Medicaid Expansion and Disparity Reduction in Surgical Cancer Care at High-Quality Hospitals
Bibliographic record
Abstract
BACKGROUND: The Affordable Care Act's Medicaid expansion has been heavily debated due to skepticism about Medicaid's ability to provide high-quality care. Particularly, little is known about whether Medicaid expansion improves access to surgical cancer care at high-quality hospitals. To address this question, we examined the effects of the 2001 New York Medicaid expansion, the largest in the pre-Affordable Care Act era, on this disparity measure. STUDY DESIGN: We identified 67,685 nonelderly adults from the New York State Inpatient Database who underwent select cancer resections. High-quality hospitals were defined as high-volume or low-mortality hospitals. Disparity was defined as model-adjusted difference in percentage of patients receiving operations at high-quality hospitals by insurance type (Medicaid/uninsured vs privately insured) or by race (African American vs white). Levels of disparity were calculated quarterly for each comparison pair and then analyzed using interrupted time series to evaluate the impact of Medicaid expansion. RESULTS: Disparity in access to high-volume hospitals by insurance type was reduced by 0.97 percentage points per quarter after Medicaid expansion (p < 0.0001). Medicaid/uninsured beneficiaries had similar access to low-mortality hospitals as the privately insured; no significant change was detected around expansion. Conversely, racial disparity increased by 0.87 percentage points per quarter (p < 0.0001) in access to high-volume hospitals and by 0.48 percentage points per quarter (p = 0.005) in access to low-mortality hospitals after Medicaid expansion. CONCLUSIONS: Pre-Affordable Care Act Medicaid expansion reduced the disparity in access to surgical cancer care at high-volume hospitals by payer. However, it was associated with increased racial disparity in access to high-quality hospitals. Addressing racial barriers in access to high-quality hospitals should be prioritized.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".