Changes in Medicaid Enrollment Among Adult Survivors of Childhood Cancer After Medicaid Expansion: A Report From the Childhood Cancer Survivor Study
Bibliographic record
Abstract
PURPOSE Little is known about whether Medicaid expansion under the Affordable Care Act affects insurance coverage among adult survivors of childhood cancer, a high-risk population requiring lifetime follow-up care. We examined the association of Medicaid expansion with Medicaid enrollment among participants in the Childhood Cancer Survivor Study. METHODS We identified 13,355 adult survivors (age 18-64 years) diagnosed with cancer at age <21 years between 1970 and 1999 and linked these survivors to Medicaid administrative data from 2010 to 2019. Outcomes included the percentage of survivors with any Medicaid enrollment and Medicaid-covered days (total number of days when a survivor was enrolled in Medicaid) each year. Multivariable difference-in-differences (DD) models were used to examine outcome changes (post- v pre-expansion) in expansion versus nonexpansion states. RESULTS Medicaid enrollment rates among survivors increased in expansion states (17.1% pre-expansion to 22.8% postexpansion) but decreased in nonexpansion states (16.6%-15.7%), leading to a net increase of 7.1 percentage points (ppts; 95% CI, 6.1 to 8.1). The DD model also showed a net mean increase of 18.7 days/year (95% CI, 15.0 to 22.4) in Medicaid-covered days in expansion relative to nonexpansion states. The expansion-associated increase in enrollment was greatest among survivors who were age 18-29 years (11.2 ppts; 95% CI, 8.3 to 14.1), non-Hispanic Black (13.6 ppts, 95% CI, 8.8 to 18.4) or Hispanic (13.4 ppts, 95% CI, 7.0 to 19.8), with <$20K in US dollars (USD) self-reported household income (13.5 ppts, 95% CI, 10.7 to 16.3), and noncollege graduates (9.3 ppts, 95% CI, 7.7 to 10.9). Similar patterns were observed when examining Medicaid-covered days. CONCLUSION To our knowledge, we provide the first evidence of increased Medicaid enrollment and longer coverage duration among adult survivors of childhood cancer after Medicaid expansion. Greater increases were seen among survivors from underrepresented racial/ethnic backgrounds, young adult survivors, and those with lower socioeconomic status, providing a mechanism to reduce disparities and ensure long-term medical care for childhood cancer 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".