Health insurance among survivors of childhood cancer following Affordable Care Act implementation
Bibliographic record
Abstract
BACKGROUND: The Affordable Care Act (ACA) increased private nonemployer health insurance options, expanded Medicaid eligibility, and provided preexisting health condition protections. We evaluated insurance coverage among long-term adult survivors of childhood cancer pre- and post-ACA implementation. METHODS: Using the multicenter Childhood Cancer Survivor Study, we included participants from 2 cross-sectional surveys: pre-ACA (2007-2009; survivors: n = 7505; siblings: n = 2175) and post-ACA (2017-2019; survivors: n = 4030; siblings: n = 987). A subset completed both surveys (1840 survivors; 646 siblings). Multivariable regression models compared post-ACA insurance coverage and type (private, public, uninsured) between survivors and siblings and identified associated demographic and clinical factors. Multinomial models compared gaining and losing insurance vs staying the same among survivors and siblings who participated in both surveys. RESULTS: The proportion with insurance was higher post-ACA (survivors pre-ACA 89.1% to post-ACA 92.0% [+2.9%]; siblings pre-ACA 90.9% to post-ACA 95.3% [+4.4%]). Post-ACA insurance increase in coverage was higher among those aged 18-25 years (survivors: +15.8% vs +2.3% or less ages 26 years and older; siblings +17.8% vs +4.2% or less ages 26 years and older). Survivors were more likely to have public insurance than siblings post-ACA (18.4% vs 6.9%; odds ratio [OR] = 1.7, 95% confidence interval [CI] = 1.1 to 2.6). Survivors with severe chronic conditions (OR = 4.7, 95% CI = 3.0 to 7.3) and those living in Medicaid expansion states (OR = 2.4, 95% CI = 1.7 to 3.4) had increased odds of public insurance coverage post-ACA. Among the subset completing both surveys, low- and mid-income survivors (<$40 000 and <$60 000, respectively) experienced insurance losses and gains in reference to highest household income survivors (≥$100 000), relative to odds of keeping the same insurance status. CONCLUSIONS: Post-ACA, more childhood cancer survivors and siblings had health insurance, although disparities remain in coverage.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".