Racial/Ethnic Disparities in Youth Mental Health Traits and Diagnoses within a Community-based Sample
Bibliographic record
Abstract
ABSTRACT Background Racial/ethnic disparities in the prevalence of mental health diagnoses have been reported but have not accounted for the prevalence of the traits that underlies these disorders. Examining rates of diagnoses in relation to traits may yield a clearer understanding of how racial/ethnic youth differ in their access to assessment and care. We sought to examine differences in self/parent-reported rates of diagnoses for obsessive-compulsive disorder (OCD), attention-deficit/ hyperactivity disorder (ADHD), and anxiety disorders after adjusting for differences in trait levels between youth from three racial/ethnic groups: White, South Asian, and East Asian. Methods We collected parent or self-reported ratings of OCD, ADHD and anxiety traits and diagnoses for youth (6-17 years) from a general population sample (Spit for Science). We examined racial/ethnic differences in trait levels and the odds of reporting a diagnosis using mixed-effects linear models and logistic regression models. Results East Asian (N = 1301) and South Asian (N = 730) youth reported significantly higher levels of OCD and anxiety traits than White youth (N = 6896). Given the same trait level, East Asian and South Asian youth had significantly lower odds of reporting a diagnosis for OCD (Odds Ratio (OR) East Asian = 0.08 [0.02, 0.41] ; OR South Asian = 0.05 [0.00, 0.81]), ADHD (OR East Asian = 0.27 [0.16, 0.45]; OR South Asian = 0.09 [0.03, 0.30]), and Anxiety (OR East Asian = 0.21 [0.11, 0.39]; OR South Asian = 0.12 [0.05, 0.32]) than White youth. Conclusions These results suggest a discrepancy between traits-levels of OCD and anxiety and rates of diagnoses for East Asian and South Asian youth. This discrepancy may be due to increased barriers for ethnically diverse youth to access mental health care. Efforts to understand racial/ethnic barriers to care are needed. Key Points Despite having lower prevalence of diagnoses, East and South Asian youth reported significantly higher anxiety and OCD trait levels than White youth Given the same trait level, East Asian youth were at 92% lower odds of having received an OCD diagnosis, 73% lower odds of having received an ADHD diagnosis, and 79% lower odds of having received an Anxiety diagnosis than White youth Given the same trait level, South Asian youth were at 95% lower odds of having received an OCD diagnosis, 91% lower odds of having received an ADHD diagnosis, and 88% lower odds of having received an anxiety diagnosis Future research is needed to understand barriers to mental health care and assessment that may underly the discrepancy between mental health traits and diagnoses for ethnic/racially diverse youth.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".