Abstract B034: Sex and Racial Disparities in Time to Treatment for Early-Onset Colorectal Cancer Across a Four-Level Rurality Classification in the United States
Bibliographic record
Abstract
Abstract Background: Adults with early-onset colorectal cancer (EOCRC, diagnosed at age < 50 years) often face diagnostic delays, making timely treatment essential for optimal outcomes. Evidence shows that male patients and racial/ethnic minorities, especially those in socioeconomically disadvantaged areas, are more likely to experience treatment delays. However, limited studies have explored how sex, racial, and geographic disparities influence treatment timeliness. This study addresses that gap by examining time to treatment across three post-diagnosis intervals, while accounting for a four-level rurality classification. Methods: We conducted a retrospective cohort analysis using the 2006–2020 Incidence Data with Census Tract Attributes from the Surveillance, Epidemiology, and End Results Program. The primary exposures included sex, race (White, Black, Hispanic, American Indian/Alaska Native [AI/AN], and Asian/Pacific Islander [Asian/PI]), and rurality (all urban, mostly urban, mostly rural, all rural). The outcome was time to treatment, categorized as initiation within 30, 60, or 90 days from diagnosis. Patients were censored if treatment was not initiated or occurred beyond the specified timeframes. Cox proportional hazards models were used, adjusting for sociodemographic, clinical factors, and diagnosis year. Multiple imputation addressed missing treatment time data (14.3%, n = 11,312). Results: Among 79,090 EOCRC patients, the average time to treatment was 20 days (SD = 32.4; IQR = 30), the shortest in mostly rural areas (17.8 days), followed by all rural (18.3 days), mostly urban (19.1 days), and all urban areas (20.7 days) (p < 0.001). In the imputed model, male patients were 5% less likely to initiate treatment across all time intervals compared to females (p < 0.05). Hispanic and Asian/PI patients were 4% (95% CI: 0.93–0.99) and 7% (95% CI: 0.91–0.95) less likely, respectively, to receive treatment within 90 days. Conversely, patients residing in non–fully urban areas were 9%–12% more likely to receive treatment across all timeframes (p < 0.05). Stratified analyses further showed that male patients in all urban areas were consistently about 5% less likely to initiate treatment (p < 0.05). Black (HR: 0.95; 95% CI: 0.92–0.98), Hispanic (HR: 0.93; 95% CI: 0.91–0.95) and Asian/PI patients (HR: 0.96; 95% CI: 0.93–0.99) patients in fully urban areas were less likely to receive treatment within 90 days, with similar patterns observed at 30 and 60 days. Conclusions: Although most patients (∼88%) initiated treatment within 30 days of diagnosis, our findings reveal persistent-albeit modest-inequities in access. Male, Hispanic, Asian/PI, and Black patients were slightly more likely to experience delays, particularly beyond 90 days. Those patients in fully urban areas also faced greater delays, suggesting potential strain on urban healthcare systems and highlighting the need for further investigation. These insights can inform targeted interventions to improve timely care for male, racial minorities, and urban populations affected by EOCRC. Citation Format: Meng-Han Tsai, Steven Coughlin, Kenneth J. Vega. Sex and Racial Disparities in Time to Treatment for Early-Onset Colorectal Cancer Across a Four-Level Rurality Classification in the United States [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 B034.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".