Prevalence of ADHD among Black Youth Compared to White, Latino and Asian Youth: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically review the prevalence of Attention Deficit Hyperactivity Disorder (ADHD) among Black children and adolescents compared to White, Latino and Asian children and adolescents. METHOD: Peer-reviewed articles were identified in seven databases and included if they reported prevalence of ADHD among Black children and adolescents living in a minority context and compared rates to at least one of White, Latino or Asian samples. A total of 7050 articles were retrieved and 155 articles were subjected to full evaluation. Twenty-three studies representing 26 independent samples were included. RESULTS: = 7) of Black, White, Latino, and Asian participants, respectively. Pooled prevalence rate of ADHD was 15.9% (95%CI 11.6% - 20.7%) among Black children and adolescents, 16.6% (95%CI 11.6% - 22.2%) among Whites, 10.1% (95%CI 6.9% - 13.8%) among Latinos and 12.4% (95%CI 1.4% - 31.8%) among Asians. There was no significant difference in prevalence between ethnic groups, whereas both Black and White children and adolescents had marginally statistically significant higher prevalence than Asians. The results of a meta-regression analysis showed no moderating effects of the type of sample and the year of publication of studies. A significant publication bias was observed, suggesting that other moderators were not identified in the present systematic review. CONCLUSION: In contrast to the assertion in the DSM-5 that clinical identification among Black children and adolescents is lower than among White children and adolescents, the present meta-analysis suggests similar rates of ADHD among these two groups. The importance of considering cultural appropriateness of assessment tools and processes is emphasized.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 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".