Changes in Insurance Coverage and Stage at Diagnosis Among Nonelderly Patients With Cancer After the Affordable Care Act
Bibliographic record
Abstract
Purpose To examine change in the percent uninsured and early-stage diagnosis among nonelderly patients with newly diagnosed cancer after the Affordable Care Act (ACA). Patients and Methods By using the National Cancer Data Base, we estimated absolute change (APC) and relative change in percent uninsured among patients with newly diagnosed cancer age 18 to 64 years between 2011 to the third quarter of 2013 (pre-ACA implementation) and the second to fourth quarter of 2014 (post-ACA) in Medicaid expansion and nonexpansion states by family income level. We also examined demographics-adjusted difference in differences in APC between Medicaid expansion and nonexpansion states. We similarly examined changes in insurance and early-stage diagnosis for the 15 leading cancers in men and women (top 17 cancers total). Results Between the pre-ACA and post-ACA periods, percent uninsured among patients with newly diagnosed cancer decreased in all income categories in both Medicaid expansion and nonexpansion states. However, the decrease was largest in low-income patients who resided in expansion states (9.6% to 3.6%; APC, -6.0%; 95% CI, -6.5% to -5.5%) versus their counterparts who resided in nonexpansion states (14.7% to 13.3%; APC, -1.4%; 95% CI, -2.0% to -0.7%), with an adjusted difference in differences of -3.3 (95% CI, -4.0 to -2.5). By cancer type, the largest decrease in percent uninsured occurred in patients with smoking- or infection-related cancers. A small but statistically significant shift was found toward early-stage diagnosis for colorectal, lung, female breast, and pancreatic cancer and melanoma in patients who resided in expansion states. Conclusion Percent uninsured among nonelderly patients with newly diagnosed cancer declined substantially after the ACA, especially among low-income people who resided in Medicaid expansion states. A trend toward early-stage diagnosis for select cancers in expansion states also was found. These results reinforce the importance of policies directed at providing affordable coverage to low-income, vulnerable populations.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".