Association Between Prenatal Cannabis Exposure and Child Health Care Use: A Retrospective Cohort Study in Ontario, Canada
Bibliographic record
Abstract
Objective: Cannabis use among expectant mothers has increased steadily over the past two decades. We compared the long-term health services use of offspring prenatally exposed to cannabis to that of matched, unexposed offspring. Study design: We conducted a retrospective cohort study using linked perinatal and health administrative databases of all live, singleton births in Ontario, Canada hospitals between April 1, 2007 and March 31, 2012. Infants were followed until March 31, 2017, with a primary outcome of primary care visits up to age 10. Secondary outcomes included rates of outpatient psychiatrist visits, emergency department visits and hospitalizations. We used adjusted Poisson regression to assess differences in rates of health service use between children with and without exposure to prenatal cannabis. Results: We included 508 025 infants, 3248 (0.6%) had cannabis exposure. Prenatal cannabis use was associated with a decreased rate of primary care physician visits (adjusted rate ratio [aRR]: 0.86, 95% confidence interval [CI]: 0.84-0.87) and an increase in the rate of outpatient psychiatrist visits (aRR: 1.29, 95% CI: 1.00-1.66), emergency department visits (aRR: 1.05, 95% CI: 1.03-1.08), and hospitalizations (aRR: 1.12, 95% CI: 1.04-1.20). Among preterm offspring, cannabis was associated with a decrease in primary care visits but no difference in other visits. Among those in the highest income quintiles, cannabis use was associated with a two-fold increase in the rate of outpatient psychiatrist visits. Conclusions: Offspring exposed to prenatal cannabis receive fewer primary care visits but have increased rates of visiting health care specialists past the neonatal period.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".