Assessing the potential population-level impacts of HIV self-testing distribution among key populations in Côte d’Ivoire, Mali, and Senegal: a mathematical modelling analysis
Bibliographic record
Abstract
Abstract Background A third of people living with HIV (PLHIV) in Western Africa had an undiagnosed infection in 2020. In 2019-2021, the ATLAS programme has distributed a total of 380 000 HIV self-testing (HIVST) kits to key populations (KP) including female sex workers (FSW) and men who have sex with men (MSM), and their partners in Côte d’Ivoire, Mali and Senegal. We predicted the potential impact of ATLAS and of national HIVST scale-up strategies among KP. Methods A deterministic model of HIV transmission was calibrated to country-specific empirical HIV and intervention data over time. We simulated scenarios reflecting 1) the actual ATLAS HIVST distribution only over 2019-2021 (∼2% of all tests done in countries), and 2) ATLAS followed by a scale-up of HIVST distribution to KP (total of ∼570 000 kits distributed each year). Impacts on HIV diagnosis, new HIV infections and deaths were derived using counterfactual scenarios without HIVST. Findings ATLAS was predicted to substantially increase HIV diagnosis among KP by the end of 2021, especially among MSM in Mali (9·3 percentage point [pp] increase), and a 1·0pp increase overall. ATLAS might have averted a median of 706 new HIV infections among KP over 2019-2028 in the 3 countries combined, especially among MSM, and 1794 new HIV infections (0·4-3·3% of all new HIV infections across countries) and 591 HIV-related deaths overall. HIVST scale-up increased HIV diagnosis at the end of 2028 by around 8pp among FSW and 33pp among MSM in every country. Overall increases ranged from 1·0pp (Côte d’Ivoire) to 11·0pp (Senegal). HIVST scale-up may avert 3-5% of new HIV infections among FSW, 3-10% among FSW clients, and 20-28% among MSM across countries (and 2-16% overall), and avert 13-18% of HIV-related deaths among MSM over 2019-2028. Interpretation Scaling-up HIVST distribution among KP in Western Africa may substantially attenuate disparities in access to HIV testing and help reduce HIV infections and deaths among KP and their partners. Funding Unitaid MRC
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".