Socioeconomic position, alcohol use and alcohol-attributable emergency department visits
Bibliographic record
Abstract
BACKGROUND: Differential vulnerability to alcohol contributes to socioeconomic inequities in alcohol-attributable harm. This study aimed to estimate the sex-/gender-specific joint effects of socioeconomic position (SEP) and heavy episodic drinking or volume of alcohol use on 100% alcohol-attributable emergency department (ED) visits. METHODS: We conducted a cohort study among 36 900 men and 39 700 women current and former alcohol consumers aged 15-64 from population-representative Canadian Community Health Surveys (2003-2008) linked to administrative ED visit data through 2017 in Ontario and Alberta. We estimated sex-/gender-specific associations between SEP (both education and income) and heavy episodic drinking (≥5 standard drinks on one occasion, at least monthly) or volume of alcohol use (standard drinks per week) on incident alcohol-attributable ED visits and assessed additive interactions using the Synergy Index (S). RESULTS: Lower levels of education (eg, less than high school vs Bachelor's degree or above: men: adjusted HR (aHR)=3.71, 95% CI 2.47 to 5.58; women: aHR=1.75, 95% CI 1.15 to 2.68) and income (eg, quintile (Q)1 vs Q5, men: aHR=2.07, 95% CI 1.35 to 3.17; women: aHR=1.84, 95% CI 0.91 to 3.71) were associated with increased rates of alcohol-attributable ED visits. Among men and women, superadditive joint effects (ie, greater than the sum of both exposures experienced independently) were observed between low SEP (education and income) and heavy episodic drinking and higher volume of alcohol use on alcohol-attributable ED visits. INTERPRETATION: Our results indicate that individuals with lower SEP experience increased vulnerability to alcohol use and related harms. These findings highlight the urgent need for population-level interventions that reduce both the high burden and socioeconomic inequities in alcohol-attributable harm.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".