Crude estimates of cannabis-attributable mortality and morbidity in Canada–implications for public health focused intervention priorities
Bibliographic record
Abstract
BACKGROUND: Cannabis is the most commonly used drug in Canada; while its use is currently controlled by criminal prohibition, debates about potential control reforms are intensifying. There is substantive evidence about cannabis-related risks to health in various key outcome domains; however, little is known about the actual extent of these harms specifically in Canada. METHODS: Based on epidemiological data (e.g. prevalence of relevant cannabis use rates and relevant risk behaviors; risk ratios; and annual numbers of morbidity/mortality cases in relevant domains), and applying the methodology of comparative risk assessment, we estimated attributable fractions for cannabis-related morbidity and mortality, specifically for: (i) motor-vehicle accidents (MVAs); (ii) use disorders; (iii) mental health (psychosis) and (iv) lung cancer. RESULTS: MVAs and lung cancer are the only domains where cannabis-attributable mortality is estimated to occur. While cannabis use results in morbidity in all domains, MVAs and use disorders by far outweigh the other domains in the number of cases; the popularly debated mental health consequences (e.g., psychosis) translate into relatively small case numbers. CONCLUSIONS: The present crude estimates should guide and help prioritize public health-oriented interventions for the cannabis-related health burden in the population in Canada; formal burden of disease calculations should be conducted.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".