Prevalence and determinants of HIV infection in South India: a heterogeneous, rural epidemic
Bibliographic record
Abstract
OBJECTIVES: To assess the prevalence and determinants of HIV infection in the general population in Bagalkot district, a largely rural district in the southern Indian state of Karnataka. METHODS: Approximately 6700 individuals aged 15-49 years were randomly sampled from 10 villages and six towns, from three of Bagalkot's six sub-districts. Each consenting respondent was administered a questionnaire, followed by blood collection and testing for HIV, syphilis, and herpes simplex virus type 2 (HSV-2) on a 25% sub-sample. RESULTS: HIV prevalence was 2.9% overall, 2.4% in urban areas and 3.6% in rural areas [odds ratio (OR), 0.65; 95% confidence interval (CI), 0.45-0.95]. Significant differences in HIV prevalence were seen between the three sub-districts, with prevalences of 1.1, 3.0 and 6.4% (P < 0.05), and HIV prevalence in the 10 villages ranged from 0 to 8.2%. Reported multiple sexual partners, receiving money for sex and a history of medical injections were significantly associated with HIV infection, as were older age, being widowed, divorced, separated or deserted, lower education levels and being a woman of a lower caste. There was a strong association between HSV-2 and HIV infection (OR, 5.2; 95% CI, 2.3-11.5). CONCLUSIONS: The rural nature of this epidemic has important implications for prevention and care programs. The striking differentials observed in HIV prevalence between sub-districts and even villages suggest that risk and vulnerability for HIV are highly heterogeneous. Further research is required to understand the individual and community-level factors behind these differentials, so that preventive interventions can be directed to where they are most needed.
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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 | 0.000 |
| 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".