecting community levels of HIV RNA and the preventive effects of HIV testing and ART (Supplement Methods). We next estimated the effect of primary prevention programming on HIV incidence; we conservatively assumed that funding cutbacks would only affect PrEP and VMMC because these services rely on PEPFAR-supplied commodities (5). We assumed behavior norms (for example, condom use, sexual practices) would not be affected by decreased HIV funding or programming. Based on current PrEP and VMMC uptake (400 000 and 173 000, respectively), we estimated these services would contribute a further 0.05 per 100 PY reduction in HIV incidence (calculated as a 4.0% reduction in a theoretical incidence of 1.24 per 100 PY projected in the absence of any HIV programming [Table 2; Supplement Methods]) (1, 40). We applied the 0.37 per 100 PY incidence rate and 0.05 per 100 PY incidence reduction to a cohort without HIV to reflect the risk for HIV acquisition with current programming (PEPFAR_100%). The resulting rate of 0.32 per 100 PY matches published incidence estimates in South Africa (1). We calculated incidence reductions of 0.02 per 100 PY (2.2% relative to a theoretical incidence of 1.24 per 100 PY with no HIV programming) with PEPFAR_50% (−$57 million) and 0.01 per 100 PY (0.5%) with PEPFAR_0% (−$113 million) (Supplement Methods) (5, 42). We assumed a fixed incidence over the model horizon to project outcomes to the epidemiologic scale with primary transmissions only (that is, 1 generation of transmissions from the cohort with prevalent HIV).
Costs
We modeled costs of ART ($3.60 to $13.70 per month), CD4 monitoring ($8.20 per test), HIV RNA monitoring ($31.10 per test), opportunistic infection treatment ($208.00 to $725.00), and routine HIV care ($14.00 to $137.00 per month) (Table 1; Supplement Methods) (35–38).
Sensitivity Analyses
In 1-way sensitivity analyses, we examined the effect of single input variation across plausible ranges (Table 1). In multiway sensitivity analyses, we examined the combined effect of varying cutbacks in HIV testing, treatment, and prevention.
Scenario Analyses
Scenario analysis is a powerful means of examining the effects of simultaneous variation in several key parameters to simulate plausible alternatives from the base case. We modeled a “resilient” scenario where programming would remain unchanged with up to 30% cutbacks in PEPFAR funding and a “susceptible” scenario where programming cutbacks would occur at a greater rate than funding cutbacks (Table 2; Supplement Methods). We modeled a “return to current status quo in 2029” scenario reflecting a hypothetical 5-year lag to reallocate and/or identify other funds to fill the budget void created by PEPFAR cutbacks. We also modeled a “larger PEPFAR funding profile” scenario where PEPFAR funding in South Africa mirrors its support in Zimbabwe (HIV prevalence, 12.6%): 55% of testing, 45% of treatment, and 77% of prevention programming (Supplement Table 2) (46).
Role of the Funding Source
The funding sources had no role in the design, analysis, or interpretation of the study.
Results
Base-Case Results
Clinical Outcomes: HIV Infections and Incidence
With status quo HIV programming in South Africa (PEPFAR_100%), 1 190 000 people are projected to acquire HIV by 10 years (incidence, 0.32 per 100 PY). This would increase to 1 476 000 (0.40 per 100 PY) with PEPFAR_50% and 1 755 000 (0.48 per 100 PY) with PEPFAR_0% (Table 3).
| Strategy | HIV Incidence per 100 PY | Population Outcomes at 10 Years, n (percentage increase)* | Per Person Outcomes | ||||
|---|---|---|---|---|---|---|---|
| HIV (n = 7.4 million) | Population (n = 45.7 million) | ||||||
| New HIV Infections, n† | HIV-Related Deaths, n‡ | Life-Years§ | Lifetime Costs, 2023 U.S. $ § | Life-Years§ | Lifetime Costs, 2023 U.S. $ § | ||
| Base case | |||||||
| PEPFAR_100% | 0.32‖ | 1 190 000 | 1 585 000 | 22.71 | 11 180 | 37.26 | 2550 |
| PEPFAR_50% | 0.40 | 1 476 000 (24) | 1 900 000 (20) | 20.69 | 10 560 | 36.63 | 2580 |
| PEPFAR_0% | 0.48 | 1 755 000 (47) | 2 186 000 (38) | 19.00 | 10 040 | 36.05 | 2620 |
| Resilient scenario | |||||||
| PEPFAR_50% | 0.36 | 1 310 000 (10) | 1 726 000 (9) | 21.79 | 10 900 | 36.97 | 2560 |
| PEPFAR_0% | 0.44 | 1 602 000 (35) | 2 040 000 (29) | 19.84 | 10 300 | 36.35 | 2600 |
| Susceptible scenario | |||||||
| PEPFAR_50% | 0.49 | 1 809 000 (52) | 2 365 000 (49) | 17.91 | 9700 | 35.77 | 2570 |
| PEPFAR_0% | 0.62 | 2 264 000 (90) | 2 911 000 (84) | 15.25 | 8830 | 34.78 | 2600 |
PEPFAR = President’s Emergency Plan for AIDS Relief; PY = person-years.
* Projections are undiscounted and rounded to the nearest 1000. Percentage increases are relative to PEPFAR_100%.
† Projected increases in new HIV infections relative to PEPFAR_100% reflect greater primary transmissions from 7 400 000 people with prevalent HIV to 38 280 760 people without HIV over 10 years.
‡ HIV-related deaths are among people with prevalent and incident HIV over 10 years.
§ Life-years and HIV-related costs are undiscounted, projected from model start, and rounded to the nearest 0.01 year and $10.
‖ Estimated based on current HIV programming (Supplement Methods). Matches published estimates in South Africa (1).
Clinical Outcomes: HIV-Related Deaths and Per-Person Life-Years
South Africa would see 1 585 000 HIV-related deaths over 10 years with PEPFAR_100%; deaths would increase by 315 000 (20%) with PEPFAR_50% and 601 000 (38%) with PEPFAR_0% (Table 3). People with prevalent HIV would live 22.71 life-years from model start with PEPFAR_100%, 20.69 with PEPFAR_50%, and 19.00 with PEPFAR_0%. People with HIV already receiving ART would experience the greatest life expectancy losses (Supplement Table 3). Among all 45 680 760 adults in South Africa, life-years lived from model start would decrease from 37.26 to 36.63 and 36.05, respectively.
Clinical Outcomes: HIV Care Continuum
The estimated 2024 HIV care continuum in South Africa is 94-80-89 (1). The projected HIV care continuum in 2029 would improve to 95-85-90 with PEPFAR_100% but worsen to 94-79-89 with PEPFAR_50% and 92-73-88 with PEPFAR_0% (Figure 1).
Figure 1. Projected HIV care continuum in 2029 with no cutbacks (PEPFAR_100%), partial cutbacks (PEPFAR_50%), and complete cutbacks (PEPFAR_0%) in 2024 PEPFAR funding.
The estimated 2024 HIV care cascade (unshaded bars) is based on published data (1). The projected HIV care cascade in 2029 is shown for PEPFAR_100% (dark green bars), PEPFAR_50% (green bars), and PEPFAR_0% (light green bars). Each set of bars represents PWH who are aware of their HIV status, receiving ART, and virally suppressed, respectively. The percentages shown are with respect to the conditional Joint United Nations Programme on HIV/AIDS (UNAIDS)'s 95-95-95 epidemic targets (proportion of all PWH who are aware of their status, proportion who are aware of their status and receiving ART, and proportion receiving ART who are virally suppressed). The estimated number of PWH alive in South Africa in 2024 was 7 400 000; the projected number of PWH alive in 2029 would be 6 913 000 with PEPFAR_100%, 6 950 000 with PEPFAR_50%, and 6 997 000 with PEPFAR_0%. The number of PWH associated with each bar is shown below the bar and is rounded to the nearest 10 000. ART = antiretroviral therapy; PEPFAR = President’s Emergency Plan for AIDS Relief; PWH = people with HIV.
Economic Outcomes: People With Prevalent HIV
Projected lifetime costs for people with prevalent HIV would decrease from $11 180 per person with PEPFAR_100% to $10 560 per person with PEPFAR_50% and $10 040 per person with PEPFAR_0% due to lower life expectancy among PWH and reduced health care access (Table 3). However, HIV-related costs would increase for people with prevalent HIV in the shorter term due to increased expenditure for PWH not receiving ART (Supplement Tables 4A and 4B).
Economic Outcomes: South African Population
Lifetime costs for all 45 680 760 South African adults would increase from $2550 per person with PEPFAR_100% to $2580 per person with PEPFAR_50% and $2620 per person with PEPFAR_0%, reflecting greater health care expenditures for a population with greater HIV-related morbidity (Table 3). Over 10 years, expenditures would increase by $880 million with PEPFAR_50% and $1.70 billion with PEPFAR_0% relative to current spending (Supplement Tables 4A and 4B). These increases reflect greater costs of acute care for an overall sicker population not using ART despite fewer routine care costs for those receiving ART.
Sensitivity Analyses
One-Way Sensitivity Analysis
In 1-way sensitivity analysis, additional infections at 10 years ranged from 211 000 to 385 000 with PEPFAR_50% and from 419 000 to 795 000 with PEPFAR_0% compared with PEPFAR_100% (Figure 2, top). The ranges of per-person life-years lost were 1.28 to 2.72 and 2.17 to 4.93 for people with prevalent HIV (Supplement Figure 1) and 0.47 to 0.90 and 0.86 to 1.72 for the entire population (Figure 2, bottom). Projected new infections were most sensitive to parameters that influenced community HIV RNA (for example, care engagement, primary prevention, CD4 count), whereas life expectancy was most sensitive to parameters that influenced disease severity (for example, age, CD4 count, HIV-related mortality) (Supplement Figures 2 to 4). Due to life-years lost, lifetime costs decreased for PWH across all ranges examined (Supplement Figure 5); these costs were offset at the population level by treatment costs of new infections unless survival among PWH was very low (Supplement Figure 6). Costs of HIV care and opportunistic infection treatment had the greatest effects on projected lifetime costs (Supplement Figures 7 and 8).
Figure 2. One-way sensitivity analysis of additional HIV infections at 10 years (top) and life-years lost among the population (bottom) with partial cutbacks (PEPFAR_50%) and complete cutbacks (PEPFAR_0%) in 2024 PEPFAR funding compared with no cutbacks (PEPFAR_100%).
A tornado diagram displays the sensitivity of a key outcome measure (additional HIV infections in the top panel and life-years lost in the bottom panel) to variation in different model input parameter values. The y-axis shows input parameters for which 1-way sensitivity analyses were performed, with the range examined in parentheses followed by the base-case value. The x-axis shows additional HIV infections (top panel) and life-years lost (bottom panel). The black vertical lines represent the base-case values with PEPFAR_50% and PEPFAR_0% compared with PEPFAR_100%. The horizontal bars (light green: PEPFAR_50%; dark green: PEPFAR_0%) represent the sensitivity of projected additional infections (top panel) and projected life-years lost (bottom panel) to the 6 most impactful parameters. Other parameters exerted little or no influence on the outcome, including proportion of female sex, status quo rates of HIV diagnosis, and 48-week viral suppression on protease inhibitor–based ART. ART = antiretroviral therapy; PEPFAR = President’s Emergency Plan for AIDS Relief; TLD = tenofovir plus lamivudine plus dolutegravir.
Multiway Sensitivity Analysis
Scaling back primary prevention programming alone would produce modest increases in new infections; these increases would compound when coupled with cutbacks in HIV treatment (Supplement Table 5). Cutbacks in treatment alone led to greater life expectancy losses than testing and primary prevention.
Scenario Analyses
Resilient and Susceptible Programming Scenarios
With greater resilience to PEPFAR cutbacks, fewer additional infections would be projected by 10 years than in the base case (120 000 with PEPFAR_50% and 412 000 with PEPFAR_0% compared with PEPFAR_100%) (Table 3). Projected life-years lost for PWH would also be lower (0.92 and 2.88, respectively). With greater susceptibility to PEPFAR cutbacks, the projected effects would be greater (619 000 and 1 074 000 additional infections and 4.80 and 7.46 life-years lost for PWH). Population lifetime costs would increase in both scenarios.
Return to Current Status Quo in 2029 Scenario
In a scenario where the PEPFAR budget deficit is filled and HIV programming returns to its current status quo in 2029, the projected effects of PEPFAR cutbacks would still be substantial. By 10 years, new infections would increase by 162 000 with PEPFAR_50% and 305 000 with PEPFAR_0% compared with PEPFAR_100% (Supplement Table 6). The costs of treating new infections would offset cost reductions associated with life-years lost, resulting in similar population-level lifetime costs ($2540 to $2550 per person).
Larger PEPFAR Funding Profile Scenario
In a scenario where PEPFAR funding in South Africa mirrors support in Zimbabwe (55% testing, 45% treatment, and 77% prevention), new infections over 10 years would increase by 72% with PEPFAR_50% and by 142% with PEPFAR_0% compared with PEPFAR_100%; PWH would lose 6.79 and 11.01 life-years, respectively (Supplement Table 7). Although these life-year losses would lead to lower HIV treatment costs, lifetime costs for the population would decrease only modestly ($10 to $50 per person) due to the cost of many more new infections.
Decremental Cost-Effectiveness Ratios
Among people with prevalent HIV, the projected DCER would be $220 saved per year of life lost for both PEPFAR_50% and PEPFAR_0% compared with PEPFAR_100% (Supplement Table 8). The DCERs would be similar across scenario analyses ($220 to $240 saved per year of life lost), demonstrating that on a lifetime horizon, any potential cost savings would be a direct result of a loss in life-years.
Discussion
Given uncertain U.S. congressional support of PEPFAR, we developed a model-based analysis to determine the clinical and economic consequences of abrupt cutbacks in $460 million of PEPFAR HIV funding allocated to South Africa. We had 3 key findings.
First, abruptly scaling back PEPFAR would reverse progress toward the 95-95-95 goals and lead to substantial life expectancy losses. Life expectancy among PWH would decrease by 3.71 life-years—a major reduction compared with other health interventions, including universal ART eligibility (47–49). South Africa has faced prior programmatic disruptions, including in the early 2000s, when political leadership delayed ART rollout due to misinformation about the cause of AIDS, leading to an estimated 330 000 additional deaths (50), and during the COVID-19 pandemic (51), which led to decreased HIV testing and ART initiations (44, 52, 53). Our analysis quantifies the effect of disruptions from scaling back PEPFAR and adds to existing evidence demonstrating PEPFAR’s role in improving survival for PWH (48, 54).
Second, withdrawing PEPFAR support would affect the health of the entire South African population, with a projected 565 000 additional HIV infections by 2034 and 1.20 life-years lost per person. Cutbacks in HIV care and treatment—which receive $217 million annually from PEPFAR—had the greatest effect on projected new infections. When combined with primary prevention cutbacks, new infections would compound due to a simultaneous greater force of infection and susceptibility to infection, as seen in other analyses (5, 42, 52, 55). These multiway sensitivity analyses demonstrate alternative budget rebalancing possibilities, reflecting the fungibility of current non-PEPFAR HIV funding. Future research is needed to inform optimal approaches to funding reallocation. Curbing HIV incidence is critical for epidemic control, and PEPFAR continues to play a vital role in these efforts (6, 56).
Third, cost reductions from defunded programs in 2024 would be offset by increased costs of treating a larger and sicker population with HIV. We projected that HIV-related costs would increase within 1 year relative to current spending, with an excess of $1.70 billion by 2034. These additional costs would primarily be used to treat new opportunistic infections that would otherwise be averted by PEPFAR-funded programming rather than treating new HIV infections. Furthermore, these extra costs would likely require diversion of funds from other effective interventions without making progress toward HIV epidemic control. Our projections are consistent with a study that found that any reduction in HIV testing would increase care costs over time due to increased HIV infections (55). Population costs only decreased in conditions with substantial life expectancy losses. We projected $220 saved per year of life lost among PWH, which is substantially less than South Africa’s willingness to pay for health ($3010 to $4100 per year of life) (13, 47, 57) and still below more affordable benchmarks for established HIV programs ($550 per year of life) (58). These cost reductions would be marginal and directly related to shorter survival of PWH.
This study likely represents a best-case scenario for PEPFAR cutbacks compared with other settings. South Africa’s economic profile has changed since PEPFAR’s inception: From 2008 to 2022, the South African government increased total dollar HIV investments by approximately 15% year after year while maintaining funding for nearly three quarters of all HIV expenditures (14). Investments from financial partners like PEPFAR have enabled overall strengthening of the health care system and decreased reliance on external funding (13, 59). However, in-country and external HIV funding have recently flattened due to slowed gross domestic product (GDP) growth and the economic toll of the COVID-19 pandemic (60–62). A sudden $460 million deficit would therefore be unlikely to be readily replaced and would jeopardize access to highly successful HIV interventions, leading to substantial morbidity and mortality. PEPFAR contributes a greater share of HIV spending in other countries, such as Zimbabwe (2023 per-capita GDP: $2160 vs. $6020 in South Africa) (4, 13), which would be even more vulnerable to abrupt PEPFAR cutbacks, with nearly 3-fold more HIV infections and life-years lost in a scenario analysis. Countries that rely more on PEPFAR support may be less equipped to withstand sudden HIV funding withdrawal. Geographic areas within South Africa and Uganda that transitioned away from PEPFAR funding have experienced decreased care engagement, more frequent commodity stockouts, and delays in implementation of national treatment guidelines (63–65).
This analysis has several limitations. First, we did not model the effect of cutbacks on pregnant people and children, which would render our projections more pessimistic (66). Although we modeled population heterogeneity within South Africa, we did not explicitly model at-risk groups who may be disproportionately affected by PEPFAR cutbacks. Second, we assumed that PEPFAR funding cutbacks would be proportional to decreases in HIV programming, implying fixed care costs, which may not reflect programmatic realities (for example, additional costs for advanced HIV disease or to reengage PWH). We examined alternative scenarios with nonlinear returns on PEPFAR investments, which demonstrated substantial clinical and epidemiologic effects even if current programming was resilient to cutbacks. Although these scenarios are intended to illustrate the range of possible outcomes resulting from funding cutbacks, they do not quantify precision or the likelihood of occurring. Third, we assumed that the programmatic effects of withdrawing PEPFAR funding would be permanent in the base case, which may have resulted in overestimation in our projections. However, refunding HIV infrastructure would take time, and an immediate loss of services would have a lasting effect on PWH. Furthermore, we did not consider the effect of reduced U.S. contributions to the Global Fund, which has consistently funded 2% to 5% of South Africa’s HIV budget (5). Even if cutbacks were limited to 5 years, new HIV infections could increase by 26%. Fourth, we assumed a fixed HIV incidence, which may have overestimated new infections that would be averted by future interventions. Although we did not account for the gradually decreasing incidence of new infections in South Africa, these trends would likely be negated by PEPFAR cutbacks. Furthermore, we only modeled the effect of PEPFAR cutbacks on primary transmissions, which may have underestimated dynamic growth of new infections (second-order transmission and vertical transmission). Finally, we did not capture PEPFAR’s indirect effects, such as facilitating decreased health care costs, increasing employment, and supporting GDP growth (67, 68).
In conclusion, this simulation modeling analysis demonstrates that abruptly scaling back PEPFAR funding would have a striking and deleterious effect on the progress South Africa has made toward HIV epidemic control. Any total cost reductions would be short-lived and would come at the expense of as many as 565 000 additional new HIV infections and 601 000 additional HIV-related deaths in South Africa by 2034. These implications highlight the importance of PEPFAR as both a life-saving foreign aid program and a bridge to self-sustaining national HIV programs.
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References
1.
Joint United Nations Programme on HIV/AIDS. UNAIDS Data 2023. 31 October 2023. Accessed at www.unaids.org/en/resources/documents/2023/2023_unaids_data on 5 June 2024.
2.
Phaswana-Mafuya RN, Phalane E, Sisel H, et al. Country ownership and sustainable programming of the HIV response in South Africa: a scoping review. South Afr J HIV Med. 2023;24:1511. [PMID: 38058847] doi: 10.4102/sajhivmed.v24i1.1511
3.
U.S. Embassy & Consulates in South Africa. United States President’s Emergency Plan for AIDS Relief (PEPFAR). 14 February 2022. Accessed at https://za.usembassy.gov/united-states-presidents-emergency-plan-for-aids-relief-pepfar/#:∼:text=To%20date%2C%20the%20United%20States,AIDS%2C%20Tuberculosis%2C%20and%20Malaria on 5 June 2024.
4.
U.S. Department of State. Country and Regional Operational Plans: the United States President’s Emergency Plan for AIDS Relief. Accessed at www.state.gov/country-operational-plans on 5 June 2024.
5.
U.S. Department of State. Country Operational Plan: PEPFAR South Africa 2022 Strategic Direction Summary. 19 April 2022. Accessed at www.state.gov/wp-content/uploads/2022/09/South-Africa-COP22_SDS.pdf on 5 June 2024.
6.
Chun HM, Dirlikov E, Cox MH, et al; CDC Global HIV Working Group. Vital Signs: Progress toward eliminating HIV as a global public health threat through scale-up of antiretroviral therapy and health system strengthening supported by the U.S. President's Emergency Plan for AIDS Relief - worldwide, 2004–2022. MMWR Morb Mortal Wkly Rep. 2023;72:317-324. [PMID: 36952290] doi: 10.15585/mmwr.mm7212e1
7.
Patel-Larson A, Ledikwe JH, West T, et al. Looking back to see forward: multidirectional learning between the US Ryan White HIV/AIDS Program and the US President's Emergency Plan for AIDS Relief. BMJ Glob Health. 2024;8:e013953. [PMID: 38395451] doi: 10.1136/bmjgh-2023-013953
8.
Nkengasong J, Zaidi I, Katz IT. PEPFAR at 20—looking back and looking ahead. JAMA. 2023;330:219-220. [PMID: 37294580] doi: 10.1001/jama.2023.9291
9.
H.R.4665 - Department of State, Foreign Operations, and Related Programs Appropriations Act, 2024. Accessed at www.congress.gov/bill/118th-congress/house-bill/4665/all-actions on 20 December 2024.
10.
Lewis S, Zengerle P. US State Dept slams Congress for failure to renew anti-AIDS program. Reuters. 3 October 2023. Accessed at www.reuters.com/business/healthcare-pharmaceuticals/us-state-dept-slams-congress-failure-renew-pepfar-anti-aids-program-2023-10-02 on 20 December 2024.
11.
Bakst D, Berry J, Burke LM, et al. Mandate for Leadership: The Conservative Promise. The Heritage Foundation; 2023. Accessed at https://static.project2025.org/2025_MandateForLeadership_FULL.pdf on 27 November 2024.
12.
Moss K, Kates J. PEPFAR’s Short-Term Reauthorization Sets an Uncertain Course for Its Long-Term Future. KFF. 27 March 2024. Accessed at www.kff.org/policy-watch/pepfars-short-term-reauthorization-sets-an-uncertain-course-for-its-long-term-future on 5 June 2024.
13.
World Bank Group. GDP per capita (current US$) - South Africa. Accessed at https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=ZA on 20 December 2024.
14.
South Africa National AIDS Council; Joint United Nations Programme on HIV/AIDS. National AIDS Spending Assessment plus (NASA+) HIV and TB Spending in South Africa: 2017/18 – 2019/20. Accessed at https://sanac.org.za/wp-content/uploads/2022/10/SA-NASA-REPORT_2017-18-to-2019-20.pdf on 5 June 2024.
15.
Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016;316:1093-1103. [PMID: 27623463] doi: 10.1001/jama.2016.12195
16.
Kent DM, Fendrick AM, Langa KM. New and dis-improved: on the evaluation and use of less effective, less expensive medical interventions. Med Decis Making. 2004;24:281-286. [PMID: 15155017] doi: 10.1177/0272989X04265478
17.
Rotteveel AH, Lambooij MS, Zuithoff NPA, et al. Valuing healthcare goods and services: a systematic review and meta-analysis on the WTA-WTP disparity. Pharmacoeconomics. 2020;38:443-458. [PMID: 32096146] doi: 10.1007/s40273-020-00890-x
18.
Walensky RP, Jacobsen MM, Bekker L-G, et al. Potential clinical and economic value of long-acting preexposure prophylaxis for South African women at high-risk for HIV infection. J Infect Dis. 2016;213:1523-1531. [PMID: 26681778] doi: 10.1093/infdis/jiv523
19.
April MD, Wood R, Berkowitz BK, et al. The survival benefits of antiretroviral therapy in South Africa. J Infect Dis. 2014;209:491-499. [PMID: 24307741] doi: 10.1093/infdis/jit584
20.
Dorward J, Sookrajh Y, Khubone T, et al. Implementation and outcomes of dolutegravir-based first-line antiretroviral therapy for people with HIV in South Africa: a retrospective cohort study. Lancet HIV. 2023;10:e284-e294. [PMID: 37001536] doi: 10.1016/S2352-3018(23)00047-4
21.
Inghels M, Niangoran S, Minga A, et al. Missed opportunities for HIV testing among newly diagnosed HIV-infected adults in Abidjan, Côte d'Ivoire. PLoS One. 2017;12:e0185117. [PMID: 28977006] doi: 10.1371/journal.pone.0185117
22.
Iwuji CC, Orne-Gliemann J, Larmarange J, et al; ANRS 12249 TasP Study Group. Universal test and treat and the HIV epidemic in rural South Africa: a phase 4, open-label, community cluster randomised trial. Lancet HIV. 2018;5:e116-e125. [PMID: 29199100] doi: 10.1016/S2352-3018(17)30205-9
23.
Gumede SB, Venter F, de Wit J, et al. Antiretroviral therapy uptake and predictors of virological failure in patients with HIV receiving first-line and second-line regimens in Johannesburg, South Africa: a retrospective cohort data analysis. BMJ Open. 2022;12:e054019. [PMID: 35428623] doi: 10.1136/bmjopen-2021-054019
24.
Losina E, Yazdanpanah Y, Deuffic-Burban S, et al. The independent effect of highly active antiretroviral therapy on severe opportunistic disease incidence and mortality in HIV-infected adults in Côte d'Ivoire. Antivir Ther. 2007;12:543-551. [PMID: 17668563]
25.
Holmes CB, Wood R, Badri M, et al. CD4 decline and incidence of opportunistic infections in Cape Town, South Africa: implications for prophylaxis and treatment. J Acquir Immune Defic Syndr. 2006;42:464-469. [PMID: 16810113] doi: 10.1097/01.qai.0000225729.79610.b7
26.
Haas AD, Lienhard R, Didden C, et al. Mental health, ART adherence, and viral suppression among adolescents and adults living with HIV in South Africa: a cohort study. AIDS Behav. 2023;27:1849-1861. [PMID: 36592251] doi: 10.1007/s10461-022-03916-x
27.
Haberer JE, Bwana BM, Orrell C, et al. ART adherence and viral suppression are high among most non-pregnant individuals with early-stage, asymptomatic HIV infection: an observational study from Uganda and South Africa. J Int AIDS Soc. 2019;22:e25232. [PMID: 30746898] doi: 10.1002/jia2.25232
28.
Aboud M, Orkin C, Podzamczer D, et al. Efficacy and safety of dolutegravir–rilpivirine for maintenance of virological suppression in adults with HIV-1: 100-week data from the randomised, open-label, phase 3 SWORD-1 and SWORD-2 studies. Lancet HIV. 2019;6:e576-e587. [PMID: 31307948] doi: 10.1016/S2352-3018(19)30149-3
29.
Boyd MA, Kumarasamy N, Moore CL, et al; SECOND-LINE Study Group. Ritonavir-boosted lopinavir plus nucleoside or nucleotide reverse transcriptase inhibitors versus ritonavir-boosted lopinavir plus raltegravir for treatment of HIV-1 infection in adults with virological failure of a standard first-line ART regimen (SECOND-LINE): a randomised, open-label, non-inferiority study. Lancet. 2013;381:2091-2099. [PMID: 23769235] doi: 10.1016/S0140-6736(13)61164-2
30.
La Rosa AM, Harrison LJ, Taiwo B, et al; ACTG A5273 Study Group. Raltegravir in second-line antiretroviral therapy in resource-limited settings (SELECT): a randomised, phase 3, non-inferiority study. Lancet HIV. 2016;3:e247-e258. [PMID: 27240787] doi: 10.1016/S2352-3018(16)30011-X
31.
Paton NI, Kityo C, Hoppe A, et al; EARNEST Trial Team. Assessment of second-line antiretroviral regimens for HIV therapy in Africa. N Engl J Med. 2014;371:234-247. [PMID: 25014688] doi: 10.1056/NEJMoa1311274
32.
Attia S, Egger M, Müller M, et al. Sexual transmission of HIV according to viral load and antiretroviral therapy: systematic review and meta-analysis. AIDS. 2009;23:1397-1404. [PMID: 19381076] doi: 10.1097/QAD.0b013e32832b7dca
33.
Meyer-Rath G, van Rensburg C, Chiu C, et al. The per-patient costs of HIV services in South Africa: systematic review and application in the South African HIV investment case. PLoS One. 2019;14:e0210497. [PMID: 30807573] doi: 10.1371/journal.pone.0210497
34.
Clinton Health Access Initiative. HIV Market Report: The State of HIV Treatment, Testing, and Prevention in Low- and Middle-Income Countries. October 2023. Accessed at https://chai19.wpenginepowered.com/wp-content/uploads/2023/11/2023-HIV-Market-Report_11.17.23.pdf on 5 June 2024.
35.
Barnabas RV, Szpiro AA, van Rooyen H, et al; Delivery Optimization of Antiretroviral Therapy (DO ART) Study Team. Community-based antiretroviral therapy versus standard clinic-based services for HIV in South Africa and Uganda (DO ART): a randomised trial. Lancet Glob Health. 2020;8:e1305-e1315. [PMID: 32971053] doi: 10.1016/S2214-109X(20)30313-2
36.
Cleary S, Okorafor O, Chitha W, et al. Financing antiretroviral treatment and primary health care services. In: Ijumba P, Barron P, eds. South African Health Review 2005. Health Systems Trust; 2005. Accessed at www.hst.org.za/publications/South%20African%20Health%20Reviews/sahr05.pdf on 5 June 2024.
37.
Thomas LS. Costing of HIV/AIDS services at a tertiary level hospital in Gauteng Province. University of Witwatersrand; 2007. Accessed at http://wiredspace.wits.ac.za/handle/10539/2008 on 5 June 2024.
38.
Anglaret X, Chêne G, Attia A, et al. Early chemoprophylaxis with trimethoprim-sulphamethoxazole for HIV-1-infected adults in Abidjan, Côte d'Ivoire: a randomised trial. Cotrimo-CI Study Group. Lancet. 1999;353:1463-1468. [PMID: 10232311] doi: 10.1016/s0140-6736(98)07399-1
39.
Bosman A, Beke A. Outcomes of community-based HIV testing modalities in a Mpumalanga district, South Africa. S Afr Med J. 2020;110:1041-1044. [PMID: 33205736] doi: 10.7196/SAMJ.2020.v110i10.14417
40.
U.S. President’s Emergency Plan for AIDS Relief. President’s Emergency Plan for AIDS Relief (PEPFAR) data. Accessed at https://data.pepfar.gov/library on 5 June 2024.
41.
Haas AD, Zaniewski E, Anderegg N, et al; African regions of the International epidemiologic Databases to Evaluate AIDS (IeDEA). Retention and mortality on antiretroviral therapy in sub-Saharan Africa: collaborative analyses of HIV treatment programmes. J Int AIDS Soc. 2018;21:e25084. [PMID: 29479867] doi: 10.1002/jia2.25084
42.
Johnson LF, Meyer-Rath G, Dorrington RE, et al. The effect of HIV programs in South Africa on national HIV incidence trends, 2000–2019. J Acquir Immune Defic Syndr. 2022;90:115-123. [PMID: 35125471] doi: 10.1097/QAI.0000000000002927
43.
Republic of South Africa National Department of Health. 2023 ART Clinical Guidelines for the Management of HIV in Adults, Pregnancy and Breastfeeding, Adolescents, Children, Infants and Neonates. 2023. Accessed at https://knowledgehub.health.gov.za/elibrary/2023-art-clinical-guidelines-management-hiv-adults-pregnancy-and-breastfeeding-adolescents on 5 June 2024.
44.
Benade M, Long L, Rosen S, et al. Reduction in initiations of HIV treatment in South Africa during the COVID pandemic. BMC Health Serv Res. 2022;22:428. [PMID: 35361209] doi: 10.1186/s12913-022-07714-y
45.
Long LC, Girdwood S, Govender K, et al. Cost and outcomes of routine HIV care and treatment: public and private service delivery models covering low-income earners in South Africa. BMC Health Serv Res. 2023;23:240. [PMID: 36906559] doi: 10.1186/s12913-023-09147-7
46.
U.S. Department of State. Zimbabwe Country Operational Plan (COP) 2022: Strategic Direction Summary (SDS). April 2022. Accessed at www.state.gov/wp-content/uploads/2022/09/Zimbabwe-COP22-SDS.pdf on 5 June 2024.
47.
Edoka IP, Stacey NK. Estimating a cost-effectiveness threshold for health care decision-making in South Africa. Health Policy Plan. 2020;35:546-555. [PMID: 32125375] doi: 10.1093/heapol/czz152
48.
Walensky RP, Borre ED, Bekker L-G, et al. Do less harm: evaluating HIV programmatic alternatives in response to cutbacks in foreign aid. Ann Intern Med. 2017;167:618-629. [PMID: 28847013] doi: 10.7326/M17-1358
49.
Ouattara EN, MacLean RL, Danel C, et al. Cost-effectiveness and budget impact of immediate antiretroviral therapy initiation for treatment of HIV infection in Côte d'Ivoire: a model-based analysis. PLoS One. 2019;14:e0219068. [PMID: 31247009] doi: 10.1371/journal.pone.0219068
50.
Chigwedere P, Seage GR 3rd, Gruskin S, et al. Estimating the lost benefits of antiretroviral drug use in South Africa. J Acquir Immune Defic Syndr. 2008;49:410-415. [PMID: 19186354] doi: 10.1097/qai.0b013e31818a6cd5
51.
Tinogona Investments; South African National AIDS Council. COVID-19 Report: The Voices of Community-Led Organisations and Their Networks. Accessed at https://sanac.org.za/wp-content/uploads/2021/06/COVID-19-Report-Final-April2021.pdf on 5 June 2024.
52.
Jewell BL, Mudimu E, Stover J, et al; HIV Modelling Consortium. Potential effects of disruption to HIV programmes in sub-Saharan Africa caused by COVID-19: results from multiple mathematical models. Lancet HIV. 2020;7:e629-e640. [PMID: 32771089] doi: 10.1016/S2352-3018(20)30211-3
53.
Dorward J, Khubone T, Gate K, et al. The impact of the COVID-19 lockdown on HIV care in 65 South African primary care clinics: an interrupted time series analysis. Lancet HIV. 2021;8:e158-e165. [PMID: 33549166] doi: 10.1016/S2352-3018(20)30359-3
54.
Gaumer G, Luan Y, Hariharan D, et al. Assessing the impact of the President's Emergency Plan for AIDS Relief on all-cause mortality. PLOS Glob Public Health. 2024;4:e0002467. [PMID: 38236797] doi: 10.1371/journal.pgph.0002467
55.
Rautenbach SP, Whittles LK, Meyer-Rath G, et al. Future HIV epidemic trajectories in South Africa and projected long-term consequences of reductions in general population HIV testing: a mathematical modelling study. Lancet Public Health. 2024;9:e218-e230. [PMID: 38553141] doi: 10.1016/S2468-2667(24)00020-3
56.
Dirlikov E, Kamoga J, Talisuna SA, et al; PEPFAR Uganda. Scale-up of HIV antiretroviral therapy and estimation of averted infections and HIV-related deaths - Uganda, 2004–2022. MMWR Morb Mortal Wkly Rep. 2023;72:90-94. [PMID: 36701255] doi: 10.15585/mmwr.mm7204a2
57.
Pichon-Riviere A, Drummond M, Palacios A, et al. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. Lancet Glob Health. 2023;11:e833-e842. [PMID: 37202020] doi: 10.1016/S2214-109X(23)00162-6
58.
Meyer-Rath G, van Rensburg C, Larson B, et al. Revealed willingness-to-pay versus standard cost-effectiveness thresholds: evidence from the South African HIV Investment Case. PLoS One. 2017;12:e0186496. [PMID: 29073167] doi: 10.1371/journal.pone.0186496
59.
Cohen RL, Li Y, Giese R, et al. An evaluation of the President's Emergency Plan for AIDS Relief effect on health systems strengthening in sub-Saharan Africa. J Acquir Immune Defic Syndr. 2013;62:471-479. [PMID: 23254150] doi: 10.1097/QAI.0b013e3182816a86
60.
Jamieson L, Meyer-Rath G, Kubjane M, et al. South African HIV Investment Case. HE2RO; 2023. Accessed at www.heroza.org/wp-content/uploads/2024/01/HIV-Investment-Case-2023-Full-Report-v1.2.pdf on 16 January 2025.
61.
World Bank. South Africa. Accessed at https://documents1.worldbank.org/curated/en/099613104052410927/pdf/IDU1ffc84f731c8e91470018c4311f71d03092e1.pdf on 16 January 2025.
62.
National Treasury, Republic of South Africa. Vote 18 Health. 2024. Accessed at www.treasury.gov.za/documents/National%20Budget/2024/ene/Vote%2018%20Health.pdf on 16 January 2025.
63.
Zakumumpa H, Paina L, Ssegujja E, et al. The impact of shifts in PEPFAR funding policy on HIV services in Eastern Uganda (2015–21). Health Policy Plan. 2024;39:i21-i32. [PMID: 38253438] doi: 10.1093/heapol/czad096
64.
Wilhelm JA, Qiu M, Paina L, et al. The impact of PEPFAR transition on HIV service delivery at health facilities in Uganda. PLoS One. 2019;14:e0223426. [PMID: 31596884] doi: 10.1371/journal.pone.0223426
65.
Chiliza J, Laing R, Feeley FG, et al. Evaluation of the impact of PEPFAR transition on retention in care in South Africa's Western Cape Province. S Afr Med J. 2023;114:44-50. [PMID: 38525641] doi: 10.7196/SAMJ.2024.v114i1.810
66.
Gaumer G, Crown WH, Kates J, et al. Analysis of maternal and child health spillover effects in PEPFAR countries. BMJ Open. 2023;13:e070221. [PMID: 38135335] doi: 10.1136/bmjopen-2022-070221
67.
Crown W, Hariharan D, Kates J, et al. Analysis of economic and educational spillover effects in PEPFAR countries. PLoS One. 2023;18:e0289909. [PMID: 38157353] doi: 10.1371/journal.pone.0289909
68.
Menzies NA, Berruti AA, Berzon R, et al. The cost of providing comprehensive HIV treatment in PEPFAR-supported programs. AIDS. 2011;25:1753-1760. [PMID: 21412127] doi: 10.1097/QAD.0b013e3283463eec
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Volume 178 • Number 4 • April 2025
Pages: 457 - 467
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Published online: 11 February 2025
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Potential Clinical and Economic Impacts of Cutbacks in the President’s Emergency Plan for AIDS Relief Program in South Africa: A Modeling Analysis. Ann Intern Med.2025;178:457-467. [Epub 11 February 2025]. doi:10.7326/ANNALS-24-01104
Potential Clinical and Economic Impacts of Cutbacks in the President’s Emergency Plan for AIDS Relief Program in South Africa: A Modeling Analysis. Ann Intern Med.2025;178:457-467. [Epub 11 February 2025]. doi:10.7326/ANNALS-24-01104
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