Public Health Insurance and Prescription Medications for Mental Illness
Bibliographic record
Abstract
Abstract Mental illnesses are prevalent in the United States and globally. Cost is a critical barrier to treatment receipt. We study the effects of the Affordable Care Act's recent expansion of Medicaid, a public insurance system for the poor in the U.S., on psychotropic prescription medications for mental illness. We estimate differences-in-differences models using administrative data on medications for which Medicaid was a third-party payer over the period 2011–2017. Our findings suggest that these expansions increased psychotropic prescriptions by 21.0%. We show that Medicaid, and not patients, financed these prescriptions. For states expanding Medicaid, the total cost of these prescriptions was $28.0 M by the second quarter of 2017. Expansion effects were experienced across most major mental illness categories and across states with different levels of patient need, system capacity, and expansion scope. We find no statistically significant evidence that Medicaid expansion reduced mental illness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".