Trends in ADHD Visits and Prescriptions before and during the COVID-19 Pandemic
Bibliographic record
Abstract
Context The stresses of the pandemic have been felt by most. Working in family medicine we have noticed an increase in patient inquiries into Attention Deficit/Hyperactivity Disorder (ADHD). Objective We set out to examine if the trends in the patients presenting to family medicine for ADHD have changed during the pandemic. Study Design We did a retrospective cohort study examining rates of patients presenting to the family physician office with a visit with the most responsible reason for visit as ADHD or a prescription for an ADHD medication. Setting We used the UTOPIAN primary care EMR database in Ontario, Canada containing patients in the Greater Toronto Area and beyond. Population Studied Over 200,000 patients annually age 5-55 years presenting to a UTOPIAN physician in 2017-2021. Outcome Measures Proportion of patients each year with a visit to a family physician where the most responsible reason for visit was documented as ADHD. Proportion of patients each year receiving a prescription for an ADHD medication. Patients visiting family physicians for ADHD were examined by age, sex, income quintile and urban vs rural residence. Results The proportion of patients with an ADHD visit in 2017-2019 ranged from 2.4 to 3.5/1000 in 2017-2019 and rose to 5.5/1000 by the end of 2021. The proportion of patients visiting for ADHD was relatively stable in 2017-2019 for all age groups but was higher than pre-pandemic for the 20-24 year olds and the 25-34 year olds by 2021. The proportion of patients with an ADHD medication prescription in 2017-2019 ranged from 12.9 to 16.5/1000 and rose to 21.9/1000 by the end of 2021. Prior to the pandemic ADHD visits were higher in rural patients compared to patients residing in urban areas (2.8 rural and 2.4/1000 urban in 2017) but the reverse occurred with the pandemic onset (3.9 rural and 5.7/1000 urban in 2021). ADHD visits and medications were higher both before and after the pandemic for males compared to females. This persistent pattern was also seen with income quintiles both before and during the pandemic with the patients in highest income quintile consistently having more ADHD visits and prescriptions compared to the lowest income quintile. Conclusions There has been an increasing proportion of patients seeing family physicians for ADHD and receiving prescriptions for ADHD medications over the past 5 years with amplification of these increases in some patient populations particularly in 2021.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.002 | 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".