Alcohol‐attributable mortality and potential years of life lost in Canada 2001: implications for prevention and policy
Bibliographic record
Abstract
BACKGROUND: Alcohol is one of the most important risk factors for burden of disease. OBJECTIVE: To estimate the number of deaths and the years of life lost attributable to alcohol for Canada 2001 using different ways to measure alcohol exposure. METHODS: Distribution of exposure was taken from a major national survey of Canada, the Canadian Addiction Survey, and corrected for per capita consumption from production and sales. For chronic disease, risk relations were taken from the published literature and combined with exposure to calculate age- and sex-specific alcohol-attributable fractions (AAFs). For injury, AAFs were taken directly from available statistics. Information on mortality, with cause of death coded according to the International Classification of Diseases version 10 (ICD-10) was obtained from Statistics Canada. RESULTS: For Canada in 2001, 4,010 of all deaths in the group below 70 years of age were attributable to alcohol, 3,132 in men and 877 in women. This constituted 6.0% of all deaths in Canada in this age group, 7.6% for men, and 3.5% for women. The 4,010 deaths are a net figure, already taking into account the deaths prevented by moderate consumption of alcohol. Main causes of alcohol-attributable death were unintentional injuries, malignant neoplasms and digestive diseases. Ischaemic heart disease (IHD) was the biggest cause of death prevented by alcohol, with 78.7% of all alcohol-attributable prevented deaths in the age groups of 70 years and above. A total of 144,143 years of life were lost prematurely in Canada in that year, 113,079 years in men and 31,063 years in women. DISCUSSION: Regardless of the assumptions made, alcohol is a major contributor to mortality in Canada. The impact of alcohol on social life is not confined to mortality, as other studies indicated that alcohol is linked even more strongly to disability and social harm. Alcohol-attributable harm could be substantially reduced, however, if known effective policies were introduced.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".