The impact of the PEPFAR funding freeze on HIV deaths and infections: a mathematical modelling study of seven countries in sub-Saharan Africa
Bibliographic record
Abstract
Summary Background On January 24, 2025, the United States government issued an executive order to freeze all foreign aid programs, including The President's Emergency Plan for AIDS Relief (PEPFAR), for 90 days. A limited waiver option became available, but its implementation remains incomplete. We estimated the impact of these policy changes on HIV deaths and new infections in seven sub-Saharan African (SSA) countries—Ethiopia, Kenya, Malawi, South Africa, Tanzania, Zambia, and Zimbabwe –, which together account for about half of all people living with HIV in SSA. Methods We used STDSIM, an established individual-based simulation model, and previously published quantifications for the seven countries. We predicted changes in HIV deaths and new infections over the period 2025–2030 for four scenarios: (1) Executive order—proportional, where treatment disruption was proportional to the country-specific PEPFAR's share of total HIV funding; (2) Executive order—realistic, assuming near-total system collapse due to program dependencies; and (3–4) Waiver scenarios where treatment was resumed after 4 or after 8 weeks. Resumptions of programs accounted for delays due to organizational and logistical challenges. Findings A 90-day funding freeze would result in 60 thousand [95% UI: 49–71 thousand] excess HIV deaths for the Executive order—proportional scenario. This number would increase to 74 thousand excess HIV deaths [95% UI: 63–89 thousand] for the Executive order—realistic scenario. Under a 4-week and 8-week waiver scenario, projected excess HIV deaths ranged between 21 thousand [95% UI: 15–28 thousand] and 28 thousand [95% UI: 22–36 thousand] respectively. Excess new infections ranged between 35 and 103 thousand for the different scenarios. Interpretation The sudden cessation of PEPFAR funding likely results in tens of thousands of HIV deaths and new infections. These losses of life and health should compel the United States government to rapidly and fully re-instate one of the most successful health programs in history. Funding None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".