P1-81 HIV, HSV-2 and syphilis among married couples in India: patterns of discordance and concordance
Bibliographic record
Abstract
Introduction Differences in sexual networks likely explain the disparity in the scale of HIV epidemics in sub-Saharan Africa and India. HIV and sexually transmitted infection (STI) discordant couple studies provide insights into important aspects of these sexual networks. We wished to quantify the role of male sexual behaviour in HIV transmission in married couples. Methods We analysed patterns of HIV, HSV-2 and syphilis sero-concordance and discordance in married couples from two community surveys in India: the National Family Health Study-3 for HIV-1 (a nationally representative household survey) and the Centre for Global Health Research health check-up for HSV-2 and syphilis. A statistical model was used to estimate the fraction of infections introduced by each of the two partners accounting for higher mortality and separation among HIV concordant couples. Results Only 0.8%, 16.0% and 3.5% of couples were infected with HIV-1, HSV-2, and syphilis, respectively. A large proportion of infected couples were discordant (73.1%, 55.0% and 84.2% for HIV-1, HSV-2, and syphilis, respectively). Among couples with any STI, the male partner introduced the infection the majority of the time (HIV-1: 85%, HSV-2: 62%, syphilis: 75%). Conclusions Male infidelity appears to be the driving force of the HIV/STI epidemic our study population and likely in the Indian population at large. Ensuring safe male client and female sex worker contacts should remain a primary target of the National AIDS Control Program in India.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".