PrEP Use Among Female Sex Workers: No Evidence for Risk Compensation
Bibliographic record
Abstract
BACKGROUND: Little is known about risk compensation among female sex workers (FSW) on HIV pre-exposure prophylaxis (PrEP), and self-report of sexual behaviors is subject to bias. SETTING: Prospective observational PrEP demonstration study conducted among FSW in Cotonou, Benin. METHODS: Over a period of 24 months, we assessed and compared trends in unprotected sex as measured by self-report (last 2 or 14 days), by detection of sexually transmitted infections (STIs), and by vaginal detection of prostate-specific antigen and Y-chromosomal DNA, 2 biomarkers of semen exposure in the last 2 or 14 days, respectively. Trends were assessed and compared using a log-binomial regression that was simultaneously fit for all unprotected sex measures. RESULTS: Of 255 participants, 120 (47.1%) completed their follow-up. Prevalence of STI decreased from 15.8% (95% confidence interval: 11.8% to 21.0%) at baseline to 2.1% (95% confidence interval: 0.4% to 10.2%) at 24 months of follow-up (P-trend = 0.04). However, we observed no trend in self-report of unprotected sex in the last 2 (P = 0.42) or 14 days (P = 0.49), nor in prostate-specific antigen (P = 0.53) or Y chromosomal DNA (P = 0.25) over the same period. We observed no statistically significant difference between trends in self-report of unprotected sex and trends in biomarkers of semen exposure in the last 2 days (P = 0.14) or in the last 14 days (P = 0.29). CONCLUSIONS: We observed no evidence of risk compensation, and a decrease in STI among FSW on PrEP. PrEP intervention may be an opportunity to control STI among FSW. Future studies should assess risk compensation with biomarkers of semen exposure when possible.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".