Healthcare use in individuals with and without attention-deficit/hyperactivity disorder: A population-based longitudinal matched cohort study.
Bibliographic record
Abstract
Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) experienced worsening symptoms during the COVID-19 pandemic resulting in increased demand for healthcare services. However, it is unclear how those with and without ADHD utilized these services during the COVID-19 pandemic. This study examined healthcare utilization among individuals with and without ADHD and as a secondary objective, investigated these trends among female and male subgroups, from April 1, 2014-March 31, 2023. We conducted a population-based longitudinal retrospective cohort study among ADHD cases identified using a validated algorithm, and controls from Ontario, Canada over the same study period. We matched ADHD cases 1:1 to controls by sex, birth year, and geographical area. Outcomes were number of outpatient visits per person per fiscal year to family physicians, for mental health and to emergency departments, stratified by sex and age group over the follow-up period. Crude visit rate differences between sex-specific cases and controls were calculated with 95% confidence intervals (CI). We matched 427 716 ADHD cases to 427 716 controls. ADHD cases were 163 528 ≤ 17 years (32% female), and 264 188 adults (52% female). From 2013-2024, where March 17, 2020 marked the onset of the COVID-19 pandemic, females aged 1-17 years with ADHD appeared to have higher visit rate differences to family physicians, emergency departments, and increased mental health services, relative to their controls, particularly in 2020 [2.66 (95% CI: 2.65-2.68)]. In the same year, males with ADHD still had a higher mental health visit rate difference, [2.02 (95% CI: 2.01-2.02)] in 2020, but lower than that observed in females. Adult females with ADHD had the highest mental health visit rate difference in 2020 [5.09 (95% CI: 5.07-5.11)] and males with ADHD had 4.41 (95% CI: 4.40-4.43). These higher service utilization differences likely reflected greater health needs among females with ADHD while males underutilized these services.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".