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 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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".