Prevalence and Correlates of Caregiver-Reported Mental Health Conditions in Youth With Autism Spectrum Disorder in the United States
Bibliographic record
Abstract
OBJECTIVE: Mental health conditions (MHCs) have substantial personal and economic costs for children with autism spectrum disorder (ASD); yet, a current population-based prevalence estimate is lacking. METHODS: This study included 42,283 caregivers of children (ages 3-17 years) from the 2016 population-based National Survey of Children's Health. Prevalence and correlates of caregiver-reported MHCs were estimated in children with ASD and compared with those in children with intellectual disability (ID), children with special health care needs (SHCN), and "all others" (no ASD, SHCN, or ID). RESULTS: 77.7% of children with ASD had ≥ 1 MHC; 49.1% had ≥ 2. The most common MHCs were behavior/conduct problem (60.8%), anxiety problem (39.5%), attention deficit disorder (ADD)/attention-deficit/hyperactivity disorder (ADHD) (48.4%), and depression (15.7%). Substance abuse was the only MHC less common in ASD. MHCs were more common in youth with ASD versus SHCN, "all other" youth, and those with ID. MHCs were common in ASD by ages 3-5 years (44.8% ≥ 1 condition) and increased with age (85.9% ≥ 1 condition, ages 12-17 years). Among children with ASD, girls had twice the odds of an anxiety problem, those with ID had 4 times the odds of behavior/conduct problem, and those with childhood adversity had greater odds of an anxiety problem (odds ratio [OR] = 2.66) and ADD/ADHD (OR = 1.99). CONCLUSIONS: Caregiver-reported MHCs are prevalent in children with ASD in the US from a young age and characterize > 85% by adolescence. There is an outsized need for effective MHC assessment and treatment of these youth that demands expedient innovation in both MHC and developmental disability policy and practice.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".