Mathematical Model Impact Analysis of a Real-Life Pre-exposure Prophylaxis and Treatment-As-Prevention Study Among Female Sex Workers in Cotonou, Benin
Bibliographic record
Abstract
BACKGROUND: Daily pre-exposure prophylaxis (PrEP) and treatment-as-prevention (TasP) reduce HIV acquisition and transmission risk, respectively. A demonstration study (2015-2017) assessed TasP and PrEP feasibility among female sex workers (FSW) in Cotonou, Benin. SETTING: Cotonou, Benin. METHODS: We developed a compartmental HIV transmission model featuring PrEP and antiretroviral therapy (ART) among the high-risk (FSW and clients) and low-risk populations, calibrated to historical epidemiological and demonstration study data, reflecting observed lower PrEP uptake, adherence and retention compared with TasP. We estimated the population-level impact of the 2-year study and several 20-year intervention scenarios, varying coverage and adherence independently and together. We report the percentage [median, 2.5th-97.5th percentile uncertainty interval (95% UI)] of HIV infections prevented comparing the intervention and counterfactual (2017 coverages: 0% PrEP and 49% ART) scenarios. RESULTS: The 2-year study (2017 coverages: 9% PrEP and 83% ART) prevented an estimated 8% (95% UI 6-12) and 6% (3-10) infections among FSW over 2 and 20 years, respectively, compared with 7% (3-11) and 5% (2-9) overall. The PrEP and TasP arms prevented 0.4% (0.2-0.8) and 4.6% (2.2-8.7) infections overall over 20 years, respectively. Twenty-year PrEP and TasP scale-ups (2035 coverages: 47% PrEP and 88% ART) prevented 21% (17-26) and 17% (10-27) infections among FSW, respectively, and 5% (3-10) and 17% (10-27) overall. Compared with TasP scale-up alone, PrEP and TasP combined scale-up prevented 1.9× and 1.2× more infections among FSW and overall, respectively. CONCLUSIONS: The demonstration study impact was modest, and mostly from TasP. Increasing PrEP adherence and coverage improves impact substantially among FSW, but little overall. We recommend TasP in prevention packages.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".