Impact of COVID-19 pandemic on prescription stimulant use among children and youth: a population-based study
Bibliographic record
Abstract
COVID-19 associated public health measures and school closures exacerbated symptoms in some children and youth with attention-deficit hyperactivity disorder (ADHD). Less well understood is how the pandemic influenced patterns of prescription stimulant use. We conducted a population-based study of stimulant dispensing to children and youth ≤ 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 the dispensing of stimulants occurred. We used interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected stimulant use. Our main outcome was the change in the monthly rate of stimulant use per 100,000 children and youth. Following an initial immediate decline of 60.1 individuals per 100,000 (95% confidence interval [CI] - 99.0 to - 21.2), the monthly rate of stimulant dispensing increased by 11.8 individuals per 100,000 (95% CI 10.0-13.6), with the greatest increases in trend observed among females, individuals in the highest income neighbourhoods, and those aged 20 to 24. Observed rates were between 3.9% (95% CI 1.7-6.2%) and 36.9% (95% CI 34.3-39.5%) higher than predicted among females from June 2020 onward and between 7.1% (95% CI 4.2-10.0%) and 50.7% (95% CI 47.0-54.4%) higher than expected among individuals aged 20-24 from May 2020 onward. Additional research is needed to ascertain the appropriateness of stimulant use and to develop strategies supporting children and youth with ADHD during future periods of long-term stressors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".