Effect of regulatory warnings on antidepressant prescription rates, use of health services and outcomes among children, adolescents and young adults
Bibliographic record
Abstract
BACKGROUND: Regulatory bodies worldwide, including Health Canada, have issued warnings about prescribing antidepressants to children and adolescents. We sought to determine whether the Health Canada warning had the desired effects on prescribing patterns and outcomes and whether it had any unintended health consequences. METHODS: We examined data from prescription and health care databases representing more than 265 000 children, adolescents and young adults annually to determine changes in the rates of antidepressant prescription, use of health services and outcomes in these populations in the 9 years before and the 2 years after the Health Canada warning. We also examined the data for unintended changes in these rates among patients with anxiety disorders. We used young adults as the comparison group because they were not targeted by the warning. RESULTS: Following the warning, the rate of antidepressant prescriptions decreased among children and adolescents (relative risk [RR] 0.86, 95% confidence interval [CI] 0.81-0.91) and among young adults (RR 0.90, 95% CI 0.86-0.93). Ambulatory visits because of depression decreased among children and adolescents (RR 0.90, 95% CI 0.85-0.96) and young adults (RR 0.91, 95% CI 0.87-0.96). The rate of completed suicides among children and adolescents rose significantly after the warning (RR 1.25, 95% CI 1.08-1.44; annual rate per 1000 = 0.04 before and 0.15 after the warning). There was no equivalent change in the rate of completed suicides among young adults (RR 1.01, 95% CI 0.93-1.10; annual rate per 1000 = 0.15 before and 0.22 after the warning). Among patients with an anxiety disorder, the prescription rates did not change among children and adolescents, except for a decrease in the use of selective serotonin reuptake inhibitors other than fluoxetine, but the rates among young adults changed similar to the pattern of changes in the overall prescribing of antidepressants. There was also a significant decrease in the rate of physician visits because of anxiety disorders among young adults after the warning. INTERPRETATION: Health advisories and warnings issued by regulatory bodies may have unintended consequences on the provision of care, delivery of health services and clinical outcomes. Further efforts are required to ensure that health warnings do not result in unexpected harm.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".