Prevalence of attention‐deficit/hyperactivity disorder in people with mood disorders: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) in mood disorders is associated with unfavorable outcomes, including more frequent mood episodes, and increased risk of suicide. The reported prevalence of ADHD in individuals with mood disorders varies widely. METHODS: , 2020, using search terms for ADHD and mood disorders. We included original data on the prevalence of ADHD in individuals with bipolar disorder (BD) or major depressive disorder (MDD). We estimated the prevalence of ADHD, by developmental period and disorder using random-effects meta-analyses. We also compared the rate of ADHD in people with MDD and BD, and with and without mood disorders. RESULTS: Based on 92 studies including 17089 individuals, prevalence of ADHD in BD is 73% (95% CI 66-79) in childhood, 43% (95% CI 35-50) in adolescence, and 17% (95% CI 14-20) in adulthood. Data from 52 studies with 16897 individuals indicated that prevalence of ADHD in MDD is 28% (95% CI 19-39) in childhood, 17% (95% CI 12-24) in adolescence, and 7% (95% CI 4-11) in adulthood. ADHD was three times more common in people with mood disorders compared to those without and 1.7 times more common in BD compared to MDD. CONCLUSION: People with mood disorders are at a significant risk for ADHD. ADHD should be assessed and treated in individuals with BD and MDD. Comprehensive assessment strategies are needed to address challenges of diagnosing ADHD alongside mood disorders.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".