A Systematic Review and Meta-Analysis of Risk Factors for Sexual Transmission of HIV in India
Bibliographic record
Abstract
BACKGROUND: Approximately 2.4 million people are living with HIV in India. This large disease burden, and potential for epidemic spread in some areas, demands a full understanding of transmission in that country. We wished to quantify the effects of key sexual risk factors for HIV infection for each gender and among high- and low-HIV risk populations in India. METHODOLOGY: We conducted a systematic review of sexual risk factors for HIV infection from 35 published studies. Risk factors analyzed were: male circumcision/religion, Herpes Simplex Virus 2, syphilis, gonorrhoea, genital ulcer, multiple sexual partners and commercial sex. Studies were included if they met predetermined criteria. Data were extracted and checked by two researchers and random-effects meta analysis of effects was conducted. Heterogeneity in effect estimates was examined by I(2) statistic. Publication bias was tested by Begg's test and funnel plots. Meta regression was used to assess effect modification by various study attributes. RESULTS: All risk factors were significantly associated with HIV status. The factor most strongly associated with HIV for both sexes was HSV-2 infection (OR(men): 5.87; 95%CI: 2.46-14.03; OR(women): 6.44; 95%CI: 3.22-12.86). The effect of multiple sexual partners was similar among men (OR = 2.46; 95%CI: 1.91-3.17,) and women (OR = 2.02; 95%CI: 1.43-2.87) and when further stratified by HIV-risk group. The association between HSV-2 and HIV prevalence was consistently stronger than other STIs or self-reported genital ulcer. If the strong associations between HSV-2 and HIV were interpreted causally, these results implied that approximately half of the HIV infections observed in our study population were attributable to HSV-2 infection. CONCLUSIONS: The risk factors examined in our analysis should remain targets of HIV prevention programs. Our results confirm that sexual risk factors for HIV infection continue to be an important part of Indian HIV epidemic 26 years after it began.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".