Variations in risks from smoking between high-income, middle-income, and low-income countries: an analysis of data from 179 000 participants from 63 countries
Bibliographic record
Abstract
BACKGROUND: Separate studies suggest that the risks from smoking might vary between high-income (HICs), middle-income (MICs), and low-income (LICs) countries, but this has not yet been systematically examined within a single study using standardised approaches. We examined the variations in risks from smoking across different country income groups and some of their potential reasons. METHODS: We analysed data from 134 909 participants from 21 countries followed up for a median of 11·3 years in the Prospective Urban Rural Epidemiology (PURE) cohort study; 9711 participants with myocardial infarction and 11 362 controls from 52 countries in the INTERHEART case-control study; and 11 580 participants with stroke and 11 331 controls from 32 countries in the INTERSTROKE case-control study. In PURE, all-cause mortality, major cardiovascular disease, cancers, respiratory diseases, and their composite were the primary outcomes for this analysis. Biochemical verification of urinary total nicotine equivalent was done in a substudy of 1000 participants in PURE. FINDINGS: In PURE, the adjusted hazard ratio (HR) for the composite outcome in current smokers (vs never smokers) was higher in HICs (HR 1·87, 95% CI 1·65-2·12) than in MICs (1·41, 1·34-1·49) and LICs (1·35, 1·25-1·46; interaction p<0·0001). Similar patterns were observed for each component of the composite outcome in PURE, myocardial infarction in INTERHEART, and stroke in INTERSTROKE. The median levels of tar, nicotine, and carbon monoxide displayed on the cigarette packs from PURE HICs were higher than those on the packs from MICs. In PURE, the proportion of never smokers reporting high second-hand smoke exposure (≥1 times/day) was 6·3% in HICs, 23·2% in MICs, and 14·0% in LICs. The adjusted geometric mean total nicotine equivalent was higher among current smokers in HICs (47·2 μM) than in MICs (31·1 μM) and LICs (25·2 μM; ANCOVA p<0·0001). By contrast, it was higher among never smokers in LICs (18·8 μM) and MICs (11·3 μM) than in HICs (5·0 μM; ANCOVA p=0·0001). INTERPRETATION: The variations in risks from smoking between country income groups are probably related to the higher exposure of tobacco-derived toxicants among smokers in HICs and higher rates of high second-hand smoke exposure among never smokers in MICs and LICs. FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".