Antidepressants and risk of suicide or self-harm in Canadian youth: A study involving common data models in Quebec and British Columbia
Bibliographic record
Abstract
Introduction The use of antidepressants (AD) in children and adolescents has become controversial due to a possible increased risk of suicide, revised evidence about effectiveness, and growing usage. Several studies have been conducted through healthcare databases, mainly restricted to hospitalized cases. Objective To evaluate the rate of self-harm among children (10–14 years) and adolescents (15–19 years) who use AD and to compare rates between classes of AD, using a common data model. Methods A retrospective cohort study was conducted in children and adolescents, age 10–19, members of the Quebec (QC) public drug plan or residents of British Columbia (BC), and who received a new AD treatment in 1997–2008 (QC) and 1997–2006 (BC). Self-harm (fatal or non-fatal) was the main outcome. Data sources consisted of prescription, medical services, and hospitalization databases. AD were categorized into: fluoxetine (only AD approved for paediatric use for depression in Canada), non-fluoxetine SSRIs, TCAs, and others. Multivariate logistic regression analyses were conducted using high dimensional propensity scores. Results Were included 51,868 and 28,200 AD users from BC and QC, respectively: 70% were females, and 60% had received a diagnosis of depression. Risk of hospitalization for self-harm was 38.15 and 19.23/1000 person-years in BC and QC, respectively. Rates were higher among adolescents (BC: 47.52; QC: 21.36) than children (BC: 17.00, QC: 12.99). For both hospitalized and non-hospitalized cases, while there was no statistically significant difference in risk associated with fluoxetine relative to non-fluoxetine SSRIs, TCAs were associated with a lower risk in BC (OR = 0.47; 95% CI: 0.31–0.72) and Quebec (OR = 0.41; 95% CI: 0.30–0.46). Conclusion In two independent large cohorts, there was no apparent differences in risk across ADs. Lower risk of TCAs may be due to residual confounding by indication.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".