HIV Prevention and Treatment Cascades Among Female Sex Workers in Benin, West Africa
Bibliographic record
Abstract
BACKGROUND: Benin has a long-standing history of HIV prevention programs aimed at female sex workers (FSWs). We used data from a national survey among FSWs (2017) to assess the prevention and care cascades in this population. METHODS: Female sex workers were recruited through cluster sampling of sex work sites. A questionnaire was administered, and HIV tested. HIV-positive participants were asked to provide dried blood spots and were tested for antiretroviral and viral load. We assessed 2 prevention cascades (HIV testing and safer sex) and the treatment cascade, using a combination of self-reported and biological variables. RESULTS: Mean age of the 1086 FSWs was 30 years. Half of them were Beninese, and two-thirds had a primary school education level or less. Almost all FSWs had ever heard of HIV/AIDS. More than half (79.1%) had ever been tested, and 84.1% of the latter had been tested in the last year. In the previous 6 months, 90.1% were exposed to prevention messages. Women exposed to any HIV prevention message reported a higher level of consistent condom use in the last month (69.0%) than those who were not (48.5%, P < 0.0001). HIV prevalence was 7.7%. Among HIV-positive women, 60.6% knew their status; among those, 90.5% were on antiretroviral and 81.8% of them had a suppressed viral load. CONCLUSIONS: Despite long-standing HIV prevention programs for FSWs, the prevention indicators were often low. Linkage to care was good, viral suppression was suboptimal, but knowledge of HIV-positive status was low. Exposing women to prevention messages is necessary, as to increase HIV testing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".