HIV incidence and risk factors for seroconversion among female sex workers and single mothers in a 10-year prospective cohort
Bibliographic record
Abstract
OBJECTIVE: To compare HIV incidence among female sex workers (FSW) and single mothers, and to determine the factors associated with seroconversion among both populations. DESIGN: Prospective cohort conducted in Lusaka and Ndola, Zambia between 2012 and 2022. METHODS: Study staff recruited FSW from common sex work locales and recruited single mothers from postnatal infant vaccination clinics. Enrolled participants were HIV-negative, aged 18-45, and identified as either a FSW or single mother. We measured HIV incidence and assessed associated factors using Poisson regression with adjusted rate ratios (aRRs) and 95% confidence intervals (CIs). RESULTS: The study enrolled 2539 women (1533 FSW and 1006 single mothers). HIV incidence was not statistically different for FSW (3.24 per 100 person-years; 95% CI: 2.63-3.95) and single mothers (2.64 per 100 person-years; 95% CI: 2.00-3.43). Factors associated with HIV seroconversion were positive syphilis (aRR: 2.03; 95% CI: 1.46-2.83) and trichomonas (aRR: 1.48; 95% CI: 1.06-2.06) diagnoses, inconsistent condom use (aRR: 1.60; 95% CI: 1.06-2.40), and greater than 6months follow-up time in the study (aRR: 2.45; 95% CI: 1.52-3.94). CONCLUSIONS: Single mothers share similar HIV risk to FSW, and both populations require targeted interventions. For single mothers, government postnatal clinics should combine comprehensive sexual education with screening and treatment for syphilis and trichomoniasis. For FSW, we recommend integrated and accessible interventions to prevent HIV and sexually transmitted infections. Future studies should investigate the social determinants of condom use among both FSW and single mothers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".