P716 HIV prevalence in at-risk adolescent girls and young women in kenya across locations associated with sex work
Bibliographic record
Abstract
<h3>Background</h3> HIV prevalence is influenced by biological, behavioural and structural factors. We sought to understand HIV prevalence among at-risk adolescent girls and young women (AGYW) in Kenya and the relative importance of location and subgroup. <h3>Methods</h3> We conducted a cross-sectional survey among AGYW aged 14–24 years in Mombasa, Kenya in 2015/16. Using probabilistic sampling we recruited participants from hotspots where female sex workers (FSWs) solicit clients, divided into venue-based (bars/brothels/hotels/brew dens) and non-venue-based (streets/public places) hotspots. We measured HIV prevalence using dried blood spot serology testing and examined predictors of risk including subgroup: engagement in formal sex work; transactional sex (TS) (exchange of sex for goods/money without pre-negotiation of price); and casual sex (CS) (did not self-identify as FSW or engaging in TS). We compared determinants of risk in venue-based and non-venue-based participants after adjusting for age. <h3>Results</h3> Overall HIV prevalence was 5.6%, with highest prevalence among FSWs (10.1%), compared to those engaged in TS (3.6%) or CS (3.6%) (p < 0.001). Daily/almost daily alcohol use was associated with higher prevalence in both venue- (AOR 2.8 (1.3–6.1)) and non-venue-based (AOR 5.3 (1.3–21.1)) hotspots. Venue-based participants were also more likely to test positive if they had ever been pregnant [AOR 1.9 (1.0–3.5)], had their first sexual experience aged <15 years [AOR2.2 (1.2–4.1)] or had ever experienced sexual [AOR 3.2 (1.8–5.7)] or physical violence [AOR 2.4 (1.3–4.3)]. Compared to CS and TS, HIV prevalence was higher among FSWs in venue-based hotspots [AOR3.4 (1.8–6.3)], but not non-venues [AOR 1.1 (0.3–3.8)]. <h3>Conclusion</h3> We identified a high prevalence of HIV among AGYW congregating in hotspots for FSW, with heterogeneity across subgroups. Determinants of HIV varied by location suggesting that one’s environment is an important contributor to risk, and HIV programmes should take this into consideration for designing delivery of interventions. <h3>Disclosure</h3> No significant relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".