State Mandate Laws for Autism Coverage and High-Deductible Health Plans
Bibliographic record
Abstract
OBJECTIVES: Most states have passed insurance mandates requiring health plans to cover services for children with autism spectrum disorder (ASD). Research reveals that these mandates increased treated prevalence, service use, and spending on ASD-related care. As employer-sponsored insurance shifts toward high-deductible health plans (HDHPs), it is important to understand how mandates affect children with ASD in HDHPs relative to traditional, low-deductible plans. METHODS: Insurance claims for 2008-2012 for children covered by 3 large US insurers (United Healthcare, Aetna, and Humana) available through the Health Care Cost Institute were used to compare the effects of mandates on ASD-related spending for children in HDHPs and traditional health plans. RESULTS: Relative to children in traditional plans, mandates were associated with higher average monthly spending increases for children in HDHPs. Mandate-attributable spending differences between children enrolled in HDHPs relative to traditional plans were $77 for ASD-specific services (95% confidence interval [CI]: $10 to $144), $125 for outpatient health services (95% CI: $26 to $223), and $144 for all health services (95% CI: $36 to $253). These spending differentials were driven by differences in plan spending and not out-of-pocket (OOP) spending. CONCLUSIONS: Spending on ASD-related services attributable to autism mandates was higher among children in HDHPs, but higher spending did not translate into a greater OOP burden. For families with consistently high health care expenditures on ASD-related services, high-deductible products may be worth considering in the context of mandate laws. Families in mandate states with children with ASD enrolled in HDHPs were able to increase service use without paying more OOP.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".