Reducing Alcohol Use to Prevent Cancer Deaths: Estimated Effects Among U.S. Adults
Bibliographic record
Abstract
INTRODUCTION: The Dietary Guidelines for Americans, 2020-2025 recommends non-drinking or no more than 2 drinks for men or 1 drink for women in a day. However, even at lower levels, alcohol use increases the risk for certain cancers. This study estimated mean annual alcohol-attributable cancer deaths and the number of cancer deaths that could potentially be prevented if all U.S. adults who drank in excess of the Dietary Guidelines had instead consumed alcohol to correspond with typical consumption of those who drink within the recommended limits. METHODS: Among U.S. residents aged ≥20 years, mean annual alcohol-attributable cancer deaths during 2020-2021 that could have been prevented with hypothetical reductions in alcohol use were estimated. Mean daily alcohol consumption prevalence estimates from the 2020-2021 Behavioral Risk Factor Surveillance System, adjusted to per capita alcohol sales to address underreporting of drinking, were applied to relative risks to calculate population-attributable fractions for cancers that can occur from drinking alcohol. Analyses were conducted during February-April 2023. RESULTS: In the U.S., an estimated 20,216 cancer deaths were alcohol-attributable/year during 2020-2021 (men: 14,562 [72.0%]; women: 5,654 [28.0%]). Approximately 16,800 deaths (83% of alcohol-attributable cancer deaths, 2.8% of all cancer deaths) could have been prevented/year if adults who drank alcohol in excess of the Dietary Guidelines had instead reduced their consumption to ≤2 drinks/day for men or ≤1 drink/day for women. Approximately 650 additional deaths could have been prevented annually if men consumed 1 drink/day, instead of 2. CONCLUSIONS: Implementing evidence-based alcohol policies (e.g., increasing alcohol taxes, regulating alcohol outlet density) to decrease drinking could reduce alcohol-attributable cancers, complementing clinical interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".