Do socioeconomic status, race and ethnicity modify the relationship between alcohol use and unintentional injury mortality?
Bibliographic record
Abstract
BACKGROUND: There is a knowledge gap regarding the potential roles that socioeconomic status (SES), race and ethnicity may play in the associations between alcohol use and injury risk. This study aimed to examine these factors as potential effect modifiers in the relationship between heavy episodic drinking (HED) and unintentional injury mortality. METHODS: We used mortality-linked data from the 1997-2018 US National Health Interview Survey. We performed survey-weighted Cox proportional hazards models to evaluate the effect modification of education, income, race and ethnicity on the relationship between the frequency of HED and motor vehicle and other unintentional injuries mortality. RESULTS: 559 442 participants were included, with 772 motor vehicle fatalities and 2003 other unintentional injury deaths. Our cohort study found no significant interaction effect between SES, race and ethnicity, and HED on motor vehicle fatalities. For other unintentional injury mortality, we identified a significant interaction effect between low education and HED once or more per month (HR 2.75, 95% CI 1.38 to 5.49). Similarly, we found a significant interaction effect between low income and HED once or more per month (HR 1.84, 95% CI 1.02 to 3.34). Finally, both Black and Hispanic participants exhibited a higher risk of other fatal unintentional injuries at varying frequencies of HED compared with White participants. CONCLUSIONS: Our results emphasise the importance of considering SES, race and ethnicity in understanding the complex interplay between alcohol consumption and unintentional injury mortality. Understanding subgroup-specific dynamics is crucial for formulating targeted interventions to address disparities and enhance public health outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".