Geographical Associations of HIV Prevalence in Female Sex Workers From Nairobi, Kenya (2014–2017)
Bibliographic record
Abstract
BACKGROUND: Kenya's HIV epidemic is heterogeneously distributed. Although HIV incidence in Kenya has shown signs of recent decline, focused interventions are still needed for female sex workers (FSWs). Geospatially informed approaches have been advocated for targeted HIV prevention. We quantified heterogeneity in HIV burden in Nairobi-based FSWs by place of origin within Kenya and hotspots and residence within Nairobi. METHODS: Data were collected as part of enrolment in the Sex Workers Outreach Program in Nairobi between 2014 and 2017. Prevalence ratios were used to quantify the risk of HIV by high-prevalence counties using modified Poisson regression analyses. Crude and fully adjusted models were fitted to the data. In heterogeneity analyses, hotspots and residences were aggregated to the Nairobi constituency level (n = 17). Inequality in the geographic distribution of HIV prevalence was measured using the Gini coefficient. RESULTS: A total of 11,899 FSWs were included. Overall HIV prevalence was 16%. FSWs originating from a high-prevalence country were at 2-fold increased risk of living with HIV in adjusted analysis (prevalence ratio 1.95; 95% CI: 1.76 to 2.17). HIV prevalence was also highly heterogeneous by hotspot, ranging from 7% to 52% by hotspot (Gini coefficient: 0.37; 95% CI: 0.23 to 0.50). By contrast, the constituency of residence had a Gini coefficient of 0.08 (95% CI: 0.06 to 0.10), suggesting minimal heterogeneity by residence. CONCLUSION: HIV prevalence in FSWs is heterogeneous by place of work within Nairobi and by county of birth within Kenya. As HIV incidence declines and financial commitments flatline, tailoring interventions to FSWs at highest HIV risk becomes increasingly important.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".