Reduced rates of HIV acquisition during unprotected sex by Kenyan female sex workers predating population declines in HIV prevalence
Bibliographic record
Abstract
OBJECTIVES: Female sex workers (FSWs) form a core group at high risk of both sexual HIV acquisition and secondary transmission. The magnitude of these risks may vary by sexual risk taking, partner HIV prevalence, host immune factors and genital co-infections. We examined temporal trends in HIV prevalence and per-act incidence, adjusted for behavioral and other variables, in FSWs from Nairobi, Kenya. METHODS: An open cohort of FSWs followed since 1985. Behavioral and clinical data were collected six monthly from 1985 to 2005, and sexually transmitted infection (STI) diagnostics and HIV serology performed. A Cox proportional hazards model with time-dependent covariables was used to estimate infection risk as a function of calendar time. RESULTS: HIV prevalence in new FSW enrollees peaked at 81% in 1986, and was consistently below 50% after 1997. Initially uninfected FSWs remained at high risk of acquiring HIV throughout the study period, but the rate of HIV acquisition during unprotected sex with a casual client declined by over four-fold. This reduction correlated closely with decreases in gonorrhea prevalence, and predated reductions in the Kenyan HIV population prevalence by over a decade. CONCLUSIONS: The per-act rate of HIV acquisition in high-risk Nairobi FSWs fell dramatically between 1985 and 2005. This decline may represent the impact of improved STI prevention/therapy, immunogenetic shifts in at-risk women, or changes in the proportion of HIV exposures occurring with clients who had acute HIV infection. Declining HIV incidence in high-risk cohorts may predict and/or be causally related to future reductions in population prevalence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".