Socioeconomic differences in heavy drinking and inequities in alcohol-attributable mortality by sex/gender and age
Bibliographic record
Abstract
In high-income countries, people with low compared to high socioeconomic position (SEP) have less alcohol use yet greater rates of alcohol-attributable mortality. Known as “the alcohol harm paradox”, this phenomenon is not explained by differences in alcohol use. This dissertation aims to better understand the alcohol harm paradox in Canada by examining a set of complex relationships among SEP, heavy episodic drinking (HED), sex/gender, age, and alcohol-attributable mortality. The first study explores age, period, and cohort trends in HED by sex/gender and SEP using 22 years of data from the Canadian Community Health Survey. Higher SEP groups report more HED on average, but this varies across the life course by sex/gender and SEP indicator. People with a bachelor’s degree or above reported comparable or greater HED than their lower education counterparts until middle adulthood, after which their HED became the lowest, particularly among men. This age-related pattern may explain the increased risk of alcohol-attributable harm among lower education groups. The second study examines SEP inequities in alcohol-attributable mortality by sex/gender and age among Canadian Census respondents with 13 years of mortality follow-up. Findings show SEP inequities in both relative and absolute terms, with greater absolute inequities in later adulthood and among men. These findings support the cumulative disadvantage hypothesis for the paradox, which suggests the accumulated impact of stress, worse health, or prolonged exposure to risks like alcohol use contributes to greater mortality for people with lower SEP. The final study uses decomposition methods with linked Canadian Community Health Survey and mortality data to assess how sociodemographic, lifestyle, healthcare access, and cumulative risk factors contribute to socioeconomic inequities in alcohol-attributable mortality. Results suggest inequities are primarily driven by greater exposure to lifestyle and cumulative risk factors among people with low SEP, with a smaller contribution from SEP differences in vulnerability to risk factors. A substantial unexplained component remains, highlighting the need for research into broader structural determinants. Together, these studies provide new insights into the complex relationships between SEP, alcohol use, and mortality in Canada, underscoring the need for public health policy addressing the root causes of socioeconomic inequities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".