Positive impact of a large-scale HIV prevention programme among female sex workers and clients in South India
Bibliographic record
Abstract
OBJECTIVE: Estimate the potential impact of Avahan, the India AIDS Initiative, among female sex workers (FSWs) and their clients in five districts of Karnataka state, south India. DESIGN: Examination of time trends in sexually transmitted infection (STI)/HIV prevalence from serial cross-sectional surveys, combined with mathematical modelling. METHODS: Survey data from each district were used to monitor changes in FSW STI/HIV prevalence during Avahan. A deterministic model, parameterized with district-specific survey data, was used to simulate HIV/HSV-2/syphilis transmission among high-risk groups in each district. Latin hypercube sampling was used to obtain multiple parameter sets that reproduced district-specific HIV prevalence trends. A Bayesian framework tested whether self-reported increases in consistent condom use (CCU) during Avahan were more compatible with FSW HIV prevalence trends than assuming no or slow (preintervention rates) CCU increases, and were used to estimate HIV incidence and infections averted. RESULTS: Declines in FSW HIV prevalence occurred over 5 years in all districts, and were statistically significant in three. Self-reported increases in CCU were more consistent with observed declines in HIV prevalence in three districts. In all five districts, an estimated 25-64% (32-70%) HIV infections were averted among FSWs (clients) over 5 years. This corresponded to 142-2092 FSW infections averted depending on the district (two-fold to nine-fold more among clients). CONCLUSION: Empirical HIV prevalence trends combined with Bayesian modelling have provided plausible evidence that Avahan has reduced HIV transmission among FSWs and their clients. If current CCU levels are sustained, FSW HIV prevalence could decline to low levels by 2015, with many more infections averted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".