Brief Report: A population-based study of the impact of the COVID-19 pandemic on benzodiazepine use among children and young adults
Bibliographic record
Abstract
The COVID-19 pandemic was associated with increases in the prevalence of depression and anxiety among children and young adults. We studied whether the pandemic was associated with changes in prescription benzodiazepine use. We conducted a population-based study of benzodiazepine dispensing to children and young adults ≤ 24 years old between January 1, 2013, and June 30, 2022. We used structural break analyses to identify the pandemic month(s) when changes in prescription benzodiazepine dispensing occurred, and interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected benzodiazepine use. A structural break occurs where there is a sudden change in the trend of a time series. We observed an immediate decline in benzodiazepine dispensing of 23.6 per 100,000 (95% confidence interval [CI]: -33.6 to -21.2) associated with a structural break in April 2020, followed by a monthly decrease in the trend of 0.3 per 100,000 (95% CI: -0.74 to 0.14). Lower than expected benzodiazepine dispensing rates were observed each month of the pandemic from April 2020 onward, with relative percent differences ranging from - 7.4% (95% CI: -10.1% to - 4.7%) to -20.9% (95% CI: -23.2% to -18.6%). Results were generally similar in analyses stratified by sex, age, neighbourhood income quintile, and urban versus rural residence. Further research is required to understand the clinical implications of these findings and whether these trends were sustained with further follow-up.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".