Venue-Based HIV Testing at Sex Work Hotspots to Reach Adolescent Girls and Young Women Living With HIV: A Cross-sectional Study in Mombasa, Kenya
Bibliographic record
Abstract
BACKGROUND: We estimated the potential number of newly diagnosed HIV infections among adolescent girls and young women (AGYW) using a venue-based approach to HIV testing at sex work hotspots. METHODS: We used hotspot enumeration and cross-sectional biobehavioral survey data from the 2015 Transition Study of AGYW aged 14-24 years who frequented hotspots in Mombasa, Kenya. We described the HIV cascade among young females who sell sex (YFSS) (N = 408) versus those young females who do not sell sex (YFNS) (N = 891) and triangulated the potential (100% test acceptance and accuracy) and feasible (accounting for test acceptance and sensitivity) number of AGYW that could be newly diagnosed through hotspot-based HIV rapid testing in Mombasa. We identified the profile of AGYW with an HIV in the past year using generalized linear mixed regression models. RESULTS: N = 37/365 (10.1%) YFSS and N = 30/828 (3.6%) YFNS were living with HIV, of whom 27.0% (N = 10/37) and 30.0% (N = 9/30) were diagnosed and aware (P = 0.79). Rapid test acceptance was 89.3%, and sensitivity was 80.4%. There were an estimated 15,635 (range: 12,172-19,097) AGYW at hotspots. The potential and feasible number of new diagnosis was 627 (310-1081), and 450 (223-776), respectively. Thus, hotspot-based testing could feasibly reduce the undiagnosed fraction from 71.6% to 20.2%. The profile of AGYW who recently tested was similar among YFSS and YFNS. YFSS were 2-fold more likely to report a recent HIV test after adjusting for other determinants [odds ratio (95% confidence interval): 2.2 (1.5 to 3.1)]. CONCLUSION: Reaching AGYW through hotspot-based HIV testing could fill gaps left by traditional, clinic-based HIV testing services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".