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Abstract B016: Survival differences after diagnosis of early-onset colorectal cancer by race/ethnicity and neighborhood-level socioeconomic status

2025· article· en· W7113904863 on OpenAlexaboutno aff

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerHazard ratioSocioeconomic statusIncidence (geometry)Proportional hazards modelCancerComorbidityConfidence intervalHousehold income

Abstract

fetched live from OpenAlex

Abstract Background: The incidence of early-onset colorectal cancer (eoCRC), diagnosed age <50 years, is increasing in the United States. Prior studies using national and state cancer databases have observed higher eoCRC mortality after diagnosis among non-Hispanic Black (NHB) patients. These studies, however, did not account for insurance status or healthcare access, which may contribute to the observed disparities. Here we examined the associations between race/ethnicity and census tract-level median household income with all-cause and colorectal cancer (CRC)-specific mortality among eoCRC cases in a large integrated healthcare delivery system whose racially/ethnically diverse members have relative equal access to care. Methods: We included Kaiser Permanente Southern California (KPSC) members diagnosed with eoCRC (age 15-49 years) between 2009-2021 and followed them through 12/31/2023. Patients with <12 months of prior KPSC membership or unspecific CRC site were excluded. Bivariate and multivariable Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between race/ethnicity and census tract-level household income and all-cause and CRC-specific mortality. Multivariable models were adjusted for age at diagnosis, sex, Charlson comorbidity score, obesity, stage at diagnosis, cancer site, and histologic subtype. Subgroup analyses were conducted by stage at diagnosis (localized, regional, distant). Results: Of 1,719 eoCRC cases included, we observed 471 deaths (423 (92.4%) were CRC-specific deaths) over a mean follow-up time of 6.8 years. In the adjusted models, NHB and non-Hispanic Asian/Pacific Islander (NH API) patients, but not Hispanic patients (HR=1.06, 95% CI: 0.84-1.30), had significantly higher all-cause mortality (HR=1.55, 95% CI: 1.10-2.20, p=0.01; HR=1.43, 95% CI: 1.07-1.91, p=0.02, respectively) compared with non-Hispanic White (NHW) patients. In the subgroup analyses, race/ethnicity was not associated with all-cause mortality among patients with localized disease. However, among patients diagnosed with regional disease, NHB patients had a significantly higher risk of all-cause mortality compared with NHW patients (HR=1.94, 95% CI: 1.07-3.50, p=0.03). Among patients diagnosed with distant stage disease, both NHB and NH API patients had elevated risk of all-cause mortality compared with NHW patients (HR=1.59, 95% CI: 0.99-2.57, p=0.06 and HR=1.63, 95% CI: 1.10-2.43, p=0.02, respectively). Similar findings were observed for CRC-specific mortality, overall and by cancer stage. Census-tract level median household income was not significantly associated with all-cause or CRC-specific mortality. Conclusions: In this insured population, NHB and NH API race/ethnicity were associated with increased risk of CRC-specific mortality among those diagnosed with advanced stage eoCRC. However, the number of NHB and NH API patients diagnosed at distant stage were small. Therefore, future research is needed to confirm these findings and better understand potential survival disparities. Citation Format: Talar S. Habeshian, Lanfang Xu, Kimberly L. Cannavale, Alec Gilfillan, Darios Getahun, Chun R. Chao. Survival differences after diagnosis of early-onset colorectal cancer by race/ethnicity and neighborhood-level socioeconomic status [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B016.

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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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.135
GPT teacher head0.488
Teacher spread0.353 · 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 teacher head, not a consensus.

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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