Early Impact of the Affordable Care Act Coverage Expansion on Safety‐Net Hospital Inpatient Payer Mix and Market Shares
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of the Affordable Care Act's coverage expansion on safety-net hospitals (SNHs). STUDY SETTING: Nine Medicaid expansion states. STUDY DESIGN: Differences-in-differences (DID) models compare payer-specific pre-post changes in inpatient stays of adults aged 19-64 years at SNHs and non-SNHs. DATA COLLECTION METHODS: 2013-2014 Healthcare Cost and Utilization Project State Inpatient Databases. PRINCIPAL FINDINGS: On average per quarter postexpansion, SNHs and non-SNHs experienced similar relative decreases in uninsured stays (DID = -2.2 percent, p = .916). Non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs (DID = 13.8 percent, p = .041). For SNHs, the average decrease in uninsured stays (-146) was similar to the increase in Medicaid stays (153); privately insured stays were stable. For non-SNHs, the decrease in uninsured (-63) plus privately insured (-33) stays was similar to the increase in Medicaid stays (105). SNHs and non-SNHs experienced a similar absolute increase in Medicaid, uninsured, and privately insured stays combined (DID = -16, p = .162). CONCLUSIONS: Postexpansion, non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs, which may reflect patients choosing non-SNHs over SNHs or a crowd-out of private insurance. More research is needed to understand these trends.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".