The Pharmacoepidemiology of Psychotropic Medication Use in Canadian Children from 2012 to 2016
Bibliographic record
Abstract
Objective: The goal of this study was to characterize the frequency and trends of psychotropic drug prescribing in Canadian children from 2010 to 2016 and to compare these results with a previous study conducted between 2005 and 2009. Methods: Using a national physician panel survey database from IQVIA Canada, aggregated frequencies of written prescriptions and therapeutic indications for antipsychotics, attention-deficit/hyperactivity disorder (ADHD) medications (psychostimulants and nonstimulants), and antidepressants were analyzed in children. Changes in frequency of written prescriptions and therapeutic indications are presented using descriptive statistics. Results: Written prescriptions for antipsychotics decreased by 10% from 2010 to 2016, in contrast to a 114% increase in written prescriptions for antipsychotics observed between 2005 and 2009. Written prescriptions for psychostimulants and antidepressants rose by 35% and 27%, respectively, between 2012 and 2016, comparable with previous results. The most common reasons for recommending an antipsychotic were ADHD and conduct disorder, although there appears to be a downward trend for ADHD compared with other conditions. In contrast, the share of written prescriptions for antipsychotics for autism increased 34% over the study period. Within the second-generation antipsychotics, written prescriptions for aripiprazole increased. An increase in the use of guanfacine extended release for ADHD was also observed. Conclusion: Several factors may be involved in stabilization and small decrease in antipsychotic use in recent years, including physician and patient awareness of adverse effects related to antipsychotic use, knowledge implementation strategies advocating short-term and judicious use of antipsychotics in children, and the approval of guanfacine extended release for use in Canada for ADHD in 2013.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".