High HIV Prevalence and Low HIV-Service Engagement Among Young Women Who Sell Sex: A Pooled Analysis Across 9 Sub-Saharan African Countries
Bibliographic record
Abstract
BACKGROUND: Epidemiological data are needed to characterize the age-specific HIV burden and engagement in HIV services among young, marginalized women in sub-Saharan Africa. SETTING: Women aged ≥18 years who reported selling sex were recruited across 9 countries in Southern, Central, and West Africa through respondent driven sampling (N = 6592). METHODS: Individual-level data were pooled and age-specific HIV prevalence and antiretroviral therapy (ART) coverage were estimated for each region using generalized linear mixed models. HIV-service engagement outcomes (prior HIV testing, HIV status awareness, and ART use) were compared among women living with HIV across age strata (18-19, 20-24, and ≥25 years) using generalized estimating equations. RESULTS: By age 18%-19%, 45.4% [95% confidence interval (CI): 37.9 to 53.0], 5.8% (95% CI: 4.3 to 7.8), and 4.0% (95% CI: 2.9 to 5.4) of young women who sell sex were living with HIV in Southern, Central, and West Africa respectively. Prevalence sharply increased during early adulthood in all regions, but ART coverage was suboptimal across age groups. Compared with adult women ≥25, young women aged 18-19 were less likely to have previously tested for HIV [prevalence ratio (PR) 0.76; 95% CI: 0.72 to 0.80], less likely to already be aware of their HIV status (PR 0.48; 95% CI: 0.35 to 0.64), and less likely to be taking ART (PR 0.67; 95% CI: 0.59 to 0.75). CONCLUSIONS: HIV prevalence was already high by age 18-19 in this pooled analysis, demonstrating the need for prevention efforts that reach women who sell sex early in their adolescence. ART coverage remained low, with women in the youngest age group the least engaged in HIV-related services. Addressing barriers to HIV service delivery among young women who sell sex is central to a comprehensive HIV response.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".