Is HIV Prevalence Declining in Southern India? Evidence From Two Rounds of General Population Surveys in Bagalkot District, Karnataka
Bibliographic record
Abstract
OBJECTIVES: To assess the levels and trends in the prevalence of HIV and sexually transmitted infections in the general population in Bagalkot district using 2 cross-sectional surveys undertaken in 2003 and 2009. METHODS: In both surveys, a target sample of 6600 adult males and females was selected systematically from a sample of 10 rural villages and 20 urban blocks in 3 of the 6 talukas (subdistrict units) in the district. Urine and blood samples were collected from all consenting participants for HIV and sexually transmitted infection testing. Changes in HIV prevalence in age subgroups were determined by logistic regression, taking into account the survey design. RESULTS: HIV prevalence fell not significantly from 3.2% to 2.5% during the 6-year study period. It decreased significantly (P = 0.023) among persons aged 15 to 24 years, from 2.4% in 2003 (95% confidence interval [95% CI]: 1.2-3.7) to 1.3% in 2009 (95% CI: 0.6 -2.0). However, among males aged 35 and above, HIV prevalence increased significantly (P = 0.008), from 3.0% (95% CI: 1.1-4.8) to 4.2% (95% CI: 1.8-6.6), a level similar to that found in the younger age-group in 2003. CONCLUSIONS: We observed a significant decline in HIV prevalence among people aged 15 to 24 years, where HIV prevalence more closely reflects incidence. The increase in HIV prevalence among older males may have been because of the aging of a cohort of men among whom HIV prevalence was highest in the 2003 survey. It may also have in part reflected increased survival, as a result of the scaling up of antiretroviral treatment programs throughout the district and state.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".