To what extent is the HIV epidemic in southern India driven by commercial sex? A modelling analysis
Bibliographic record
Abstract
BACKGROUND: In south India, general population HIV prevalence estimates range from 0.5 to 3%. To focus HIV prevention efforts, it is important to understand whether HIV transmission is driven by commercial sex. METHODS: A dynamic HIV/sexually transmitted infection transmission model was parameterized using data from Belgaum and Mysore in south India. Fits to sexually transmitted infection/HIV data from female sex workers (FSWs) and their clients for each district were obtained. Model HIV/herpes simplex virus-2 (HSV-2) prevalence projections for the general population were cross-validated against empirical estimates not used to fit model. The model estimated the proportion of incident HIV/HSV-2 infections due to HIV/HSV-2 transmission between FSWs/clients, their noncommercial partners and other low-risk partnerships. The relative impact of a generic intervention targeting different partnerships was explored. RESULTS: The model's general population HIV/HSV-2 prevalence projections agreed well with empirical estimates. Recent increases in condom use resulted in decreasing HIV epidemics in both settings. For men, most incident HIV/HSV-2 infections (>90%) directly result from commercial sex, whereas for women most are due to bridging infections from clients of FSWs (80-90%) with the remainder mainly due to commercial sex. Less than 1.5% of incident infections are due to low-risk partnerships. Intervention impact is maximized through targeting commercial sex but substantial impact could also be achieved through targeting noncommercial partners of clients. DISCUSSION: In southern India, HIV transmission could be driven by FSWs and their clients. While efforts to reduce HIV transmission due to commercial sex must continue, prevention programmes should also consider strategies to prevent transmission from clients to their noncommercial partners.
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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".