Modelling the cost-effectiveness of TasP and PrEP in female sex workers in Cotonou, Benin
Bibliographic record
Abstract
Abstract Introduction Treatment as prevention (TasP) and pre-exposure prophylaxis (PrEP) could impact the HIV epidemic among Female Sex Workers (FSW) but their cost-effectiveness is uncertain in this group. This study aims to model the cost-effectiveness of TasP and PrEP among FSW in Cotonou, Benin. Methods A demonstration study assessed TasP and PrEP use among FSW in Cotonou. A dynamic HIV transmission model was developed to estimate the impact of this intervention and published elsewhere. Incremental economic costs of the study were collected prospectively capturing both provider and FSW costs. The incremental cost-effectiveness ratio per HIV infection and disability-adjusted life years (DALY) averted were estimated over a 20 year time horizon with costs converted to USD 2020 and both costs and DALYs discounted at 4.5% per year. Different cost scenarios were modelled to investigate the cost-effectiveness of the intervention as delivered by the government reflecting current day implementation and resource costs. Results The mean provider annual economic cost per FSW on TasP was $646, with an initiation cost of $347 and mean annual user costs were $16. The mean initiation costs for PrEP were $268, mean annual provider costs were $359-$499 and annual user cost $15-$21 depending on adherence level. TasP was found to be cost saving for all cost scenarios examined compared to routine HIV care for FSW. PrEP was not cost-effective for any cost scenario, population coverage or adherence level examined. Conclusion Our results support TasP but not PrEP for FSW in our setting. A streamlined, outreach delivery model with reduced costs should be investigated to assess its cost-effectiveness in this setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".