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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".