Trends of incident adult Attention-deficit/hyperactivity disorder diagnoses before, during and after the pandemic provincial state of emergency in British Columbia (2013–2023): a population-based study
Bibliographic record
Abstract
Background: Emerging evidence indicates a potential rise in attention-deficit/hyperactivity disorder (ADHD) incidence worldwide and in British Columbia (BC) since the pandemic. Given the high comorbidity of ADHD with substance use disorder (SUD) and other mental disorders, understanding changes in ADHD diagnosis among adults is crucial for healthcare planning amid BC's drug poisoning (overdose) crisis. We aimed to report how rates of newly diagnosed adult ADHD changed before, during and after the pandemic by demographic variables and histories of SUD or mental disorders. Methods: We conducted interrupted time series analyses on overall and stratified monthly incidence rates of ADHD diagnosis between Jan, 2013, and Nov, 2023 in BC, using data from linked population-based administrative databases. Findings: The pre-pandemic average of newly diagnosed adult ADHD was 8.8 cases per 100,000 population monthly. During the pandemic (Mar, 2020-Jun, 2021), this rose to 19.2 driven by a 4.9% (95% confidence interval: [3.7, 6.2]) month-over-month increase. When the pandemic ended, the monthly rate jumped by 107.3% [68.5, 155.0] in Jul, 2021 and grew 1.5% [0.4, 2.7] per month thereafter, averaging 34.8 cases per 100,000 post-pandemic. Substantial differences in trends emerged when stratified by sex and SUD histories. Interpretation: This exponential rise in adult ADHD may be explained by pandemic-related sociocultural changes and the broader societal evolution in mental health awareness in recent years and decades. This rise could foreshadow a potential increase in the population at risk of SUD, underscoring the urgent need for bidirectional integration of ADHD and SUD services. Funding: We acknowledge the UBC Psychiatry Stimulus Grants Initiative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".