Estimating chronic-disease deaths and hospitalizations due to alcohol use in Canada in 2002. Implications for policy and prevention strategies
Bibliographic record
Abstract
INTRODUCTION: Alcohol consumption is a factor that increases risk of chronic disease. This study estimates various indicators of alcohol-attributable premature chronic-disease morbidity and mortality for Canada in 2002. METHODS: Information on mortality and morbidity was obtained from Statistics Canada and from the Canadian Institute for Health Information database. Data on alcohol use were obtained from the Canadian Addiction Survey and weighted for per capita consumption. Risk information was taken from published literature and combined with alcohol consumption information to calculate age- and sex-specific alcohol-attributable chronic disease morbidity and mortality. RESULTS: In Canada in 2002, there were 1631 chronic disease deaths among adults aged 69 years and younger attributed to alcohol consumption, and these deaths were 2.4% of the deaths in Canada for this age group. The net number of deaths comprised 2577 deaths caused and 947 deaths prevented by alcohol consumption. Moderate drinking was involved in 25% of deaths caused and 85% of deaths prevented by alcohol. There were 42,996 years of life lost prematurely in Canada due to alcohol consumption in 2002, 28,890 for men and 14,106 for women. In Canada in 2002, there were 91,970 net chronic disease hospitalizations attributed to alcohol consumption among individuals aged 69 years and younger. The net numbers were 124,621 hospitalizations caused and 32,651 hospitalizations prevented by alcohol consumption. CONCLUSION: With rising rates of alcohol consumption and extensive high-risk drinking, both chronic and acute damage from alcohol are expected to increase. Attention is needed to 1) create effective policies and interventions; 2) control access to alcohol; 3) reduce high-risk drinking; and 4) provide brief interventions for high-risk drinkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".