Attention-Deficit/Hyperactivity Disorder and Alcohol and Other Substance Use Disorders in Young Adulthood: Findings from a Canadian Nationally Representative Survey
Bibliographic record
Abstract
AIM: (a) To document the prevalence and odds of (i) alcohol use disorders, (ii) cannabis use disorders, (iii) other drug use disorders and (iv) any substance use disorder (SUD), among young adults with and without ADHD, and (b) to investigate the degree to which the association between ADHD and SUDs is attenuated by socio-demographics, early adversities and mental health. METHOD: Secondary analysis of the nationally representative Canadian Community Health Survey-Mental Health (CCHS-MH). The sample included 6872 respondents aged 20-39, of whom 270 had ADHD. The survey response rate was 68.9%. MEASUREMENTS: Substance Use Disorder: World Health Organization's Composite International Diagnostic Interview criteria, SUDs, were derived from lifetime algorithms for alcohol, cannabis and other substance abuse or dependence. ADHD was based on self-report of a health professional's diagnosis. FINDINGS: One in three young adults with ADHD had a lifetime alcohol use disorder (36%) compared to 19% of those without ADHD (P < 0.001). After adjusting for all control variables, those with ADHD had higher odds of developing alcohol use disorders (OR = 1.38, 95% CI: 1.05, 1.81), cannabis use disorders (OR = 1.46, 95% CI: 1.06, 2.00), other drug use disorders (OR = 2.07, 95% CI: 1.46, 2.95) and any SUD (OR = 1.69, 95% CI: 1.28, 2.23). History of depression and anxiety led to the largest attenuation of the ADHD-SUD relationship, followed by childhood adversities and socioeconomic status. CONCLUSIONS: Young adults with ADHD have a high prevalence of alcohol and other SUDs. Targeted outreach and interventions for this extremely vulnerable population are warranted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".