Cannabis use disorder and adverse cardiovascular outcomes: A population‐based retrospective cohort analysis of adults from Alberta, Canada
Bibliographic record
Abstract
AIM: To measure the association between cannabis use disorder (CUD) and adverse cardiovascular disease (CVD) outcomes. DESIGN AND SETTING: We conducted a matched, population-based retrospective cohort study involving five linked administrative health databases from Alberta, Canada. PARTICIPANTS: We identified participants with CUD diagnosis codes and matched them to participants without CUD codes by gender, year of birth and time of presentation to the health system. We included 29 764 pairs (n = 59 528 individuals in total). MEASUREMENTS: CVD events were defined by at least one incident diagnostic code within the study period (1 January 2012-31 December 2019). Covariates included comorbidity, socio-economic status, prescription medication use and health service use. Using mortality-censored Poisson regression models, we computed survival analyses for time to incident CVD stratified by CUD status. In addition, we calculated crude and stratified risk ratios (RRs) across various covariates using the Mantel-Haenszel technique. FINDINGS: The overall prevalence of documented CUD was 0.8%. Approximately 2.4% and 1.5% of participants in the CUD and unexposed groups experienced an incident adverse CVD event (RR = 1.57; 95% confidence interval = 1.40-1.77). CUD was significantly associated with reduced time to incident CVD event. Individuals who appeared to have greater RRs for incident CVD were those without mental health comorbidity, who had not used health-care services in the previous 6 months, who were not on prescription medications and who did not have comorbid conditions. CONCLUSIONS: Canadian adults with cannabis use disorder appear to have an approximately 60% higher risk of experiencing incident adverse cardiovascular disease events than those without cannabis use disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".