Medicaid Expansion Slowed Rates Of Health Decline For Low-Income Adults In Southern States
Bibliographic record
Abstract
Of the fourteen states that have not expanded eligibility for Medicaid, nine are in the southern census region, and two others border that region. Ongoing debate over the merits of Medicaid expansion in these states has focused, in part, on whether the safety net provides sufficient access for uninsured low-income Americans. We analyzed longitudinal survey and vital status data from the twelve-state Southern Community Cohort Study (SCCS) for 15,356 nonelderly adult participants with low incomes, 86 percent of whom were enrolled at community health centers. In difference-in-differences analyses, we compared changes in self-reported health between participants in four expansion and eight nonexpansion states before (2008-13) and after (2015-17) Medicaid expansion. We found that a higher proportion of SCCS participants in expansion states reported increases in Medicaid coverage (a differential change of 7.6 percentage points), a lower proportion experienced a health status decline (-1.8 percentage points), and a higher proportion maintained their baseline health status (1.4 percentage points). The magnitude of estimated reductions in health declines would meaningfully affect a nonexpansion state's health ranking in our sample if that state elected to expand Medicaid. Our results suggest that for low-income adults in the South, Medicaid expansion yielded health benefits-even for those with established access to safety-net care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".