Anal Intercourse Among Female Sex Workers in Côte d’Ivoire: Prevalence, Determinants, and Model-Based Estimates of the Population-Level Impact on HIV Transmission
Bibliographic record
Abstract
Current evidence suggests that anal intercourse (AI) during sex work is common in sub-Saharan Africa, but there have been few studies in which the contribution of heterosexual AI to human immunodeficiency virus (HIV) epidemics has been investigated. Using a respondent-driven sampling survey of female sex workers (FSWs; n = 466) in Abidjan, Côte d'Ivoire, in 2014, we estimated AI prevalence and frequency. Poisson regressions were used to identify AI determinants. Approximately 20% of FSWs engaged in AI during a normal week (95% confidence interval: 15, 26). Women who performed AI were generally younger, had been selling sex for longer, were born in Côte d'Ivoire, and reported higher sex-work income, more frequent sex in public places, and violence from clients than women not reporting AI. Condom use was lower, condom breakage/slippage more frequent, and use of water-based lubricants was less frequently reported for AI than for vaginal intercourse. Using a dynamic transmission model, we estimated that 22% (95% credible interval: 11, 37% of new HIV infections could have been averted among FSWs during 2000-2015 if AI had been substituted for vaginal intercourse. Despite representing a small fraction of all sex acts, AI is an underestimated source of HIV transmission. Increasing availability and uptake of condoms, lubricants, and pre-exposure prophylaxis for women engaging in AI could help mitigate HIV risk.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".