Racial and Ethnic Disparities in Alcohol Consumption and Mortality in the U.S.
Bibliographic record
Abstract
INTRODUCTION: Although there are racial/ethnic differences in alcohol use, there is little information about differences in mortality from all alcohol-related conditions or by cause of death. Furthermore, little is known about the degree to which racial/ethnic differences in mortality persist after adjusting for ethanol consumption. The purpose of this cross-sectional study was to comprehensively assess racial/ethnic differences in alcohol-attributable deaths and reduced life expectancy. METHODS: Alcohol prevalence data were from the Behavioral Risk Factor Surveillance System, and mortality data were from the National Vital Statistics System. Alcohol-attributable fractions and the Alcohol-Related Disease Impact application were used to assess alcohol-attributable deaths from 58 partially or wholly alcohol-attributable conditions in the U.S. during 2020-2021 (analyzed in 2024). RESULTS: White persons (60.9% of the population) accounted for 70.8% of all alcohol-attributable deaths and had the second-highest death rate (63.8 per 100,000) among racial/ethnic groups. American Indian/Alaska Native persons had the highest alcohol-attributable death rate (145.3) and the lowest average age of death (48.1 years). White and Asian, Native Hawaiian, or Pacific Islander persons tended to die of alcohol-attributable conditions from chronic diseases at relatively older ages, whereas people in other racial/ethnic groups tended to die at younger ages from alcohol-attributable acute causes of death. After adjusting for differences in per capita alcohol consumption, there remained fourfold differences in alcohol-attributable deaths by race/ethnicity. CONCLUSIONS: Large differences in alcohol-attributable deaths across racial/ethnic groups were only partially explained by racial/ethnic differences in alcohol consumption. Implementing effective alcohol policies and addressing social determinants of health could reduce alcohol-related harms across race/ethnicities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".