Can pharmacare reduce suicide-related behaviors? A quasi-experimental study in Ontario Youth
Bibliographic record
Abstract
BACKGROUND: Canada's Ontario Health Insurance Plan Plus (OHIP+), implemented in 2018, provided universal prescription drug coverage to youth under 25. Because youth face elevated risks of suicide-related behaviors (SRBs) and frequently encounter cost-related barriers to accessing mental health medications, this study evaluated whether OHIP + impacted emergency department (ED) visits for SRBs among Ontario youth. METHODS: We used linked data from the 2016 Canadian Census and the National Ambulatory Care Reporting System, identifying SRB-related ED visits between April 2016 and March 2020. A single interrupted time series (ITS) and a comparative ITS (CITS) with Coarsened Exact Matching were employed to assess changes in SRB-related ED visits associated with the implementation of OHIP+. The CITS compared low-versus high-socioeconomic status (SES) youths, based on the assumption that low-SES youths are more likely to benefit from public drug coverage due to greater financial barriers. RESULTS: OHIP + implementation was associated with an immediate reduction of 9.4 SRB-related ED visits per 100,000 youth per month (95% CI: -18.2 to -0.6). In the CITS analysis, low-SES youth experienced a significantly greater reduction compared to high-SES youth (difference: -19.6; 95% CI: -37.7 to -1.5). Sex-stratified models indicated that these effects were driven primarily by reductions among females. CONCLUSION: Public drug coverage through OHIP+ was associated with a significant reduction in ED visits for SRBs, particularly among low-income and female youth. Our findings underscore the potential of pharmacare to reduce acute mental health crises and promote health equity among vulnerable youth.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".