Global, regional, and national cancer deaths and disability‐adjusted life‐years (DALYs) attributable to alcohol consumption in 204 countries and territories, 1990‐2019
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption is a risk factor for a number of communicable and non-communicable diseases, including several types of cancer. This article reports the burden of cancers attributable to alcohol consumption by age, sex, location, sociodemographic index (SDI), and cancer type from 1990 to 2019. METHODS: The Comparative Risk Assessment approach was used in the 2019 Global Burden of Disease study to report the burden of cancers attributable to alcohol consumption between 1990 and 2019. RESULTS: In 2019, there were globally an estimated 494.7 thousand cancer deaths (95% uncertainty interval [UI], 439.7 to 554.1) and 13.0 million cancer disability-adjusted life-years (DALYs; 95% UI, 11.6 to 14.5) that were attributable to alcohol consumption. The alcohol-attributable DALYs were much higher in men (10.5 million; 95% UI, 9.2 to 11.8) than women (2.5 million; 95% UI, 2.2 to 2.9). The global age-standardized death and DALY rates of cancers attributable to alcohol decreased by 14.7% (95% UI, 6.4% to 23%) and 18.1% (95% UI, 9.2% to 26.5%), respectively, over the study period. Central Europe had the highest age-standardized death rates that were attributable to alcohol consumption(10.3; 95% UI, 8.7 to12.0). Moreover, there was an overall positive association between SDI and the regional age-standardized DALY rate for alcohol-attributable cancers. CONCLUSIONS: Despite decreases in age-standardized deaths and DALYs, substantial numbers of cancer deaths and DALYs are still attributable to alcohol consumption. Because there is a higher burden in males, the elderly, and developed regions (based on SDI), these groups and regions should be prioritized in any prevention programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".