Trends and correlates of cannabis use in pregnancy: a population-based study in Ontario, Canada from 2012 to 2017
Bibliographic record
Abstract
OBJECTIVE: Forthcoming legislative changes will legalize and make cannabis widely available in Canada. We conducted an analysis of Ontario's birth registry to determine recent trends and correlates of cannabis use in pregnancy. METHODS: We conducted a population-based retrospective cohort study assembled from the Better Outcomes Registry & Network (BORN) Ontario database, covering live births and stillbirths in Ontario between April 2012 and December 2017. Trends in self-reported cannabis use in pregnancy were analyzed according to maternal age and area-level socio-economic status (SES) using log binomial regression analysis. RESULTS: A total of 10,731 women reported cannabis use in pregnancy. Prevalence increased from 1.2% in 2012 to 1.8% in 2017 (p-trend, < 0.001), equivalent to a relative increase of 61% (relative risk [RR] 1.61, 95% confidence interval [CI] 1.51 to 1.72). The crude prevalence of cannabis use in pregnancy among women aged 15 to 24 years and in the lowest two area-level income quintiles was 6.7%, compared to 0.3% among women aged 35 years and over in the highest three income quintiles (RR 24.59, 95% CI 21.98 to 27.52). A majority (52.0%) of cannabis users were aged 15-24 years and 54.7% of users were in the lowest two income quintiles. CONCLUSION: Cannabis use in pregnancy has increased since 2012 in Ontario and was reported in about 2% of pregnancies in 2017. Increases were predominately among women of younger ages and those of lower SES, and these groups account for half of users. Promoting cannabis cessation in pregnancy could lead to improved perinatal and later childhood outcomes and reduce health inequalities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".