Adolescent cannabis use and young adult healthcare use in a population-based birth cohort linked to healthcare administrative records
Bibliographic record
Abstract
Introduction Evidence links early adolescent cannabis use (CU) to long-term health risks, but most studies lack comprehensive early-life confounder data and rely on subjective health measures. Objectives To assess the association between adolescent CU trajectories and healthcare use for physical and mental health problems (P&MHP) in young adulthood. Methods Data from the Québec Longitudinal Study of Child Development, a 23-year population-based birth cohort (N = 1,591), were linked to healthcare administrative records (hospitalizations, outpatient, and ER visits). CU trajectories (exposure) were derived from age of onset and frequency data (ages 12-17) using group-based trajectory modeling. Missing data on pre-exposure confounders were multiply imputed. Overlap-weighted logistic regression was used to assess the adjusted associations between these trajectories and healthcare use for P&MHP between ages 18-23. Results Three CU trajectories were identified: non-users, late users, and early users (Figure 1). Early users had a higher risk of healthcare use for any mental disorder (OR 1.55, 95% CI 1.17-2.06), common mental disorders (OR 1.69, 95% CI 1.19-2.39), substance-related disorders (OR 2.25, 95% 1.24-4.10), and hospitalizations for physical diseases (OR 1.57, 95% CI 1.03-2.38) compared to non-users. No significant differences were found between late and non-users. Image 1: Conclusions These findings highlight the need for targeted interventions during adolescence to mitigate long-term health risks. Prevention efforts should prioritize early users, and be focused on integrated social, mental, and physical care. Disclosure of Interest None Declared
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".