Public Insurance As a Proxy Measure of Household Poverty-Exposures Among Children with Hematologic Malignancies
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».