Socioeconomic inequities in alcohol-attributable mortality by sex/gender and age in Canada: a 13-year population-representative cohort study
Bibliographic record
Abstract
Individuals with low socioeconomic position (SEP) experience greater rates of alcohol-attributable mortality, contributing to health inequities in mortality and life expectancy. We examined the association between SEP and alcohol-attributable mortality by sex/gender and age in Canada. Census records from the 2006 Canadian Census Health and Environment Cohort (ages 12+; n = 5 038 790) were linked to mortality data from 2006 to 2019. SEP was measured by educational attainment and household income. Poisson and Fine and Gray subdistribution hazard models estimated rate differences (RDs) per 100 000 person-years and hazard ratios (HRs). Both educational attainment and household income were inversely associated with alcohol-attributable mortality. Absolute SEP inequities were greater among men than women, with an RD of 30.81 (95% CI, 28.04-33.57) for men and 9.86 (95% CI, 8.49-11.22) for women when comparing the lowest to the highest income quintile. Age-stratified analyses showed absolute SEP inequities were most pronounced in middle and older adulthood, above age 30 for women and age 50 for men, with smaller RDs in ages 12 to 29. Relative SEP inequities were similar in women and men, with greater HRs at younger ages. Public health policies addressing social determinants and population-level alcohol policies should consider patterning of SEP inequities by sex/gender and age group.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".