Impact of the COVID-19 pandemic on antidepressant and antipsychotic use among children and adolescents: a population-based study
Bibliographic record
Abstract
Background: The COVID-19 pandemic was associated with increases in the prevalence of depression, anxiety and behavioural problems among children and youth. Less well understood is the influence of the pandemic on antidepressant and antipsychotic use among children. This is important, as it is possible that antidepressants and antipsychotics were used as a "stop-gap" measure to treat mental health symptoms when in-person access to outpatient care and school-based supportive services was disrupted. Furthermore, antipsychotics and antidepressants have been associated with harm in children and youth. We examined trends in dispensing of these medications two years following the pandemic among children 18 years of age and under in Ontario, Canada. Methods: We conducted a population-based time-series study of antidepressant and antipsychotic medication dispensing to children and adolescents ≤18 years old between September 1, 2014, and March 31, 2022. We measured monthly population-adjusted rates of antidepressant and antipsychotics obtained from the IQVIA Geographic Prescription Monitor (GPM) database. We used structural break analyses to identify the pandemic month(s) when changes in the dispensing of antidepressants and antipsychotics occurred. We used interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected use of these drugs. Results: Overall, we found higher-than-expected dispensing of antidepressants and antipsychotics in children and youth. Specifically, we observed an immediate step decrease in antidepressant dispensing associated with a structural break in April 2020 (-55.8 units per 1,000 individuals; 95% confidence intervals [CI] CI: -117.4 to 5.8), followed by an increased monthly trend in the rate of antidepressant dispensing of 13.0 units per 1,000 individuals (95% CI: 10.2-15.9). Antidepressant dispensing was consistently greater than predicted from September 2020 onward. Antipsychotic dispensing increased immediately following a June 2020 structural break (26.4 units per 1,000 individuals; 95% CI: 15.8-36.9) and did not change appreciably thereafter. Antipsychotic dispensing was higher than predicted at all time points from June 2020 onward. Conclusion: We found higher-than-expected dispensing of antidepressants and antipsychotics in children and youth. These increases were sustained through nearly two years of observation and are especially concerning in light of the potential for harm with the long-term use of antipsychotics in children. Further research is required to understand the clinical implications of these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".