Medicaid enrollment changes among U.S. adult survivors of childhood cancer following medicaid expansion: A report from the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10011 Background: Little is known about whether Medicaid expansion under the Affordable Care Act (ACA) affected insurance coverage among adult survivors of childhood cancer, a population at high-risk for poor health outcome. We addressed this gap by evaluating the association between ACA Medicaid expansion and Medicaid enrollment among participants in the Childhood Cancer Survivor Study (CCSS). Methods: The CCSS cohort of 5-year survivors of childhood cancer was linked to administrative Medicaid insurance data in 2010-2016. We identified 13,895 adult survivors (aged 18-64 years) diagnosed with cancer under age 21 and between 1970 and 1999. Outcomes included (1) percentage with any Medicaid enrollment by year; and (2) Medicaid-covered days (number of days when a survivor was enrolled in Medicaid) during each year. Multivariable difference-in-differences (DD) models were used to examine outcome changes pre vs. post ACA Medicaid expansion, in expansion- vs. non-expansion states, adjusting for age, sex, race/ethnicity, income, education, and chronic conditions. Multivariable models were conducted overall and then stratified by cancer type, race/ethnicity, income, and education. Results: Medicaid enrollment rates increased more in expansion states (17.6% pre-expansion to 24.1% post-expansion) than non-expansion states (16.4% to 16.9%), leading to a net increase in enrollment of 6.6 percentage points (ppt; 95% CI = 5.5-7.7) in the multivariable DD model. Multivariable DD model showed a net increase of 18.4 days (95% CI = 13.8-23.1) in Medicaid-covered days in expansion states relative to non-expansion states. The expansion-associated increase in Medicaid enrollment rates was greatest among survivors of leukemia (multivariable DD estimate: 8.9 ppt, 95% CI = 6.9-11.0) and non-Hodgkin lymphoma (8.0 ppt, 95% CI = 5.0-10.9). Greater increases in Medicaid enrollment were seen in non-Hispanic Black (13.5 ppt, 95% CI = 8.0-19.1) and Hispanic survivors (15.8 ppt, 95% CI = 10.6-21.0) than non-Hispanic White peers (5.1 ppt; 95% CI = 4.0-6.2); in survivors with < $20K household income (11.7 ppt, 95% CI = 8.9-14.4) compared to those with ≥$60K household income (2.9 ppt, 95% CI = 1.6-4.2); and in survivors with high school or lower education (9.3 ppt, 95% CI = 6.9-11.8) compared to those with a college or higher degree (3.9 ppt, 95% CI = 2.6-5.3). Similar patterns were observed across survivor subgroups when examining Medicaid-covered days. Conclusions: We provide the first evidence on increased Medicaid enrollment and longer coverage duration among adult survivors of childhood cancer following Medicaid expansion, with greater increases seen among survivors of underrepresented racial/ethnic populations, those with low socioeconomic status, and high medical need survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".