European burden of cancer in 2020 attributable to alcohol use
Bibliographic record
Abstract
Abstract Background Alcohol use can increase the risk of at least seven different cancer types. We present regional and national estimates of alcohol-attributable cancer burden in 2020 to inform alcohol policy and cancer control in Europe. Methods In this population-based study, we calculated population attributable fractions (PAFs) using relative risk estimates and alcohol use prevalence by age, sex, and country. Assuming a 10-year latency period between alcohol consumption and cancer occurrence, we used alcohol consumption prevalence from 2010 and GLOBOCAN 2020 data to estimate new cancer cases attributable to alcohol consumption. We also calculated the contribution of moderate (<20 g alcohol per day), risky (20 to 60 g per day), and heavy (>60 g per day) drinking to the total alcohol-attributable cancer burden. Results Within Europe, an estimated 181,000, or 4%, of all new cases of cancer in 2020 were attributable to alcohol consumption. Males represented two thirds (68%) of the total alcohol-attributable cancer cases in Europe. The cancer sites which contributed the most alcohol-attributable cases were cancers of the colorectum (59,000 cases), breast (38,500 cases), and oral cavity (22,000 cases). Among women in the European regions, the burden of alcohol-attributable cancers was highest in Western Europe and Northern Europe; among men, the burden was highest in Central and Eastern Europe. Heavy drinking contributed most to the burden of alcohol-attributable cancers in Europe (52% of alcohol-attributable cases), and risky and moderate drinking contributed 37% and 11%, respectively. Conclusions Our findings highlight the need for effective policies to increase awareness of the link between alcohol use and cancer and decrease overall alcohol consumption to reduce this preventable burden of cancer in Europe. Key messages An estimated 181,000 cancers in Europe in 2020 were attributable to alcohol use. Effective policies are needed to increase awareness of the link between alcohol and cancer and decrease alcohol use in Europe.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".