On HIV Prevalence and AIDS Deaths in India
Bibliographic record
Abstract
The National AIDS Control Organization (NACO) of India had estimated, before this year, that there were 5.134 million HIV positive people in India at the end of 2004 and that they were increasing at the rate of more than a quarter of a million people every year. In a recent publication, we estimated that, if the number of reported AIDS cases in India are only 50% efficient, i.e. if the number of actual AIDS cases in India is no more than twice the reported number, then the number of HIV positive people in India should have been no more than 2.5 million at the end of 2004. Many other people in the epidemiology community have the same point of view. Now, the government of India is also of the same view and “The latest data released by the government shows that the country has around 2 to 3 million people with HIV, much lower than last year's figure of 5.7 million”. However, our assumption that the actual number of AIDS cases in India is only about twice the number reported, has been questioned, and it has been suggested that the Indian system of AIDS reporting is woefully inaccurate and the actual number of AIDS cases there could be three, four or even five times the reported number. In this paper, we consider this suggestion and show that, even if the actual number of AIDS cases was three, four, or even five, times the reported number, the number of HIV positive people in India, at the end of 2004, should still be no more than 2.5 million. This is because our previous estimate was an over estimate and had room to accommodate considerably more number of AIDS cases. We also estimate the number of AIDS deaths in India and show that it should be considerably less than those estimated by the World Health Organisation (WHO).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".