HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: HIV incidence among women in sub-Saharan Africa (SSA) has declined steadily, but it is unknown whether new infections among women who engage in sex work (WESW) have declined at a similar rate. We synthesised estimates of HIV incidence among WESW in SSA and compared these to the wider female population to understand levels and trends in incidence over time. Methods: We searched Medline, Embase, Global Health, Popline, Web of Science, and Google Scholar from January 1990 to October 2022, and grey literature for estimates of HIV incidence among WESW in SSA. We included studies reporting empirical estimates in any SSA country. We calculated incidence rate ratios (IRR) compared to age-district-year matched total female population incidence estimates. We conducted a meta-analysis of IRRs and used a continuous mixed-effects model to estimate changes in IRR over time. Results: From 32 studies between 1985 and 2020, 2,194 new HIV infections were observed in WESW over 51,000 person-years (py). Median HIV incidence was 4.3/100py (IQR 2.8-7.0/100py), declining from a median of 5.96/100py between 1985 and 1995 to a median of 3.2/100py between 2010 and 2020. Incidence among WESW was nine times higher than in matched total population women (RR 8.6, 95%CI: 5.7-12.9), and greater in Western and Central Africa (RR 22.4, 95%CI: 11.3-44.3) than in Eastern and Southern Africa (RR 5.3, 95%CI: 3.7-7.6). Annual changes in log IRRs were minimal (-0.1% 95%CI: -6.9 to +6.8%). Conclusions: Across SSA, HIV incidence among WESW remains disproportionately high compared to the total female population but showed similar rates of decline between 1990 and 2020. Improved surveillance and standardisation of approaches to obtain empirical estimates of sex worker incidence would enable a clearer understanding of whether we are on track to meet global targets for this population and better support data-driven HIV prevention programming.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".