Global burden of cancer in 2020 attributable to alcohol consumption: a population-based study
Bibliographic record
Abstract
BACKGROUND: Alcohol use is causally linked to multiple cancers. We present global, regional, and national estimates of alcohol-attributable cancer burden in 2020 to inform alcohol policy and cancer control across different settings globally. METHODS: In this population-based study, population attributable fractions (PAFs) calculated using a theoretical minimum-risk exposure of lifetime abstention and 2010 alcohol consumption estimates from the Global Information System on Alcohol and Health (assuming a 10-year latency period between alcohol consumption and cancer diagnosis), combined with corresponding relative risk estimates from systematic literature reviews as part of the WCRF Continuous Update Project, were applied to cancer incidence data from GLOBOCAN 2020 to estimate new cancer cases attributable to alcohol. We also calculated the contribution of moderate (<20 g per day), risky (20-60 g per day), and heavy (>60 g per day) drinking to the total alcohol-attributable cancer burden, as well as the contribution by 10 g per day increment (up to a maximum of 150 g). 95% uncertainty intervals (UIs) were estimated using a Monte Carlo-like approach. FINDINGS: Globally, an estimated 741 300 (95% UI 558 500-951 200), or 4·1% (3·1-5·3), of all new cases of cancer in 2020 were attributable to alcohol consumption. Males accounted for 568 700 (76·7%; 95% UI 422 500-731 100) of total alcohol-attributable cancer cases, and cancers of the oesophagus (189 700 cases [110 900-274 600]), liver (154 700 cases [43 700-281 500]), and breast (98 300 cases [68 200-130 500]) contributed the most cases. PAFs were lowest in northern Africa (0·3% [95% UI 0·1-3·3]) and western Asia (0·7% [0·5-1·2]), and highest in eastern Asia (5·7% [3·6-7·9]) and central and eastern Europe (5·6% [4·6-6·6]). The largest burden of alcohol-attributable cancers was represented by heavy drinking (346 400 [46·7%; 95% UI 227 900-489 400] cases) and risky drinking (291 800 [39·4%; 227 700-333 100] cases), whereas moderate drinking contributed 103 100 (13·9%; 82 600-207 200) cases, and drinking up to 10 g per day contributed 41 300 (35 400-145 800) cases. INTERPRETATION: Our findings highlight the need for effective policy and interventions to increase awareness of cancer risks associated with alcohol use and decrease overall alcohol consumption to prevent the burden of alcohol-attributable cancers. FUNDING: None.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".