Antidepressant Prescriptions, Including Tricyclics, Continue to Increase in Canadian Children
Bibliographic record
Abstract
Objective: Few studies have longitudinally followed trends in antidepressant prescribing for Canadian children following the Black Box warning issued in 2004. Using a national data source, we aim to describe trends in antidepressant recommendations for Canadian children ages 1–18 during 2012 to 2016. Methods: A database called the Canadian Disease and Therapeutic Index (CDTI), provided by IQVIA, was used to conduct analyses. The CDTI dataset collects a quarterly sample of pediatric antidepressant recommendations, projected using a weight procedure from a dynamic sample of 652 Canadian office-based physicians. The term “recommendations” is used because nonprescription drugs may be recommended and there is no confirmation in the database that the prescriptions were filled or medications taken. The data were collected from 2012 to 2016 and the sample population was projected by IQVIA to be representative of the entire Canadian pediatric population. Results: The total number of projected antidepressant recommendations for children increased from 2012 to 2016. Selective serotonin reuptake inhibitors were the most recommended class of antidepressants. Analysis indicated that fluoxetine was the most frequently recommended drug. Findings also suggest that recommendations for tricyclic antidepressants (TCAs) are increasing, but predominantly for reasons other than treatment of depression. Conclusions: Overall, antidepressant use in Canadian children increased over the study period. Unsurprisingly, fluoxetine was the most recommended antidepressant for Canadian children. However, the observed increase in TCA use for a pediatric population is unexpected. The data source is descriptive and lacks detailed measures supporting comprehensive explanation of the findings, therefore, further research is required.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".