Abstract
Introduction
We investigated the impact of racial/ethnic disparities in therapy initiation on colorectal cancer (CRC) mortality using Surveillance, Epidemiology, and End Results Program (SEER) database.
Methods
Adults aged 18–84 years with CRC were identified. Cox models for 60-month all-cause and cancer-specific mortality were adjusted for demographics, stage, tumor site, income, and rural–urban residence.
Results
Therapy initiation was slower for Hispanics (HR 0.85) and non-Hispanic Black (NHB) patients (HR 0.80) compared with non-Hispanic Whites (p < 0.001). Each additional month of delay was associated with a 3% increase in cancer mortality (p < 0.001). Findings were consistent across diagnosis eras, with no significant race-by-era interaction, and adjustment for socioeconomic and geographic factors resulted in minimal attenuation of racial disparities.
Conclusion
Treatment delays independently contribute to all-cause and cancer-specific mortality, disproportionately affecting NHB and lower-SES patients.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1007/s10620-026-09897-4.
Keywords: Colorectal neoplasms, Health status disparities, Time-to-treatment, Survival analysis, Ethnic groups
Introduction
Colorectal cancer (CRC) is the second leading cause of cancer-related mortality in the United States, with more than 150,000 new cases diagnosed annually [1]. The degree to which delays in therapy are driven by socioeconomic status (SES), residential setting remains less well defined. Prior research has largely focused on survival differences by race/ethnicity [2–4], but few studies have examined whether therapy initiation delays are mediated by SES and geography. This study uses the Surveillance, Epidemiology, and End Results (SEER) Research Plus database to characterize differences in therapy initiation across racial/ethnic groups to determine the extent to which SES and rural–urban residence explain these disparities.
Methods
This retrospective cohort used SEER Research Plus (18 registries; 2000–2019, November 2022 submission) to identify adults aged 18–84 years with histologically confirmed colorectal cancer [5]. We excluded appendiceal tumors, benign/in situ disease, incomplete survival or cause-of-death data, missing/invalid treatment information, and treatment intervals < 1 or > 12 months to reduce misclassification from emergent or undocumented care. Time to treatment (TTT) was defined as the interval in months from the date of diagnosis to the first definitive therapy (surgery, chemotherapy, or radiation). Race/ethnicity was categorized as non-Hispanic White (NHW), non-hispanic black (NHB), Hispanic, non-Hispanic Asian/Pacific Islander (NHAPI), and non-Hispanic American Indian/Alaska Native (NHAIAN). We summarized baseline characteristics and calculated the proportion initiating treatment by 3, 6, and 12 months, as well as the number who died before treatment or remained untreated by 12 months. Cox proportional hazards models estimated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for TTT by race/ethnicity, adjusting for age, sex, stage, tumor site, income quartile, rural/urban residence, and year of diagnosis, with race-stratified Kaplan–Meier curves compared using log-rank and Wilcoxon tests. For mortality, patients were followed from diagnosis to death or censoring at 60 months; separate Cox models for all-cause and cancer-specific mortality used the same covariates. Sensitivity analyses added treatment and TTT to mortality models, used a 1-month landmark to reduce immortal-time bias, stratified by tumor site, and specified individual treatment modalities. Additional exploratory analyses were performed to assess the robustness and potential mechanisms underlying observed disparities. Attenuation analyses were conducted by comparing models with and without adjustment for socioeconomic and geographic variables. Temporal trends were evaluated using era-stratified models (2000–2009 vs 2010–2019) and race-by-era interaction terms. Effect modification by age was assessed using race-by-age interaction terms. Finally, sensitivity analyses were performed using simplified socioeconomic and geographic categorizations. All analyses used Stata/SE 19.5 (StataCorp, College Station, TX).
Results
A total of 102,910 patients met inclusion criteria (Table 1). The median age was 65 years (IQR 55–74), 44.2% were female, and racial/ethnic distribution was 67.1% NHW, 12.1% NHB, 11.3% Hispanic, 8.5% NHAPI, and 0.9% NHAIAN. Median time from diagnosis to first therapy was 1.5 months (SD 1.0). By 60 months, 29,311 (28.5%) had died of CRC and 41,077 (39.9%) of any cause.
Table 1.
Baseline characteristics of colorectal cancer patients by Race/Ethnicity (SEER Research Plus, 18 Registries, November 2022 submission, 2000–2019; N = 102,910)
| Factor | Hispanic (n = 13,677) | NHAIAN (n = 783) | NHAPI (n = 8840) | NHB (n = 12,464) | NH Unknown (n = 185) | NHW (n = 66,961) | p-value |
|---|---|---|---|---|---|---|---|
| N | 13,677 | 783 | 8840 | 12,464 | 185 | 66,961 | |
| Sex | < 0.001 | ||||||
| Male | 7,803 (57.1%) | 434 (55.4%) | 4,996 (56.5%) | 6,657 (53.4%) | 105 (56.8%) | 38,481 (57.5%) | |
| Female | 5,874 (42.9%) | 349 (44.6%) | 3,844 (43.5%) | 5,807 (46.6%) | 80 (43.2%) | 28,480 (42.5%) | |
| Age group at diagnosis | < 0.001 | ||||||
| < 50 | 2,197 (16.1%) | 136 (17.4%) | 1139 (12.9%) | 1472 (11.8%) | 25 (13.5%) | 5,617 (8.4%) | |
| 50–64 | 5,475 (40.0%) | 344 (43.9%) | 3,423 (38.7%) | 5,234 (42.0%) | 89 (48.1%) | 21,917 (32.7%) | |
| 65–74 | 3,623 (26.5%) | 208 (26.6%) | 2497 (28.2%) | 3533 (28.3%) | 48 (25.9%) | 20,427 (30.5%) | |
| 75–84 | 2,382 (17.4%) | 95 (12.1%) | 1781 (20.1%) | 2225 (17.9%) | 23 (12.4%) | 19,000 (28.4%) | |
| Primary site group (Colon vs Rectum) | < 0.001 | ||||||
| Colon | 8,499 (62.1%) | 483 (61.7%) | 5,292 (59.9%) | 8,676 (69.6%) | 107 (57.8%) | 42,303 (63.2%) | |
| Rectum | 5178 (37.9%) | 300 (38.3%) | 3548 (40.1%) | 3788 (30.4%) | 78 (42.2%) | 24,658 (36.8%) | |
| Stage | < 0.001 | ||||||
| Localized | 4907 (35.9%) | 288 (36.8%) | 3257 (36.8%) | 4495 (36.1%) | 108 (58.4%) | 27,203 (40.6%) | |
| Regional | 6015 (44.0%) | 318 (40.6%) | 3965 (44.9%) | 4955 (39.8%) | 63 (34.1%) | 27,471 (41.0%) | |
| Distant | 2755 (20.1%) | 177 (22.6%) | 1618 (18.3%) | 3014 (24.2%) | 14 (7.6%) | 12,287 (18.3%) | |
| Median household income (category) | < 0.001 | ||||||
| $100,000–$109,999 | 1,180 (8.6%) | 40 (5.1%) | 1375 (15.6%) | 871 (7.0%) | 25 (13.5%) | 7522 (11.2%) | |
| $110,000–$119,999 | 548 (4.0%) | 18 (2.3%) | 563 (6.4%) | 507 (4.1%) | 11 (5.9%) | 4638 (6.9%) | |
| $120,000 + | 563 (4.1%) | 6 (0.8%) | 783 (8.9%) | 208 (1.7%) | 9 (4.9%) | 3197 (4.8%) | |
| $40,000–$44,999 | 78 (0.6%) | 30 (3.8%) | 2 (< 1%) | 374 (3.0%) | 0 (0.0%) | 1178 (1.8%) | |
| $45,000–$49,999 | 222 (1.6%) | 35 (4.5%) | 13 (0.1%) | 758 (6.1%) | 3 (1.6%) | 1700 (2.5%) | |
| $50,000–$54,999 | 324 (2.4%) | 27 (3.4%) | 24 (0.3%) | 781 (6.3%) | 1 (0.5%) | 2604 (3.9%) | |
| $55,000–$59,999 | 413 (3.0%) | 38 (4.9%) | 62 (0.7%) | 590 (4.7%) | 5 (2.7%) | 3253 (4.9%) | |
| $60,000–$64,999 | 896 (6.6%) | 90 (11.5%) | 148 (1.7%) | 894 (7.2%) | 4 (2.2%) | 4526 (6.8%) | |
| $65,000–$69,999 | 635 (4.6%) | 28 (3.6%) | 192 (2.2%) | 831 (6.7%) | 5 (2.7%) | 3456 (5.2%) | |
| $70,000–$74,999 | 2,313 (16.9%) | 59 (7.5%) | 1,017 (11.5%) | 1,612 (12.9%) | 29 (15.7%) | 6136 (9.2%) | |
| $75,000–$79,999 | 3,487 (25.5%) | 72 (9.2%) | 1,673 (18.9%) | 2,259 (18.1%) | 31 (16.8%) | 9,786 (14.6%) | |
| $80,000–$84,999 | 1,262 (9.2%) | 51 (6.5%) | 602 (6.8%) | 935 (7.5%) | 19 (10.3%) | 6449 (9.6%) | |
| $85,000–$89,999 | 727 (5.3%) | 23 (2.9%) | 478 (5.4%) | 795 (6.4%) | 15 (8.1%) | 5075 (7.6%) | |
| $90,000–$94,999 | 479 (3.5%) | 211 (26.9%) | 672 (7.6%) | 436 (3.5%) | 9 (4.9%) | 3264 (4.9%) | |
| $95,000–$99,999 | 478 (3.5%) | 22 (2.8%) | 1,232 (13.9%) | 422 (3.4%) | 18 (9.7%) | 3171 (4.7%) | |
| < $40,000 | 70 (0.5%) | 33 (4.2%) | 4 (< 1%) | 191 (1.5%) | 1 (0.5%) | 1002 (1.5%) | |
| Unknown/missing/no match/Not 1990–2023 | 2 (< 1%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 4 (< 1%) | |
| Rural–urban continuum code | < 0.001 | ||||||
| Metro ≥ 1,000,000 | 9,750 (71.3%) | 217 (27.7%) | 6,629 (75.0%) | 8,320 (66.8%) | 132 (71.4%) | 39,978 (59.7%) | |
| Metro 250 k–1 M | 2,647 (19.4%) | 121 (15.5%) | 1,728 (19.5%) | 2,147 (17.2%) | 36 (19.5%) | 12,730 (19.0%) | |
| Metro < 250 k | 687 (5.0%) | 88 (11.2%) | 211 (2.4%) | 914 (7.3%) | 4 (2.2%) | 5,102 (7.6%) | |
| Non-metro, adjacent | 302 (2.2%) | 82 (10.5%) | 33 (0.4%) | 832 (6.7%) | 9 (4.9%) | 5,359 (8.0%) | |
| Non-metro, not adjacent | 289 (2.1%) | 90 (11.5%) | 239 (2.7%) | 251 (2.0%) | 4 (2.2%) | 3,788 (5.7%) | |
| AK/HI statewide | 0 (0.0%) | 185 (23.6%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
| Unknown/missing/Not 1990–2023 | 2 (< 1%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 4 (< 1%) | |
| Year of diagnosis, median (IQR) | 2011.0 (2007.0, 2015.0) | 2012.0 (2008.0, 2015.0) | 2011.0 (2007.0, 2015.0) | 2010.0 (2006.0, 2014.0) | 2013.0 (2008.0, 2016.0) | 2010.0 (2005.0, 2014.0) | < 0.001 |
| ttmonths, median (IQR) | 1.6 (1.2, 2.2) | 1.5 (1.2, 2.0) | 1.5 (1.2, 2.0) | 1.6 (1.2, 2.3) | 1.6 (1.2, 2.2) | 1.4 (1.2, 1.9) | < 0.001 |
| Survival months, median (IQR) | 71.0 (30.0, 118.0) | 68.0 (28.0, 116.0) | 77.0 (35.0, 129.0) | 65.0 (22.0, 117.0) | 88.0 (62.0, 151.0) | 72.0 (28.0, 123.0) | < 0.001 |
| Surgery | 11,378 (83.2%) | 627 (80.1%) | 7572 (85.7%) | 10,118 (81.2%) | 162 (87.6%) | 57,074 (85.2%) | < 0.001 |
| Chemotherapy | 7662 (56.0%) | 463 (59.1%) | 4875 (55.1%) | 6447 (51.7%) | 65 (35.1%) | 33,578 (50.1%) | < 0.001 |
| Radiation | 4,209 (30.8%) | 282 (36.0%) | 2816 (31.9%) | 2985 (23.9%) | 41 (22.2%) | 19,492 (29.1%) | < 0.001 |
| Any treatment | 13,653 (99.8%) | 778 (99.4%) | 8827 (99.9%) | 12,426 (99.7%) | 185 (100.0%) | 66,846 (99.8%) | 0.002 |
Compared with NHW, therapy initiation was slower for NHB (HR 0.80, 95% CI 0.79–0.82), Hispanic (HR 0.85, 95% CI 0.83–0.86), NHAIAN (HR 0.85, 95% CI 0.78–0.92), and NHAPI patients (HR 0.92, 95% CI 0.90–0.94; all p < 0.001). Lower- and middle-income ZIP code residents had slower initiation vs the highest income bracket ($120 k +) (Supplementary Table 1). Patients in smaller metropolitan counties (< 250 k) also had slower treatment (HR 1.08, 95% CI 1.05–1.11; HR 1.08, 95% CI 1.05–1.12). Kaplan–Meier curves showed significant racial disparities (log-rank p < 0.001; Wilcoxon p < 0.001) (Fig. 1A).
Fig. 1.
Racial/Ethnic Differences in Time to Treatment, Cancer-Specific Mortality, and All-Cause Mortality in Colorectal Cancer (SEER Research Plus, 18 registries; 2000–2019; N = 102,910). A. Time-to-treatment curves show slower initiation for non-Hispanic Black and American Indian/Alaska Native patients compared with non-Hispanic White patients. B. Cancer-specific mortality curves demonstrate highest risks among non-Hispanic Black patients and lower risks among Hispanic and Asian/Pacific Islander patients. C. All-cause mortality curves show elevated mortality for non-Hispanic Black and American Indian/Alaska Native patients, with lower mortality for Hispanic and Asian/Pacific Islander patients
By 3 months, initiation was 92.3% for NHW vs 86.7% NHB (− 5.6 pp), 88.6% Hispanic (− 3.8 pp), 90.9% NHAPI (− 1.5 pp), and 88.9% NHAIAN (− 3.5 pp). By 12 months, nearly all patients had initiated therapy (Supplementary Table 2).
After adjustment, NHB patients had higher cancer specific (CSM) vs NHW (HR 1.22, 95% CI 1.18–1.26). NHAPI patients had lower mortality (HR 0.94, 95% CI 0.90–0.99). Hispanic and NHAIAN patients did not differ significantly (HR 0.98, 95% CI 0.95–1.02; HR 0.94, 95% CI 0.81–1.09). Patients in < $40 k ZIP codes had 36% higher CSM (HR 1.36, 95% CI 1.21–1.52), and those in $40–55 k had 23–39% higher risk. Smaller metropolitan counties also had higher CSM (HR 1.05, 95% CI 1.00–1.11). Each additional month of delay increased cancer mortality by 3% (HR 1.03, 95% CI 1.02–1.04). Receipt of any treatment reduced CSM by 43% (HR 0.57, 95% CI 0.49–0.67) (Fig. 1B).
NHB patients had higher all-cause mortality (ACM) vs NHW (HR 1.17, 95% CI 1.13–1.20). Hispanic (HR 0.94, 95% CI 0.91–0.97) and NHAPI patients (HR 0.88, 95% CI 0.84–0.91) had lower mortality. Patients in < $40 k ZIP codes had 37% higher ACM (HR 1.37, 95% CI 1.25–1.51), with 25–39% higher risk in $40–55 k categories. Non-metropolitan, non-adjacent counties had higher ACM (HR 1.07, 95% CI 1.01–1.13). Each additional month to treatment increased ACM by 6% (HR 1.06, 95% CI 1.05–1.07). Receipt of any treatment reduced ACM by 40% (HR 0.60, 95% CI 0.51–0.70) (Fig. 1C).
Results were consistent across colon vs rectal cancers; major resection and chemotherapy remained protective. Models without treatment or TTT and a 1-month landmark analysis confirmed higher mortality hazards for NHB (Supplementary Table 3).
In attenuation analyses, adjustment for socioeconomic and geographic variables resulted in minimal changes in racial disparities in time-to-treatment. The hazard ratio for treatment initiation among non-Hispanic Black patients changed from 0.79 (95% CI 0.77–0.80) to 0.80 (95% CI 0.78–0.82), and for Hispanic patients from 0.83 (95% CI 0.81–0.84) to 0.84 (95% CI 0.83–0.86), indicating limited attenuation (Supplementary Table 4).
Era-stratified analyses demonstrated consistent associations between race and cancer-specific mortality across time periods. Non-Hispanic Black patients had higher mortality both before 2010 (HR 1.25, 95% CI 1.19–1.31; p < 0.001) and after 2010 (HR 1.21, 95% CI 1.15–1.26; p < 0.001). Race-by-era interaction terms were not statistically significant (e.g., NH Black × post-2010 HR 0.96, 95% CI 0.90–1.03), indicating stable disparities over time (Supplementary Table 5).
In race-by-age interaction analyses, non-Hispanic Black patients had higher mortality overall (HR 1.44, 95% CI 1.32–1.58), with attenuation of effect in older age groups (interaction HR 0.89 for ages 50–64, 0.83 for 65–74, and 0.72 for 75–84; all p < 0.05). Similar attenuation patterns were observed for Asian patients, although no consistent pattern suggesting clinically meaningful effect modification was identified (Supplementary Table S6).
In sensitivity analyses using simplified socioeconomic and geographic variables, results remained consistent, with persistent higher mortality among non-Hispanic Black patients (CSM HR 1.26, 95% CI 1.22–1.31) and increased risk associated with non-metropolitan residence (HR 1.19, 95% CI 1.15–1.23) (Supplementary Table S7).
Discussion
Despite extensive efforts, CRC survival disparities persist [2, 3]. Prior studies consistently show racial and ethnic gaps relative to NHW patients. [4, 6, 7] Our study report that inequities appear early at therapy initiation and these delays affect both all-cause and cancer-specific mortality, with the largest differences in the first three months. NHB and Hispanic patients had ~ 4–5-day longer median times to treatment (49 vs 44 days for NHB; 48 vs 44 for Hispanic), corresponding to 12–20% lower hazards of starting therapy after adjustment for income and rural–urban residence, consistent with systemic barriers noted previously. [8–10] At 3 months, NHB patients had a 5.6-pp lower probability of initiating treatment, with differences narrowing by 6–12 months. Each additional month before therapy increased all-cause mortality by 6% and cancer-specific mortality by 3%, with greater overall mortality impact among NHB patients. [11] NHAPI patients showed shorter delays and lower mortality, consistent with the “Asian survival advantage.” [12] Patients in ZIP codes with median incomes < $40,000 had 36–37% higher mortality, yet racial disparities remained after adjusting for income and rural–urban residence. Prior work shows worse survival in deprived areas [13] and among rural residents independent of income [14, 15].
Our additional analyses provide important context regarding the mechanisms underlying disparities. Adjustment for socioeconomic and geographic variables resulted in only minimal attenuation of racial differences in time-to-treatment, suggesting that structural factors beyond measurable SES likely contribute to inequities. Furthermore, the persistence of disparities across diagnosis eras indicates that advances in colorectal cancer care have not eliminated these gaps. Although interaction analyses demonstrated some heterogeneity by age, the absence of a consistent pattern suggests that disparities are broadly present across age groups rather than confined to specific subpopulations. Sensitivity analyses using simplified socioeconomic and geographic categorizations yielded consistent findings, supporting the robustness of our results.
The strengths of this study include its large, racially diverse cohort and use of contemporary SEER Research Plus data spanning nearly two decades. However, this study has limitations. The retrospective design limits causal inference, and residual confounding from unmeasured clinical factors is possible. SEER lacks granular access-to-care measures and does not reliably capture chemotherapy or radiation, leading to potential under-ascertainment and misclassification of time-to-treatment. Treatment timing reflects only the first recorded therapy. Conditioning models on treatment may introduce selection effects; thus, we also reported models without treatment and used a 1-month landmark to reduce immortal-time bias. Key contributors to delay, patient preferences, referral pathways, and institutional scheduling, are not available and warrant further study.
Supplementary Information
Below is the link to the electronic supplementary material.
Author Contributions
Hassam Ali, MD: Study concept/design, data analysis, drafting, critical revision, final approval Vishali Moond, MD: Interpretation, critical revisions, final approval Dushyant Singh Dahiya MD: Data interpretation, literature review, final approval Umar Hayat, MD, MPH: Statistical review, manuscript revisions, final approval Mohammad Bilal, MD: Clinical interpretation, revisions, mentorship, final approval Aasma Shaukat, MD, MPH: Supervision, critical review, final approval.
Funding
Open access funding provided by the Carolinas Consortium. No external funding was received for this study.
Data Availability
This study used publicly available, de-identified SEER Research Plus data; all datasets can be accessed through the SEER Program with standard user agreements.
Declarations
Conflicts of interest
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study used publicly available, de-identified SEER Research Plus data; all datasets can be accessed through the SEER Program with standard user agreements.