Abstract B016: Survival differences after diagnosis of early-onset colorectal cancer by race/ethnicity and neighborhood-level socioeconomic status
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".