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Record W4391031264 · doi:10.1542/hpeds.2023-007482

Inequitable Poverty Exposures: A Subspecialty Opportunity to Address Disparities

2024· article· en· W4391031264 on OpenAlexaffabout
Kristine Karvonen, Puja J. Umaretiya, Victoria Koch, Yael Flamand, Rahela Aziz‐Bose, Lenka Ilcisin, Ariana Valenzuela, Peter D. Cole, Lisa Gennarini, Justine M. Kahn, Kara M. Kelly, Thai Hoa Tran, Bruno Michon, Jennifer Welch, Joanne Wolfe, Lewis B. Silverman, Abby R. Rosenberg, Kira Bona

Bibliographic record

VenueHospital Pediatrics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité de Montréal
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineSubspecialtyPovertyEnvironmental healthMEDLINEFamily medicineEconomic growth

Abstract

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Subspecialty pediatrics have lagged behind primary care pediatrics in recognizing adverse social determinants of health (SDOH) as salient to outcomes, key drivers of inequity, and worthy of systematic investigation.1 A population frequently hospitalized with chronic illness with well-defined inequities is children with cancer. More than 1 in 5 pediatric oncology families report low-income, and at least 1 household material hardship (HMH; food, housing, or utility insecurity) at diagnosis.2 Identifying whether children from marginalized racial/ethnic groups are disproportionately exposed to poverty, a modifiable SDOH, can inform intervention opportunities for children with chronic illness to mitigate disparities.3 We leveraged parent-reported poverty data collected as a prospective aim of a clinical trial for children with newly diagnosed acute lymphoblastic leukemia (ALL) to characterize modifiable poverty exposures by race/ethnicity.The Dana Farber Cancer Institute ALL Consortium phase III randomized clinical trial 16-001 (NCT03020030) enrolled children aged 1 to 21 years with de novo ALL from 2016 to 2022 at 8 US and Canadian centers. It included an embedded prospective cohort study evaluating parent-reported SDOH via survey within 32 days of enrollment.4,5 The study was approved by enrolling sites’ institutional review boards.Child’s parent-reported race and ethnicity were collected using US and Canadian federal reporting guidelines and combined to reflect populations per best practice (Supplemental Table 1). HMH was defined as at least 1 of 3 resource insecurities (housing, food, or utilities) using a standardized instrument,5 and additionally examined as ordinal number of unmet resource needs (0–3). Low income was defined as annual household income <200% US Federal Poverty Level for subject year of enrollment.6 To allow comparison across the trial cohort, Canadian to US dollar conversion was calculated via the July 2022 exchange rate of 1 CAD to 0.7765 USD.7 Comparisons were made using the χ2 test or Fisher exact test, as appropriate. Analyses were performed using SAS, version 9.The analytic cohort included 375 subjects, including 247 (66%) treated at US sites and 128 (34%) at Canadian sites. Parent-reported race/ethnicity and poverty exposures are displayed in Supplemental Table 2.One hundred and twenty (32%) families reported HMH at diagnosis, including 47% (n = 17) of Black families (P < .001) and 68% (n = 45) of Hispanic families (P < .001) vs 19% (n = 46) of non-Hispanic White (NHW) families (Fig 1). Housing insecurity was present in 33% (n = 12) of Black families (P < .001) and 47% (n = 31) of Hispanic families (P < .001) vs 11% (n = 26) of NHW families. Many Black and Hispanic families reported more than 1 resource insecurity; specifically, 8% (n = 3) of Black families (P = .10) and 14% (n = 9) of Hispanic families (P = .001) vs 3% (n = 6) of NHW families reported 3 HMH domains.Among 336 (89%) families with available income data, 131 (39%) reported low income, including 52% (14/27) of Black families and 74% (40/54) of Hispanic families vs 27% (62/226) of NHW families (P = .009 and <.001, respectively).Overall, among 179 families who reported any poverty exposures, 40% (n = 72) reported both low-income and HMH poverty exposures (Fig 2).In a subspecialty pediatric patient cohort, we demonstrate that Black and Hispanic children with ALL experience high frequencies of modifiable poverty exposures at diagnosis. Implications of these poverty exposures include differential health care access and inferior disease outcomes—including higher rates of relapse and death.2 These data identify actionable risk exposures disproportionately experienced by marginalized children with complex chronic illness. They provide immediate opportunities for subspecialist and hospital-based pediatric providers to address disparities rooted in systemic racism.In this cohort, HMH and income poverty distinguished overlapping but nonidentical populations (Fig 2),8 identifying opportunities for exposure-specific (income vs resource poverty) interventions. For example, children living in low-income households may benefit from guaranteed income pilots or interventions to increase means-tested governmental program participation,9 whereas those facing resource insecurities absent low income may require direct resource provision—such as food or transportation vouchers—during cancer treatment.10 Cancer-specific interventions targeting both are currently in development (NCT03638453).Our data are limited by a geographically restricted cohort, with underrepresentation of racial/ethnic identities. Merging of US and Canadian racial/ethnic groups risks misclassification. Replication of these data using larger, more diverse cohorts is ongoing in the Children’s Oncology Group (NCT03914625, NCT03126916).These data identify marked inequities in modifiable SDOH experienced by marginalized populations within a paradigmatic subspecialty population requiring frequent hospitalization. They provide immediate targets for interventions aimed at addressing racial/ethnic outcome disparities applicable to pediatric populations with complex chronic illness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.121
GPT teacher head0.423
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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