Abstract 43: Global Burden of Cancer in 2020 Attributable to Alcohol Consumption: A Population-Based Study
Bibliographic record
Abstract
Abstract Purpose: Alcohol use is causally linked to multiple cancer sites. 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, 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: Globally, an estimated 702 900, or 3.9%, of all new cases of cancer in 2020 were attributable to alcohol consumption. Males represented 71.1% of the total alcohol-attributable cancer cases. The cancer sites which contributed the most alcohol-attributable cases were cancers of the esophagus (183 000 cases), liver (142 600 cases), and breast (114 300 cases). PAFs were lowest in Northern Africa and Western Asia (less than 1%) in both sexes, and highest in Eastern Asia (7.7%) and Central and Eastern Europe (6.9%) in men, and in Central and Eastern Europe (3.8%), Western Europe (3.5%) and Australia and New Zealand (3.55%) in women. Risky and heavy drinking contributed most to the burden of alcohol-attributable cancers (42.6% and 42.5%, respectively), and moderate drinking contributed 14.9%. Conclusion: 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 avoid future rises in alcohol-attributable cancer burden in several regions of the world. Citation Format: Harriet Rumgay, Kevin Shield, Hadrien Charvat, Pietro Ferrari, Bundit Sornpaisarn, Isidore Obot, Farhad Islami, Valery Lemmens, Jürgen Rehm, Isabelle Soerjomataram. Global Burden of Cancer in 2020 Attributable to Alcohol Consumption: A Population-Based Study [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 43.
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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 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.002 |
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".