Alcohol consumption and mortality among Canadian drinkers: A national population‐based survival analysis (2000–2017)
Bibliographic record
Abstract
INTRODUCTION: Alcohol contributes significantly to global disease burden. Over 50 countries, including Canada, have established low-risk drinking guidelines to reduce alcohol-related harm. Canada's Guidance on Alcohol and Health (CGAH) was released in 2023. This study examines the relationship between weekly alcohol consumption, CGAH risk zones and mortality patterns among Canadian drinkers aged 15 and older. METHODS: A retrospective cohort study was conducted using data from three cycles of the national, population-based Canadian Community Health Survey (2000-2006) linked to mortality data up to 2017. The sample included 145,760 respondents aged 15 and older who reported alcohol consumption in the past week. Average weekly alcohol consumption was assessed using the Timeline Followback method (i.e., 7-day recall). Outcomes included all-cause mortality, alcohol-related mortality and mortality from conditions with an alcohol-attributable fraction ≥15%. RESULTS: Alcohol consumption was significantly positively associated with increased risks of all-cause (hazard ratio = 1.01, p < 0.001), alcohol-related (hazard ratio = 1.01, p = 0.001) and alcohol-attributable fraction-related mortality (hazard ratio = 1.02, p < 0.001). Each additional standard drink per week raised mortality risk, with women experiencing a greater increase in risk compared to men. DISCUSSION AND CONCLUSION: The findings support the CGAH recommendations, highlighting the importance of lower alcohol consumption limits to reduce health risks. Public health efforts should focus on increasing awareness and adherence to these guidelines, particularly among women who face greater mortality risks at higher consumption levels. Ongoing monitoring of alcohol consumption is critical for tracking and evaluating low-risk drinking guideline effectiveness in reducing alcohol-related 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".