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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".