Prevalence of attention deficit hyperactivity disorder/hyperkinetic disorder of pediatric and adult populations in clinical settings: a systematic review, meta-analysis and meta-regression
Bibliographic record
Abstract
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD)/Hyperkinetic Disorder (HD) is linked to increased risks of morbidity, comorbidity and mortality, with higher prevalence in clinical populations. The differential prevalence of ADHD/HD across adult and pediatric clinical populations, influenced by factors such as time trends, sex, age, geographic regions, and comorbidities, has not been systematically assessed. METHODS: MEDLINE, CINAHL, Embase and PsycINFO databases were searched from inception to 1st August 2023 for eligible full-text papers published in English, and reviewing reference lists of identified studies and review papers. Studies reporting ADHD/HD prevalence in adult and pediatric clinical populations were included. Meta-regression evaluated the effects of geographic region, year of publication and sample size. RESULTS: From 30,740 citations, we reviewed 521 full-text articles, yielding 311 studies for inclusion (including 653,558 pediatric and 43,311 adult participants). Overall, worldwide pooled prevalence of ADHD/HD in clinical settings for pediatrics was 32.4% (95% CI 31-34%), and in adults 21.4% (95% CI 20-23%). Prevalence was higher in outpatient settings than inpatient settings. Prevalence based on rating scales was higher than studies using diagnostic interviews or clinical record review. Prevalence varied significantly across subspecialist settings for children and adults. No significant time trend was detected between 1981-2023. Pediatric prevalence appears influenced by geographic region but not year of publication or sample size. For adults, larger sample sizes were associated with lower prevalence estimates. CONCLUSIONS: ADHD/HD prevalence in clinical populations is 8-9-fold higher than community estimates. With these patients at risk for many adverse outcomes, our findings underscore the critical importance of resource allocation for screening, diagnosing and treatment.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.059 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".