Survivability in Patients with Rare Colorectal Adenocarcinoma Variants: Exploring the Influence of Rural-Urban Continuum Codes and Social Determinants of Health in the United States

Survivability in Patients with Rare Colorectal Adenocarcinoma Variants: Exploring the Influence of Rural-Urban Continuum Codes and Social Determinants of Health in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Survivability in Patients with Rare Colorectal Adenocarcinoma Variants: Exploring the Influence of Rural-Urban Continuum Codes and Social Determinants of Health in the United States Md Roungu Ahmmad, Rodney P. Rocconi, Fazlay Faruque, Emran Hossain, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7005373/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Survival disparities in rare sigmoid colon adenocarcinoma are influenced by socioeconomic and geographic factors, particularly rural-urban residence and household income. This study examines the impact of RUCC and median household income (MHI) on survival outcomes, adjusting for clinical and demographic variables. Methods We analyzed data from the SEER database (1998–2017), including 94,697 patients diagnosed with rare histologic subtypes of sigmoid colon adenocarcinoma: signet-ring cell carcinoma (SRCC) and mucinous adenocarcinoma (MAC). Rectal cancer cases were excluded. Kaplan-Meier survival analysis and Cox proportional hazards models were used to assess survival, incorporating RUCC, MHI, age, sex, race, cancer stage, and treatment modalities. Interaction effects between RUCC and MHI were also evaluated. Results Patients residing in rural regions (RUCC 4–5) with low MHI ( $ 100,000) had the highest survival probability (0.72, 95% CI: 0.58–0.86). Older age, male sex, distant cancer stage, and Black race were significantly associated with increased mortality (p < 0.001). Surgical treatment was strongly associated with improved survival (HR = 0.41, 95% CI: 0.40–0.42, p < 0.001). Chemotherapy also conferred a protective effect in adjusted models (HR = 0.62, 95% CI: 0.61–0.64, p < 0.001). Conclusion Significant geographic and socioeconomic disparities exist in survival outcomes for patients with rare sigmoid colon adenocarcinoma. Targeted public health efforts, including improved access to screening and treatment in rural and low-income areas, are urgently needed to promote equitable cancer care. Colorectal cancer survival disparities rural-urban continuum median household income nomogram prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer (CRC) ranks as the third most common cancer globally, accounting for approximately 10% of all cancer cases (Ahadi et al., 2021 ). Adenocarcinoma is the predominant histological subtype, comprising about 90% of all CRC cases (DiSario et al., 1994 ). However, rarer adenocarcinoma subtypes in the colon exhibit distinct clinical and pathological features that may influence patient survival outcomes. Despite significant advancements in colorectal cancer (CRC) screening, surgical techniques, and adjuvant therapies, survival outcomes remain varied, especially among patients diagnosed with rare adenocarcinoma subtypes, such as SRCC and MAC.(DiSario et al., 1994 ; Zhang et al., 2023 ). For instance, signet-ring cell carcinoma (SRCC), a rare histological variant, is associated with poorer survival compared to conventional adenocarcinomas (Benesch & Mathieson, 2020 ). Research has shown that the 5-year survival rate for SRCC ranges from less than 30% to approximately 31.3% (Wan et al., 2017 ; Zhu et al., 2023 ). Similarly, mucinous adenocarcinoma (MAC) presents distinct clinical features and survival outcomes, with research indicating a 5-year overall survival rate of 78.6%, which tends to decrease as the disease advances (Huang et al., 2021 ). These disparities underscore the need for tailored treatment approaches and further research into the prognostic factors influencing outcomes in these rare CRC subtypes. In addition, understanding the demographic, socioeconomic, and treatment-related factors associated with survival for cancer patients is crucial for optimizing patient care and improving prognostic predictions (Horgan et al., 2024 ). Existing literature highlights that factor such as rural-urban status, median household income, race, age, cancer stage, and treatment modalities (surgery, radiotherapy, chemotherapy) significantly influence survival outcomes in CRC patients (Sepassi et al., 2024 ). However, limited studies exist regarding how these factors uniquely impact patients with rare adenocarcinoma variants, especially when excluding rectal cancer cases. A measure of socioeconomic status (SES) is median household income (MHI), which is crucial in determining access to healthcare and treatment outcomes (Finegan et al., 2018 ). Patients from lower-income households are at a significantly higher risk of late-stage cancer diagnosis and mortality due to several socioeconomic barriers (Shah & Chan, 2021 ). Lower-income individuals are less likely to undergo routine CRC screening (colonoscopy, FIT tests, Cologuard, sigmoidoscopy), leading to delayed detection and higher rates of advanced-stage disease at diagnosis (Smith et al., 2021 ). Out-of-pocket expenses and lack of insurance coverage often prevent individuals from following screening guidelines, increasing the likelihood of late-stage detection. The high cost of cancer treatment also contributes to financial strain for low-income patients, potentially causing delays or discontinuation of treatment (Carrera et al., 2018 ). Low-income patients are less likely to receive targeted therapies or participate in clinical trials, which can reduce survival outcomes (Nze & Herrera, 2025 ; Wells et al., 2021 ). In addition, patients from lower-income backgrounds may have reduced access to post-treatment surveillance, increasing the risk of undetected recurrence (Sanchez et al., 2022 ). Social determinants of health (SDOH) also play a crucial role in cancer survival. Factors such as access to healthcare, socioeconomic status, and community support significantly affect treatment options and outcomes for individuals with colon cancer (Lee et al., 2012 ; Liss & Baker, 2014 ). The CDC identifies five domains of SDOH that impact health outcomes, emphasizing that these determinants can have a more profound effect on health than genetic factors or healthcare access alone (Ganatra et al., 2024 ; Hacker et al., 2022 ). Additionally, studies have found a correlation between employment status and colorectal cancer survival, suggesting that financial stability is a key factor in patient prognosis (Manser & Bauerfeind, 2014 ). In addition, RUCC is a critical factor influencing patient survival, as geographic location significantly impacts access to healthcare and overall health outcomes. Rural cancer patients frequently experience lower survival rates due to limited healthcare access, lower screening rates, and delayed diagnoses (Bhatia et al., 2022 ). The RUCC categorize geographic regions into nine levels of urbanization which allows for a systematic assessment of rural-urban health disparities (Erly et al., 2024 ). Prior studies indicate that CRC patients in rural regions are more likely to present with later-stage disease and to receive less aggressive treatment, resulting in poorer prognoses (Burnett-Hartman et al., 2019 ). Rural populations also experience a higher burden of comorbidities, lower socioeconomic status, and reduced availability of specialized oncology care, which further contributes to disparities in survival outcomes (Bhatia et al., 2022 ; Ramkumar et al., 2022 ). Emerging research suggests that interventions such as telemedicine, patient navigation programs, and rural healthcare workforce expansion can help bridge the rural-urban divide in CRC outcomes (Batool & Lopez, 2023 ). Figure 1 illustrates the distribution of MHI and RUCC across the United States, using a color gradient to represent median income levels and degrees of rurality/urbanicity. This visualization highlights significant geographic disparities in income distribution, emphasizing the additional barriers rural patients face in accessing specialized cancer care, which may contribute to disparities in survival outcomes. This study aims to assess survival disparities in sigmoid colon adenocarcinoma across different RUCC and MHI levels. Additionally, it will examine the interaction effects between these two predictors to better understand their combined impact on survival outcomes. Materials and Methods Study Design and Data Sources This study utilized a retrospective, population-based cancer study data from the Surveillance, Epidemiology, and End Results (SEER) Program (1998–2017). SEER provides high-quality, population-based cancer data, capturing approximately 40% of the U.S. population (Penberthy & Friedman, 2024). The study focuses on patients diagnosed with rare adenocarcinoma variants of the colon cancer who died exclusively from colon cancer (excluding rectum cases). Detailed information about these data items can be found on the SEER website (http://www.seer.cancer.gov). We also used the USDA’s Rural-Urban Continuum Codes (RUCC) dataset to classify patient's place of residence. The USDA's RUCC database categorizes U.S. counties by their population size (metro) and urbanization and proximity to metro areas (nonmetro). County boundaries are classified as metropolitan or non-metropolitan based on Office of Management and Budget (OMB) delineation of metro areas in 2023. The data information can be found on the USDA website (https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/). Primary Outcome: The primary outcome is overall survival (OS), defined as the time from diagnosis to death due to colon cancer (excluding rectum cases) or last follow-up. Primary Predictors RUCCs, which categorize counties based on population density and proximity to metropolitan areas, serve as the measure of rurality/urbanicity. County-level median household income (MHI) was categorized into [specify categories, e.g., low, medium, and high] for the patient’s household income. Covariates: We included patients baseline predictors age, sex, race, histologic factors (e.g., cancer stage), as well as therapeutical interventions (chemotherapy, radiotherapy, surgery and radiosurgery sequency). Statistical Analysis Descriptive statistics were summarized using means (SD) for continuous variables and frequencies (%) for categorical variables, with comparisons by survival status (alive vs. deceased) using Chi-square tests. Overall survival (OS) was assessed using Kaplan-Meier curves. Cox proportional hazards regression quantified the impact of RUCC and MHI on survival, estimating hazard ratios (HR) with 95% confidence intervals (CIs) while adjusting for demographic, and clinical factors. Interaction terms assessed whether income modified the association between rurality and survival. A nomogram was developed to predict five-year survival probabilities based on RUCC and MHI disparities. Analyses were conducted in R programming (v4.4.2), with statistical significance set at p < 0.05. Results Participant Characteristics This study analyzed 94,697 patients diagnosed with rare adenocarcinoma variants of colon cancer (excluding rectal cancer) from the SEER database (1998–2017). The cohort was divided into two groups: 52.2% were alive, while 47.8% had died, with significant survival disparities based on baseline characteristics, geographic location, socioeconomic status, and clinical factors. Urban residents (RUCC 1–2) constituted 75.3% of the cohort and had higher survival rates than rural patients, suggesting that access to specialized medical care, facilities, and resources in urban settings may significantly impact patient outcomes Similarly, income influenced survival, with those earning > $ 100,000 experiencing the highest survival (55.2%) compared to 49.6% in lower-income groups (< $ 70,000). This finding underscores the critical role of socioeconomic factors in cancer prognosis. Demographically, the mean age at diagnosis was 65.3 years, with survivors being younger (61.5 years) than non-survivors (69.5 years). Women (52.6%) and Asian/Pacific Islander (API) patients (59.7%) had the highest survival, whereas Black patients had the lowest (48.0%). These demographic disparities reflect the complex interplay between race, healthcare access, and possibly biologically distinct cancer subtypes. Clinically, survival was highest among patients with localized disease (81.6%) and those who underwent surgery (58.1%). Conversely, chemotherapy recipients had lower survival (44.4%), which may indicate that patients receiving chemotherapy had more advanced diseases. The lower survival rate among chemotherapy patients might be explained by the fact that chemotherapy is typically administered in later-stage cancer, where the disease is harder to treat. These clinical observations suggest that interventions aimed at early diagnosis and reducing barriers to surgery could improve overall survival rates. These findings emphasize the need for further research into survival patterns, particularly the impact of rurality and socioeconomic disparities on patient survival outcomes. Table 1 Characteristics of the study population and grouped by patients’ survival status for the patients with rare tumors with adenocarcinoma with variants of colon and died only for colon cancer excluding rectum in SEER during 1998–2017 (N = 94,697). Predictors Overall N = 94,697 Alive N = 49,456 Dead N = 45,241 pval RUCC < 0.001 1 46738 (49.4) 24949 (53.4) 21789 (46.6) 2 24525 (25.9) 13126 (53.5) 11399 (46.5) 3 7866 (8.3) 3962 (50.4) 3904 (49.6) 4 7910 (8.4) 3758 (47.5) 4152 (52.5) 5 7658 (8.1) 3661 (47.8) 3997 (52.2) MHI < 0.001 Below 70,000 22216 (23.5) 11018 (49.6) 11198 (50.4) 70,000–90,000 35287 (37.3) 18429 (52.2) 16858 (47.8) 90,000–100,000 20732 (21.9) 10927 (52.7) 9805 (47.3) Above 100,000 16462 (17.4) 9082 (55.2) 7380 (44.8) Age : Mean (SD) 65.3 (13.6) 61.5 (12.3) 69.5 (13.9) < 0.001 Sex : Female 47790 (50.5) 25127 (52.6) 22663 (47.4) 0.029 Race/Ethnicity < 0.001 API 11322 (12.0) 6760 (59.7) 4562 (40.3) Black 8841 (9.3) 4244 (48.0) 4597 (52.0) White 74534 (78.7) 38452 (51.6) 36082 (48.4) Cancer Stage < 0.001 Localized 32942 (34.8) 26868 (81.6) 6074 (18.4) Regional 37655 (39.8) 20646 (54.8) 17009 (45.2) Distant 24100 (25.4) 1942 (8.1) 22158 (91.9) Surgery : Yes 83196 (89.4) 48299 (58.1) 34897 (41.9) < 0.001 Radiotherapy : Yes 4160 (4.4) 1941 (46.7) 2219 (53.3) < 0.001 Rad-Surgy Sequence < 0.001 Intraoperative radiation 28 (0.0) 15 (53.6) 13 (46.4) Radiation after surgery 2479 (2.6) 1164 (47.0) 1315 (53.0) Rad before and after surgy 32 (0.0) 13 (40.6) 19 (59.4) Rad prior to surgy 844 (0.9) 604 (71.6) 240 (28.4) Sequency Unknown 91314 (96.4) 47660 (52.2) 43654 (47.8) Chemotherapy : Yes 37972 (40.1) 16863 (44.4) 21109 (55.6) < 0.001 Continuous variables: mean (SD); SD = Standard deviation, Categorical variables: N (%), P-value calculated by Chi-square test. Statistical Analysis Figure 2 presents Kaplan–Meier survival curves representing the survival probabilities by RUCC and MHI. The left panel (A) illustrates survival differences based on RUCC, while the right panel (B) displays disparities by MHI. In panel A, patients from urban areas (RUCC 1 and 2) demonstrate higher survival probabilities over time compared to those from rural regions (RUCC 3–5). Notably, patients in RUCC 5 (most rural) consistently have the lowest survival rates, suggesting that geographic disparities significantly impact long-term survival outcomes. However, despite these trends, the survival predictions between different RUCC codes show no statistically significant differences, highlighting the complexity of how rurality/urbanicity affects survival (Fig. 2A). Similarly, Panel B reveals socioeconomic disparities throughout the follow-up in survival probability, showing that patients with income over $ 100,000 survive more than those below $ 70,000. The income gradient in the survival curves also revealed that economic disadvantages are correlated with poorer survival outcomes (Fig. 2B). These survival trends emphasize the influence of both geographical and economic disparities on this rare cancer prognosis. The key findings of the hazard ratio (HR) analysis are shown in Table 2 , incorporating both univariate (unadjusted) and multivariate (adjusted) Cox regression models. The results highlight notable links between survival outcomes and various demographic, socioeconomic, and clinical variables. Patients residing in RUCC 3 exhibited a significantly increased hazard of death compared to those in RUCC 1. Specifically, the adjusted hazard ratios (HRs) for RUCC 3, 4, and 5 were 1.06 (95% CI: 1.02–1.11), 1.10 (95% CI: 1.06–1.14), and 1.11 (95% CI: 1.06–1.15), respectively, all with p-values < 0.001, indicating a substantial survival disadvantage for rural patients. Regarding median household income (MHI), lower-income patients ( $ 100,000) showed a protective effect with an HR of 0.86 (95% CI: 0.84–0.89, p < 0.001). However, after adjustment, this effect was somewhat attenuated (HR = 0.95, p = 0.004), suggesting that while income is a strong factor, other covariates also contribute to survival disparities for this disease conditions. Table 2 An analysis of sigmoid colon cancer hazard risk using both simple univariate and multiple regression models in the United States (N = 94,697). Predictors Unadjusted HR (95%CI) Unadj p-val Adjusted HR (95%CI) Adjusted p-val PLR test p-val RUCC: Ref = rucc 1 < 0.001 rucc 2 0.99 (0.97,1.02) 0.650 0.92 (0.97,1.02) 0.951 rucc 3 1.09 (1.05,1.12) < 0.001 1.06 (1.02,1.11) 0.003 rucc 4 1.17 (1.14,1.21) < 0.001 1.10 (1.06,1.14) < 0.001 rucc 5 1.18 (1.13,1.21) < 0.001 1.11 (1.06,1.15) < 0.001 Median HH Income: Ref = Below $70, 000 < 0.001 $ 70.000- $ 90.000 0.92 (0.9,0.95) < 0.001 1.05 (1.02,1.09) < 0.001 $ 90.000- $ 100.000 0.89 (0.87,0.92) < 0.001 1.03 (0.99,1.07) 0.097 Above $ 100,000 0.86 (0.84,0.89) < 0.001 0.95 (0.91,0.98) 0.004 Age (in Years) 1.04 (1.03,1.04) < 0.001 1.05 (1.04,1.06) < 0.001 < 0.001 Male vs Female 1.01 (0.91,1.02) 0.641 1.07 (1.05,1.1) < 0.001 < 0.001 Race: Ref = API < 0.001 Black 1.42 (1.36,1.48) < 0.001 1.35 (1.29,1.41) < 0.001 White 1.28 (1.24,1.32) < 0.001 1.14 (1.11,1.18) < 0.001 Cancer Stage: ref = Localized < 0.001 Regional 2.96 (2.87,3.05) < 0.001 3.76 (3.65,3.88) < 0.001 Distant 13.51 (13.12,13.91) < 0.001 16.23 (15.69,16.78) < 0.001 Surgery: Yes vs No 0.15 (0.15,0.16) < 0.001 0.41 (0.4,0.42) < 0.001 < 0.001 Chemotherapy: Yes vs No 1.31 (1.28,1.33) < 0.001 0.62 (0.61,0.64) < 0.001 < 0.001 Radiotherapy: Yes vs No 1.11 (1.07,1.16) < 0.001 1.02 (0.98,1.07) 0.323 0.325 HR: Hazard Ratio, CI: Confidence Interval, PLR: Partial Likelihood Ratio Increasing age was significantly associated with a higher risk of mortality (adjusted HR = 1.05, p < 0.001), demonstrating that older patients had poorer survival outcomes. Male patients also had a higher risk compared to females (adjusted HR = 1.07, p < 0.001). Additionally, racial disparities in mortality were evident. Compared to Asian/Pacific Islanders (API, reference group), Black patients had the highest hazard of death (adjusted HR = 1.35, p < 0.001), followed by White patients (adjusted HR = 1.14, p < 0.001). These disparities may reflect differences in healthcare access, tumor biology, or treatment responses, highlighting the demand for targeted intrusions to address inequities in cancer care. As anticipated, the cancer stage emerged as the most influential factor in mortality risk. Patients with regional disease exhibited an adjusted hazard ratio (HR) of 3.76 (p < 0.001), whereas those with distant metastases faced a significantly higher HR of 16.23 (p < 0.001). The results emphasize the significance of prompt diagnosis and timely medical intervention in enhancing patient survival rates. In terms of treatment, surgical intervention significantly reduced the hazard risk, with an adjusted HR of 0.41 (p < 0.001), emphasizing its vital role in improving survival outcomes. Interestingly, while chemotherapy initially appeared to increase the hazard risk (unadjusted HR = 1.31), the adjusted model revealed a protective effect (adjusted HR = 0.62, p < 0.001), suggesting that patients receiving chemotherapy were likely to have more aggressive disease at baseline. Radiotherapy, however, did not show a significant adjusted effect (p = 0.323). These results emphasize the necessity of early detection and appropriate treatment selection in managing this rare cancer. Figure 3 presents a hazard ratio (HR) analysis evaluating the interaction between RUCC and MHI on survival outcomes for patients with this rare cancer. This analysis explores how socioeconomic factors modify the impact of geographic location on survival outcomes. The interaction between higher-income categories ( $ 80,000– $ 100,000 and > $ 100,000) and rural residence (RUCC 4 and 5) generally indicate a lower hazard of mortality, suggesting a protective effect of higher income in rural areas. Patients in RUCC 4 with incomes over $ 100,000 had a noticeably lower hazard risk (HR = 0.57, 95% CI: 0.40–0.80), suggesting that financial stability plays a crucial role in improving this rare cancer survival outcomes, even in geographically disadvantaged areas. In contrast, hazard risk estimates for RUCC 3 individuals earning above $ 100,000 were unavailable, likely due to a smaller sample size limiting statistical power. Among the general effects of MHI alone, patients with incomes $ 80,000 - $ 100,000 exhibited a significantly increased hazard compared to those earning less than $ 70,000 (HR = 1.08, 95% CI: 1.01–1.16). In addition, those with incomes between $ 70,000– $ 80,000 had insignificant HR (1.03, 95% CI: 0.96–1.11), suggesting that the survival benefits of income might be non-linear and influenced by other social determinants. Regarding the independent effect of RUCC categories, patients residing in RUCC 3, 4, and 5 had a significantly elevated hazard of death compared to RUCC 1 (urban reference). Specifically, RUCC 4 and 5 both had an HR of 1.26 (95% CI: 1.17–1.35), and RUCC 2 showed a weaker, non-significant effect (HR = 1.06, 95% CI: 0.98–1.15). Survival rates for patients with this rare cancer are influenced by a complex interplay of geographic and socioeconomic factors. While a higher income can help appear to mitigate some of the survival disadvantages associated with rural residence, disparities persist, particularly among lower-income individuals in these regions. Figure 4 presents dynamic nomogram model-based five-year survival probabilities for patients with this rare cancer, adjusting for histological, therapeutic, and baseline predictors. The survival probabilities are stratified by RUCC and MHI levels, illustrating the interaction between socioeconomic and geographic factors in predicting survival outcomes. The results indicate a clear gradient in survival probabilities, where patients residing in urban areas (RUCC 1–2) with higher incomes (> $ 100,000) have the best survival outcomes, with probabilities ranging from 0.60 to 0.72. In contrast, those in more rural settings (RUCC 4–5) with lower incomes (< $ 70,000) exhibit significantly worse survival, with probabilities as low as 0.53. This trend highlights the compounded impact of rurality and lower socioeconomic status on this rare cancer survival. Interestingly, for middle-income groups ( $ 70,000– $ 80,000 and $ 80,000– $ 100,000), survival probabilities exhibit a modest variation across rural-urban settings, without a consistent trend favoring extreme. This could indicate that while income provides some survival advantage, other unmeasured factors—such as healthcare accessibility, comorbidities, and treatment disparities—also could be a modifier for survival outcome for this disease conditions. Patients in the highest income bracket (> $ 100,000) consistently demonstrate the highest survival across all RUCC categories, emphasizing the protective role of financial resources. Conversely, individuals in rural areas with lower incomes face a disproportionate survival disadvantage. These results underscore the importance of targeted interventions to bridge healthcare disparities, particularly in rural and lower-income populations. Discussion This analysis reveals significant geographic, socioeconomic, racial, and clinical disparities in survival among patients with rare colon adenocarcinomas cancer in the U.S. Worse survival outcomes were observed among rural residents, lower-income groups, older patients, males, and Black individuals, while surgery and chemotherapy demonstrated protective effects. The study highlights the impact of socioeconomic determinants of health (SDOH) and geographic disparities, emphasizing the interaction between RUCC and MHI as key factors requiring policy attention to ensure equitable cancer care and survival outcomes for this disease condition. Our analysis indicates that patients residing in urban areas (RUCC 1) with higher income levels (MHI > $ 100,000) had the highest five-year survival probability (0.72, 95% CI: 0.58–0.86). Thus, Patients in urban areas often benefit from greater access to healthcare facilities, improved screening programs, and specialized cancer treatment centers. As a result, early diagnosis is more common, allowing for timely and effective medical interventions (Siegel et al., 2018 ). Conversely, individuals from rural areas (RUCC 4–5) with lower incomes (< $ 70,000) exhibited significantly lower survival rates, with probabilities dropping to 0.53 (95% CI: 0.42–0.64). These results align with existing literature that rural cancer patients experience delayed diagnosis, reduced access to oncological care, and lower adherence to treatment guidelines (Acquah et al., 2022 ; Bhatia et al., 2022 ; George et al., 2022; Strickland & Strickland, 1996 ). Even among middle-income groups ( $ 70,000– $ 100,000), survival probability varied across RUCC levels. Patients in semi-rural settings (RUCC 2–3) exhibited modestly better survival than those in more isolated rural areas (RUCC 4–5). This suggests that while income remains a crucial determinant of survival, geographic location further exacerbates disparities, potentially due to limited healthcare infrastructure, transportation challenges, and specialist shortages in rural communities (Batool & Lopez, 2023 ; George et al., 2022; Wilson et al., 2009 ) Increasing age was significantly associated with poorer survival outcomes, with older patients exhibiting a higher risk of mortality (adjusted HR = 1.05, p < 0.001). This finding is consistent with prior research indicating that older individuals are more likely to present with advanced-stage disease and experience higher comorbidity burdens, which can limit treatment options and affect prognosis (Siegel et al., 2018 ; Wilson et al., 2009 ). Additionally, older patients often have lower adherence to follow-up care and cancer screenings, leading to delayed diagnoses and poorer outcomes (Sepassi et al., 2024 ; Zapka et al., 2010 ). In addition, male patients had a significantly higher mortality risk compared to females (adjusted HR = 1.07, p < 0.001). Prior research suggests this may be due to hormonal, behavioral, and healthcare utilization differences (Horgan et al., 2024 ; Zapka et al., 2010 ). Estrogen has been found to exert a protective effect against colorectal cancer progression, potentially explaining the improved survival observed in female patients (Lavasani et al., 2015 ). Additionally, men are generally less likely to engage in routine cancer screenings and preventive healthcare, which may contribute to later-stage diagnoses and poorer outcomes (Lavasani et al., 2015 ; Siegel et al., 2018 ). Significant racial disparities were observed, with Black patients exhibiting the highest hazard of death (adjusted HR = 1.35, p < 0.001), followed by White patients (adjusted HR = 1.14, p < 0.001), compared to Asian/Pacific Islander (API) individuals, who had the most favorable survival outcomes. These disparities likely reflect systemic differences in healthcare access, socioeconomic status, and treatment quality (Siegel et al., 2018 ; Smith et al., 2021 ). Black patients have been reported to experience delays in diagnosis, lower rates of guideline-concordant treatment, and limited access to high-quality oncology care, all of which contribute to poorer survival (Ramkumar et al., 2022 ). Cancer stages of diagnosis emerged as the most critical determinant of survival. Patients diagnosed with localized disease had the highest survival rates (81.6%), whereas those with regional and distant metastases exhibited significantly lower survival rates (54.8% and 8.1%, respectively). The adjusted hazard ratios confirmed a substantial survival disadvantage for patients with advanced-stage disease (regional: HR = 3.76, p < 0.001; distant: HR = 16.23, p < 0.001). These findings align with previous research emphasizing the importance of early detection and timely treatment in improving colorectal cancer survival outcomes (Lavasani et al., 2015 ; Shah & Chan, 2021 ). Late-stage diagnoses are often linked to disparities in healthcare access, screening uptake, and socioeconomic barriers that delay medical intervention. Another factor contributing to survival disparities is treatment modality and quality. Prior research has shown that rural patients are less likely to receive guideline-concordant care, including adjuvant chemotherapy, radiotherapy, and advanced surgical techniques (Burnett-Hartman et al., 2019 ; Shah & Chan, 2021 ). Additionally, income disparities influence insurance coverage and out-of-pocket costs, affecting patients' ability to afford targeted therapies and post-treatment follow-ups (Nicoll et al., 2022 ; Ramkumar et al., 2022 ; Zapka et al., 2010 ). The results of this study reinforce these findings, as lower-income patients—especially those in rural settings—experience disproportionately lower survival probabilities despite adjustments for clinical and histopathological factors. Public Health Implications This study underscores the crucial need for targeted public health interventions to address survival disparities among patients with this rare cancer. Expanding access to early screening, particularly in rural and low-income communities, is crucial for improving early diagnosis and treatment outcomes. Strengthening healthcare infrastructure through telemedicine, patient navigation programs, and financial assistance can mitigate geographic and socioeconomic barriers to care. Additionally, implementing multidisciplinary care teams that include oncologists, nurses, social workers, dietitians, and mental health professionals can provide holistic care to improve patient outcomes. Addressing racial and gender disparities through culturally competent healthcare strategies and tailored outreach programs can further improve survival rates. Policymakers must integrate social determinants of health into cancer care models, ensuring equitable access to high-quality treatment. Increasing access to clinical trials and research opportunities for rare cancers will also help underserved populations benefit from the latest advancements in cancer treatment. Long-term survivorship care, including follow-up monitoring and health maintenance, is essential for patients who overcome cancer. Furthermore, improving insurance coverage and advocating for policy reforms can reduce financial barriers to care, particularly for low-income individuals. By implementing these strategies, public health efforts can reduce disparities and improve survival outcomes for these cancer patients, ensuring a more equitable and comprehensive approach to this cancer care. Limitations and Future Directions While the findings of the study give us robust insights into the interaction between socioeconomic status and geographic location on sigmoid colon cancer survival, several limitations should be acknowledged. At first, the study is based on data from the SEER (1998–2017) dataset, which, despite its comprehensiveness, may not capture recent advancements in colorectal cancer treatment. Second, comorbidities that were not considered, lifestyle factors (diet, exercise), and patient adherence to treatment recommendations could contribute to the observed survival differences. Third, our study does not account for racial and ethnic disparities within rural and urban populations, which warrants further investigation. Therefore, future research should emphasize prospective cohort studies to better understand the long-term effects of policy interventions on survival disparities. Moreover, integrating machine learning and predictive modeling could aid in creating personalized risk assessment tools that consider clinical, socioeconomic, and geographic variables to support more precise treatment planning. Conclusion This study reveals marked socioeconomic and geographic disparities in survival among patients diagnosed with the rare subtype of sigmoid colon adenocarcinoma. Patients residing in rural areas, specifically those classified under RUCC codes 4 and 5, as well as individuals from communities with lower median household incomes, exhibit significantly elevated mortality rates. These disparities may be attributed to limited access to timely cancer screening, delays in diagnosis, and insufficient availability of specialized oncology care. The findings emphasize the critical need for targeted interventions, including the expansion of colorectal cancer screening programs, investment in healthcare infrastructure within rural and medically underserved areas, and policy initiatives aimed at reducing structural barriers to care. Such efforts are essential to improving outcomes and achieving equity in cancer care delivery for these high-risk populations. Declarations Competing interests The authors declare no competing interests. Funding Open Access funding enabled and organized by USF Health, University of South Florida Author Contribution Md Roungu Ahmmad conceptualized the study, performed the data analysis, and led the manuscript writing. Rodney P. Rocconi contributed to the clinical interpretation of findings and provided critical revisions of the manuscript. Fazlay Faruque assisted in the study design and data interpretation and offered guidance on the methodological framework. Emran Hossain contributed to data acquisition, preprocessing, and assisted with references. Salwa Musarikandy supported the literature review, data, and manuscript formatting and referencing. All authors reviewed and approved the final version of the manuscript. Acknowledgement We would like to thank the SEER and USDA for giving permission to access the data. Data availability The publicly available data from SEER and USDA website used for this project. All meta data will be provided upon potential request. References Acquah, I., Hagan, K., Valero-Elizondo, J., Javed, Z., Butt, S. A., Mahajan, S., Taha, M. B., Hyder, A. A., Mossialos, E., Cainzos-Achirica, M., & Nasir, K. (2022). Delayed medical care due to transportation barriers among adults with atherosclerotic cardiovascular disease. American Heart Journal , 245 , 60–69. https://doi.org/10.1016/j.ahj.2021.11.019 Ahadi, M., Sokolova, A., Brown, I., Chou, A., & Gill, A. J. (2021). The 2019 World Health Organization Classification of appendiceal, colorectal and anal canal tumours: An update and critical assessment. Pathology , 53 (4), 454–461. https://doi.org/10.1016/j.pathol.2020.10.010 Batool, D. A., & Lopez, D. A. (2023). Healthcare Access and Regional Connectivity: Bridging the Gap. Journal of Regional Connectivity and Development , 2 (2), Article 2. Benesch, M. G. K., & Mathieson, A. (2020). Epidemiology of Signet Ring Cell Adenocarcinomas. Cancers , 12 (6), Article 6. https://doi.org/10.3390/cancers12061544 Bhatia, S., Landier, W., Paskett, E. D., Peters, K. B., Merrill, J. K., Phillips, J., & Osarogiagbon, R. U. (2022). Rural–Urban Disparities in Cancer Outcomes: Opportunities for Future Research. JNCI: Journal of the National Cancer Institute , 114 (7), 940–952. https://doi.org/10.1093/jnci/djac030 Burnett-Hartman, A. N., Powers, J. D., Chubak, J., Corley, D. A., Ghai, N. R., McMullen, C. K., Pawloski, P. A., Sterrett, A. T., & Feigelson, H. S. (2019). Treatment patterns and survival differ between early-onset and late-onset colorectal cancer patients: The patient outcomes to advance learning network. Cancer Causes & Control , 30 (7), 747–755. https://doi.org/10.1007/s10552-019-01181-3 Carrera, P. M., Kantarjian, H. M., & Blinder, V. S. (2018). The financial burden and distress of patients with cancer: Understanding and stepping-up action on the financial toxicity of cancer treatment. CA: A Cancer Journal for Clinicians , 68 (2), 153–165. https://doi.org/10.3322/caac.21443 DiSario, J. A., Burt, R. W., Kendrick, M. L., & McWhorter, W. P. (1994). Colorectal cancers of rare histologic types compared with adenocarcinomas. Diseases of the Colon & Rectum , 37 (12), 1277. https://doi.org/10.1007/BF02257796 Erly, S., Mocha, C. M., Amiya, R. M., & Glick, S. N. (2024). Development of a rural–urban classification system for public health research that accommodates structural differences between states. American Journal of Epidemiology , 193 (12), 1840–1847. https://doi.org/10.1093/aje/kwae119 Finegan, M., Firth, N., Wojnarowski, C., & Delgadillo, J. (2018). Associations between socioeconomic status and psychological therapy outcomes: A systematic review and meta-analysis. Depression and Anxiety , 35 (6), 560–573. https://doi.org/10.1002/da.22765 Ganatra, S., Khadke, S., Kumar, A., Khan, S., Javed, Z., Nasir, K., Rajagopalan, S., Wadhera, R. K., Dani, S. S., & Al-Kindi, S. (2024). Standardizing social determinants of health data: A proposal for a comprehensive screening tool to address health equity a systematic review. Health Affairs Scholar , 2 (12), qxae151. https://doi.org/10.1093/haschl/qxae151 George, M., Smith ,Alexandra, Ranmuthugula ,Geetha, & and Sabesan, S. (2022). Barriers to Accessing, Commencing and Completing Cancer Treatment Among Geriatric Patients in Rural Australia: A Qualitative Perspective. International Journal of General Medicine , 15 , 1583–1594. https://doi.org/10.2147/IJGM.S338128 Hacker, K., Auerbach, J., Ikeda, R., Philip, C., & Houry, D. (2022). Social Determinants of Health—An Approach Taken at CDC. Journal of Public Health Management and Practice , 28 (6), 589. https://doi.org/10.1097/PHH.0000000000001626 Horgan, D., Van den Bulcke, M., Malapelle, U., Normanno, N., Capoluongo, E. D., Prelaj, A., Rizzari, C., Stathopoulou, A., Singh, J., Kozaric, M., Dube, F., Ottaviano, M., Boccia, S., Pravettoni, G., Cattaneo, I., Malats, N., Buettner, R., Lekadir, K., de Lorenzo, F., … Hofman, P. (2024). Demographic Analysis of Cancer Research Priorities and Treatment Correlations. Current Oncology , 31 (4), Article 4. https://doi.org/10.3390/curroncol31040139 Huang, A., Yang, Y., Shi, J.-Y., Li, Y.-K., Xu, J.-X., Cheng, Y., & Gu, J. (2021). Mucinous adenocarcinoma: A unique clinicopathological subtype in colorectal cancer. World Journal of Gastrointestinal Surgery , 13 (12), 1567–1583. https://doi.org/10.4240/wjgs.v13.i12.1567 Lavasani, S., Chlebowski, R. T., Prentice, R. L., Kato, I., Wactawski-Wende, J., Johnson, K. C., Young, A., Rodabough, R., Hubbell, F. A., Mahinbakht, A., & Simon, M. S. (2015). Estrogen and colorectal cancer incidence and mortality. Cancer , 121 (18), 3261–3271. https://doi.org/10.1002/cncr.29464 Lee, W., Nelson, R., Mailey, B., Duldulao, M. P., Garcia-Aguilar, J., & Kim, J. (2012). Socioeconomic Factors Impact Colon Cancer Outcomes in Diverse Patient Populations. Journal of Gastrointestinal Surgery , 16 (4), 692–704. https://doi.org/10.1007/s11605-011-1809-y Liss, D. T., & Baker, D. W. (2014). Understanding Current Racial/Ethnic Disparities in Colorectal Cancer Screening in the United States: The Contribution of Socioeconomic Status and Access to Care. American Journal of Preventive Medicine , 46 (3), 228–236. https://doi.org/10.1016/j.amepre.2013.10.023 Manser, C. N., & Bauerfeind, P. (2014). Impact of socioeconomic status on incidence, mortality, and survival of colorectal cancer patients: A systematic review. Gastrointestinal Endoscopy , 80 (1), 42-60.e9. https://doi.org/10.1016/j.gie.2014.03.011 Nicoll, I., Lockwood, G., Longo, C. J., Loiselle, C. G., & Fitch, M. I. (2022). Relationships between Canadian adult cancer survivors’ annual household income and emotional/practical concerns, help-seeking and unmet needs. Health & Social Care in the Community , 30 (4), e1290–e1301. https://doi.org/10.1111/hsc.13536 Nze, C., & Herrera, A. F. (2025). New strategies for enhancing enrollment of underrepresented minorities in lymphoma clinical trials. Blood Advances , 9 (4), 774–782. https://doi.org/10.1182/bloodadvances.2024012981 Penberthy, L., & Friedman, S. (2024). The SEER Program’s evolution: Supporting clinically meaningful population-level research. JNCI Monographs , 2024 (65), 110–117. https://doi.org/10.1093/jncimonographs/lgae022 Ramkumar, N., Colla, C. H., Wang, Q., O’Malley, A. J., Wong, S. L., & Brooks, G. A. (2022). Association of Rurality, Race and Ethnicity, and Socioeconomic Status With the Surgical Management of Colon Cancer and Postoperative Outcomes Among Medicare Beneficiaries. JAMA Network Open , 5 (8), e2229247. https://doi.org/10.1001/jamanetworkopen.2022.29247 Sanchez, J. I., Shankaran, V., Unger, J. M., Madeleine, M. M., Espinoza, N., & Thompson, B. (2022). Disparities in post-operative surveillance testing for metastatic recurrence among colorectal cancer survivors. Journal of Cancer Survivorship , 16 (3), 638–649. https://doi.org/10.1007/s11764-021-01057-z Sepassi, A., Li, M., A. Zell, J., Chan, A., Saunders, I. M., & Mukamel, D. B. (2024). Rural-Urban Disparities in Colorectal Cancer Screening, Diagnosis, Treatment, and Survivorship Care: A Systematic Review and Meta-Analysis. The Oncologist , 29 (4), e431–e446. https://doi.org/10.1093/oncolo/oyad347 Shah, R., & Chan, K. K. W. (2021). The impact of socioeconomic status on stage at presentation, receipt of diagnostic imaging, receipt of treatment and overall survival in colorectal cancer patients. International Journal of Cancer , 149 (5), 1031–1043. https://doi.org/10.1002/ijc.33622 Siegel, R. L., Miller, K. D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians , 68 (1), 7–30. https://doi.org/10.3322/caac.21442 Smith, R. A., Fedewa, S., & Siegel, R. (2021). Chapter Three—Early colorectal cancer detection—Current and evolving challenges in evidence, guidelines, policy, and practices. In F. G. Berger & C. R. Boland (Eds.), Advances in Cancer Research (Vol. 151, pp. 69–107). Academic Press. https://doi.org/10.1016/bs.acr.2021.03.005 Strickland, J., & Strickland, D. L. (1996). Barriers to Preventive Health Services for Minority Households in the Rural South. The Journal of Rural Health , 12 (3), 206–217. https://doi.org/10.1111/j.1748-0361.1996.tb00795.x Wan, Z., Huang, Z., & Chen, L. (2017). Survival predictors associated with signet ring cell carcinoma of the esophagus (SRCCE): A population-based retrospective cohort study. PLOS ONE , 12 (7), e0181845. https://doi.org/10.1371/journal.pone.0181845 Wells, J. C., Sharma, S., Del Paggio, J. C., Hopman, W. M., Gyawali, B., Mukherji, D., Hammad, N., Pramesh, C. S., Aggarwal, A., Sullivan, R., & Booth, C. M. (2021). An Analysis of Contemporary Oncology Randomized Clinical Trials From Low/Middle-Income vs High-Income Countries. JAMA Oncology , 7 (3), 379–385. https://doi.org/10.1001/jamaoncol.2020.7478 Wilson, N. W., Couper, I. D., De, V. E., Reid, S., Fish, T., & Marais, B. J. (2009). A critical review of interventions to redress the inequitable distribution of healthcare professionals to rural and remote areas. Rural and Remote Health , 9 (2), 1–21. https://doi.org/10.3316/informit.496326474031125 Zapka, J., Taplin, S. H., Anhang Price, R., Cranos, C., & Yabroff, R. (2010). Factors in Quality Care—The Case of Follow-Up to Abnormal Cancer Screening Tests—Problems in the Steps and Interfaces of Care. JNCI Monographs , 2010 (40), 58–71. https://doi.org/10.1093/jncimonographs/lgq009 Zhang, F., Xu, B., Peng, Y., Mao, Z., & Tong, S. (2023). Incidence and survival of adenocarcinoma with mixed subtypes in patients with colorectal cancer. International Journal of Colorectal Disease , 38 (1), 215. https://doi.org/10.1007/s00384-023-04508-4 Zhu, Y., Thandar, M., Cheng, J., Zhang, X., Zhao, Z., Huang, S., & Chi, P. (2023). Comparison of survival outcomes and survival prediction in patients with primary colorectal MANEC and primary colorectal SRCC: A population-based propensity-score matching study. Journal of Cancer Research and Clinical Oncology , 149 (14), 13279–13300. https://doi.org/10.1007/s00432-023-05043-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7005373","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478405202,"identity":"00dd8a7f-18bc-40d7-a9c6-66ea01215765","order_by":0,"name":"Md Roungu Ahmmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDACCRDBZsPAwHyAgYGHBC1pQJxAmpbDJGjhn92duuFH2fnE+W0MjA/ethFjyZ2z2272nLuduOEYA7PhXGK0MNzI3XaDtw2oRb6BTZqXGC3yQC03/7adAzmM/TdRWgyAWm7zth1IbDjGwMZMlBZDkBaZc8nGG44xNkvOOUeEFjmQw96U2cnOb2M++OFNGRFakABjA2nqR8EoGAWjYBTgBgDrEDuWww1PdwAAAABJRU5ErkJggg==","orcid":"","institution":"USF Health, University of South Florida","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Roungu","lastName":"Ahmmad","suffix":""},{"id":478405203,"identity":"ed3c09a1-6832-4b27-a741-010b7837341c","order_by":1,"name":"Rodney P. Rocconi","email":"","orcid":"","institution":"University of Mississippi Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Rodney","middleName":"P.","lastName":"Rocconi","suffix":""},{"id":478405204,"identity":"27b53ef4-b5e9-493c-8df3-e0b82a2c12dd","order_by":2,"name":"Fazlay Faruque","email":"","orcid":"","institution":"University of Mississippi Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Fazlay","middleName":"","lastName":"Faruque","suffix":""},{"id":478405205,"identity":"433e0133-65cd-4e22-b1d5-42ee3016e714","order_by":3,"name":"Emran Hossain","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Emran","middleName":"","lastName":"Hossain","suffix":""},{"id":478405206,"identity":"d9baaf46-bbf4-4fa5-81a4-72e173bcf963","order_by":4,"name":"Salwa Musarikandy","email":"","orcid":"","institution":"University Southern Mississippi","correspondingAuthor":false,"prefix":"","firstName":"Salwa","middleName":"","lastName":"Musarikandy","suffix":""}],"badges":[],"createdAt":"2025-06-30 01:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7005373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7005373/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85837585,"identity":"451ac897-e875-4889-b492-7e8b562c026e","added_by":"auto","created_at":"2025-07-02 08:27:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUS median Household income per county and US the degree of rurality (RUCC) per county. The color code represents blue, which is high income country whereas green represents a low-income country, and the color code represents blue is urban country whereas green represent rural county.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7005373/v1/4b7459893628a1c73cf8b333.png"},{"id":85837596,"identity":"d18f3400-952d-41a9-adf7-5de5a4defa58","added_by":"auto","created_at":"2025-07-02 08:27:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival probability among RUCC and SODH for patients diagnosed with rare tumors with adenocarcinoma with variants of colon and died only for colon cancer excluding rectum in SEER (1998–2017) (Kaplan–Meier).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7005373/v1/7b2df2fbff3c43633b38b809.png"},{"id":85837877,"identity":"b2c12b96-b99d-4013-8d7c-a2eeeeec1469","added_by":"auto","created_at":"2025-07-02 08:35:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":214364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA regression analysis of the interaction effects between RUCC and SODH and survival for the patients diagnosed with rare tumors with adenocarcinoma with variants of colon and died only for colon cancer excluding rectum varying SDOH and RUCC interaction in SEER (1998–2017).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7005373/v1/297f7d04ff9a988b343ef32b.png"},{"id":85837595,"identity":"9e83813c-c1df-4c4e-8acd-915e36651eb3","added_by":"auto","created_at":"2025-07-02 08:27:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel based survival prediction by adjusting histological, therapeutical and baseline predictors. 5-year survival probability prediction by using nomogram for the patients diagnosed with rare tumors with adenocarcinoma with variants of colon and died only for colon cancer excluding rectum by interaction of SDOH and RUCC in SEER dataset (1998–2017).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7005373/v1/cc602a156ee68476c698b6c0.png"},{"id":86542276,"identity":"147831f8-9e28-4790-919f-c41b81acfede","added_by":"auto","created_at":"2025-07-11 21:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1790433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7005373/v1/b16b67c5-f415-4d24-b6dd-8bc9cd540363.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survivability in Patients with Rare Colorectal Adenocarcinoma Variants: Exploring the Influence of Rural-Urban Continuum Codes and Social Determinants of Health in the United States","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks as the third most common cancer globally, accounting for approximately 10% of all cancer cases (Ahadi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Adenocarcinoma is the predominant histological subtype, comprising about 90% of all CRC cases (DiSario et al., \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). However, rarer adenocarcinoma subtypes in the colon exhibit distinct clinical and pathological features that may influence patient survival outcomes. Despite significant advancements in colorectal cancer (CRC) screening, surgical techniques, and adjuvant therapies, survival outcomes remain varied, especially among patients diagnosed with rare adenocarcinoma subtypes, such as SRCC and MAC.(DiSario et al., \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, signet-ring cell carcinoma (SRCC), a rare histological variant, is associated with poorer survival compared to conventional adenocarcinomas (Benesch \u0026amp; Mathieson, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has shown that the 5-year survival rate for SRCC ranges from less than 30% to approximately 31.3% (Wan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, mucinous adenocarcinoma (MAC) presents distinct clinical features and survival outcomes, with research indicating a 5-year overall survival rate of 78.6%, which tends to decrease as the disease advances (Huang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). These disparities underscore the need for tailored treatment approaches and further research into the prognostic factors influencing outcomes in these rare CRC subtypes.\u003c/p\u003e\n\u003cp\u003eIn addition, understanding the demographic, socioeconomic, and treatment-related factors associated with survival for cancer patients is crucial for optimizing patient care and improving prognostic predictions (Horgan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing literature highlights that factor such as rural-urban status, median household income, race, age, cancer stage, and treatment modalities (surgery, radiotherapy, chemotherapy) significantly influence survival outcomes in CRC patients (Sepassi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, limited studies exist regarding how these factors uniquely impact patients with rare adenocarcinoma variants, especially when excluding rectal cancer cases.\u003c/p\u003e\n\u003cp\u003eA measure of socioeconomic status (SES) is median household income (MHI), which is crucial in determining access to healthcare and treatment outcomes (Finegan et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Patients from lower-income households are at a significantly higher risk of late-stage cancer diagnosis and mortality due to several socioeconomic barriers (Shah \u0026amp; Chan, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Lower-income individuals are less likely to undergo routine CRC screening (colonoscopy, FIT tests, Cologuard, sigmoidoscopy), leading to delayed detection and higher rates of advanced-stage disease at diagnosis (Smith et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Out-of-pocket expenses and lack of insurance coverage often prevent individuals from following screening guidelines, increasing the likelihood of late-stage detection. The high cost of cancer treatment also contributes to financial strain for low-income patients, potentially causing delays or discontinuation of treatment (Carrera et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Low-income patients are less likely to receive targeted therapies or participate in clinical trials, which can reduce survival outcomes (Nze \u0026amp; Herrera, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wells et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, patients from lower-income backgrounds may have reduced access to post-treatment surveillance, increasing the risk of undetected recurrence (Sanchez et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSocial determinants of health (SDOH) also play a crucial role in cancer survival. Factors such as access to healthcare, socioeconomic status, and community support significantly affect treatment options and outcomes for individuals with colon cancer (Lee et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liss \u0026amp; Baker, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The CDC identifies five domains of SDOH that impact health outcomes, emphasizing that these determinants can have a more profound effect on health than genetic factors or healthcare access alone (Ganatra et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hacker et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, studies have found a correlation between employment status and colorectal cancer survival, suggesting that financial stability is a key factor in patient prognosis (Manser \u0026amp; Bauerfeind, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition, RUCC is a critical factor influencing patient survival, as geographic location significantly impacts access to healthcare and overall health outcomes. Rural cancer patients frequently experience lower survival rates due to limited healthcare access, lower screening rates, and delayed diagnoses (Bhatia et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The RUCC categorize geographic regions into nine levels of urbanization which allows for a systematic assessment of rural-urban health disparities (Erly et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Prior studies indicate that CRC patients in rural regions are more likely to present with later-stage disease and to receive less aggressive treatment, resulting in poorer prognoses (Burnett-Hartman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rural populations also experience a higher burden of comorbidities, lower socioeconomic status, and reduced availability of specialized oncology care, which further contributes to disparities in survival outcomes (Bhatia et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ramkumar et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Emerging research suggests that interventions such as telemedicine, patient navigation programs, and rural healthcare workforce expansion can help bridge the rural-urban divide in CRC outcomes (Batool \u0026amp; Lopez, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e illustrates the distribution of MHI and RUCC across the United States, using a color gradient to represent median income levels and degrees of rurality/urbanicity. This visualization highlights significant geographic disparities in income distribution, emphasizing the additional barriers rural patients face in accessing specialized cancer care, which may contribute to disparities in survival outcomes. This study aims to assess survival disparities in sigmoid colon adenocarcinoma across different RUCC and MHI levels. Additionally, it will examine the interaction effects between these two predictors to better understand their combined impact on survival outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized a retrospective, population-based cancer study data from the Surveillance, Epidemiology, and End Results (SEER) Program (1998\u0026ndash;2017). SEER provides high-quality, population-based cancer data, capturing approximately 40% of the U.S. population (Penberthy \u0026amp; Friedman, 2024). The study focuses on patients diagnosed with rare adenocarcinoma variants of the colon cancer who died exclusively from colon cancer (excluding rectum cases). Detailed information about these data items can be found on the SEER website (http://www.seer.cancer.gov).\u003c/p\u003e\n\u003cp\u003eWe also used the USDA\u0026rsquo;s Rural-Urban Continuum Codes (RUCC) dataset to classify patient\u0026apos;s place of residence. The USDA\u0026apos;s RUCC database categorizes U.S. counties by their population size (metro) and urbanization and proximity to metro areas (nonmetro). County boundaries are classified as metropolitan or non-metropolitan based on Office of Management and Budget (OMB) delineation of metro areas in 2023. The data information can be found on the USDA website (https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Outcome:\u0026nbsp;\u003c/strong\u003eThe primary outcome is overall survival (OS), defined as the time from diagnosis to death due to colon cancer (excluding rectum cases) or last follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eRUCCs, which categorize counties based on population density and proximity to metropolitan areas, serve as the measure of rurality/urbanicity.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;County-level median household income (MHI) was categorized into [specify categories, e.g., low, medium, and high] for the patient\u0026rsquo;s household income.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates:\u0026nbsp;\u003c/strong\u003eWe included patients baseline predictors age, sex, race, histologic factors (e.g., cancer stage), as well as therapeutical interventions (chemotherapy, radiotherapy, surgery and radiosurgery sequency).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were summarized using means (SD) for continuous variables and frequencies (%) for categorical variables, with comparisons by survival status (alive vs. deceased) using Chi-square tests. Overall survival (OS) was assessed using Kaplan-Meier curves. Cox proportional hazards regression quantified the impact of RUCC and MHI on survival, estimating hazard ratios (HR) with 95% confidence intervals (CIs) while adjusting for demographic, and clinical factors. Interaction terms assessed whether income modified the association between rurality and survival. A nomogram was developed to predict five-year survival probabilities based on RUCC and MHI disparities. Analyses were conducted in R programming (v4.4.2), with statistical significance set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\n \u003cp\u003eThis study analyzed 94,697 patients diagnosed with rare adenocarcinoma variants of colon cancer (excluding rectal cancer) from the SEER database (1998\u0026ndash;2017). The cohort was divided into two groups: 52.2% were alive, while 47.8% had died, with significant survival disparities based on baseline characteristics, geographic location, socioeconomic status, and clinical factors. Urban residents (RUCC 1\u0026ndash;2) constituted 75.3% of the cohort and had higher survival rates than rural patients, suggesting that access to specialized medical care, facilities, and resources in urban settings may significantly impact patient outcomes Similarly, income influenced survival, with those earning \u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000 experiencing the highest survival (55.2%) compared to 49.6% in lower-income groups (\u0026lt;\u003cspan\u003e$\u003c/span\u003e70,000). This finding underscores the critical role of socioeconomic factors in cancer prognosis.\u003c/p\u003e\n \u003cp\u003eDemographically, the mean age at diagnosis was 65.3 years, with survivors being younger (61.5 years) than non-survivors (69.5 years). Women (52.6%) and Asian/Pacific Islander (API) patients (59.7%) had the highest survival, whereas Black patients had the lowest (48.0%). These demographic disparities reflect the complex interplay between race, healthcare access, and possibly biologically distinct cancer subtypes. Clinically, survival was highest among patients with localized disease (81.6%) and those who underwent surgery (58.1%). Conversely, chemotherapy recipients had lower survival (44.4%), which may indicate that patients receiving chemotherapy had more advanced diseases. The lower survival rate among chemotherapy patients might be explained by the fact that chemotherapy is typically administered in later-stage cancer, where the disease is harder to treat. These clinical observations suggest that interventions aimed at early diagnosis and reducing barriers to surgery could improve overall survival rates. These findings emphasize the need for further research into survival patterns, particularly the impact of rurality and socioeconomic disparities on patient survival outcomes.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the study population and grouped by patients\u0026rsquo; survival status for the patients with rare tumors with adenocarcinoma with variants of colon and died only for colon cancer excluding rectum in SEER during 1998\u0026ndash;2017 (N\u0026thinsp;=\u0026thinsp;94,697).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94,697\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;49,456\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;45,241\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRUCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46738 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24949 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21789 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24525 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13126 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11399 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7866 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3962 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3904 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7910 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3758 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4152 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7658 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3661 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3997 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow 70,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22216 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11018 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11198 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70,000\u0026ndash;90,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35287 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18429 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16858 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90,000\u0026ndash;100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20732 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10927 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9805 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16462 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9082 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7380 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e: Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.3 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.5 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e: Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47790 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25127 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22663 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11322 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6760 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4562 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8841 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4244 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4597 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74534 (78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38452 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36082 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32942 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26868 (81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6074 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37655 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20646 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17009 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24100 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1942 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22158 (91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e: Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83196 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48299 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34897 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e: Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4160 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1941 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2219 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRad-Surgy Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntraoperative radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiation after surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2479 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1164 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1315 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRad before and after surgy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRad prior to surgy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e604 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSequency Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91314 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47660 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43654 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e: Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37972 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16863 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21109 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eContinuous variables: mean (SD); SD\u0026thinsp;=\u0026thinsp;Standard deviation, Categorical variables: N (%), P-value calculated by Chi-square test.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e presents Kaplan\u0026ndash;Meier survival curves representing the survival probabilities by RUCC and MHI. The left panel (A) illustrates survival differences based on RUCC, while the right panel (B) displays disparities by MHI. In panel A, patients from urban areas (RUCC 1 and 2) demonstrate higher survival probabilities over time compared to those from rural regions (RUCC 3\u0026ndash;5). Notably, patients in RUCC 5 (most rural) consistently have the lowest survival rates, suggesting that geographic disparities significantly impact long-term survival outcomes. However, despite these trends, the survival predictions between different RUCC codes show no statistically significant differences, highlighting the complexity of how rurality/urbanicity affects survival (Fig. 2A).\u003c/p\u003e\n \u003cp\u003eSimilarly, Panel B reveals socioeconomic disparities throughout the follow-up in survival probability, showing that patients with income over \u003cspan\u003e$\u003c/span\u003e100,000 survive more than those below \u003cspan\u003e$\u003c/span\u003e70,000. The income gradient in the survival curves also revealed that economic disadvantages are correlated with poorer survival outcomes (Fig.\u0026nbsp;2B). These survival trends emphasize the influence of both geographical and economic disparities on this rare cancer prognosis.\u003c/p\u003e\n \u003cp\u003eThe key findings of the hazard ratio (HR) analysis are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, incorporating both univariate (unadjusted) and multivariate (adjusted) Cox regression models. The results highlight notable links between survival outcomes and various demographic, socioeconomic, and clinical variables. Patients residing in RUCC 3 exhibited a significantly increased hazard of death compared to those in RUCC 1. Specifically, the adjusted hazard ratios (HRs) for RUCC 3, 4, and 5 were 1.06 (95% CI: 1.02\u0026ndash;1.11), 1.10 (95% CI: 1.06\u0026ndash;1.14), and 1.11 (95% CI: 1.06\u0026ndash;1.15), respectively, all with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a substantial survival disadvantage for rural patients.\u003c/p\u003e\n \u003cp\u003eRegarding median household income (MHI), lower-income patients (\u0026lt;\u003cspan\u003e$\u003c/span\u003e70,000) had worse survival outcomes. In unadjusted models, higher-income groups (\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000) showed a protective effect with an HR of 0.86 (95% CI: 0.84\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, after adjustment, this effect was somewhat attenuated (HR\u0026thinsp;=\u0026thinsp;0.95, p\u0026thinsp;=\u0026thinsp;0.004), suggesting that while income is a strong factor, other covariates also contribute to survival disparities for this disease conditions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAn analysis of sigmoid colon cancer hazard risk using both simple univariate and multiple regression models in the United States (N\u0026thinsp;=\u0026thinsp;94,697).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadj\u003c/p\u003e\n \u003cp\u003ep-val\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted p-val\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLR test\u003c/p\u003e\n \u003cp\u003ep-val\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eRUCC: Ref\u0026thinsp;=\u0026thinsp;rucc 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erucc 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.97,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.97,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erucc 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (1.05,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (1.02,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erucc 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (1.14,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (1.06,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erucc 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (1.13,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.06,1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian HH Income: Ref\u0026thinsp;=\u0026thinsp;Below $70, 000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e70.000- \u003cspan\u003e$\u003c/span\u003e90.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.9,0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.02,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e90.000- \u003cspan\u003e$\u003c/span\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.87,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.99,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove \u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.84,0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.91,0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (in Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (1.03,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.04,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale vs Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.91,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (1.05,1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace: Ref\u0026thinsp;=\u0026thinsp;API\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.36,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.29,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (1.24,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.11,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Stage: ref\u0026thinsp;=\u0026thinsp;Localized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96 (2.87,3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76 (3.65,3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.51 (13.12,13.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.23 (15.69,16.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery: Yes vs No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.15,0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (0.4,0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy: Yes vs No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (1.28,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.61,0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy: Yes vs No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.07,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.98,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eHR: Hazard Ratio, CI: Confidence Interval, PLR: Partial Likelihood Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIncreasing age was significantly associated with a higher risk of mortality (adjusted HR\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating that older patients had poorer survival outcomes. Male patients also had a higher risk compared to females (adjusted HR\u0026thinsp;=\u0026thinsp;1.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, racial disparities in mortality were evident. Compared to Asian/Pacific Islanders (API, reference group), Black patients had the highest hazard of death (adjusted HR\u0026thinsp;=\u0026thinsp;1.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by White patients (adjusted HR\u0026thinsp;=\u0026thinsp;1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These disparities may reflect differences in healthcare access, tumor biology, or treatment responses, highlighting the demand for targeted intrusions to address inequities in cancer care.\u003c/p\u003e\n \u003cp\u003eAs anticipated, the cancer stage emerged as the most influential factor in mortality risk. Patients with regional disease exhibited an adjusted hazard ratio (HR) of 3.76 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas those with distant metastases faced a significantly higher HR of 16.23 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results emphasize the significance of prompt diagnosis and timely medical intervention in enhancing patient survival rates. In terms of treatment, surgical intervention significantly reduced the hazard risk, with an adjusted HR of 0.41 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing its vital role in improving survival outcomes. Interestingly, while chemotherapy initially appeared to increase the hazard risk (unadjusted HR\u0026thinsp;=\u0026thinsp;1.31), the adjusted model revealed a protective effect (adjusted HR\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that patients receiving chemotherapy were likely to have more aggressive disease at baseline. Radiotherapy, however, did not show a significant adjusted effect (p\u0026thinsp;=\u0026thinsp;0.323). These results emphasize the necessity of early detection and appropriate treatment selection in managing this rare cancer.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents a hazard ratio (HR) analysis evaluating the interaction between RUCC and MHI on survival outcomes for patients with this rare cancer. This analysis explores how socioeconomic factors modify the impact of geographic location on survival outcomes. The interaction between higher-income categories (\u003cspan\u003e$\u003c/span\u003e80,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e100,000 and \u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000) and rural residence (RUCC 4 and 5) generally indicate a lower hazard of mortality, suggesting a protective effect of higher income in rural areas. Patients in RUCC 4 with incomes over \u003cspan\u003e$\u003c/span\u003e100,000 had a noticeably lower hazard risk (HR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.40\u0026ndash;0.80), suggesting that financial stability plays a crucial role in improving this rare cancer survival outcomes, even in geographically disadvantaged areas. In contrast, hazard risk estimates for RUCC 3 individuals earning above \u003cspan\u003e$\u003c/span\u003e100,000 were unavailable, likely due to a smaller sample size limiting statistical power.\u003c/p\u003e\n \u003cp\u003eAmong the general effects of MHI alone, patients with incomes \u003cspan\u003e$\u003c/span\u003e80,000 - \u003cspan\u003e$\u003c/span\u003e100,000 exhibited a significantly increased hazard compared to those earning less than \u003cspan\u003e$\u003c/span\u003e70,000 (HR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.01\u0026ndash;1.16). In addition, those with incomes between \u003cspan\u003e$\u003c/span\u003e70,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e80,000 had insignificant HR (1.03, 95% CI: 0.96\u0026ndash;1.11), suggesting that the survival benefits of income might be non-linear and influenced by other social determinants. Regarding the independent effect of RUCC categories, patients residing in RUCC 3, 4, and 5 had a significantly elevated hazard of death compared to RUCC 1 (urban reference). Specifically, RUCC 4 and 5 both had an HR of 1.26 (95% CI: 1.17\u0026ndash;1.35), and RUCC 2 showed a weaker, non-significant effect (HR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 0.98\u0026ndash;1.15). Survival rates for patients with this rare cancer are influenced by a complex interplay of geographic and socioeconomic factors. While a higher income can help appear to mitigate some of the survival disadvantages associated with rural residence, disparities persist, particularly among lower-income individuals in these regions.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents dynamic nomogram model-based five-year survival probabilities for patients with this rare cancer, adjusting for histological, therapeutic, and baseline predictors. The survival probabilities are stratified by RUCC and MHI levels, illustrating the interaction between socioeconomic and geographic factors in predicting survival outcomes. The results indicate a clear gradient in survival probabilities, where patients residing in urban areas (RUCC 1\u0026ndash;2) with higher incomes (\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000) have the best survival outcomes, with probabilities ranging from 0.60 to 0.72. In contrast, those in more rural settings (RUCC 4\u0026ndash;5) with lower incomes (\u0026lt;\u003cspan\u003e$\u003c/span\u003e70,000) exhibit significantly worse survival, with probabilities as low as 0.53.\u003c/p\u003e\n \u003cp\u003eThis trend highlights the compounded impact of rurality and lower socioeconomic status on this rare cancer survival. Interestingly, for middle-income groups (\u003cspan\u003e$\u003c/span\u003e70,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e80,000 and \u003cspan\u003e$\u003c/span\u003e80,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e100,000), survival probabilities exhibit a modest variation across rural-urban settings, without a consistent trend favoring extreme. This could indicate that while income provides some survival advantage, other unmeasured factors\u0026mdash;such as healthcare accessibility, comorbidities, and treatment disparities\u0026mdash;also could be a modifier for survival outcome for this disease conditions. Patients in the highest income bracket (\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000) consistently demonstrate the highest survival across all RUCC categories, emphasizing the protective role of financial resources. Conversely, individuals in rural areas with lower incomes face a disproportionate survival disadvantage. These results underscore the importance of targeted interventions to bridge healthcare disparities, particularly in rural and lower-income populations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis analysis reveals significant geographic, socioeconomic, racial, and clinical disparities in survival among patients with rare colon adenocarcinomas cancer in the U.S. Worse survival outcomes were observed among rural residents, lower-income groups, older patients, males, and Black individuals, while surgery and chemotherapy demonstrated protective effects. The study highlights the impact of socioeconomic determinants of health (SDOH) and geographic disparities, emphasizing the interaction between RUCC and MHI as key factors requiring policy attention to ensure equitable cancer care and survival outcomes for this disease condition.\u003c/p\u003e \u003cp\u003e Our analysis indicates that patients residing in urban areas (RUCC 1) with higher income levels (MHI \u0026gt; \u003cspan\u003e$\u003c/span\u003e100,000) had the highest five-year survival probability (0.72, 95% CI: 0.58\u0026ndash;0.86). Thus, Patients in urban areas often benefit from greater access to healthcare facilities, improved screening programs, and specialized cancer treatment centers. As a result, early diagnosis is more common, allowing for timely and effective medical interventions (Siegel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ). Conversely, individuals from rural areas (RUCC 4\u0026ndash;5) with lower incomes (\u0026lt; \u003cspan\u003e$\u003c/span\u003e70,000) exhibited significantly lower survival rates, with probabilities dropping to 0.53 (95% CI: 0.42\u0026ndash;0.64). These results align with existing literature that rural cancer patients experience delayed diagnosis, reduced access to oncological care, and lower adherence to treatment guidelines (Acquah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e ; Bhatia et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e ; George et al., 2022; Strickland \u0026amp; Strickland, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e ). Even among middle-income groups (\u003cspan\u003e$\u003c/span\u003e70,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e100,000), survival probability varied across RUCC levels. Patients in semi-rural settings (RUCC 2\u0026ndash;3) exhibited modestly better survival than those in more isolated rural areas (RUCC 4\u0026ndash;5). This suggests that while income remains a crucial determinant of survival, geographic location further exacerbates disparities, potentially due to limited healthcare infrastructure, transportation challenges, and specialist shortages in rural communities (Batool \u0026amp; Lopez, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e ; George et al., 2022; Wilson et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e ) \u003c/p\u003e \u003cp\u003eIncreasing age was significantly associated with poorer survival outcomes, with older patients exhibiting a higher risk of mortality (adjusted HR\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is consistent with prior research indicating that older individuals are more likely to present with advanced-stage disease and experience higher comorbidity burdens, which can limit treatment options and affect prognosis (Siegel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, older patients often have lower adherence to follow-up care and cancer screenings, leading to delayed diagnoses and poorer outcomes (Sepassi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zapka et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, male patients had a significantly higher mortality risk compared to females (adjusted HR\u0026thinsp;=\u0026thinsp;1.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prior research suggests this may be due to hormonal, behavioral, and healthcare utilization differences (Horgan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zapka et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Estrogen has been found to exert a protective effect against colorectal cancer progression, potentially explaining the improved survival observed in female patients (Lavasani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, men are generally less likely to engage in routine cancer screenings and preventive healthcare, which may contribute to later-stage diagnoses and poorer outcomes (Lavasani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Siegel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Significant racial disparities were observed, with Black patients exhibiting the highest hazard of death (adjusted HR\u0026thinsp;=\u0026thinsp;1.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by White patients (adjusted HR\u0026thinsp;=\u0026thinsp;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to Asian/Pacific Islander (API) individuals, who had the most favorable survival outcomes. These disparities likely reflect systemic differences in healthcare access, socioeconomic status, and treatment quality (Siegel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Black patients have been reported to experience delays in diagnosis, lower rates of guideline-concordant treatment, and limited access to high-quality oncology care, all of which contribute to poorer survival (Ramkumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCancer stages of diagnosis emerged as the most critical determinant of survival. Patients diagnosed with localized disease had the highest survival rates (81.6%), whereas those with regional and distant metastases exhibited significantly lower survival rates (54.8% and 8.1%, respectively). The adjusted hazard ratios confirmed a substantial survival disadvantage for patients with advanced-stage disease (regional: HR\u0026thinsp;=\u0026thinsp;3.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; distant: HR\u0026thinsp;=\u0026thinsp;16.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings align with previous research emphasizing the importance of early detection and timely treatment in improving colorectal cancer survival outcomes (Lavasani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shah \u0026amp; Chan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Late-stage diagnoses are often linked to disparities in healthcare access, screening uptake, and socioeconomic barriers that delay medical intervention.\u003c/p\u003e \u003cp\u003eAnother factor contributing to survival disparities is treatment modality and quality. Prior research has shown that rural patients are less likely to receive guideline-concordant care, including adjuvant chemotherapy, radiotherapy, and advanced surgical techniques (Burnett-Hartman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shah \u0026amp; Chan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, income disparities influence insurance coverage and out-of-pocket costs, affecting patients' ability to afford targeted therapies and post-treatment follow-ups (Nicoll et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ramkumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zapka et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The results of this study reinforce these findings, as lower-income patients\u0026mdash;especially those in rural settings\u0026mdash;experience disproportionately lower survival probabilities despite adjustments for clinical and histopathological factors.\u003c/p\u003e\n\u003ch3\u003ePublic Health Implications\u003c/h3\u003e\n\u003cp\u003eThis study underscores the crucial need for targeted public health interventions to address survival disparities among patients with this rare cancer. Expanding access to early screening, particularly in rural and low-income communities, is crucial for improving early diagnosis and treatment outcomes. Strengthening healthcare infrastructure through telemedicine, patient navigation programs, and financial assistance can mitigate geographic and socioeconomic barriers to care. Additionally, implementing multidisciplinary care teams that include oncologists, nurses, social workers, dietitians, and mental health professionals can provide holistic care to improve patient outcomes. Addressing racial and gender disparities through culturally competent healthcare strategies and tailored outreach programs can further improve survival rates. Policymakers must integrate social determinants of health into cancer care models, ensuring equitable access to high-quality treatment. Increasing access to clinical trials and research opportunities for rare cancers will also help underserved populations benefit from the latest advancements in cancer treatment. Long-term survivorship care, including follow-up monitoring and health maintenance, is essential for patients who overcome cancer. Furthermore, improving insurance coverage and advocating for policy reforms can reduce financial barriers to care, particularly for low-income individuals. By implementing these strategies, public health efforts can reduce disparities and improve survival outcomes for these cancer patients, ensuring a more equitable and comprehensive approach to this cancer care.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eWhile the findings of the study give us robust insights into the interaction between socioeconomic status and geographic location on sigmoid colon cancer survival, several limitations should be acknowledged. At first, the study is based on data from the SEER (1998\u0026ndash;2017) dataset, which, despite its comprehensiveness, may not capture recent advancements in colorectal cancer treatment. Second, comorbidities that were not considered, lifestyle factors (diet, exercise), and patient adherence to treatment recommendations could contribute to the observed survival differences. Third, our study does not account for racial and ethnic disparities within rural and urban populations, which warrants further investigation. Therefore, future research should emphasize prospective cohort studies to better understand the long-term effects of policy interventions on survival disparities. Moreover, integrating machine learning and predictive modeling could aid in creating personalized risk assessment tools that consider clinical, socioeconomic, and geographic variables to support more precise treatment planning.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals marked socioeconomic and geographic disparities in survival among patients diagnosed with the rare subtype of sigmoid colon adenocarcinoma. Patients residing in rural areas, specifically those classified under RUCC codes 4 and 5, as well as individuals from communities with lower median household incomes, exhibit significantly elevated mortality rates. These disparities may be attributed to limited access to timely cancer screening, delays in diagnosis, and insufficient availability of specialized oncology care. The findings emphasize the critical need for targeted interventions, including the expansion of colorectal cancer screening programs, investment in healthcare infrastructure within rural and medically underserved areas, and policy initiatives aimed at reducing structural barriers to care. Such efforts are essential to improving outcomes and achieving equity in cancer care delivery for these high-risk populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eOpen Access funding enabled and organized by USF Health, University of South Florida\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMd Roungu Ahmmad conceptualized the study, performed the data analysis, and led the manuscript writing. Rodney P. Rocconi contributed to the clinical interpretation of findings and provided critical revisions of the manuscript. Fazlay Faruque assisted in the study design and data interpretation and offered guidance on the methodological framework. Emran Hossain contributed to data acquisition, preprocessing, and assisted with references. Salwa Musarikandy supported the literature review, data, and manuscript formatting and referencing. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the SEER and USDA for giving permission to access the data.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe publicly available data from SEER and USDA website used for this project. All meta data will be provided upon potential request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcquah, I., Hagan, K., Valero-Elizondo, J., Javed, Z., Butt, S. A., Mahajan, S., Taha, M. B., Hyder, A. A., Mossialos, E., Cainzos-Achirica, M., \u0026amp; Nasir, K. (2022). Delayed medical care due to transportation barriers among adults with atherosclerotic cardiovascular disease. \u003cem\u003eAmerican Heart Journal\u003c/em\u003e, \u003cem\u003e245\u003c/em\u003e, 60\u0026ndash;69. https://doi.org/10.1016/j.ahj.2021.11.019\u003c/li\u003e\n\u003cli\u003eAhadi, M., Sokolova, A., Brown, I., Chou, A., \u0026amp; Gill, A. J. (2021). The 2019 World Health Organization Classification of appendiceal, colorectal and anal canal tumours: An update and critical assessment. \u003cem\u003ePathology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(4), 454\u0026ndash;461. https://doi.org/10.1016/j.pathol.2020.10.010\u003c/li\u003e\n\u003cli\u003eBatool, D. A., \u0026amp; Lopez, D. A. (2023). Healthcare Access and Regional Connectivity: Bridging the Gap. \u003cem\u003eJournal of Regional Connectivity and Development\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), Article 2.\u003c/li\u003e\n\u003cli\u003eBenesch, M. G. K., \u0026amp; Mathieson, A. (2020). Epidemiology of Signet Ring Cell Adenocarcinomas. \u003cem\u003eCancers\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(6), Article 6. https://doi.org/10.3390/cancers12061544\u003c/li\u003e\n\u003cli\u003eBhatia, S., Landier, W., Paskett, E. D., Peters, K. B., Merrill, J. K., Phillips, J., \u0026amp; Osarogiagbon, R. U. (2022). Rural\u0026ndash;Urban Disparities in Cancer Outcomes: Opportunities for Future Research. \u003cem\u003eJNCI: Journal of the National Cancer Institute\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e(7), 940\u0026ndash;952. https://doi.org/10.1093/jnci/djac030\u003c/li\u003e\n\u003cli\u003eBurnett-Hartman, A. N., Powers, J. D., Chubak, J., Corley, D. A., Ghai, N. R., McMullen, C. K., Pawloski, P. A., Sterrett, A. T., \u0026amp; Feigelson, H. S. (2019). Treatment patterns and survival differ between early-onset and late-onset colorectal cancer patients: The patient outcomes to advance learning network. \u003cem\u003eCancer Causes \u0026amp; Control\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(7), 747\u0026ndash;755. https://doi.org/10.1007/s10552-019-01181-3\u003c/li\u003e\n\u003cli\u003eCarrera, P. M., Kantarjian, H. M., \u0026amp; Blinder, V. S. (2018). The financial burden and distress of patients with cancer: Understanding and stepping-up action on the financial toxicity of cancer treatment. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(2), 153\u0026ndash;165. https://doi.org/10.3322/caac.21443\u003c/li\u003e\n\u003cli\u003eDiSario, J. A., Burt, R. W., Kendrick, M. L., \u0026amp; McWhorter, W. P. (1994). Colorectal cancers of rare histologic types compared with adenocarcinomas. \u003cem\u003eDiseases of the Colon \u0026amp; Rectum\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(12), 1277. https://doi.org/10.1007/BF02257796\u003c/li\u003e\n\u003cli\u003eErly, S., Mocha, C. M., Amiya, R. M., \u0026amp; Glick, S. N. (2024). Development of a rural\u0026ndash;urban classification system for public health research that accommodates structural differences between states. \u003cem\u003eAmerican Journal of Epidemiology\u003c/em\u003e, \u003cem\u003e193\u003c/em\u003e(12), 1840\u0026ndash;1847. https://doi.org/10.1093/aje/kwae119\u003c/li\u003e\n\u003cli\u003eFinegan, M., Firth, N., Wojnarowski, C., \u0026amp; Delgadillo, J. (2018). Associations between socioeconomic status and psychological therapy outcomes: A systematic review and meta-analysis. \u003cem\u003eDepression and Anxiety\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(6), 560\u0026ndash;573. https://doi.org/10.1002/da.22765\u003c/li\u003e\n\u003cli\u003eGanatra, S., Khadke, S., Kumar, A., Khan, S., Javed, Z., Nasir, K., Rajagopalan, S., Wadhera, R. K., Dani, S. S., \u0026amp; Al-Kindi, S. (2024). Standardizing social determinants of health data: A proposal for a comprehensive screening tool to address health equity a systematic review. \u003cem\u003eHealth Affairs Scholar\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(12), qxae151. https://doi.org/10.1093/haschl/qxae151\u003c/li\u003e\n\u003cli\u003eGeorge, M., Smith ,Alexandra, Ranmuthugula ,Geetha, \u0026amp; and Sabesan, S. (2022). Barriers to Accessing, Commencing and Completing Cancer Treatment Among Geriatric Patients in Rural Australia: A Qualitative Perspective. \u003cem\u003eInternational Journal of General Medicine\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1583\u0026ndash;1594. https://doi.org/10.2147/IJGM.S338128\u003c/li\u003e\n\u003cli\u003eHacker, K., Auerbach, J., Ikeda, R., Philip, C., \u0026amp; Houry, D. (2022). Social Determinants of Health\u0026mdash;An Approach Taken at CDC. \u003cem\u003eJournal of Public Health Management and Practice\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), 589. https://doi.org/10.1097/PHH.0000000000001626\u003c/li\u003e\n\u003cli\u003eHorgan, D., Van den Bulcke, M., Malapelle, U., Normanno, N., Capoluongo, E. D., Prelaj, A., Rizzari, C., Stathopoulou, A., Singh, J., Kozaric, M., Dube, F., Ottaviano, M., Boccia, S., Pravettoni, G., Cattaneo, I., Malats, N., Buettner, R., Lekadir, K., de Lorenzo, F., \u0026hellip; Hofman, P. (2024). Demographic Analysis of Cancer Research Priorities and Treatment Correlations. \u003cem\u003eCurrent Oncology\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), Article 4. https://doi.org/10.3390/curroncol31040139\u003c/li\u003e\n\u003cli\u003eHuang, A., Yang, Y., Shi, J.-Y., Li, Y.-K., Xu, J.-X., Cheng, Y., \u0026amp; Gu, J. (2021). Mucinous adenocarcinoma: A unique clinicopathological subtype in colorectal cancer. \u003cem\u003eWorld Journal of Gastrointestinal Surgery\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(12), 1567\u0026ndash;1583. https://doi.org/10.4240/wjgs.v13.i12.1567\u003c/li\u003e\n\u003cli\u003eLavasani, S., Chlebowski, R. T., Prentice, R. L., Kato, I., Wactawski-Wende, J., Johnson, K. C., Young, A., Rodabough, R., Hubbell, F. A., Mahinbakht, A., \u0026amp; Simon, M. S. (2015). Estrogen and colorectal cancer incidence and mortality. \u003cem\u003eCancer\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(18), 3261\u0026ndash;3271. https://doi.org/10.1002/cncr.29464\u003c/li\u003e\n\u003cli\u003eLee, W., Nelson, R., Mailey, B., Duldulao, M. P., Garcia-Aguilar, J., \u0026amp; Kim, J. (2012). Socioeconomic Factors Impact Colon Cancer Outcomes in Diverse Patient Populations. \u003cem\u003eJournal of Gastrointestinal Surgery\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 692\u0026ndash;704. https://doi.org/10.1007/s11605-011-1809-y\u003c/li\u003e\n\u003cli\u003eLiss, D. T., \u0026amp; Baker, D. W. (2014). Understanding Current Racial/Ethnic Disparities in Colorectal Cancer Screening in the United States: The Contribution of Socioeconomic Status and Access to Care. \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 228\u0026ndash;236. https://doi.org/10.1016/j.amepre.2013.10.023\u003c/li\u003e\n\u003cli\u003eManser, C. N., \u0026amp; Bauerfeind, P. (2014). Impact of socioeconomic status on incidence, mortality, and survival of colorectal cancer patients: A systematic review. \u003cem\u003eGastrointestinal Endoscopy\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(1), 42-60.e9. https://doi.org/10.1016/j.gie.2014.03.011\u003c/li\u003e\n\u003cli\u003eNicoll, I., Lockwood, G., Longo, C. J., Loiselle, C. G., \u0026amp; Fitch, M. I. (2022). Relationships between Canadian adult cancer survivors\u0026rsquo; annual household income and emotional/practical concerns, help-seeking and unmet needs. \u003cem\u003eHealth \u0026amp; Social Care in the Community\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), e1290\u0026ndash;e1301. https://doi.org/10.1111/hsc.13536\u003c/li\u003e\n\u003cli\u003eNze, C., \u0026amp; Herrera, A. F. (2025). New strategies for enhancing enrollment of underrepresented minorities in lymphoma clinical trials. \u003cem\u003eBlood Advances\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4), 774\u0026ndash;782. https://doi.org/10.1182/bloodadvances.2024012981\u003c/li\u003e\n\u003cli\u003ePenberthy, L., \u0026amp; Friedman, S. (2024). The SEER Program\u0026rsquo;s evolution: Supporting clinically meaningful population-level research. \u003cem\u003eJNCI Monographs\u003c/em\u003e, \u003cem\u003e2024\u003c/em\u003e(65), 110\u0026ndash;117. https://doi.org/10.1093/jncimonographs/lgae022\u003c/li\u003e\n\u003cli\u003eRamkumar, N., Colla, C. H., Wang, Q., O\u0026rsquo;Malley, A. J., Wong, S. L., \u0026amp; Brooks, G. A. (2022). Association of Rurality, Race and Ethnicity, and Socioeconomic Status With the Surgical Management of Colon Cancer and Postoperative Outcomes Among Medicare Beneficiaries. \u003cem\u003eJAMA Network Open\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(8), e2229247. https://doi.org/10.1001/jamanetworkopen.2022.29247\u003c/li\u003e\n\u003cli\u003eSanchez, J. I., Shankaran, V., Unger, J. M., Madeleine, M. M., Espinoza, N., \u0026amp; Thompson, B. (2022). Disparities in post-operative surveillance testing for metastatic recurrence among colorectal cancer survivors. \u003cem\u003eJournal of Cancer Survivorship\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 638\u0026ndash;649. https://doi.org/10.1007/s11764-021-01057-z\u003c/li\u003e\n\u003cli\u003eSepassi, A., Li, M., A. Zell, J., Chan, A., Saunders, I. M., \u0026amp; Mukamel, D. B. (2024). Rural-Urban Disparities in Colorectal Cancer Screening, Diagnosis, Treatment, and Survivorship Care: A Systematic Review and Meta-Analysis. \u003cem\u003eThe Oncologist\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(4), e431\u0026ndash;e446. https://doi.org/10.1093/oncolo/oyad347\u003c/li\u003e\n\u003cli\u003eShah, R., \u0026amp; Chan, K. K. W. (2021). The impact of socioeconomic status on stage at presentation, receipt of diagnostic imaging, receipt of treatment and overall survival in colorectal cancer patients. \u003cem\u003eInternational Journal of Cancer\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e(5), 1031\u0026ndash;1043. https://doi.org/10.1002/ijc.33622\u003c/li\u003e\n\u003cli\u003eSiegel, R. L., Miller, K. D., \u0026amp; Jemal, A. (2018). Cancer statistics, 2018. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(1), 7\u0026ndash;30. https://doi.org/10.3322/caac.21442\u003c/li\u003e\n\u003cli\u003eSmith, R. A., Fedewa, S., \u0026amp; Siegel, R. (2021). Chapter Three\u0026mdash;Early colorectal cancer detection\u0026mdash;Current and evolving challenges in evidence, guidelines, policy, and practices. In F. G. Berger \u0026amp; C. R. Boland (Eds.), \u003cem\u003eAdvances in Cancer Research\u003c/em\u003e (Vol. 151, pp. 69\u0026ndash;107). Academic Press. https://doi.org/10.1016/bs.acr.2021.03.005\u003c/li\u003e\n\u003cli\u003eStrickland, J., \u0026amp; Strickland, D. L. (1996). Barriers to Preventive Health Services for Minority Households in the Rural South. \u003cem\u003eThe Journal of Rural Health\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 206\u0026ndash;217. https://doi.org/10.1111/j.1748-0361.1996.tb00795.x\u003c/li\u003e\n\u003cli\u003eWan, Z., Huang, Z., \u0026amp; Chen, L. (2017). Survival predictors associated with signet ring cell carcinoma of the esophagus (SRCCE): A population-based retrospective cohort study. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(7), e0181845. https://doi.org/10.1371/journal.pone.0181845\u003c/li\u003e\n\u003cli\u003eWells, J. C., Sharma, S., Del Paggio, J. C., Hopman, W. M., Gyawali, B., Mukherji, D., Hammad, N., Pramesh, C. S., Aggarwal, A., Sullivan, R., \u0026amp; Booth, C. M. (2021). An Analysis of Contemporary Oncology Randomized Clinical Trials From Low/Middle-Income vs High-Income Countries. \u003cem\u003eJAMA Oncology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 379\u0026ndash;385. https://doi.org/10.1001/jamaoncol.2020.7478\u003c/li\u003e\n\u003cli\u003eWilson, N. W., Couper, I. D., De, V. E., Reid, S., Fish, T., \u0026amp; Marais, B. J. (2009). A critical review of interventions to redress the inequitable distribution of healthcare professionals to rural and remote areas. \u003cem\u003eRural and Remote Health\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 1\u0026ndash;21. https://doi.org/10.3316/informit.496326474031125\u003c/li\u003e\n\u003cli\u003eZapka, J., Taplin, S. H., Anhang Price, R., Cranos, C., \u0026amp; Yabroff, R. (2010). Factors in Quality Care\u0026mdash;The Case of Follow-Up to Abnormal Cancer Screening Tests\u0026mdash;Problems in the Steps and Interfaces of Care. \u003cem\u003eJNCI Monographs\u003c/em\u003e, \u003cem\u003e2010\u003c/em\u003e(40), 58\u0026ndash;71. https://doi.org/10.1093/jncimonographs/lgq009\u003c/li\u003e\n\u003cli\u003eZhang, F., Xu, B., Peng, Y., Mao, Z., \u0026amp; Tong, S. (2023). Incidence and survival of adenocarcinoma with mixed subtypes in patients with colorectal cancer. \u003cem\u003eInternational Journal of Colorectal Disease\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 215. https://doi.org/10.1007/s00384-023-04508-4\u003c/li\u003e\n\u003cli\u003eZhu, Y., Thandar, M., Cheng, J., Zhang, X., Zhao, Z., Huang, S., \u0026amp; Chi, P. (2023). Comparison of survival outcomes and survival prediction in patients with primary colorectal MANEC and primary colorectal SRCC: A population-based propensity-score matching study. \u003cem\u003eJournal of Cancer Research and Clinical Oncology\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e(14), 13279\u0026ndash;13300. https://doi.org/10.1007/s00432-023-05043-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, survival disparities, rural-urban continuum, median household income, nomogram prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7005373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7005373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSurvival disparities in rare sigmoid colon adenocarcinoma are influenced by socioeconomic and geographic factors, particularly rural-urban residence and household income. This study examines the impact of RUCC and median household income (MHI) on survival outcomes, adjusting for clinical and demographic variables.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the SEER database (1998\u0026ndash;2017), including 94,697 patients diagnosed with rare histologic subtypes of sigmoid colon adenocarcinoma: signet-ring cell carcinoma (SRCC) and mucinous adenocarcinoma (MAC). Rectal cancer cases were excluded. Kaplan-Meier survival analysis and Cox proportional hazards models were used to assess survival, incorporating RUCC, MHI, age, sex, race, cancer stage, and treatment modalities. Interaction effects between RUCC and MHI were also evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients residing in rural regions (RUCC 4\u0026ndash;5) with low MHI (\u0026lt;\u003cspan\u003e$\u003c/span\u003e70,000) had the lowest five-year survival probability (0.53, 95% CI: 0.42\u0026ndash;0.64), while urban residents (RUCC 1) with high MHI (\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000) had the highest survival probability (0.72, 95% CI: 0.58\u0026ndash;0.86). Older age, male sex, distant cancer stage, and Black race were significantly associated with increased mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Surgical treatment was strongly associated with improved survival (HR\u0026thinsp;=\u0026thinsp;0.41, 95% CI: 0.40\u0026ndash;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chemotherapy also conferred a protective effect in adjusted models (HR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: 0.61\u0026ndash;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSignificant geographic and socioeconomic disparities exist in survival outcomes for patients with rare sigmoid colon adenocarcinoma. Targeted public health efforts, including improved access to screening and treatment in rural and low-income areas, are urgently needed to promote equitable cancer care.\u003c/p\u003e","manuscriptTitle":"Survivability in Patients with Rare Colorectal Adenocarcinoma Variants: Exploring the Influence of Rural-Urban Continuum Codes and Social Determinants of Health in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 08:27:21","doi":"10.21203/rs.3.rs-7005373/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01d3f0ab-5736-4489-accf-88e7a3bb5318","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-11T21:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 08:27:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7005373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7005373","identity":"rs-7005373","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}