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Social determinants of health (SDOH) and late mortality among survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).

2025· article· en· W4410803096 on OpenAlexaff
Cindy Im, Fang Wang, Yan Chen, Carrie R. Howell, Kristine Karvonen, Val Nolan, Aaron McDonald, Lucie M. Turcotte, Eric J. Chow, Yutaka Yasui, Paul C. Nathan, Claire Snyder, Gregory T. Armstrong, I‐Chan Huang

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineChildhood cancerCancerSocial determinants of healthSurvivorship curveCancer survivorshipCancer survivorGerontologyDemographyPublic healthInternal medicine

Abstract

fetched live from OpenAlex

10021 Background: Neighborhood-level SDOH may increase disparities in adverse cancer-related outcomes. The US CDC-constructed Social Vulnerability Index (SVI) reflects 4 SDOH domains (socioeconomic status [SES]; household composition; minority status/language; housing/transportation) and captures the vulnerability of underserved communities. The impact of neighborhood-level SDOH on late mortality among survivors of childhood cancer is not known. Methods: Analyses included 5-year survivors in the US diagnosed in 1970-1999 participating in the CCSS, a multi-institutional retrospective cohort study. We evaluated geocoded SVI quintiles (Q1 to Q5, from least to most vulnerable) based on residential addresses and personal SES factors including income, education level, and health insurance status collected at CCSS baseline. The impact of SVI and personal-level SES on all-cause and cause-specific mortality rates were evaluated using cumulative incidence and relative rates (RRs) from piecewise exponential regression models adjusted for age, sex, diagnosis age, and treatments. Results: Among 20,261 survivors with geocode data (mean age at cancer diagnosis and baseline evaluation, 7y and 24y respectively, with a mean follow up of 17y), 2,439 survivors died. All-cause late mortality was greater in survivors living in more vulnerable areas (Q5 vs. Q1, at 20y: 14.7% vs. 10.8%, P<0.001). We observed a dose-response relationship between worsening SVI and the all-cause mortality rate (Q5 vs. Q1 RR 1.52, 95% CI 1.32-1.76, P trend <0.001) as well as for mortality rates due to specific health causes (Table). Among the SDOH domains, neighborhood SES (Q5 vs. Q1 RR 1.68, 95% CI 1.45-1.95) showed the strongest association with all-cause mortality followed by household composition (RR 1.43, 95% CI 1.24-1.66). Notably, these findings remained largely consistent after adjusting for personal-level SES as well as in analyses stratified by income and insurance coverage. Conclusions: Living in socially vulnerable neighborhoods during young adulthood is associated with a ~50% increased risk for late mortality among survivors of childhood cancer and is largely unaffected by favorable personal-level SES. Policies and interventions targeting neighborhood-level SDOH during the transition to survivorship care are needed to reduce mortality risk in this population. Adjusted RRs and 95% confidence intervals for overall and cause-specific mortality. SVI All cause Subsequent neoplasm cause Cardiovascular cause Other health causes Q2 1.00 (0.88 - 1.14) 0.90 (0.74 - 1.11) 1.09 (0.76 - 1.55) 1.09 (0.85 - 1.39) Q3 1.16 (1.02 - 1.32) 1.03 (0.84 - 1.27) 1.18 (0.82 - 1.70) 1.24 (0.97 - 1.59) Q4 1.24 (1.08 - 1.42) 1.12 (0.90 - 1.39) 1.29 (0.88 - 1.90) 1.44 (1.11 - 1.86) Q5 1.52 (1.32 - 1.76) 1.35 (1.07 - 1.69) 1.54 (1.02 - 2.33) 1.83 (1.38 - 2.42) SVI Q1 (least vulnerable) is the referent.

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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)0.001
metaresearch head score (Gemma)0.002
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.138
GPT teacher head0.523
Teacher spread0.385 · 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
Published2025
Admission routes1
Has abstractyes

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