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Abstract PR007: Persistent poverty is associated with risk of early mortality among children with cancer: An analysis of SEER data

2024· article· en· W4402267132 on OpenAlexaboutno aff
Emma Hymel, Josiane Kabayundo, Krishtee Napit, Shinobu Watanabe‐Galloway

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerPovertyDemographyChildhood cancerGerontologyInternal medicineEconomic growthSociology

Abstract

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Abstract Objectives: Social determinants of health are understudied in the context of pediatric cancer. Persistent poverty, a measure developed by the National Cancer Institute and the US Department of Agriculture’s Economic Research Service, is defined as having 20% or more of an area’s population having lived below the poverty level for a period of about 30 years. The effect of persistent poverty on pediatric cancer outcomes is not well understood. Methods: This population-based longitudinal study uses data from the Surveillance, Epidemiology, and End Results (SEER)-22 registries Incidence Data with Census Tract Attributes Database. All primary cases of malignant cancer diagnosed among children aged 0-19 at diagnosis from 2006-2020 were included. Early mortality was defined as death within three months of diagnosis. Cox proportional hazards models were used to compute crude and adjusted hazard rations (aHRs) for the association between persistent poverty and early mortality. Based on a directed acyclic graph, models were also adjusted for age, sex, race/ethnicity, cancer type, and rurality (defined using Rural Urban Commuting Area codes). Results: The study included 97,132 children; 12.63% resided in a persistent poverty census tract at diagnosis and 1.66% died within three months of diagnosis. In the adjusted model, living in a persistent poverty census tract was associated with a higher risk of early mortality (aHR=1.26, 95% CI: 1.10-1.45). Additionally, a higher risk of early mortality was observed among Non-Hispanic Black (aHR=1.44, 95% CI: 1.23-1.70), Non-Hispanic Asian and Pacific Islander (aHR=1.39, 95% CI: 1.15-1.68), and Hispanic children (aHR=1.33, 95% CI: 1.18-1.49) compared to Non-Hispanic White children. Children residing in rural areas also had a higher risk of death compared to children in urban areas (aHR=1.29, 95% CI: 1.11-1.50). In sensitivity analyses looking at the effect of persistent poverty on risk of death within one month of diagnosis, a similar effect size was observed (aHR=1.26, 95% CI: 1.07-1.48). Conclusions: Our study observed an increased risk of early mortality among children living in persistent poverty census tracts and among racial/ethnic minorities. Future studies should explore potential mediators of the association between persistent poverty and early mortality, including health insurance, treatment access, and individual-level socioeconomic status. Citation Format: Emma Hymel, Josiane Kabayundo, Krishtee Napit, Shinobu Watanabe-Galloway. Persistent poverty is associated with risk of early mortality among children with cancer: An analysis of SEER data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr PR007.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.134
GPT teacher head0.440
Teacher spread0.305 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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