Interprovincial Variation in Antipsychotic and Antidepressant Prescriptions Dispensed in the Canadian Pediatric Population
Bibliographic record
Abstract
OBJECTIVE: Although antidepressants and antipsychotics are valuable medications in the treatment of select psychiatric disorders, there is increasing focus on the balance of risks and benefits of these drugs as prescribed, particularly in the pediatric population. We examined recent national trends and interprovincial variation in dispensing of antipsychotic and antidepressant prescriptions to the Canadian pediatric population. METHOD: We conducted a population-based cross-sectional study of antidepressant and antipsychotic prescriptions dispensed by Canadian pharmacies to the pediatric population (≤18 years) between 2010 and 2013. Prescription volumes were obtained from IMS Health. Analysis was stratified by drug, year, quarter, and province and population-standardized using age-adjusted population estimates. RESULTS: From the first quarter of 2010 to the fourth quarter of 2013, dispensing of antipsychotics to the pediatric population increased 33% (from 34 to 45 prescriptions per 1000) and dispensing of antidepressants increased 63% (from 34 to 55 per 1000). We observed a 1.5-fold interprovincial difference in dispensing rates for antidepressants (range: 189 per 1000 to 275 per 1000) and a 3.0-fold difference for antipsychotics (range: 85 per 1000 to 253 per 1000) in 2013. Among antidepressants, selective serotonin reuptake inhibitors were the most dispensed (76%), with fluoxetine being the leading agent. Among antipsychotics, atypical antipsychotics were the most dispensed (97%), with risperidone being the leading agent. CONCLUSIONS: Antipsychotic and antidepressant dispensing to the Canadian pediatric population increased from 2010 to 2013, with considerable interprovincial variation. Future research is required to explore reasons for observed patterns to optimize care for the Canadian pediatric population.
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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 | 0.000 |
| 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".