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Longitudinal associations between chronic health condition burden and financial hardship among adult survivors of childhood cancer: A report from the Childhood Cancer Survivor study (CCSS).

2025· article· en· W4410805340 on OpenAlexaff
Tara K. Suntum, Yan Chen, Nickhill Bhakta, Wendy M. Leisenring, Anne C. Kirchhoff, Tara O. Henderson, K. Robin Yabroff, Rena M. Conti, Elyse Richelle Park, Melissa M. Hudson, Kirsten K. Ness, Claire Snyder, Eric J. Chow, Yutaka Yasui, Gregory T. Armstrong, Paul C. Nathan, 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 cancerCancerSurvivorship curveCancer survivorLongitudinal studyGerontologyInternal medicinePathology

Abstract

fetched live from OpenAlex

10057 Background: Childhood cancer survivors experience a large burden of chronic health conditions (CHCs) with the progression of these conditions facilitating potential economic burden. This study examined the association between CHC progression and financial hardship in adult survivors of childhood cancer. Methods: The study included CCSS participants diagnosed with pediatric cancer (1970–1999) who survived > 5 years post-diagnosis and were ≥26 years old at the assessment of financial burden. Participants completed surveys (2017-2019) assessing three financial hardship domains: behavioral, material, and psychological. CHCs were self-reported at baseline and on up to 4 follow-ups. CHC severity was graded using CTCAE v4.03. To estimate the impact of multiple CHCs, a severity score was calculated based on published methods (PMID: 17595271) accounting for the frequency and grade of conditions. Notable CHC burden was defined as any CHC above low severity grade. Multivariable logistic regression evaluated associations of CHC burden with financial hardship adjusting for age at diagnosis, attained age, sex, insurance, personal income, education, marital status, smoking status, and body mass index. Additional analyses examined whether neighborhood deprivation using the Area Deprivation Index (ADI) (range 0-100) modified the relationship between CHC burden and financial hardship. Results: Among 3,638 evaluable participants, the prevalence of notable CHC burden was 66%, material hardship 16%, psychological hardship 26%, and behavioral hardship 21%. Survivors with very high CHC burden had 2.6-fold (95%CI 1.6-4.1) higher odds of material and 1.6-fold (95%CI 1.0-2.4) higher odds of psychological hardship vs. those with low CHC burden. Survivors who progressed to moderate, high, or very high CHC burden had 1.7-fold (95%CI 1.2-2.5) higher odds of material hardship and 1.6-fold (95%CI 1.1-2.2) higher odds of psychological hardship vs. those with persistent low CHC burden. For survivors living in more deprived neighborhoods (ADI≥50), having notable CHC burden was associated with 2.5-fold (95%CI 1.5-4.3) higher odds of material hardship vs. those without notable CHC burden. For survivors living in less deprived neighborhoods (ADI < 50), having notable CHC burden was associated with 1.5-fold (95%CI 1.1-2.2) higher odds of psychological hardship and 1.6-fold (95%CI 1.1-2.1) higher odds of behavioral hardship vs. those without notable CHC burden. Conclusions: Longitudinal CHC burden shows strong temporal associations with material and psychological financial hardship. Neighborhood deprivation is associated with financial hardship, beyond individual sociodemographic factors. Multi-level interventions will be crucial to address financial hardship in survivors who develop CHCs earlier than peers.

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 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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.082
GPT teacher head0.471
Teacher spread0.389 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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