Racial/ethnic disparities in childhood cancer survival in the United States: Mediation effects of health insurance coverage and area-level social deprivation.
Bibliographic record
Abstract
143 Background: Childhood cancer survival varies by race/ethnicity in the United States. This study evaluated the impact of potentially modifiable characteristics - health insurance and area-level social deprivation - on racial/ethnic disparities in childhood cancer survival nationwide. Methods: We identified 65,113 childhood cancer patients aged < 18 years newly diagnosed with any of 10 common cancer types (e.g. central nervous system (CNS) neoplasms, acute lymphoblastic leukemia (ALL), Hodgkin lymphoma) from the 2004-2014 National Cancer Database. Cox proportional hazard models were used to compare survival probabilities by race and ethnicity (non-Hispanic white (NHW) vs non-Hispanic black (NHB), Hispanic, and non-Hispanic other (NH other)) for each cancer type. We conducted mediation analyses by the mma R package to evaluate the racial/ethnic survival disparities mediated by health insurance (private, Medicaid, and uninsured) and social deprivation index (SDI) quartile. SDI is a composite measure of deprivation based on seven characteristics (e.g. income, education, employment). Results: Compared to NHW, worse survival were observed for NHB (HR (hazard ratio): 1.4, 95% CI: 1.3-1.5), Hispanic (HR: 1.2, 95% CI: 1.1-1.2), and NH other (HR: 1.2, 95% CI: 1.1-1.3) for all cancer sites combined after adjusting for sociodemographic characteristics other than health insurance and SDI. Health insurance explained 20% of the survival disparities and SDI explained 19% of the disparity between NHB vs NHW; health insurance explained 48% of the survival disparities and SDI explained 45% of the disparity between Hispanic vs NHW. For ALL, health insurance significantly explained 15% and 18% of the survival disparities between NHB and Hispanic vs NHW, respectively. SDI significantly explained 19% and 31% of the disparities, respectively. Conclusions: Health insurance and SDI mediated racial/ethnic survival disparities for several childhood cancers. Expanding insurance coverage and improving healthcare access in disadvantaged areas may effectively reduce disparities for these cancer sites.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".