Smoking-attributable mortality in Bangladesh: proportional mortality study
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To directly estimate how much smoking contributes to cause-specific mortality in Bangladesh. METHODS: A case-control study was conducted with surveillance data from Matlab, a rural subdistrict. Cases (n = 2213) and controls (n = 261) were men aged 25 to 69 years who had died between 2003 and 2010 from smoking-related and non-smoking-related causes, respectively. Cause-specific odds ratios (ORs) were calculated for "ever-smokers" versus "never-smokers", with adjustment for education, tobacco chewing status and age. Smoking-attributable deaths among cases, national attributable fractions and cumulative probability of surviving from 25 to 69 years of age among ever-smokers and never-smokers were also calculated. FINDINGS: The fraction of ever-smokers was about 84% among cases and 73% among controls (OR: 1.7; 99% confidence interval, CI: 1.1-2.5). ORs were highest for cancers and lower for respiratory, vascular and other diseases. A dose-response relationship was noted between age at smoking initiation and daily number of cigarettes or bidis smoked and the risk of death. Among 25-year-old Bangladeshi men, 32% of ever-smokers will die before reaching 70 years of age, compared with 19% of never-smokers. In 2010, about 25% of all deaths observed in Bangladeshi men aged 25 to 69 years (i.e. 42,000 deaths) were attributable to smoking. CONCLUSION: Smoking causes about 25% of all deaths in Bangladeshi men aged 25 to 69 years and an average loss of seven years of life per smoker. Without a substantial increase in smoking cessation rates, which are low among Bangladeshi men, smoking-attributable deaths in Bangladesh are likely to increase.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it