Public Insurance As a Proxy Measure of Household Poverty-Exposures Among Children with Hematologic Malignancies
Bibliographic record
Abstract
Background: Poverty is associated with adverse outcomes in pediatric hematologic malignancies. However, the measures commonly used to characterize poverty-exposure-ZIP code and insurance-are proxies used in the absence of self-reported data. These proxies are both non-modifiable and risk misclassification. Identifying poverty-exposures amenable to intervention is essential to address outcome inequities. Both income-poverty and household material hardship ([HMH], defined as self-reported food, housing, or utility insecurity), are associated with inferior child health outcomes and modifiable with intervention. We aimed to characterize the utility of insurance as a proxy for household-level poverty-exposure by describing the relationship between public versus private insurance coverage among those with HMH-exposure and low-income. Methods: This post-hoc analysis pooled parent/guardian-reported survey data from US pediatric patients with acute lymphoblastic leukemia enrolled in a multicenter phase III therapeutic trial (NCT03020030) conducted at 6 Northeastern sites, with survey data from pediatric patients diagnosed with a hematologic malignancy (acute leukemias, non-Hodgkin lymphomas, Hodgkin lymphoma and Langerhans cell histiocytosis) enrolled in a single-center sociodemographic banking study. Survey data for both cohorts were collected within 6-weeks of child's diagnosis from 2017-2023. For patients participating in both the phase III trial and sociodemographic banking study, data from the trial were utilized. Consistent with published pediatric oncology disparities analyses, insurance was dichotomized as sole public coverage (Medicaid or Children's Health Insurance Program) versus any private coverage (private or dual private/public). Income was dichotomized as low-income (parent-reported annual household income <200% federal poverty level [FPL]) and higher-income (≥200% FPL). HMH was defined as food, housing or utility insecurity measured using validated scales; participants with affirmative responses to any HMH domain were considered HMH-exposed. We calculated HMH frequency by insurance type, as well as the frequency of public versus private insurance across HMH-exposed and low-income families. Results: The analytic cohort included 349 patients, with a median age of 6.9 years (IQR 3.9-12.4), with 6% self-identifying as Asian, 13% as Black, and 25% as Hispanic ethnicity; 22% spoke a primary language other than English. Thirty-seven percent (n=130) had public insurance, and 63% (n=219) private insurance. Thirty-five percent (n=121) of the cohort reported HMH-exposure, and 30% (n=104) reported low-income, with 19% (n=65) reporting both. Participants with public insurance were significantly more likely to report HMH (n=83/130, 64%) than those with private insurance (n=38/219, 17%; p<0.0001). Among participants with HMH, 31% (n=38) had private insurance and 69% (n=83) had public insurance. Among participants with low-income, 25% (n=26) had private insurance and 75% (n=78) public insurance. Conclusions: Though HMH is experienced more frequently by families with public insurance, use of public insurance to proxy modifiable household-level poverty-exposures-HMH or low-income-fails to identify up to one-third of families with these exposures. These findings highlight the inadequacy of insurance status to proxy social risk. Insurance-associated survival disparities are well defined across pediatric hematologic malignancies, but provide no opportunity for intervention to mitigate these inequities. Supportive care equity interventions targeting both HMH and income-poverty are currently in development. These data highlight the immediate need for systematic collection of family-reported social determinants of health data across the cooperative group setting to facilitate targeted health equity intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".