Determinants of HIV prevalence among female sex workers in four south Indian states: analysis of cross-sectional surveys in twenty-three districts
Bibliographic record
Abstract
OBJECTIVE: In four states in southern India we explored the determinants of HIV prevalence among female sex workers (FSW), as well as factors associated with district-level variations in HIV prevalence among FSW. METHODS: Data from cross-sectional surveys in 23 districts were analysed, with HIV prevalence as the outcome variable, and sociodemographic and sex work characteristics as predictor variables. Multilevel logistic regression was applied to identify factors that could explain variations in HIV prevalence among districts. RESULTS: HIV prevalence among the 10 096 FSW surveyed was 14.5% (95% confidence interval 14.0-15.4), with a large interdistrict variation, ranging from 2% to 38%. Current marital status and the usual place of solicitation emerged as important factors that determine individual probability of being HIV positive, as well as the HIV prevalence within districts. In multivariate analysis, compared with home-based FSW, the odds of being HIV positive was greater for brothel-based FSW [adjusted odds ratio (AOR) 2.17, P <or= 0.001] and for public place-based FSW (AOR 1.32, P = 0.005). Unmarried FSW and those who were widowed/divorced/separated, or from the devadasi tradition, had higher odds of being HIV positive (AOR 1.79, P <or= 0.001 and 1.98, P < 0.001, respectively), than those currently married. The estimated district level variance in HIV prevalence was lowest (0.152) for brothel-based unmarried FSW, followed by brothel-based widowed/divorced/separated or devadasi FSW (0.192). CONCLUSION: Heterogeneity in the organization and structure of sex work is an important determinant of variations in HIV prevalence among FSW across districts in India, much more so than the districts themselves. This understanding should help to improve the design of HIV preventive interventions.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".