The impact of cigarette smoking on life expectancy between 1980 and 2010: a global perspective
Bibliographic record
Abstract
INTRODUCTION: Tobacco smoking is among the leading causes of preventable mortality worldwide. We assessed the impact of smoking on life expectancy worldwide between 1980 and 2010. METHODS: We retrieved cause-specific mortality data from the WHO Mortality Database by sex, year and age for 63 countries with high or moderate quality data (1980-2010). Using the time of the peak of the smoking epidemic by country, relative risks from the three waves of the Cancer Prevention Study were applied to calculate the smoking impact ratio and population attributable fraction. Finally, we estimated the potential gain in life expectancy at age 40 if smoking-related deaths in middle age (40-79 years) were eliminated. RESULTS: Currently, tobacco smoking is related to approximately 20% of total adult mortality in the countries in this study (24% in men and 12% in women). If smoking-related deaths were eliminated, adult life expectancy would increase on average by 2.4 years in men (0.1 in Uzbekistan to 4.8 years in Hungary) and 1 year in women (0.1 in Kyrgyzstan to 2.9 years in the USA). The proportion of smoking-related mortality among men has declined in most countries, but has increased in the most populous country in the world, that is, China from 4.6% to 7.3%. Increases in the impact of tobacco on life expectancy were observed among women in high-income countries. CONCLUSIONS: Recent trends indicate a substantial rise in the population-level impact of tobacco smoking on life expectancy in women and in middle-income countries. High-quality local data are needed in most low-income countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".