Declines in HIV prevalence in female sex workers accessing an HIV treatment and prevention programme in Nairobi, Kenya over a 10-year period
Bibliographic record
Abstract
OBJECTIVES: Empirical time trends in HIV prevalence in female sex workers (FSWs) are helpful to understand the evolving HIV epidemic, and to monitor the scale-up, coverage, and impact of ongoing HIV prevention and treatment programmes. DESIGN: Serial HIV prevalence study. METHODS: We analyzed time trends in HIV prevalence in FSWs accessing services at seven Sex Worker Outreach Programme (SWOP) clinics in Nairobi from 2008 to 2017 (N = 33 560). The Mantel--Haenszel test for trend and independent samples Kruskal--Wallis test were used to analyze categorical and continuous variables, respectively. Multivariable binomial regression was used to estimate prevalence ratios/year, adjusting for several covariates. RESULTS: HIV prevalence decreased over time in all age groups. This was particularly evident among FSWs less than 25 years of age; HIV was 17.5% in 2008-2009, decreasing to 12.2% in 2010-2011, 8.3% in 2012-2013, 7.3% in 2014-2015, and 4.8% in 2016-2017 (P < 0.0001). Over time, FSWs reported increased condom use, particularly with regular partners, more frequent prior HIV testing, and were less likely to report a history of vaginal discharge (P < 0.0001). In adjusted analyses compared with 2008, HIV prevalence decreased in 2011 (aPR 0.64; 95% CI: 0.46-0.90), 2012 (aPR 0.58; 95% CI: 0.41-0.81), 2013 (aPR 0.53; 95% CI: 0.38-0.73), 2014 (aPR 0.48; 95% CI: 0.34-0.67), 2015 (aPR 0.50; 95% CI: 0.35-0.70), 2016 (aPR 0.40; 95% CI: 0.28-0.57), and 2017 (aPR 0.33; 95% CI: 0.22-0.50). CONCLUSION: HIV prevalence has decreased among FSW accessing SWOP in Nairobi, Kenya. This decline is consistent with the scale-up of HIV prevention and treatment efforts, both in FSWs and in the general population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".