Potential impact of pre‐exposure prophylaxis for female sex workers and men who have sex with men in Bangalore, India: a mathematical modelling study
Bibliographic record
Abstract
INTRODUCTION: In Bangalore, new HIV infections of female sex workers and men who have sex with men continue to occur, despite high condom use. Pre-exposure prophylaxis (PrEP) has high anti-HIV efficacy for men who have sex with men. PrEP demonstration projects are underway amongst Indian female sex workers. We estimated the impact and efficiency of prioritizing PrEP to female sex workers and/or men who have sex with men in Bangalore. METHODS: A mathematical model of HIV transmission and treatment for female sex workers, clients, men who have sex with men and low-risk groups was parameterized and fitted to Bangalore data. The proportion of transmission attributable (population attributable fraction) to commercial sex and sex between men was calculated. PrEP impact (infections averted, life-years gained) and efficiency (life-years gained/infections averted per 100 person-years on PrEP) were estimated for different levels of PrEP adherence, coverage and prioritization strategies (female sex workers, high-risk men who have sex with men, both female sex workers and high-risk men who have sex with men, or female sex workers with lower condom use), under current conditions and in a scenario with lower baseline condom use amongst key populations. RESULTS: Population attributable fractions for commercial sex and sex between men have declined over time, and they are predicted to account for 19% of all new infections between 2016 and 2025. PrEP could prevent a substantial proportion of infections amongst female sex workers and men who have sex with men in this setting (23%/27% over 5/10 years, with 60% coverage and 50% adherence), which could avert 2.9%/4.3% of infections over 5/10 years in the whole Bangalore population. Impact and efficiency in the whole population was greater if female sex workers were prioritized. Efficiency increased, but impact decreased, if only female sex workers with lower condom use were given PrEP. Greater impact and efficiency was predicted for the scenario with lower condom use. CONCLUSIONS: PrEP could be beneficial for female sex workers and men who have sex with men in Bangalore, and give some benefits in the general population, especially in similar settings with lower condom use levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".