Differential Burden of HIV Among Adolescent Girls and Young Women by Places Associated With Sex Work: An Observational Study in Mombasa, Kenya
Bibliographic record
Abstract
BACKGROUND: The design of HIV prevention programs for adolescent girls and young women (AGYW) are informed by data on who is at highest risk and where they can be reached. Places (hotspots) associated with selling sex are an established outreach strategy for sex work (SW) programs but could be used to reach other AGYW at high risk. SETTING: This study took place in Mombasa, Kenya. METHODS: We conducted a cross-sectional, bio-behavioural survey among (N = 1193) sexually active AGYW aged 14-24 years recruited at hotspots. We compared HIV prevalence by subgroup (SW; transactional sex, TS; and non-transactional sex), stratified by hotspot type (venues and nonvenues). We examined whether associations between HIV prevalence and hotspot/subgroup remained after adjustment for individual-level risk factors, and estimated HIV prevalence ratio with and without adjustment for these individual-level factors. RESULTS: Overall HIV prevalence was 5.6%, 5.3% in venues and 7.3% in nonvenues. Overall SW HIV prevalence was 2-fold higher than among participants engaged in nontransactional sex. After adjusting for age and individual-level risk factors, HIV prevalence was 2.72 times higher among venue-based SWs (95% confidence interval: 1.56 to 4.85) and 2.11 times higher among nonvenue AGYW not engaged in SW (95% confidence interval: 0.97 to 4.30) compared with venue-based AGYW not engaged in SW. CONCLUSION: AGYW who sell sex remain at high risk of HIV across types of hotspots. The residual pattern of elevated HIV burden by AGWY subgroup and hotspot type suggests that unmeasured, network-level factors underscore differential risks. As such, hotspots constitute a "place" to reach AGYW at high risk of HIV.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".