Reductions in smoking due to ratification of the Framework Convention for Tobacco Control in 171 countries
Bibliographic record
Abstract
Smoking globally kills over half of long-term smokers and causes about 7 million annual deaths. The World Health Organization Framework Convention for Tobacco Control (FCTC) is the main global policy strategy to combat smoking, but its effectiveness is uncertain. Our interrupted time series analyses compared before- and after-FCTC trends in the numbers and prevalence of smokers below the age of 25 years (when smoking initiation occurs and during which response to interventions is greatest) and on cessation at 45-59 years (when quitting probably occurs) in 170 countries, excluding China. Contrasting the 10 years after FCTC ratification with the income-specific before-FCTC trends, we observed cumulative decreases of 15.5% (95% confidence interval = -33.2 to -0.7) for the numbers of current smokers and decreases of -7.5% (95% CI = -10.6 to -4.5) for the prevalence of smoking below age 25 years. The quit ratio (comparing the numbers of former and ever smokers) at 45-59 years increased by 1.8% (1.2 to 2.3) 10 years after FCTC ratification. Countries raising taxes by at least 10 percentage points concurrent with ratification observed steeper decreases in all three outcomes than countries that did not. Over a decade across 170 countries, the FCTC was associated with 24 million fewer young smokers and 2 million more quitters.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".