The Differential Effects of Insurance Mandates on Health Care Spending for Children’s Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVES: There is substantial variation in treatment intensity among children with autism spectrum disorder (ASD). This study asks whether policies that target health care utilization for ASD affect children differentially based on this variation. Specifically, we examine the impact of state-level insurance mandates that require commercial insurers to cover certain treatments for ASD for any fully-insured plan. METHODS: Using insurance claims between 2008 and 2012 from three national insurers, we used a difference-in-differences approach to compare children with ASD who were subject to mandates to children with ASD who were not. To allow for differential effects, we estimated quantile regressions that evaluate the impact of mandates across the spending distributions of three outcomes: (1) monthly spending on ASD-specific outpatient services; (2) monthly spending on ASD-specific inpatient services; and (3) quarterly spending on psychotropic medications. RESULTS: The change in spending on ASD-specific outpatient services attributable to mandates varied based on the child's level of spending. For those children with ASD who were subject to the mandate, monthly spending for a child in the 95th percentile of the ASD-specific outpatient spending distribution increased by $1460 (P<0.001). In contrast, the effect was only $2 per month for a child in the fifth percentile (P<0.001). Mandates did not significantly affect spending on ASD-specific inpatient services or psychotropic medications. CONCLUSIONS: State-level insurance mandates have larger effects for those children with higher levels of spending. To the extent that spending approximates treatment intensity and the underlying severity of ASD, these results suggest that mandates target children with greater service needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".