Cannabis use disorder and the future risk of cardiovascular disease in parous women: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Cannabis use is increasing in women of reproductive age, but whether cannabis use disorders increase the long-term risk of cardiovascular disease in this population is not known. Cannabis may cause tachycardia, hypertension, cerebral vasoconstriction, and other adverse cardiovascular effects and has been associated with acute myocardial infarction and stroke. Data on the long-term effects of cannabis on the cardiovascular system are more limited. We assessed the relationship between cannabis use disorders early in life and the future risk of cardiovascular disease in women. METHODS: We analyzed a longitudinal cohort of 1,247,035 pregnant women in Quebec, Canada, between 1989 and 2019. The main exposure was current or past history of cannabis use disorders at cohort entry. The main outcome measure included future hospital admission for any cardiovascular disorder during 18,998,986 person years of follow-up. We used Cox proportional hazards regression models adjusted for patient characteristics to compute hazard ratios (HR) and 95% confidence intervals (CI) for the association of cannabis use disorder with the later risk of cardiovascular hospitalization. RESULTS: Women with cannabis use disorders had a higher incidence of cardiovascular hospitalization than unexposed women (58.4 vs. 33.6 per 10,000 person years). Cannabis use disorder was associated with 1.48 times the risk of cardiovascular hospitalization (95% CI 1.27-1.72), compared with no cannabis use disorder. The association was greater for cannabis with concomitant use of other substances (HR 1.84, 95% CI 1.53-2.21) than for cannabis alone (HR 1.30, 95% CI 0.99-1.72). Cannabis use disorder was strongly associated with hemorrhagic stroke, even with adjustment for other substance use (HR 2.08, CI 1.07-4.05). CONCLUSIONS: Cannabis use disorders may increase the long-term risk of cardiovascular disease in women, particularly hemorrhagic stroke. However, some of the excess risk may be due to concomitant use of other substances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".