{"paper_id":"43fd8c3b-1fdf-474e-8432-2f8b30555786","body_text":"Patient-Reported Barriers to Healthcare Access Among Patients with Gastrointestinal Cancer: Insights from 45,000 Participants in the All of Us Research Program | 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 Patient-Reported Barriers to Healthcare Access Among Patients with Gastrointestinal Cancer: Insights from 45,000 Participants in the All of Us Research Program Samuel D. Butensky, Kurt S. Schultz, Elizabeth L. Godfrey, Jihoon Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6855375/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 Background: Gastrointestinal (GI) cancer patients often face care delays and cost-related unmet needs, increasing the probability for treatment nonadherence and adverse outcomes. The extent of these barriers within the first three months of diagnosis remains unclear. We aimed to identify early barriers to care for targeted interventions. Methods: A retrospective analysis using the All of Us database included patients with esophageal, stomach, small intestine, pancreatic, hepatocellular, biliary, colorectal, or anal cancer. Patients were stratified into two cohorts based on survey completion. Reasons for delays in care and cost-related unmet needs were included as dependent variables. Propensity score matching (PSM) and logistic regression evaluated the impact of time from diagnosis. Results: Among 45,061 GI cancer patients, 89.4% were underrepresented in biomedical research. Patients surveyed within three months of diagnosis had higher rates of delays in care (16.9% vs. 14.0%, p < 0.001), driven by affordability, childcare, and transportation (all p < 0.001). Overall cost-related unmet needs did not differ significantly (< 3 months 20.9% vs. >3 months 19.7%, p = 0.204), but differences in unmet prescription and alternative therapy needs persisted. After PSM, early-diagnosis patients had no differences in delays in care but were more likely to report cost-saving behaviors such as using lower-cost prescriptions (OR 1.28, 95% CI 1.05–1.54) and alternative therapies (OR 1.48, 95% CI 1.08–2.01) to save money. Conclusion: Cost-related unmet needs exist in the first three months after GI cancer diagnosis. This study underscores the importance of addressing social determinants of health early in cancer care. Healthcare Access Gastrointestinal Cancer All of Us Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cancer patients face significant challenges in accessing healthcare, including affording medications, arranging transportation to appointments, securing time off work, and managing caregiving or childcare responsibilities. Cancer care can also create substantial financial strain, with patients citing cancer as the most common reason to file for bankruptcy due to medical expenses. 1 – 3 Furthermore, cancer patients are more likely to report delays in care compared to adults without a cancer history, with approximately 476,000 patients in 2018 experiencing delays. 4 Ultimately, patients who experience financial hardship and delays in care during cancer treatment have been shown to be at higher risk for treatment nonadherence, poor quality of life, and worse survival. 5 – 7 Prior studies utilizing data from the National Health Interview Survey (NHIS) have highlighted these disparities in care access. Analysis of NHIS data has demonstrated a U-shaped pattern in cost-related delayed or forgone care, with early cancer patients—those surveyed within the first year of diagnosis—and long-term cancer patients (10 + years post-diagnosis) reporting the highest rates of financial barriers to care. 8 However, the patient population captured by NHIS may not be fully representative of the diverse U.S. population, as the cohort has historically been predominantly female, white, and non-Hispanic. 4 , 8 Recognizing the need for more representative research, the All of Us Research Program was developed with the goal of building a diverse cohort, prioritizing the recruitment of populations underrepresented in biomedical research (UBR). As part of its data collection efforts, All of Us developed the Healthcare Access and Utilization (HAU) survey, which was modeled after the NHIS survey to assess barriers to care in a more diverse and representative population. 9 This is especially critical in the context of gastrointestinal (GI) cancers, which often requires multimodal therapy and potentially results in greater out-of-pocket costs, work disruption, and care fragmentation. Additionally, GI cancer patients’ care may disrupt their ability to afford other aspects of their medical needs, such as prescription medications, resulting in cost-related unmet needs. Given that GI cancer represents one-quarter of all cancer diagnoses worldwide, there is a societal need to understand how delays in care and cost-related unmet needs most impact these patients. 10 Our study aims to assess barriers to care among GI cancer patients using the prospective survey data from the All of Us database relative to a cancer diagnosis. We hypothesize that patients within three months of diagnosis experience more delays in care and cost-related unmet needs than those diagnosed later. Methods Data Source and Study Sample We conducted a retrospective analysis of prospectively collected data from the All of Us Research Program, a nationwide initiative designed to create a diverse health database of over one million United States residents. 11 The All of Us database has been described in detail. 12 We used the Controlled Tier Dataset v8, released on October 1st, 2023, encompassing 633,540 patients. We included individuals aged ≥ 18 years old with a diagnosis of esophageal, stomach, small intestine, pancreas, hepatocellular, biliary, colon, rectum, or anal cancer at any time, defined by SNOMED codes (Supplemental Figure S1 ). Patients who did not complete the Basics and Healthcare Access and Utilization surveys were excluded. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As the All of Us database is deidentified, this research was not considered human subjects research and thus was not subject to review under Yale University Institutional Review Board guidelines. Survey Responses The Healthcare Access and Utilization survey is a 57-question survey derived from the National Health Interview Survey (NHIS) that asks questions about access and use of health care. 13 The survey was subdivided into two categories based on theme: delays in care and cost-related unmet needs. Individual reasons for reporting delays in care and cost-related unmet needs were included as dependent variables in logistic regression. A binary variable was also created to capture any delay in care and any issue affording care: patients who reported ≥ 1 reason for a delay in care or issue affording care were marked “Yes” as having a delay in care or issue affording care, respectively. Covariates Demographic information was obtained from the Basics Survey. Deprivation index was obtained for each person from the “Zip Code Socioeconomic Status Data” concept set within All of Us; deprivation index is an All of Us population-weighted average of the index for the census tracts covered by the 3-digit ZIP code tabulation area (ZCTA). UBR was defined by race, ethnicity, age, household income, disability, education, gender identity, sex at birth, and sexual orientation. 14 Charlson-Deyo comorbidities were obtained from the electronic medical record using the SNOMED codes (Supplemental Table S1 ). Race, gender, ethnicity, education, and employment are self-described. Patients with missing data for date of birth, gender, race, or ethnicity were excluded. Responses of “I prefer not to answer,” “None of these,” and “PMI: Skip,” for the demographic and socioeconomic questions were treated as “Prefer not to answer.” Justification for 3-Month Cutoff A binary variable called “Time from Diagnosis to Survey Completion” was created to separate the cohort into patients who answered the survey < 3 months from their cancer diagnosis, and those who completed the survey ≥ 3 months from their diagnosis. While delays in care can occur throughout the first two years post-diagnosis, this interval is too broad to guide timely, targeted interventions. Prior work by Shankaran et al. in the metastatic colorectal cancer population showed that 25% of patients in the first three months after their diagnosis have major financial hardship, and this financial hardship negatively impacts their health-related quality of life. 15 Based on this literature and institutional experience, the first 90 days post-diagnosis represent a particularly vulnerable period marked by intense decision-making, care coordination, and potential disruption to employment and insurance coverage. Using this time frame allows for more actionable insights into early care disruptions and cost-related unmet needs. Statistical Analysis The patient population was summarized with descriptive statistics. Chi square and Student’s T-Test were used to compare demographic variables. Survey responses were stratified by time from diagnosis to survey completion and statistically compared using Chi square analysis. Propensity score matching (PSM) was performed to match these two cohorts using nearest neighbor methodology in a 2:1 ratio for the following covariates: age, race, ethnicity, gender, comorbidities, deprivation index, income, education, employment, and healthcare insurance. These covariates were chosen due to their relevance to the causal pathway and/or potential for confounding. Standard mean differences were checked before and after matching to ensure balance. Statistical significance was set at an α of 0.05, with a 2-sided P value of < 0.05 indicating significance. All analyses were conducted using R in the secure All of Us researcher workbench (workspace: “Gastrointestinal Cancer – SDOH and HUA CT V8”). Sensitivity Analysis To assess the robustness of our findings, two additional analyses evaluated a 6-month and 1-year cutoff of time from diagnosis to survey completion. PSM was used to match these cohorts in similar fashion to the main analysis (Supplemental Figure S2). Results A total of 45,061 patients with a prior diagnosis of gastrointestinal tract cancer met our inclusion criteria (Fig. 1 ). Most patients were white (70.0%), non-Hispanic (86.2%), and female (54.2%) (Table 1 ). The majority of patients earned less than $ 100,000 (54.9%), and the most common cancer diagnosis was colorectal cancer (67.8%). 89.4% were UBR; of those who were UBR, 77% were > 65 years old, 25% had at least one disability; 36% earned < $ 50,000, and 34% were non-white. There were significant differences between diagnosis to survey cohorts in all covariates except gender (p = 0.36820). 9% of patients who completed the survey within 3 months had cost-related unmet needs, compared to 19.7% of patients who completed the survey ≥ 3 months after their diagnosis (p = 0.204). The number of reasons for delays and cost-related unmet needs decreased over time (Supplemental Figure S3). Table 1 Clinical and demographic information of patients with gastrointestinal cancer in our study population stratified by time from diagnosis to survey completion. Variable Overall Diagnosis to Survey Time P Value < 3 Months > 3 Months n 45061 1959 43102 UBR, n (%) 40271 (89.4) 1664 (84.9) 38607 (89.6) < 0.001 Delays in Care (Yes), n (%) 6345 (14.1) 332 (16.9) 6013 (14.0) < 0.001 Cost-Related Unmet Needs (Yes), n (%) 8893 (19.7) 409 (20.9) 8484 (19.7) 0.204 Diagnosis to Survey (Years), n (%) 6.39 (5.41) 0.11 (0.07) 6.67 (5.36) < 0.001 Age, mean (sd) 68.72 (11.61) 62.18 (13.15) 69.02 (11.44) < 0.001 Gender, n (%) 0.368 Male 20042 (44.5) 840 (42.9) 19202 (44.6) Female 24442 (54.2) 1098 (56.0) 23344 (54.2) Non Binary 98 ( 0.2) 4 ( 0.2) 94 ( 0.2) None Indicated 479 ( 1.1) 17 ( 0.9) 462 ( 1.1) Ethnicity, n (%) < 0.001 Hispanic 4764 (10.6) 335 (17.1) 4429 (10.3) Non-Hispanic 38843 (86.2) 1568 (80.0) 37275 (86.5) Prefer not to answer 1454 ( 3.2) 56 ( 2.9) 1398 ( 3.2) Race, n (%) < 0.001 White 31533 (70.0) 1195 (61.0) 30338 (70.4) Asian 787 ( 1.7) 56 ( 2.9) 731 ( 1.7) Black or African American 6270 (13.9) 311 (15.9) 5959 (13.8) Other 6471 (14.4) 397 (20.3) 6074 (14.1) Comorbidities, n (%) < 0.001 < 1 Comorbidity 19006 (42.2) 865 (44.2) 18141 (42.1) 1–3 Comorbidities 18420 (40.9) 852 (43.5) 17568 (40.8) 4 + Comorbidities 7635 (16.9) 242 (12.4) 7393 (17.2) Deprivation Index, mean (sd) 0.31 (0.06) 0.32 (0.07) 0.31 (0.06) < 0.001 Income, n (%) < 0.001 150k+ 6293 (14.0) 282 (14.4) 6011 (13.9) 100k-150k 5756 (12.8) 223 (11.4) 5533 (12.8) 50k-100k 10418 (23.1) 365 (18.6) 10053 (23.3) < 50k 14347 (31.8) 650 (33.2) 13697 (31.8) Prefer not to answer 8247 (18.3) 439 (22.4) 7808 (18.1) Education, n (%) < 0.001 Advanced degree 12268 (27.2) 462 (23.6) 11806 (27.4) College 10986 (24.4) 479 (24.5) 10507 (24.4) High School 18543 (41.2) 833 (42.5) 17710 (41.1) Did not graduate high school 2467 ( 5.5) 151 ( 7.7) 2316 ( 5.4) Prefer not to answer 797 ( 1.8) 34 ( 1.7) 763 ( 1.8) Employment, n (%) < 0.001 Employed 14893 (33.1) 775 (39.6) 14118 (32.8) Unemployed 29405 (65.3) 1141 (58.2) 28264 (65.6) Other 763 ( 1.7) 43 ( 2.2) 720 ( 1.7) Insurance, n (%) 0.011 No health insurance 708 ( 1.6) 47 ( 2.4) 661 ( 1.5) Yes health insurance 43633 (96.8) 1881 (96.0) 41752 (96.9) Unknown 720 ( 1.6) 31 ( 1.6) 689 ( 1.6) Cancer, n (%) < 0.001 Colorectal 30543 (67.8) 1144 (58.4) 29399 (68.2) Anus 11827 (26.2) 568 (29.0) 11259 (26.1) Biliary 412 ( 0.9) 48 ( 2.5) 364 ( 0.8) Esophageal 197 ( 0.4) 25 ( 1.3) 172 ( 0.4) Hepatocellular 230 ( 0.5) 34 ( 1.7) 196 ( 0.5) Pancreas 571 ( 1.3) 74 ( 3.8) 497 ( 1.2) Small Intestine 266 ( 0.6) 23 ( 1.2) 243 ( 0.6) Stomach 1015 ( 2.3) 43 ( 2.2) 972 ( 2.3) Delays in Care 14.1% of all respondents reported delays in cancer care, with 16.9% of patients < 3 months from their diagnosis having delays in care as compared to 14.0% in the other cohort (p < 0.001) (Table 1 ). 5.8% of patients delayed care because they had to pay out of pocket for their visit. 4.1% of patients delayed care because they were nervous, and 3.3% of patients delayed care due to issues with transportation (Fig. 2 A). When stratified by diagnosis to survey completion time (Fig. 2 B), there were significant differences in every category. When compared to patients greater than 3 months after their diagnosis, more patients within 3 months of their diagnosis delayed care because of issues affording their copay (3.4% vs. 2.3%, p < 0.001), childcare (1% vs. 0.5%, p < 0.001), elderly care (1.4% vs. 0.9%, p = 0.025), issues getting time off work (3.9% vs. 2.4%, p < 0.001), and difficulty finding transportation (4.3% vs. 3.2%, p < 0.001). After PSM, there were no significant differences in these two cohorts (Fig. 4 A). Cost-related Unmet Needs 19.7% of all patients reported cost-related unmet needs, with 20.9% of patients < 3 months from their diagnosis having cost-related unmet needs compared to 19.7% in the other cohort (p = 0.204). 9.9% of patients asked their doctor for a lower cost medication to save money. 6.7% of patients needed dental care, 5% needed prescription medications, and 4.4% needed eyeglasses but were unable to obtain them due to financial constraints (Fig. 3 A). 3.1% of patients took less medication to save money. When stratified by diagnosis to survey completion time (Fig. 3 B), all categories, except buying a prescription from another country to save money, had significant differences. As compared to patients who completed the survey more than 3 months after their cancer diagnosis, more patients within three months of their diagnosis took a lower cost prescription to save money (10.8% vs. 9.8%, p < 0.001), needed dental care but could not afford it (7.5% vs. 6.6%), needed prescription medication but could not afford it (6.5% vs. 4.8%, p < 0.001), and used alternative therapies to save money (3.0% vs. 2.4%, p < 0.001). After PSM, two significant differences remained (Fig. 4 B). Patients within 3 months of their diagnosis had increased odds of taking a lower cost prescription (OR 1.28, [95% CI: 1.05–1.54]) and trying alternative therapies (OR 1.48, [95% CI: 1.08–2.01]) to save money, as compared to patients more than three months after their cancer diagnosis. Sensitivity Analysis For the binomial logistic regression on delays of care, there remained no significant differences closer to patients’ cancer diagnosis as compared to later on at both the 6-month and 1-year threshold (Supplemental Figure S4). With regard to cost-related unmet needs, taking a lower cost prescription to save money remained persistently higher closer to the time of diagnosis at both the 6-month and 1-year threshold (Supplemental Figure S5). Trying alternative therapies to save money was not robust after a sensitivity analysis. All other reasons for cost-related unmet needs remained non-significant in the sensitivity analysis, except that patients at the 6-month cutoff were more likely to need prescription medications but not be able to afford them (OR 1.23 [95% CI: 1.03–1.46]) (Supplemental Figure S5B); this finding was not significant at the 3-month and 1-year cut points. Discussion This study examines challenges related to healthcare access and affordability among a national cohort of patients with GI cancer. Overall, 14.1% reported care delays, with patients in their first three months of diagnosis more likely to report problems related to out-of-pocket costs, transportation, childcare, and time off work. Similarly, 19.7% reported cost-related unmet needs, with early respondents more often reporting medication non-adherence, unmet dental or prescription needs, and use of alternative therapies. These results suggest that the acute time period after a cancer diagnosis is a sensitive period that may impact their cancer care as well as their ability to access other aspects of their healthcare. While it has been well-established that living with cancer can increase cardiovascular diseases, suicide and psychiatric symptoms and disorders, the diagnosis itself can also be highly stressful. 16 – 18 Lu et al. showed that compared to the two years before a cancer diagnosis, patients who were diagnosed with cancer had a peak in depression, anxiety, substance abuse, and stress reactions by the first week of their diagnosis. 19 While the number of disorders decreased rapidly, they still remained elevated 10 years after diagnosis. This peak in emotional stress after a cancer diagnosis is compounded by the logistical maze patients must navigate to secure treatment while keeping the rest of their lives moving forward. Our study reflects these challenges, with patients within the first three months of their diagnosis experiencing increased delays in care and cost-related unmet needs than patients more than 3 months out. It might also explain why patients within the first three months of their cancer diagnosis were more likely to use lower cost prescriptions and seek alternative therapies, and they associated these behaviors with cost considerations. The use of complementary and alternative medicine (CAM) in cancer care is widespread, with recent estimates indicating that up to 87% of individuals diagnosed with cancer utilize at least one form of alternative therapy following their diagnosis. 20 Reasons for using CAM vary, including managing cancer and treatment-related symptoms and side effects, enhancing quality of life, and providing a sense of control. 21 – 25 With regards to delays in care, the process of delaying treatment is complex. Several studies have shown that traditional risk factors such as age, race, ethnicity, clinical staging, tumor size, treatment facility, and the number of comorbidities contribute to treatment delays. 26 – 28 Others have examined the role of social determinants of health (SDOH), including insurance status and geography, in these delays. 29 – 32 However, our study utilizes patient-level data to focus on care delays, particularly those based on psychosocial factors. We found that 4.1% of GI cancer patients delayed care due to nervousness, 2.4% postponed care because they could not take time off work, and 0.5% (~ 225 patients) delayed care owing to childcare issues. The existing literature on the impact of these patient-level factors is limited. Frosch et al. interviewed 22 cancer patients and discovered that psychological distress both contributes to and results from treatment delays. 33 Conversely, other studies suggest that higher anxiety levels and awareness of cancer symptoms can reduce treatment delays. Regarding childcare, women are especially vulnerable to postponing care due to childcare responsibilities—Gaur et al. found that among patients awaiting ambulatory services, 54.5% of women reported missing or delaying care because of these issues. 34 Further explanatory studies are essential to clarify the effects of these barriers on delays in care. In addition to delays in care, prior studies utilizing NHIS or Medical Expenditure Panel Survey (MEPS) have demonstrated significant a tendency to forgo treatment altogether because of cost, resulting in cost-related unmet needs; 2 , 8 , 35 while NHIS gathers data from face-to-face interviews with patients, the MEPS also includes medical providers and employers across the United States. Weaver et al. examined 6,602 cancer survivors using the NHIS and found 10% deferred prescription medication and 11.3% forewent dental care. 8 Our findings indicate that 2.9% forwent prescription medications and 6.7% forwent dental care. Part of the reason that our results have lower proportions than Weaver et al. is that their study population is not UBR. In turn, given the high proportion of UBR in our research, our findings may offer a more accurate representation of financial delays in care. Additionally, our study focuses specifically on GI cancer patients rather than all cancer patients. Several actions can be taken in light of our study's findings. Oncology clinics should consider enhancing access to financial navigation programs to optimize insurance, co-pay assistance, and prescription affordability. Evidence-based complementary therapies could be incorporated into cancer care with insurance coverage. Patient navigators and social workers should proactively address financial toxicity within the first three months after diagnosis. Hospitals should provide subsidized childcare and flexible work accommodation to minimize care delays. Transportation assistance programs, such as ACS’s Road to Recovery, should be expanded. EHR-based psychosocial screenings should result in mental health referrals. Finally, policy advocacy should concentrate on Medicaid expansion, drug pricing reform, and increased federal funding to alleviate financial toxicity in oncology care. Future studies should incorporate the role of psychosocial risk factors on delays and affordability of care. 36 All of Us contains an SDOH survey with several psychosocial domains that provide unprecedented patient-level reporting on risk factors that have been poorly studied in the literature. Additionally, we would like to look at the impact of time from treatment or intervention to survey completion on delays and care affordability. These next steps will inform the design of targeted psychosocial interventions to improve access to care and outcomes in patients with cancer. Limitations As a retrospective analysis, this study cannot establish causal relationships between the time from diagnosis to survey completion and healthcare access challenges in GI cancer patients. Response bias remains a limitation, as our cohort was disproportionately female, highly educated, and non-Hispanic white, which may limit generalizability. However, our cohort was 90% UBR, comparable to or exceeding prior All of Us studies. 37 , 38 Additionally, insurance type is not available in the All of Us database. Lastly, we did not assess cancer-specific delays and costs-related unmet needs, limiting disease-specific insights. Conclusion This study identifies significant financial and access barriers among patients with GI cancers, particularly within the first three months following their cancer diagnosis. Nearly 90% of the cohort was UBR, and despite 97% being insured, cost-related unmet needs persisted. Delays in care were driven by affordability, childcare, and transportation barriers, but these factors were non-significant after PSM. After PSM, patients who completed the survey within 3 months of their cancer diagnosis were more likely to take lower-cost prescriptions and use alternative therapies to save money. While delays were no longer significant after adjustment, affordability challenges remained. Addressing affordability of care is essential for equitable GI cancer care. Declarations Corresponding Author Samuel D. Butensky Department of Surgery – Yale School of Medicine PO Box 208062 New Haven, CT, 06520-8062 [email protected] Author Contributions Conceptualization: SDB, SAK Data Curation: SDB, JK Formal Analysis: SDB, ELG, KSS Funding acquisition: SAK, CHJ Investigation: SDB, ELG, KSS Methodology: SDB, ELG, KSS Project Administration: SDB, SAK, IL Resources: SAK, CHJ, IL Software: SDB, JK Supervision: SAK Validation: SDB Visualization: SDB Writing – original draft: SDB, SAK Writing – review & editing: SDB, KSS, ELG, IL, SAK Conflict of Interest Disclosures: No other disclosures were reported. Acknowledgements We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data [and/or samples and/or cohort] examined in this study. During the preparation of this work the authors used a large language model (ChatGPT 4o) to revise the manuscript text for coherence and clarity. After using this service, the authors reviewed and edited the content as needed, and they take full responsibility for the content of the publication. Data Availability The All of Us Research Program is a publicly available, de-identified database (https://databrowser.researchallofus.org/). All supporting code used to analyze the data can be found in the All of Us researcher workbench (workspace: “Gastrointestinal Cancer – SDOH and HUA CT V8”). Funding/Support This study was supported by R21CA280372 (Drs. Johnson and Khan), CTSA UL1 TR001863 (Dr. Khan), and 5P30CA016359-41 (Drs. Johnson and Khan) from the NIH. 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Annals of oncology : official journal of the European Society for Medical Oncology . 2005 Apr;16(4)doi:10.1093/annonc/mdi110 CF K, M K. Association of distress symptoms and use of complementary medicine among patients with cancer - PubMed. Journal of clinical nursing . 2012 Mar;21(5-6)doi:10.1111/j.1365-2702.2011.03884.x N K, LG B, GT L, et al. Surveys of Cancer Patients and Cancer Health Care Providers Regarding Complementary Therapy Use, Communication, and Information Needs - PubMed. Integrative cancer therapies . 2015 Nov;14(6)doi:10.1177/1534735415589984 T T, JL B. Decision making related to complementary therapies: a process of regaining control - PubMed. Patient education and counseling . 1999 Oct;38(2)doi:10.1016/s0738-3991(99)00060-9 KY B, CY K, JS T, et al. Wait times for cancer surgery in the United States: trends and predictors of delays - PubMed. Annals of surgery . 2011 Apr;253(4)doi:10.1097/SLA.0b013e318211cc0f DL W, S T, J T. Factors associated with delays to diagnosis and treatment of breast cancer in women in a Louisiana urban safety net hospital - PubMed. Women & health . 2010 Dec;50(8)doi:10.1080/03630242.2010.530928 K I, ML S, KM H, et al. Diagnosis and treatment delays among elderly breast cancer patients with pre-existing mental illness - PubMed. Breast cancer research and treatment . 2017 Nov;166(1)doi:10.1007/s10549-017-4399-x RJ B, K R, ER S, et al. Preoperative delays in the US Medicare population with breast cancer - PubMed. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 12/20/2012;30(36)doi:10.1200/JCO.2012.41.7972 JM M, RT A, AK F, EE S, R B, ED P. Effect on survival of longer intervals between confirmed diagnosis and treatment initiation among low-income women with breast cancer - PubMed. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 12/20/2012;30(36)doi:10.1200/JCO.2012.39.7695 EC S, A Z, H A-C. Delay in surgical treatment and survival after breast cancer diagnosis in young women by race/ethnicity - PubMed. JAMA surgery . 2013 Jun;148(6)doi:10.1001/jamasurg.2013.1680 EA B, KL S, AR D, T M, J N. Determinants of Treatment Delays Among Underserved Hispanics With Lung and Head and Neck Cancers - PubMed. Cancer control : journal of the Moffitt Cancer Center . 2016 Oct;23(4)doi:10.1177/107327481602300410 Frosch ZAK, Jacobs LM, O’Brien CS, et al. “Cancer’s a demon”: a qualitative study of fear and multilevel factors contributing to cancer treatment delays. Supportive Care in Cancer 2023 32:1 . 2023-12-07;32(1)doi:10.1007/s00520-023-08200-9 Gaur P, Ganguly AP, Kuo M, et al. Childcare needs as a barrier to healthcare among women in a safety-net health system. BMC Public Health . 2024 Jun 17;24(1)doi:10.1186/s12889-024-19125-1 EE K, LP F, KR Y, et al. Are survivors who report cancer-related financial problems more likely to forgo or delay medical care? - PubMed. Cancer . 10/15/2013;119(20)doi:10.1002/cncr.28262 Schultz KS, Richburg CE, Park EY, Leeds IL. Identifying and optimizing psychosocial frailty in surgical practice. Seminars in Colon and Rectal Surgery . 2024/12/01;35(4)doi:10.1016/j.scrs.2024.101061 Tesfaye S, Cronin RM, Lopez-Class M, et al. Measuring social determinants of health in the All of Us Research Program. Scientific Reports 2024 14:1 . 2024-04-16;14(1)doi:10.1038/s41598-024-57410-6 Ramirez AH, Sulieman L, Schlueter DJ, et al. The All of Us Research Program: Data quality, utility, and diversity. Patterns . 2022/08/12;3(8)doi:10.1016/j.patter.2022.100570 Additional Declarations No competing interests reported. Supplementary Files SCCAoUHAUSupplemental.docx 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. 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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-6855375\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":490368412,\"identity\":\"1990ad55-6b9b-445c-9b63-38513ee5d574\",\"order_by\":0,\"name\":\"Samuel D. Butensky\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYNCCAoYECOMAgxyYekBQiwFQCxtEizGYSiBFS2IDiManRb79dJrEBwOGPIP7zcce/jhzOH1+2OGHQFvs5HQbcJh/Jneb5AwDhmKDY2zpxjw3DuduvJ1mANSSbGx2AJeTcrdJ8xgwJG44xmMmzfABqGV2AkjLgcRtOLTI97/dJv0HrIX/m+SPD4fTDWenf8CrheEG0BYGiC1sEkCHJchL5+C3xeDG282WPQYSxZLH0sykec6kG26Qzik4kGCA2y/y/bkbb/yosMnjO3z4meSPY9by8rPTN3/4UGEnh0sLFEjAGM0MBgcgwUI0qGOQbyBe9SgYBaNgFIwMAADSgWY1+SVWVAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Yale New Haven Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Samuel\",\"middleName\":\"D.\",\"lastName\":\"Butensky\",\"suffix\":\"\"},{\"id\":490368413,\"identity\":\"e0a34789-7fb7-4d32-aad9-fb2ff722041d\",\"order_by\":1,\"name\":\"Kurt S. 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Johnson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yale University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Caroline\",\"middleName\":\"H.\",\"lastName\":\"Johnson\",\"suffix\":\"\"},{\"id\":490368417,\"identity\":\"e7bee9cf-131d-491b-bb95-1c668299fd11\",\"order_by\":5,\"name\":\"Ira Leeds\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yale New Haven Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ira\",\"middleName\":\"\",\"lastName\":\"Leeds\",\"suffix\":\"\"},{\"id\":490368418,\"identity\":\"d7599132-9164-4a15-84ef-c9e543dde871\",\"order_by\":6,\"name\":\"Sajid A. Khan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yale New Haven Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sajid\",\"middleName\":\"A.\",\"lastName\":\"Khan\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-09 14:23:27\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6855375/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6855375/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":87801815,\"identity\":\"b40ad0f7-17cc-45c3-908e-b8203aa2621d\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 07:55:07\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":30665,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePatient selection criteria for evaluating access to care in patients with gastrointestinal cancer within the All of Us research database\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/bf721a64ae49e7a7bb7a33fe.png\"},{\"id\":87801816,\"identity\":\"a9071ca3-fe12-44e6-94f5-126fd6e99aa9\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 07:55:07\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":54844,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReasons for delays in care in A) patients with gastrointestinal cancer then B) stratified by time from cancer diagnosis to survey completion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/b97f1f75499afd6fb26e9b18.png\"},{\"id\":87801819,\"identity\":\"b1c49428-934e-4c3a-bdee-18b477cc209c\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 07:55:07\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":65141,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReasons for cost-related unmet needs in A) patients with gastrointestinal cancer then B) stratified by time from cancer diagnosis to survey completion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/465e5036ff7395d3d09738fa.png\"},{\"id\":87802443,\"identity\":\"5f4282b2-a581-4eca-913e-a9ee7f2642a7\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 08:03:07\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":169465,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBinomial logistic regression evaluating the effect of diagnosis from survey time on A) delays in care and B) cost-related unmet needs in a propensity score matched cohort\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/e56aa3a712a6c59727a77871.png\"},{\"id\":90847290,\"identity\":\"1e659923-58f0-42b3-aff6-25d3417d0022\",\"added_by\":\"auto\",\"created_at\":\"2025-09-09 01:16:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1281631,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/817c8166-5e78-4391-8ca6-20f0fdb85fc5.pdf\"},{\"id\":87801817,\"identity\":\"b95b0cdb-7dc8-4026-bb1c-8c4fee96c866\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 07:55:07\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":927102,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SCCAoUHAUSupplemental.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6855375/v1/8196bf8cc347a0e6b72d3ff5.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Patient-Reported Barriers to Healthcare Access Among Patients with Gastrointestinal Cancer: Insights from 45,000 Participants in the All of Us Research Program\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCancer patients face significant challenges in accessing healthcare, including affording medications, arranging transportation to appointments, securing time off work, and managing caregiving or childcare responsibilities. Cancer care can also create substantial financial strain, with patients citing cancer as the most common reason to file for bankruptcy due to medical expenses.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e Furthermore, cancer patients are more likely to report delays in care compared to adults without a cancer history, with approximately 476,000 patients in 2018 experiencing delays.\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e Ultimately, patients who experience financial hardship and delays in care during cancer treatment have been shown to be at higher risk for treatment nonadherence, poor quality of life, and worse survival.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003ePrior studies utilizing data from the National Health Interview Survey (NHIS) have highlighted these disparities in care access. Analysis of NHIS data has demonstrated a U-shaped pattern in cost-related delayed or forgone care, with early cancer patients\\u0026mdash;those surveyed within the first year of diagnosis\\u0026mdash;and long-term cancer patients (10\\u0026thinsp;+\\u0026thinsp;years post-diagnosis) reporting the highest rates of financial barriers to care.\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e However, the patient population captured by NHIS may not be fully representative of the diverse U.S. population, as the cohort has historically been predominantly female, white, and non-Hispanic.\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eRecognizing the need for more representative research, the All of Us Research Program was developed with the goal of building a diverse cohort, prioritizing the recruitment of populations underrepresented in biomedical research (UBR). As part of its data collection efforts, All of Us developed the Healthcare Access and Utilization (HAU) survey, which was modeled after the NHIS survey to assess barriers to care in a more diverse and representative population.\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e This is especially critical in the context of gastrointestinal (GI) cancers, which often requires multimodal therapy and potentially results in greater out-of-pocket costs, work disruption, and care fragmentation.\\u003c/p\\u003e\\u003cp\\u003eAdditionally, GI cancer patients\\u0026rsquo; care may disrupt their ability to afford other aspects of their medical needs, such as prescription medications, resulting in cost-related unmet needs. Given that GI cancer represents one-quarter of all cancer diagnoses worldwide, there is a societal need to understand how delays in care and cost-related unmet needs most impact these patients.\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e Our study aims to assess barriers to care among GI cancer patients using the prospective survey data from the All of Us database relative to a cancer diagnosis. We hypothesize that patients within three months of diagnosis experience more delays in care and cost-related unmet needs than those diagnosed later.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData Source and Study Sample\\u003c/h2\\u003e\\u003cp\\u003eWe conducted a retrospective analysis of prospectively collected data from the All of Us Research Program, a nationwide initiative designed to create a diverse health database of over one million United States residents.\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e The All of Us database has been described in detail.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e We used the Controlled Tier Dataset v8, released on October 1st, 2023, encompassing 633,540 patients.\\u003c/p\\u003e\\u003cp\\u003eWe included individuals aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years old with a diagnosis of esophageal, stomach, small intestine, pancreas, hepatocellular, biliary, colon, rectum, or anal cancer at any time, defined by SNOMED codes (Supplemental Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Patients who did not complete the Basics and Healthcare Access and Utilization surveys were excluded. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As the All of Us database is deidentified, this research was not considered human subjects research and thus was not subject to review under Yale University Institutional Review Board guidelines.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eSurvey Responses\\u003c/h3\\u003e\\n\\u003cp\\u003eThe Healthcare Access and Utilization survey is a 57-question survey derived from the National Health Interview Survey (NHIS) that asks questions about access and use of health care.\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e The survey was subdivided into two categories based on theme: delays in care and cost-related unmet needs. Individual reasons for reporting delays in care and cost-related unmet needs were included as dependent variables in logistic regression. A binary variable was also created to capture any delay in care and any issue affording care: patients who reported\\u0026thinsp;\\u0026ge;\\u0026thinsp;1 reason for a delay in care or issue affording care were marked \\u0026ldquo;Yes\\u0026rdquo; as having a delay in care or issue affording care, respectively.\\u003c/p\\u003e\\n\\u003ch3\\u003eCovariates\\u003c/h3\\u003e\\n\\u003cp\\u003eDemographic information was obtained from the Basics Survey. Deprivation index was obtained for each person from the \\u0026ldquo;Zip Code Socioeconomic Status Data\\u0026rdquo; concept set within All of Us; deprivation index is an All of Us population-weighted average of the index for the census tracts covered by the 3-digit ZIP code tabulation area (ZCTA). UBR was defined by race, ethnicity, age, household income, disability, education, gender identity, sex at birth, and sexual orientation.\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e Charlson-Deyo comorbidities were obtained from the electronic medical record using the SNOMED codes (Supplemental Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Race, gender, ethnicity, education, and employment are self-described. Patients with missing data for date of birth, gender, race, or ethnicity were excluded. Responses of \\u0026ldquo;I prefer not to answer,\\u0026rdquo; \\u0026ldquo;None of these,\\u0026rdquo; and \\u0026ldquo;PMI: Skip,\\u0026rdquo; for the demographic and socioeconomic questions were treated as \\u0026ldquo;Prefer not to answer.\\u0026rdquo;\\u003c/p\\u003e\\n\\u003ch3\\u003eJustification for 3-Month Cutoff\\u003c/h3\\u003e\\n\\u003cp\\u003eA binary variable called \\u0026ldquo;Time from Diagnosis to Survey Completion\\u0026rdquo; was created to separate the cohort into patients who answered the survey\\u0026thinsp;\\u0026lt;\\u0026thinsp;3 months from their cancer diagnosis, and those who completed the survey\\u0026thinsp;\\u0026ge;\\u0026thinsp;3 months from their diagnosis. While delays in care can occur throughout the first two years post-diagnosis, this interval is too broad to guide timely, targeted interventions. Prior work by Shankaran et al. in the metastatic colorectal cancer population showed that 25% of patients in the first three months after their diagnosis have major financial hardship, and this financial hardship negatively impacts their health-related quality of life.\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e Based on this literature and institutional experience, the first 90 days post-diagnosis represent a particularly vulnerable period marked by intense decision-making, care coordination, and potential disruption to employment and insurance coverage. Using this time frame allows for more actionable insights into early care disruptions and cost-related unmet needs.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe patient population was summarized with descriptive statistics. Chi square and Student\\u0026rsquo;s T-Test were used to compare demographic variables. Survey responses were stratified by time from diagnosis to survey completion and statistically compared using Chi square analysis. Propensity score matching (PSM) was performed to match these two cohorts using nearest neighbor methodology in a 2:1 ratio for the following covariates: age, race, ethnicity, gender, comorbidities, deprivation index, income, education, employment, and healthcare insurance. These covariates were chosen due to their relevance to the causal pathway and/or potential for confounding. Standard mean differences were checked before and after matching to ensure balance. Statistical significance was set at an α of 0.05, with a 2-sided \\u003cem\\u003eP\\u003c/em\\u003e value of \\u0026lt;\\u0026thinsp;0.05 indicating significance. All analyses were conducted using R in the secure All of Us researcher workbench (workspace: \\u0026ldquo;Gastrointestinal Cancer \\u0026ndash; SDOH and HUA CT V8\\u0026rdquo;).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSensitivity Analysis\\u003c/h2\\u003e\\u003cp\\u003eTo assess the robustness of our findings, two additional analyses evaluated a 6-month and 1-year cutoff of time from diagnosis to survey completion. PSM was used to match these cohorts in similar fashion to the main analysis (Supplemental Figure S2).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eA total of 45,061 patients with a prior diagnosis of gastrointestinal tract cancer met our inclusion criteria (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Most patients were white (70.0%), non-Hispanic (86.2%), and female (54.2%) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The majority of patients earned less than \\u003cspan\\u003e$\\u003c/span\\u003e100,000 (54.9%), and the most common cancer diagnosis was colorectal cancer (67.8%). 89.4% were UBR; of those who were UBR, 77% were \\u0026gt;\\u0026thinsp;65 years old, 25% had at least one disability; 36% earned \\u0026lt; \\u003cspan\\u003e$\\u003c/span\\u003e50,000, and 34% were non-white. There were significant differences between diagnosis to survey cohorts in all covariates except gender (p\\u0026thinsp;=\\u0026thinsp;0.36820). 9% of patients who completed the survey within 3 months had cost-related unmet needs, compared to 19.7% of patients who completed the survey\\u0026thinsp;\\u0026ge;\\u0026thinsp;3 months after their diagnosis (p\\u0026thinsp;=\\u0026thinsp;0.204). The number of reasons for delays and cost-related unmet needs decreased over time (Supplemental Figure S3).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eClinical and demographic information of patients with gastrointestinal cancer in our study population stratified by time from diagnosis to survey completion.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eOverall\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eDiagnosis to Survey Time\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eP Value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;3 Months\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;3 Months\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003en\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e45061\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1959\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e43102\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eUBR, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40271 (89.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1664 (84.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e38607 (89.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDelays in Care (Yes), n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6345 (14.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e332 (16.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6013 (14.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCost-Related Unmet Needs (Yes), n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8893 (19.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e409 (20.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8484 (19.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.204\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDiagnosis to Survey (Years), n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.39 (5.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.11 (0.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.67 (5.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge, mean (sd)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e68.72 (11.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e62.18 (13.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e69.02 (11.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGender, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.368\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e20042 (44.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e840 (42.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19202 (44.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e24442 (54.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1098 (56.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e23344 (54.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNon Binary\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e98 ( 0.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 ( 0.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e94 ( 0.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNone Indicated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e479 ( 1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e17 ( 0.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e462 ( 1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEthnicity, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHispanic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4764 (10.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e335 (17.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4429 (10.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNon-Hispanic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e38843 (86.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1568 (80.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e37275 (86.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePrefer not to answer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1454 ( 3.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e56 ( 2.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1398 ( 3.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eRace, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e31533 (70.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1195 (61.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e30338 (70.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsian\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e787 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e56 ( 2.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e731 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBlack or African American\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6270 (13.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e311 (15.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5959 (13.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6471 (14.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e397 (20.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6074 (14.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eComorbidities, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt; 1 Comorbidity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19006 (42.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e865 (44.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e18141 (42.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u0026ndash;3 Comorbidities\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18420 (40.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e852 (43.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17568 (40.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e4\\u0026thinsp;+\\u0026thinsp;Comorbidities\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7635 (16.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e242 (12.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7393 (17.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDeprivation Index, mean (sd)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.31 (0.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.32 (0.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.31 (0.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIncome, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e150k+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6293 (14.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e282 (14.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6011 (13.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e100k-150k\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5756 (12.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e223 (11.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5533 (12.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e50k-100k\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10418 (23.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e365 (18.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10053 (23.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt; 50k\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14347 (31.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e650 (33.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e13697 (31.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePrefer not to answer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8247 (18.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e439 (22.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7808 (18.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEducation, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAdvanced degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e12268 (27.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e462 (23.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11806 (27.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCollege\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10986 (24.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e479 (24.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10507 (24.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh School\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18543 (41.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e833 (42.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17710 (41.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDid not graduate high school\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2467 ( 5.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e151 ( 7.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2316 ( 5.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePrefer not to answer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e797 ( 1.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e763 ( 1.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEmployment, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEmployed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14893 (33.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e775 (39.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14118 (32.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnemployed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e29405 (65.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1141 (58.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e28264 (65.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e763 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43 ( 2.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e720 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInsurance, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo health insurance\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e708 ( 1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e47 ( 2.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e661 ( 1.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes health insurance\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43633 (96.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1881 (96.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e41752 (96.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnknown\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e720 ( 1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31 ( 1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e689 ( 1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCancer, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eColorectal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30543 (67.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1144 (58.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e29399 (68.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAnus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11827 (26.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e568 (29.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11259 (26.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBiliary\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e412 ( 0.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e48 ( 2.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e364 ( 0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEsophageal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e197 ( 0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25 ( 1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e172 ( 0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHepatocellular\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e230 ( 0.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34 ( 1.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e196 ( 0.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePancreas\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e571 ( 1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e74 ( 3.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e497 ( 1.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmall Intestine\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e266 ( 0.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e23 ( 1.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e243 ( 0.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStomach\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1015 ( 2.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43 ( 2.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e972 ( 2.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eDelays in Care\\u003c/h3\\u003e\\n\\u003cp\\u003e14.1% of all respondents reported delays in cancer care, with 16.9% of patients\\u0026thinsp;\\u0026lt;\\u0026thinsp;3 months from their diagnosis having delays in care as compared to 14.0% in the other cohort (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). 5.8% of patients delayed care because they had to pay out of pocket for their visit. 4.1% of patients delayed care because they were nervous, and 3.3% of patients delayed care due to issues with transportation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). When stratified by diagnosis to survey completion time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB), there were significant differences in every category. When compared to patients greater than 3 months after their diagnosis, more patients within 3 months of their diagnosis delayed care because of issues affording their copay (3.4% vs. 2.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), childcare (1% vs. 0.5%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), elderly care (1.4% vs. 0.9%, p\\u0026thinsp;=\\u0026thinsp;0.025), issues getting time off work (3.9% vs. 2.4%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and difficulty finding transportation (4.3% vs. 3.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). After PSM, there were no significant differences in these two cohorts (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCost-related Unmet Needs\\u003c/h2\\u003e\\u003cp\\u003e19.7% of all patients reported cost-related unmet needs, with 20.9% of patients\\u0026thinsp;\\u0026lt;\\u0026thinsp;3 months from their diagnosis having cost-related unmet needs compared to 19.7% in the other cohort (p\\u0026thinsp;=\\u0026thinsp;0.204). 9.9% of patients asked their doctor for a lower cost medication to save money. 6.7% of patients needed dental care, 5% needed prescription medications, and 4.4% needed eyeglasses but were unable to obtain them due to financial constraints (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). 3.1% of patients took less medication to save money. When stratified by diagnosis to survey completion time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), all categories, except buying a prescription from another country to save money, had significant differences. As compared to patients who completed the survey more than 3 months after their cancer diagnosis, more patients within three months of their diagnosis took a lower cost prescription to save money (10.8% vs. 9.8%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), needed dental care but could not afford it (7.5% vs. 6.6%), needed prescription medication but could not afford it (6.5% vs. 4.8%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and used alternative therapies to save money (3.0% vs. 2.4%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). After PSM, two significant differences remained (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). Patients within 3 months of their diagnosis had increased odds of taking a lower cost prescription (OR 1.28, [95% CI: 1.05\\u0026ndash;1.54]) and trying alternative therapies (OR 1.48, [95% CI: 1.08\\u0026ndash;2.01]) to save money, as compared to patients more than three months after their cancer diagnosis.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSensitivity Analysis\\u003c/h2\\u003e\\u003cp\\u003eFor the binomial logistic regression on delays of care, there remained no significant differences closer to patients\\u0026rsquo; cancer diagnosis as compared to later on at both the 6-month and 1-year threshold (Supplemental Figure S4). With regard to cost-related unmet needs, taking a lower cost prescription to save money remained persistently higher closer to the time of diagnosis at both the 6-month and 1-year threshold (Supplemental Figure S5). Trying alternative therapies to save money was not robust after a sensitivity analysis. All other reasons for cost-related unmet needs remained non-significant in the sensitivity analysis, except that patients at the 6-month cutoff were more likely to need prescription medications but not be able to afford them (OR 1.23 [95% CI: 1.03\\u0026ndash;1.46]) (Supplemental Figure S5B); this finding was not significant at the 3-month and 1-year cut points.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study examines challenges related to healthcare access and affordability among a national cohort of patients with GI cancer. Overall, 14.1% reported care delays, with patients in their first three months of diagnosis more likely to report problems related to out-of-pocket costs, transportation, childcare, and time off work. Similarly, 19.7% reported cost-related unmet needs, with early respondents more often reporting medication non-adherence, unmet dental or prescription needs, and use of alternative therapies. These results suggest that the acute time period after a cancer diagnosis is a sensitive period that may impact their cancer care as well as their ability to access other aspects of their healthcare.\\u003c/p\\u003e\\u003cp\\u003eWhile it has been well-established that living with cancer can increase cardiovascular diseases, suicide and psychiatric symptoms and disorders, the diagnosis itself can also be highly stressful.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR17\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e Lu et al. showed that compared to the two years before a cancer diagnosis, patients who were diagnosed with cancer had a peak in depression, anxiety, substance abuse, and stress reactions by the first week of their diagnosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e While the number of disorders decreased rapidly, they still remained elevated 10 years after diagnosis. This peak in emotional stress after a cancer diagnosis is compounded by the logistical maze patients must navigate to secure treatment while keeping the rest of their lives moving forward.\\u003c/p\\u003e\\u003cp\\u003eOur study reflects these challenges, with patients within the first three months of their diagnosis experiencing increased delays in care and cost-related unmet needs than patients more than 3 months out. It might also explain why patients within the first three months of their cancer diagnosis were more likely to use lower cost prescriptions and seek alternative therapies, and they associated these behaviors with cost considerations. The use of complementary and alternative medicine (CAM) in cancer care is widespread, with recent estimates indicating that up to 87% of individuals diagnosed with cancer utilize at least one form of alternative therapy following their diagnosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e Reasons for using CAM vary, including managing cancer and treatment-related symptoms and side effects, enhancing quality of life, and providing a sense of control.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR22 CR23 CR24\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eWith regards to delays in care, the process of delaying treatment is complex. Several studies have shown that traditional risk factors such as age, race, ethnicity, clinical staging, tumor size, treatment facility, and the number of comorbidities contribute to treatment delays.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR27\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e Others have examined the role of social determinants of health (SDOH), including insurance status and geography, in these delays.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR30 CR31\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e However, our study utilizes patient-level data to focus on care delays, particularly those based on psychosocial factors. We found that 4.1% of GI cancer patients delayed care due to nervousness, 2.4% postponed care because they could not take time off work, and 0.5% (~\\u0026thinsp;225 patients) delayed care owing to childcare issues.\\u003c/p\\u003e\\u003cp\\u003eThe existing literature on the impact of these patient-level factors is limited. Frosch et al. interviewed 22 cancer patients and discovered that psychological distress both contributes to and results from treatment delays.\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e Conversely, other studies suggest that higher anxiety levels and awareness of cancer symptoms can reduce treatment delays. Regarding childcare, women are especially vulnerable to postponing care due to childcare responsibilities\\u0026mdash;Gaur et al. found that among patients awaiting ambulatory services, 54.5% of women reported missing or delaying care because of these issues.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e Further explanatory studies are essential to clarify the effects of these barriers on delays in care.\\u003c/p\\u003e\\u003cp\\u003eIn addition to delays in care, prior studies utilizing NHIS or Medical Expenditure Panel Survey (MEPS) have demonstrated significant a tendency to forgo treatment altogether because of cost, resulting in cost-related unmet needs;\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e while NHIS gathers data from face-to-face interviews with patients, the MEPS also includes medical providers and employers across the United States. Weaver et al. examined 6,602 cancer survivors using the NHIS and found 10% deferred prescription medication and 11.3% forewent dental care.\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e Our findings indicate that 2.9% forwent prescription medications and 6.7% forwent dental care. Part of the reason that our results have lower proportions than Weaver et al. is that their study population is not UBR. In turn, given the high proportion of UBR in our research, our findings may offer a more accurate representation of financial delays in care. Additionally, our study focuses specifically on GI cancer patients rather than all cancer patients.\\u003c/p\\u003e\\u003cp\\u003eSeveral actions can be taken in light of our study's findings. Oncology clinics should consider enhancing access to financial navigation programs to optimize insurance, co-pay assistance, and prescription affordability. Evidence-based complementary therapies could be incorporated into cancer care with insurance coverage. Patient navigators and social workers should proactively address financial toxicity within the first three months after diagnosis. Hospitals should provide subsidized childcare and flexible work accommodation to minimize care delays. Transportation assistance programs, such as ACS\\u0026rsquo;s Road to Recovery, should be expanded. EHR-based psychosocial screenings should result in mental health referrals. Finally, policy advocacy should concentrate on Medicaid expansion, drug pricing reform, and increased federal funding to alleviate financial toxicity in oncology care.\\u003c/p\\u003e\\u003cp\\u003eFuture studies should incorporate the role of psychosocial risk factors on delays and affordability of care.\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e All of Us contains an SDOH survey with several psychosocial domains that provide unprecedented patient-level reporting on risk factors that have been poorly studied in the literature. Additionally, we would like to look at the impact of time from treatment or intervention to survey completion on delays and care affordability. These next steps will inform the design of targeted psychosocial interventions to improve access to care and outcomes in patients with cancer.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eLimitations\\u003c/h2\\u003e\\u003cp\\u003eAs a retrospective analysis, this study cannot establish causal relationships between the time from diagnosis to survey completion and healthcare access challenges in GI cancer patients. Response bias remains a limitation, as our cohort was disproportionately female, highly educated, and non-Hispanic white, which may limit generalizability. However, our cohort was 90% UBR, comparable to or exceeding prior All of Us studies.\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, insurance type is not available in the \\u003cem\\u003eAll of Us\\u003c/em\\u003e database. Lastly, we did not assess cancer-specific delays and costs-related unmet needs, limiting disease-specific insights.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study identifies significant financial and access barriers among patients with GI cancers, particularly within the first three months following their cancer diagnosis. Nearly 90% of the cohort was UBR, and despite 97% being insured, cost-related unmet needs persisted. Delays in care were driven by affordability, childcare, and transportation barriers, but these factors were non-significant after PSM. After PSM, patients who completed the survey within 3 months of their cancer diagnosis were more likely to take lower-cost prescriptions and use alternative therapies to save money. While delays were no longer significant after adjustment, affordability challenges remained. Addressing affordability of care is essential for equitable GI cancer care.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorresponding Author\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSamuel D. Butensky\\u003c/p\\u003e\\n\\u003cp\\u003eDepartment of Surgery \\u0026ndash; Yale School of Medicine\\u003c/p\\u003e\\n\\u003cp\\u003ePO Box 208062\\u003c/p\\u003e\\n\\u003cp\\u003eNew Haven, CT, 06520-8062\\u003c/p\\u003e\\n\\u003cp\\u003eSamuel.butensky@gmail.com \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eConceptualization:\\u0026nbsp;\\u003c/em\\u003eSDB, SAK\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eData Curation:\\u0026nbsp;\\u003c/em\\u003eSDB, JK\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFormal Analysis:\\u0026nbsp;\\u003c/em\\u003eSDB, ELG, KSS\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFunding acquisition:\\u0026nbsp;\\u003c/em\\u003eSAK, CHJ\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eInvestigation:\\u0026nbsp;\\u003c/em\\u003eSDB, ELG, KSS\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eMethodology:\\u0026nbsp;\\u003c/em\\u003eSDB, ELG, KSS\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eProject Administration:\\u0026nbsp;\\u003c/em\\u003eSDB, SAK, IL\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eResources:\\u0026nbsp;\\u003c/em\\u003eSAK, CHJ, IL\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSoftware:\\u0026nbsp;\\u003c/em\\u003eSDB, JK\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSupervision:\\u0026nbsp;\\u003c/em\\u003eSAK\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eValidation:\\u0026nbsp;\\u003c/em\\u003eSDB\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eVisualization:\\u0026nbsp;\\u003c/em\\u003eSDB\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eWriting \\u0026ndash; original draft:\\u0026nbsp;\\u003c/em\\u003eSDB, SAK\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eWriting \\u0026ndash; review \\u0026amp; editing:\\u0026nbsp;\\u003c/em\\u003eSDB, KSS, ELG, IL, SAK\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest Disclosures:\\u0026nbsp;\\u003c/strong\\u003eNo other disclosures were reported.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health\\u0026rsquo;s All of Us Research Program for making available the participant data [and/or samples and/or cohort] examined in this study. During the preparation of this work the authors used a large language model (ChatGPT 4o) to revise the manuscript text for coherence and clarity. After using this service, the authors reviewed and edited the content as needed, and they take full responsibility for the content of the publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe All of Us Research Program is a publicly available, de-identified database (https://databrowser.researchallofus.org/). All supporting code used to analyze the data can be found in the All of Us researcher workbench (workspace: \\u0026ldquo;Gastrointestinal Cancer \\u0026ndash; SDOH and HUA CT V8\\u0026rdquo;).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding/Support\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by R21CA280372 (Drs. Johnson and Khan), CTSA UL1 TR001863 (Dr. Khan), and 5P30CA016359-41 (Drs. Johnson and Khan) from the NIH.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRole of the Funder/Sponsor\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe funder did not play a role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eZafar SY, Peppercorn JM, Schrag D, et al. 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The All of Us Research Program: Data quality, utility, and diversity. \\u003cem\\u003ePatterns\\u003c/em\\u003e. 2022/08/12;3(8)doi:10.1016/j.patter.2022.100570\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"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\":\"info@researchsquare.com\",\"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\":\"Healthcare Access, Gastrointestinal, Cancer, All of Us\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6855375/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6855375/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground:\\u003c/h2\\u003e\\u003cp\\u003eGastrointestinal (GI) cancer patients often face care delays and cost-related unmet needs, increasing the probability for treatment nonadherence and adverse outcomes. The extent of these barriers within the first three months of diagnosis remains unclear. We aimed to identify early barriers to care for targeted interventions.\\u003c/p\\u003e\\u003ch2\\u003eMethods:\\u003c/h2\\u003e\\u003cp\\u003eA retrospective analysis using the \\u003cem\\u003eAll of Us\\u003c/em\\u003e database included patients with esophageal, stomach, small intestine, pancreatic, hepatocellular, biliary, colorectal, or anal cancer. Patients were stratified into two cohorts based on survey completion. Reasons for delays in care and cost-related unmet needs were included as dependent variables. Propensity score matching (PSM) and logistic regression evaluated the impact of time from diagnosis.\\u003c/p\\u003e\\u003ch2\\u003eResults:\\u003c/h2\\u003e\\u003cp\\u003eAmong 45,061 GI cancer patients, 89.4% were underrepresented in biomedical research. Patients surveyed within three months of diagnosis had higher rates of delays in care (16.9% vs. 14.0%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), driven by affordability, childcare, and transportation (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Overall cost-related unmet needs did not differ significantly (\\u0026lt;\\u0026thinsp;3 months 20.9% vs. \\u0026gt;3 months 19.7%, p\\u0026thinsp;=\\u0026thinsp;0.204), but differences in unmet prescription and alternative therapy needs persisted. After PSM, early-diagnosis patients had no differences in delays in care but were more likely to report cost-saving behaviors such as using lower-cost prescriptions (OR 1.28, 95% CI 1.05\\u0026ndash;1.54) and alternative therapies (OR 1.48, 95% CI 1.08\\u0026ndash;2.01) to save money.\\u003c/p\\u003e\\u003ch2\\u003eConclusion:\\u003c/h2\\u003e\\u003cp\\u003eCost-related unmet needs exist in the first three months after GI cancer diagnosis. This study underscores the importance of addressing social determinants of health early in cancer care.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Patient-Reported Barriers to Healthcare Access Among Patients with Gastrointestinal Cancer: Insights from 45,000 Participants in the All of Us Research Program\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-29 07:55:02\",\"doi\":\"10.21203/rs.3.rs-6855375/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"c2b88fc7-2511-4249-8bac-6c34c8b6f982\",\"owner\":[],\"postedDate\":\"July 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-09T01:08:15+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-29 07:55:02\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6855375\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6855375\",\"identity\":\"rs-6855375\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}