The burden of disease attributable to cannabis use in Canada in 2012
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cannabis use is associated with several adverse health effects. However, little is known about the cannabis-attributable burden of disease. This study quantified the age-, sex- and adverse health effect-specific cannabis-attributable (1) mortality, (2) years of life lost due to premature mortality (YLLs), (3) years of life lost due to disability (YLDs) and (4) disability-adjusted life years (DALYs) in Canada in 2012. DESIGN: Epidemiological modeling. SETTING: Canada. PARTICIPANTS: Canadians aged ≥ 15 years in 2012. MEASUREMENTS: Using comparative risk assessment methodology, cannabis-attributable fractions were computed using Canadian exposure data and risk relations from large studies or meta-analyses. Outcome data were obtained from Canadian databases and the World Health Organization. The 95% confidence intervals (CIs) were computed using Monte Carlo methodology. FINDINGS: Cannabis use was estimated to have caused 287 deaths (95% CI = 108, 609), 10,533 YLLs (95% CI = 4760, 20,833), 55,813 YLDs (95% CI = 38,175, 74,094) and 66,346 DALYs (95% CI = 47,785, 87,207), based on causal impacts on cannabis use disorders, schizophrenia, lung cancer and road traffic injuries. Cannabis-attributable burden of disease was highest among young people, and males accounted for twice the burden than females. Cannabis use disorders were the most important single cause of the cannabis-attributable burden of disease. CONCLUSIONS: The cannabis-attributable burden of disease in Canada in 2012 included 55,813 years of life lost due to disability, caused mainly by cannabis use disorders. Although the cannabis-attributable burden of disease was substantial, it was much lower compared with other commonly used legal and illegal substances. Moreover, the evidence base for cannabis-attributable harms was smaller.
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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".