Identifying priority populations for HIV interventions using acquisition and transmission indicators: a combined analysis of 15 mathematical models from ten African countries
Bibliographic record
Abstract
Background Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We aimed to assess how indicators measuring HIV acquisitions, transmissions, or potential long-term infections influence estimates of the contribution of different populations to new infections, including key populations (including female sex workers, their clients, men who have sex with men). Methods In this mathematical model comparison analysis, we evaluated four indicators using nine models representing 15 different settings across Africa. The acquisition indicator (I 1 ) measured the annual proportion of all new infections acquired by a specific population, the direct transmission indicator (I 2 ) measured the annual proportion of all new infections directly transmitted by a specific population, and the 1-year transmission population-attributable fractions (tPAFs; I 3 ) and 10-year tPAFs (I 4 ) measured the proportion of new infections averted if transmission involving a specific population was blocked over a specific time period. We compared estimates of the four indicators across seven populations and 15 settings and assessed if the contribution of specific populations ranked differently across indicators for ten settings. Findings Different indicators identified distinct priority populations as the largest contributors: I 1 identified women aged 25 years and older outside key populations as contributing the most to acquired infections in eight of ten settings in 2020, but to direct transmissions (I 2 ) in only two settings. In six of ten settings, I 4 identified non-key population men aged 25 years and older and clients of female sex workers as the largest contributors to HIV transmission. Notably, non-key population women aged 15–24 years acquired (I 1 ) more infections in 2020 (median of 1·7 times higher across models) than they directly transmitted (I 2 ), whereas more infections were transmitted than acquired in non-key population men aged 25 years and older (median 1·4 times more) and clients of female sex workers (1·6 times more) in all but one model. Estimates of the 10-year tPAFs accounting for transmission in the long-term were substantially larger than the direct transmission indicator for all populations, especially for female sex workers (2·0 times higher). Interpretation Indicators that reflect HIV acquisitions and transmissions in the short and long term can be used to capture the complexity of HIV epidemics across different populations and timeframes. The added nuance would improve the effectiveness of the HIV prevention response across all populations at risk. Funding US National Institutes of Health and UK Medical Research Council. Translation For the French translation of the abstract see Supplementary Materials section.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".