States' Performance in Reducing Uninsurance Among Black, Hispanic, and Low-Income Americans Following Implementation of the Affordable Care Act
Bibliographic record
Abstract
Purpose: To assess state-level variation in changes in uninsurance among Black, Hispanic, and low-income Americans after implementation of the Affordable Care Act (ACA). Methods: We analyzed data from the Behavioral Risk Factor Surveillance System from 2012 to 2016, excluding 2014. For Black, Hispanic, and low-income (<$35,000/year) adults 18–64 years of age, we estimated multivariable regression adjusted pre- (2012–2013) to post-ACA (2015–2016) percentage point changes in uninsurance for each U.S. state. We compared absolute and relative changes and the proportion remaining uninsured post-ACA across states. We also examined whether state-level variation in coverage gains was associated with changes in forgoing needed care due to cost. Results: The range in the percentage point reduction in uninsurance varied substantially across states: 19-fold for Black (0.9–17.4), 18-fold for Hispanic (1.2–21.5), and 23-fold for low-income (1.0–27.8) adults. State-level variation in changes in uninsurance relative to baseline uninsurance rates also varied substantially. In some states, more than one quarter of Black, one half of Hispanic, and approaching one half of low-income adults remained uninsured after full implementation of the ACA. Compared with states in the lowest quintile of change in coverage, states in the highest quintile experienced greater improvements in ability to see a physician. Conclusions: Performance on reducing uninsurance for Black, Hispanic, and low-income Americans under the ACA varied substantially among U.S. states with some making substantial progress and others making little. Post-ACA uninsurance rates remained high for these populations in many states.
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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 | 0.004 | 0.011 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".