Cost‐effectiveness analysis of community‐led HIV self‐testing among key populations in Côte d'Ivoire, Mali, and Senegal
Bibliographic record
Abstract
INTRODUCTION: HIV self-testing (HIVST) is a promising strategy to improve diagnosis coverage among key populations (KP). The ATLAS (Auto Test VIH, Libre d'Accéder à la connaissance de son Statut) programme implemented HIVST in three West African countries, distributing over 380,000 kits up between 2019 and 2021, focussing on community-led distribution by KP to their peers and subsequent secondary distribution to their partners and clients. We aim to evaluate the cost-effectiveness of community-led HIVST in Côte d'Ivoire, Mali and Senegal. METHODS: An HIV transmission dynamics model was adapted and calibrated to country-specific epidemiological data and used to predict the impact of HIVST. We considered the distribution of HIVST among two KP-female sex workers (FSW), and men who have sex with men (MSM)-and their sexual partners and clients. We compared the cost-effectiveness of two scenarios against a counterfactual without HIVST over a 20-year horizon (2019-2039). The ATLAS-only scenario mimicked the 2-year implemented ATLAS programme, whereas the ATLAS-scale-up scenario achieved 95% coverage of HIVST distribution among FSW and MSM by 2025 onwards. The primary outcome is the number of disability-adjusted life-years (DALY) averted. Scenarios were compared using incremental cost-effectiveness ratios (ICERs). Costing was performed using a healthcare provider's perspective. Costs were discounted at 4%, converted to $USD 2022 and estimated using a cost-function to accommodate economies of scale. RESULTS: The ATLAS-only scenario was highly cost-effective over 20 years, even at low willingness-to-pay thresholds. The median ICERs were $126 ($88-$210) per DALY averted in Côte d'Ivoire, $92 ($88-$210) in Mali and 27$ ($88-$210) in Senegal. Scaling-up the ATLAS programme would also be cost-effective, and substantial epidemiological impacts would be achieved. The ICERs for the scale-up scenario were $199 ($122-$338) per DALY averted in Côte d'Ivoire, $224 ($118-$415) in Mali and $61 ($18-$128) in Senegal. CONCLUSIONS: Both the implemented and the potential scale-up of community-led HIVST programmes in West Africa, where KP are important to overall transmission dynamics, have the potential to be highly cost-effective, as compared to a scenario without HIVST. These findings support the scale-up of community-led HIVST to reach populations that otherwise may not access conventional testing services.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".