Trends in cannabis-attributable hospitalizations and emergency department visits: data from the Canadian Substance Use Costs and Harms Study (2007–2020)
Bibliographic record
Abstract
INTRODUCTION: The prevalence of cannabis use continues to increase among certain populations in Canada. This study focussed on the increase in cannabis-attributable hospitalizations and emergency department (ED) visits from 2007 to 2020. METHODS: To estimate the counts of hospitalizations and ED visits attributable to cannabis use, we acquired record-level hospital discharge data with ICD-10 diagnostic information for all fiscal years 2006/07 to 2020/21. Diagnostic information was used to associate each record to a health condition category for eight substances, including cannabis. The prevalence of cannabis use was estimated for each province or territory, calendar year, sex and age using national survey information. These estimates were used to adjust relative risk estimates derived from cannabis literature to calculate cannabisattributable fractions, which were in turn used to estimate the proportion of hospitalizations and ED visits that were attributable to cannabis use. RESULTS: Between 2007 and 2020, the overall rate of cannabis-attributable inpatient hospitalizations increased by 120%, from 6.4 in 2007 to 14.0 per 100 000 in 2020. Cannabis-attributable ED visits increased by 113%, from 52.1 per 100 000 in 2007 to 111.0 per 100 000 in 2019, and then decreased by 12% in 2020. This study found that the increases in hospitalizations and ED visits were partly attributed to neuropsychiatric conditions, particularly hospitalizations due to psychotic disorders and ED visits due to acute intoxication among children and youth. CONCLUSION: Ongoing monitoring of cannabis-attributable harms is necessary to understand the harms related to use and the factors that influence the ways in which people use cannabis and seek care. Further research may distinguish the early effects of legalization trends from the early pandemic period data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".