Save Lives by Counting the Dead: Counting the World's Deaths and Finding out Why People Die Is One of the Most Important Goals to Improve Public Health. Professor Prabhat Jha Tells Why He Is Obsessed with Death Numbers
Bibliographic record
Abstract
Q: You recently led the Million Death Study in India--the first nationally representative study of the effect of tobacco-smoking on health that covered more than one million households and six million people. Why do you think it has taken so long to do this when studies were done in Europe and the United States of America many years ago? A: It was assumed that smoking risks were widely known because of the studies from Europe and the USA. It had also been assumed that the risks of smoking in India were smaller than in other industrialized nations, as most Indians smoke bidis, a locally manufactured small cigarette that contains only one quarter the tobacco of other [commercially manufactured] cigarettes. Also, the age of smoking onset in India is older than in the USA and the daily per smoker is less. But our study showed, surprisingly, that smoking is as hazardous in India as in the west. Compared to non-smokers, men who smoke bidis lose about six years of life, women who smoke bidis lose about eight years, and men who smoke cigarettes lose a full 10 years. Sir Richard Peto, who has documented tobacco hazards better than anyone, was also surprised. Smoking appears to turn sub-clinical infection with the tuberculosis (TB) bacillus into active disease, so smoking may well be contributing to the spread of TB in India, and probably elsewhere. Importantly, the leading cause of smoking-related deaths in rural India was TB, and perhaps 40% of all TB deaths in Indian middle-aged males are due to smoking. Q: How can tobacco control move forward in south-eastern Asia? A: Cessation and taxes. Any meaningful reduction in deaths over the next few decades needs to focus on cessation by the world's 1.1 billion smokers, well over two-thirds of whom live in south and east Asia. Preventing children from starting is important, but that will not reduce deaths until after 2050. The key strategy for cessation is higher taxes. We have conservatively estimated that a tripling of the excise tax worldwide would avoid over 115 million premature deaths by getting current smokers to quit. Unfortunately, bidis are largely untaxed in India. But I am hopeful that taxes will rise, in part because the huge health toll of tobacco is being taken more seriously, and also because of attention to taxation by the World Health Organization and the Bill & Melinda Gates Foundation, among others. Some economists argue that higher taxes on tobacco are undesirable because the tax burdens fall more heavily on the poor. But we have shown that, in several countries, tobacco smoking alone causes at least half of the inequalities in adult male mortality associated with lower socioeconomic status. And because the poor quit more in response to price hikes than the rich, it's not necessarily the case that increased tobacco taxes hurt the poor financially. Q: One of the keys to this tobacco study was the sample registration system established by the Registrar-General of India. How important is routinely collected vital registration data for analytic epidemiology? A: Epidemiologists are number crunchers like accountants, minus the personality [he jokes]. For us, routine death numbers are crucial. Vital statistics have helped identify major trends in fertility, child survival and child mortality. They have revealed good news, such as the large declines in tuberculosis and under-five mortality in the early twentieth century. They have also sounded alarms, identifying, for example, the dramatic increases in lung cancer deaths in British and American men around the time of the Second World War. Mortality data also revealed the alarming increase in immune-related deaths among young men in San Francisco in the early 1980s, a trend that marked the beginning of the HIV-1 epidemic in the United States. Rigorous monitoring of the results of health interventions is essential and mortality data are a good basis for monitoring. …
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".