HIV mortality and infection in India: estimates from nationally representative mortality survey of 1.1 million homes
Bibliographic record
Abstract
OBJECTIVE: To determine the rates of death and infection from HIV in India. DESIGN: Nationally representative survey of deaths. SETTING: 1.1 million homes in India. Population 123,000 deaths at all ages from 2001 to 2003. MAIN OUTCOME MEASURES: HIV mortality and infection. RESULTS: HIV accounted for 8.1% (99% confidence interval 5.0% to 11.2%) of all deaths among adults aged 25-34 years. In this age group, about 40% of deaths from HIV were due to AIDS, 26% were due to tuberculosis, and the rest were attributable to other causes. Nationally, HIV infection accounted for about 100,000 (59,000 to 140,000) deaths or 3.2% (1.9% to 4.6%) of all deaths among people aged 15-59 years. Deaths from HIV were concentrated in the states and districts with higher HIV prevalence and in men. The mortality results imply an HIV prevalence at age 15-49 years of 0.26% (0.13% to 0.39%) in 2004, comparable to results from a 2005/6 household survey that tested for HIV (0.28%). Collectively, these data suggest that India had about 1.4-1.6 million HIV infected adults aged 15-49 years in 2004-6, about 40% lower than the official estimate of 2.3 million for 2006. All cause mortality increased in men aged 25-34 years between 1997 and 2002 in the states with higher HIV prevalence but declined after that. HIV prevalence in young pregnant women, a proxy measure of incidence in the general population, fell between 2000 and 2007. Thus, HIV mortality and prevalence may have fallen further since our study. CONCLUSION: HIV attributable death and infection in India is substantial, although it is lower than previously estimated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".