Disparities in Timeliness of Cancer Diagnosis Across a Multi-Site Academic Health System

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Ochoa Dominguez, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8767825/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Early cancer diagnosis improves survival and quality of life, yet disparities in stage at diagnosis persist. This study evaluates demographic, clinical, insurance, and neighborhood-level socioeconomic factors associated with late-stage cancer diagnosis within an academic health system. Methods We conducted a retrospective cohort study of 27,064 adults diagnosed with breast, colorectal, or lung and bronchus cancer between 2015 and 2025 in the University of California Health System. Late-stage disease was defined as AJCC stage III/IV. Multivariable logistic regression examined associations between late-stage diagnosis and patient characteristics, insurance status, comorbidity burden, and neighborhood socioeconomic measures, including the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI). Results 17.6% of patients were diagnosed at a late stage. Cancer type was the strongest predictor, with lung (aOR ≈ 13–14) and colorectal cancer (aOR ≈ 8) associated with higher odds of late-stage diagnosis compared with breast cancer. Residence in medium and high ADI tertiles and Medicaid insurance (OR = 1.16; 95% CI: 1.06–1.28) were associated with higher odds of late-stage diagnosis, while Veterans Affairs coverage was associated with lower odds (OR = 0.76; 95% CI: 0.58–1.01). SVI was not associated with stage at diagnosis, whereas higher HPI scores were modestly protective. Conclusion Late-stage cancer diagnosis is driven primarily by cancer type and insurance status, with additional contributions from neighborhood disadvantage. Introduction Context Early cancer detection is a fundamental public health strategy recognized by the World Health Organization (WHO) for reducing global cancer morbidity and mortality. 1,2 Early-stage diagnosis is consistently associated with improved survival, reduced treatment intensity, and better quality of life across cancer types. 2,3 In the United States (US), sustained declines in cancer mortality since the early 1990s have been attributed in part to advances in screening and early detection, with an estimated 3.8 million cancer-related deaths averted during this period. 3 Despite these gains, a substantial proportion of cancers continue to be diagnosed at advanced stages, underscoring persistent barriers to timely diagnosis and access to care. 3,4 Stage-specific survival data reveal that early diagnosis is critical for improving patient outcomes. 2 For localized cancer, when the disease is confined to its primary site, the 5-year relative survival rates are significantly higher compared to distant (metastatic) disease across the majority of cancer types. 5 For instance, with colorectal cancer, survival falls from 91% for localized disease to 13% for distant disease, and with lung and bronchus cancers survival declines from 64.7% (localized) to 9.7% (distant). 6,7 Despite the proven efficacy of early-stage screening programs, a substantial proportion of late-stage cancer diagnoses persist nationally, underscoring significant systemic barriers in the timely delivery of cancer care. 3,4 The burden of advanced disease varies significantly by cancer type: lung and bronchus cancer patients comprising the highest proportion, with 45.1% of diagnoses occurring at the distant stage, compared to 23.4% for colon cancer, 18.9% for rectum cancer, and 31.0% for female breast cancer (where late-stage includes regional and distant disease). 4 The continued diagnosis of late-stage cancers, particularly for cancers with well-established screening guidelines (e.g. colorectal, breast), reveals persistent disparities in access to and utilization of these life-saving technologies across the nation. 4,8 Late-stage cancer diagnoses are closely associated with patient demographics and systemic socioeconomic inequities. 9,10 Men and women living in low-income neighborhoods face a 10%-point lower 5-year cancer survival rate than their more affluent counterparts, underscoring the influence of area-level socioeconomic factors on cancer outcomes. 11 Lower rates of screening adherence among economically vulnerable groups have also been documented. 3 Colorectal cancer screening rates are substantially lower among uninsured persons (21%) and recent immigrants (29%) and breast cancer screening rates were the lowest among uninsured women (29%) and recent immigrants (37%). 3 Racial and ethnic disparities in cancer survival also persist, indicating that economic factors alone do not fully account for inequities in cancer survival. 9,11 Even after adjusting for poverty levels, African American, American Indian, and Alaskan Native men and women exhibited lower 5-year survival rates than non-Hispanic Whites. 11 To obtain a comprehensive understanding of cancer stage disparities, the effect of the social environment also needs to be assessed, rather than relying solely on patient-level data. The Area Deprivation Index (ADI) is a validated composite measure that quantifies neighborhood-level socioeconomic disadvantage across factors associated with income, employment, education level, and housing quality and is typically aggregated to the census block group level. 12 The ADI, often represented as a State Rank from 1 (least deprived) to 10 (most deprived) on a local scale or 1 (most affluent) to 100 (most deprived) on a national scale, ranks areas by disadvantage such that higher values reflect greater structural disadvantage across these domains. 12 For cancer research, neighborhood disadvantage as measured by the ADI has been shown to be significantly associated with advanced-stage cancer diagnosis and subsequently worse survival, often independent of individual demographic and socioeconomic factors. 13,14 However, much of the existing evidence is derived from population-based registries or single-site studies, with limited evaluation of how neighborhood-level disadvantage interacts with individual clinical and access-related factors within large, multi-site academic health systems. The objective of this study was to quantify the independent associations between patient demographics, cancer type, insurance status, comorbidity burden, and neighborhood-level socioeconomic disadvantage with the likelihood of a late-stage (AJCC stage III/IV) cancer diagnosis among patients with breast, colorectal, and lung and bronchus cancer treated within a large, multi-site academic health system. Specifically, we evaluated whether neighborhood-level socioeconomic measures, including the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI), were associated with stage at diagnosis after adjustment for individual-level demographic and clinical factors. We hypothesized that patients with lung and colorectal cancers would have substantially higher odds of late-stage diagnosis compared to those with breast cancer and that residence in more socioeconomically disadvantaged neighborhoods would be associated with higher odds of late-stage diagnosis. Materials & Methods Study Design and Data Source This study employed a retrospective cohort design utilizing patient data extracted from the University of California Data Discovery Platform (UCDDP), a HIPAA-compliant repository of standardized electronic health records from all six UC Health academic medical centers (Davis, Irvine, Los Angeles, Riverside, San Diego, and San Francisco). The UCDDP employs the Observational Medical Outcomes Partnership (OMOP) Common Data Model version 5.4, which enables standardized querying and analysis of healthcare data across multiple institutions. Data extraction occurred in October 2025. Under IRB protocol 1604619-1, the University of California Health System has granted this study an exemption from human subjects protection. Study Population The final study cohort included patients over the age of 18 diagnosed with breast, colorectal, or lung and bronchus cancers that were diagnosed from January 1, 2015 to October 1, 2025. Age at diagnosis was recorded for all patients and modeled as a continuous variable in regression analyses. Cancer diagnoses were identified using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes mapped to standardized OMOP concept identifiers. The cancer types included breast cancer (ICD-10-CM codes C50.x), colorectal cancer (C18.x-C21.x), and lung and bronchus cancer (C34.x). Patients were excluded for having an unknown cancer type, unknown staging information, or if their residential address could not be linked to geospatial socioeconomic data. Primary Exposure The primary exposure variables focused on neighborhood-level socioeconomic disadvantage, linked to each patient’s residential address at diagnosis using three validated indices. The Area Deprivation Index (ADI) State Rank is a composite measure of socioeconomic disadvantage based on 17 factors across income, employment, education, and housing quality, which ranks neighborhoods within a state such that a higher rank indicates greater deprivation. 12 To improve interpretability and facilitate comparison across levels of disadvantage, ADI State Rank was categorized into tertiles representing low, medium, and high neighborhood deprivation based on the distribution of ADI values within the study population, with higher tertiles indicating greater deprivation. ADI tertiles were used as the primary neighborhood exposure. In secondary analyses, the neighborhood socioeconomic context was further evaluated using two additional, validated indices. The Social Vulnerability Index (SVI) is a composite measure developed by the Centers for Disease Control and Prevention (CDC) that captures vulnerability related to socioeconomic status, household composition, minority status, and housing and transportation, with higher scores associated with greater social vulnerability. 15 The Healthy Places Index (HPI) measures neighborhood conditions that support health, including economic opportunity, education, transportation access, housing, and environmental quality, with higher scores reflecting more favorable neighborhood health conditions and greater access to community resources. 16 SVI and HPI were modeled as continuous variables in regression analyses to assess whether alternative dimensions of neighborhood context were independently associated with stage at diagnosis. Primary Outcomes The primary outcome was the stage at diagnosis, represented as a binary outcome based on the American Joint Committee on Cancer (AJCC) staging system. Stage 0, I, and II were categorized as early-stage disease (n = 10,927; 85.9%) and served as the reference group, while stages III and IV were categorized as late-stage disease (n = 1,794; 14.1%). This outcome was selected given its established association with cancer survival and sensitivity to delays in screening, diagnostic evaluation, and care initiation. Secondary Outcomes Secondary analyses compared neighborhood-level socioeconomic characteristics between patients diagnosed at early versus late-stages. Mean values of ADI, SVI, and HPI were compared across stage groups using bivariate statistical tests to characterize unadjusted differences in neighborhood context by stage at diagnosis. Covariates Clinical and demographic covariates were selected based on existing literature and clinical relevance. Covariates included cancer type (categorical: breast, colorectal, and lung and bronchus, with breast cancer as the reference group), age at diagnosis (modeled as a continuous variable in years), sex (categorical: female or male, with female as the reference group), race (categorical: White, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, Multiracial, Other race, and Unknown, with White as the reference group), and ethnicity (categorical: Not Hispanic or Latino, Hispanic or Latino, and Unknown). Insurance status and comorbidity burden were also included as key covariates given their relevance to healthcare access and utilization. Comorbidity burden was measured using the Charlson Comorbidity Index (CCI), a validated weighted index that summarizes the presence of chronic conditions associated with mortality risk based on diagnosis codes, and was modeled as a continuous variable. 17 Statistical Analysis Descriptive statistics for the cohort were calculated with continuous variables (i.e. age, ADI, SVI, and HPI) summarized using means ± standard deviations (SD), and categorical variables (i.e. gender, race, cancer type) reported as frequencies and percentages (n (%)). Bivariate analyses compared patient characteristics between the early-stage and late-stage diagnosis groups using chi-square tests for categorical variables and independent samples t-tests for continuous variables. As this was a retrospective observational study, participants were not randomized to exposure groups. Blinding was not applicable, as all analyses were conducted on pre-existing, de-identified electronic health record data. To determine adjusted associations with late-stage diagnosis, a series of sequential multivariable logistic regression models were constructed with results presented as odds ratios (OR) and 95% confidence intervals (CI). A formal a priori power calculation was not performed, as the study included all eligible patients meeting inclusion criteria during the study period. The base model (Model 1) included patient-level demographic and clinical covariates, including age at diagnosis, sex, race, ethnicity, cancer type, insurance status, and the CCI. Breast cancer, female sex, White race, and private insurance served as reference groups. Subsequent models evaluated the independent contribution of neighborhood-level socioeconomic factors. Model 2 added ADI tertiles to the base model. Model 3A added SVI as a continuous variable to the base model and Model 3B also added HPI as a continuous variable to the base model. This approach allowed for the direct comparison of neighborhood indices while maintaining a consistent set of individual-level covariates across models. Model fit and performance were assessed using Akaike Information Criterion (AIC) and pseudo-R² statistics. All analyses were performed using Python (version 3.14) with the statsmodels package (version 0.14.6) and PySpark for data management. Data Availability This study utilized data from the University of California Data Discovery Platform (UCDDP), a HIPAA-limited dataset comprising patient records from the six UC Health academic medical centers. To protect patient confidentiality, the data are not publicly accessible. The data used in this study are not publicly available due to HIPAA and institutional data use agreements but may be accessed through the University of California Data Discovery Platform following appropriate approvals. Results Sample Characteristics The final analytic cohort consisted of 27,064 adult patients diagnosed with breast, colorectal, or lung and bronchus cancer between January 1, 2015 and October 1, 2025 across six University of California academic medical centers (Table 1). The mean age at diagnosis was 61.1 ± 13.1 years, and the cohort was predominantly female (80.2%), reflecting the large proportion of breast cancer cases. More than half of patients identified as White (55.8%), followed by Asian patients (14.4%), Black or African American patients (4.0%), and small proportions of other racial groups. Breast cancer accounted for 62.9% of diagnoses, while colorectal and lung and bronchus cancers represented 19.6% and 17.5% of cases, respectively. Overall, 17.6% of patients were diagnosed at a late stage (AJCC stage III/IV). Insurance coverage varied substantially across the cohort. Over half of patients were covered by private insurance, nearly one-fifth were insured through Medicaid, and a notable proportion had other or unknown insurance coverage (Table 1). On average, patients resided in neighborhoods characterized by moderate socioeconomic disadvantage, as indicated by mean ADI, SVI, and HPI scores. Demographic, Clinical, and Insurance Factors Associated with Late-Stage Diagnosis In multivariable analyses adjusting for demographic, clinical, insurance, and comorbidity factors, cancer type emerged as the most influential predictor of late-stage diagnosis (Table 2). Compared with breast cancer, lung and bronchus cancer was associated with nearly fourteen-fold higher odds of late-stage diagnosis (OR = 13.89; 95% CI: 12.55–15.38), while colorectal cancer was associated with approximately eight-fold higher odds (OR = 8.39; 95% CI: 7.62–9.25). Beyond cancer type, several individual-level characteristics were independently associated with stage at diagnosis. Male sex was associated with modestly higher odds of late-stage diagnosis compared with female sex (OR = 1.09; 95% CI: 1.00–1.18). Insurance status demonstrated a significant association with diagnostic stage. Patients insured through Medicaid (OR = 1.16; 95% CI: 1.06–1.28) and those with other or unknown insurance (OR = 1.39; 95% CI: 1.26–1.52) had significantly higher odds of late-stage diagnosis compared with privately insured patients. In contrast, patients receiving care through the Veterans Affairs system experienced lower odds of late-stage diagnosis (OR = 0.76; 95% CI: 0.58–1.01). Increasing age at diagnosis (OR = 0.99 per year; 95% CI: 0.98–0.99) and higher Charlson Comorbidity Index scores (OR = 0.83; 95% CI: 0.79–0.87) were associated with lower odds of late-stage diagnosis. This inverse association likely reflects more frequent healthcare contact, monitoring, and opportunities for cancer detection among older individuals and those with greater comorbidity burden rather than a protective effect of comorbidity itself (Table 2). Neighborhood Context and Late-Stage Diagnosis After incorporating neighborhood-level socioeconomic context, residence in more deprived neighborhoods remained independently associated with late-stage diagnosis (Table 3). Relative to patients residing in neighborhoods in the lowest ADI tertile, those in the medium deprivation tertile had approximately 21% higher odds of late-stage diagnosis (95% CI), while those in the highest tertile had approximately 10% higher odds (95% CI). The stronger association observed in the medium tertile suggests a non-linear relationship between neighborhood deprivation and diagnostic delays. Evaluation of alternative neighborhood indices yielded mixed findings. The Social Vulnerability Index was not independently associated with late-stage diagnosis after adjustment for individual-level factors (Table 4). In contrast, higher Healthy Places Index scores were significantly associated with lower odds of late-stage diagnosis (Table 5). These findings suggest that neighborhood characteristics reflecting access to health-promoting resources may support earlier detection. Model Performance and Comparison Model performance metrics were similar across sequential multivariable logistic regression models, with comparable pseudo-R² values indicating similar overall explanatory power (Table 6). Although models incorporating neighborhood-level indices demonstrated slightly lower Akaike Information Criterion values, these differences should be interpreted cautiously due to reduced sample sizes resulting from missing neighborhood-level data. Discussion In this large, multi-site academic health system, we found that stage at cancer diagnosis was shaped by a combination of clinical, individual-level, and neighborhood-level factors, with cancer type emerging as the most influential determinant of late-stage presentation. Lung and colorectal cancers were associated with substantially higher odds of late-stage diagnosis compared with breast cancer. These findings reinforce the central role of cancer-specific biology, symptom presentation, and screening access in determining stage at diagnosis. The significantly higher odds of late-stage diagnosis observed for lung and colorectal cancers are consistent with prior evidence demonstrating substantial variation in stage distribution, access to, and utilization of recommended screening across cancer types. 18,19 Lung cancer, in particular, is frequently diagnosed at advanced stages due to non-specific early symptoms and historically limited screening uptake, while colorectal cancer continues to experience gaps in screening participation despite well-established guidelines. 18,19 These persistent disparities highlight the need for enhanced early detection strategies, particularly for cancers with high late-stage burden. Beyond cancer type, individual-level access-related factors played an important role in diagnostic timeliness. Insurance status was a strong and consistent predictor of late-stage diagnosis, with Medicaid-insured patients and those with other or unknown insurance experiencing higher odds of advanced disease at presentation compared with privately insured patients. These findings align with prior literature demonstrating association between insurance coverage, access to preventative services, and stage at diagnosis. 8,10 In contrast, patients receiving care through the Veterans Affairs system experienced lower odds of late-stage diagnosis, suggesting that integrated healthcare delivery models with coordinated preventive and diagnostic services may mitigate delays in cancer detection. Age at diagnosis and comorbidity burden were inversely associated with late-stage diagnosis, likely reflecting greater healthcare engagement among older individuals and patients with higher comorbidity burden, who may have more frequent clinical encounters and opportunities for cancer screening or incidental detection. Residence in more socioeconomically deprived neighborhoods, as measured by ADI tertiles, was independently associated with higher odds of late-stage diagnosis. This finding is consistent with prior studies demonstrating that neighborhood disadvantage is associated with advanced-stage cancer presentation and worse outcomes, often independent of individual socioeconomic characteristics. 13,20 These findings suggest that structural barriers within disadvantaged environments such as transportation challenges, competing social demands, and limited access to preventative services continue to influence diagnostic timeliness. Alternatively, individuals in the most deprived neighborhoods may be more likely to qualify for targeted public health programs or safety-net services that can mediate barriers to care. These findings highlight the complexity of neighborhood effects and suggest that deprivation does not operate uniformly across contexts. In contrast to ADI, the Social Vulnerability Index was not independently associated with stage at diagnosis after adjustment for individual-level factors. While SVI captures aspects of vulnerability relevant to emergency preparedness and population-level risk, it may be less sensitive to barriers specific to cancer screening and diagnostic pathways within academic health systems. 15 Conversely, the Healthy Places Index demonstrated a protective association, indicating that neighborhood characteristics reflecting access to health-promoting resources and infrastructure may facilitate earlier cancer detection. 16 These findings suggest that the relevance of neighborhood indices may vary depending on the specific outcome of interest and the healthcare context in which they are applied. Across all models, the inclusion of neighborhood-level measures resulted in relatively minimal improvements in model performance compared with models including demographic, clinical, and insurance variables alone. This suggests that within a large academic health system, individual-level access and clinical factors explain most of the variation in stage at diagnosis, while neighborhood context provides additional context. The strengths of the study include its large, diverse cohort drawn from multiple academic medical centers and the use of standardized electronic health record data to ensure consistently measured exposures and outcomes. Additionally, the evaluation of multiple neighborhood-level indices allowed for comparison of different dimensions of socioeconomic context. Limitations include restriction to a single academic health system, potential misclassification of neighborhood exposure based on residential address at diagnosis, missing neighborhood-level data for a subset of patients and the impact of unmeasured factors such as individual screening history, health literacy, or primary care access. Conclusion Findings from this large, multi-site study confirm that the risk of a late-stage cancer diagnosis is not distributed randomly but is shaped by a combination of clinical, individual-level, and structural factors. The persistence of neighborhood-level associations after adjustment for individual demographic, insurance, and comorbidity factors suggest that the physical and social environment acts as an important structural determinant of diagnostic timeliness, underscoring the need for interventions that target community-level infrastructure and access to care. Together, these findings suggest that efforts to reduce late-stage cancer diagnosis should prioritize cancer-specific early detection strategies, particularly for lung and colorectal cancers, while also addressing structural barriers related to insurance coverage and neighborhood disadvantage. Leveraging geospatial tools such as the ADI may help health systems and public health agencies identify communities at elevated risk for delayed diagnosis and guide targeted outreach, screening, and patient navigation efforts to advance equity in cancer outcomes. Declarations Conflict of Interest Statement The authors declare no potential conflicts of interest. Ethics Statement This study was conducted using de-identified, retrospective electronic health record data from the University of California Data Discovery Platform. The study protocol was reviewed and deemed exempt from human subjects research by the University of California Health Institutional Review Board (IRB protocol 1604619-1). The requirement for informed consent was waived. Funding Statement This research was conducted without external funding. Individual author funding is as follows: VHT: None; SMR: None; CYOD: National Institutes of Health/National Cancer Institute under Award Number K00CA264294 (P.I.: C.Y.O.-D.) and the Burroughs Wellcome Fund Postdoctoral Enrichment Program (Award #1057518); JD: National Institutes of Health/National Cancer Institute (R00CA267181; Principal Investigator); HPJ: National Cancer Institute (K01CA234317), the SDSU/UCSD Cancer Center Comprehensive Partnership (U54CA285117 and U54CA285115), and the Alzheimer’s Disease Resource Center for Advancing Minority Aging Research at the University of California San Diego (P30AG059299); EM: None; CM: None; NC: University Grants Program, San Diego State University; American Heart Association Scientist Development Grant; National Institutes of Health/National Cancer Institute (R01; Co-Investigator); MS: National Institutes of Health/National Institute on Minority Health and Health Disparities (L32MD013114) and National Institutes of Health/National Institute on Aging (K01AG068592); WM: None; JM: National Institutes of Health/National Cancer Institute Cancer Research and Education to Advance HealTh Equity (CREATE) Partnership (U54CA285115 and U54CA285117; Multiple Principal Investigator), National Institutes of Health/National Cancer Institute (R25CA274175; Principal Investigator), and California Institute for Regenerative Medicine (EDUC3-13126; Principal Investigator); BR: None; MPB: National Institutes of Health/National Cancer Institute (1R01CA298021; Heintzman/Banegas, MPI). Author Contribution VT and SR contributed to the conceptualization of the study. MPB and CYOD provided mentorship and guidance during the study ideation and outlining process. VT developed the study outline and led manuscript drafting. SR conducted all data analyses and generated the tables. HPJ reviewed and provided feedback on the study outline. All authors contributed to critical revision of the manuscript and approved the final version for submission. Data Availability This study utilized data from the University of California Data Discovery Platform (UCDDP), a HIPAA-limited dataset comprising patient records from the six UC Health academic medical centers. To protect patient confidentiality, the data are not publicly accessible. The data used in this study are not publicly available due to HIPAA and institutional data use agreements but may be accessed through the University of California Data Discovery Platform following appropriate approvals. References Promoting cancer early diagnosis. Accessed October 26, 2025. https://www.who.int/activities/promoting-cancer-early-diagnosis Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science . 2022;375(6586):eaay9040. doi:10.1126/science.aay9040 Cancer Prevention and Early Detection Facts & Figures 2023-2024. Accessed October 26, 2025. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/cancer-prevention-and-early-detection-facts-and-figures/2024-cped-files/cped-2024-cff.pdf Stage at Diagnosis. Accessed October 26, 2025. https://progressreport.cancer.gov/diagnosis/stage Miller KD, Nogueira L, Devasia T, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin . 2022;72(5):409-436. doi:10.3322/caac.21731 Colorectal Cancer Survival Rates | Colorectal Cancer Prognosis. Accessed October 26, 2025. https://www.cancer.org/cancer/types/colon-rectal-cancer/detection-diagnosis-staging/survival-rates.html Cancer of the Lung and Bronchus - Cancer Stat Facts. SEER. Accessed October 26, 2025. https://seer.cancer.gov/statfacts/html/lungb.html Disparities in Cancer Screening for Early Detection - CDPR24. Cancer Progress Report. Accessed October 26, 2025. https://cancerprogressreport.aacr.org/disparities/cdpr24-contents/cdpr24-disparities-in-cancer-screening-for-early-detection/ Islami F, Kahn AR, Bickell NA, Schymura MJ, Boffetta P. Disentangling the effects of race/ethnicity and socioeconomic status of neighborhood in cancer stage distribution in New York City. Cancer Causes Control CCC . 2013;24(6):1069-1078. doi:10.1007/s10552-013-0184-2 Wang F, Luo L, McLafferty S. Healthcare access, socioeconomic factors and late-stage cancer diagnosis: an exploratory spatial analysis and public policy implication. Int J Public Policy . 2010;5(2-3):237-258. doi:10.1504/IJPP.2010.030606 Ward E, Jemal A, Cokkinides V, et al. Cancer Disparities by Race/Ethnicity and Socioeconomic Status. CA Cancer J Clin . 2004;54(2):78-93. doi:10.3322/canjclin.54.2.78 About the Neighborhood Atlas® and Area Deprivation Index (ADI). Cent Health Disparities Res Univ Wis Sch Med Public Health . https://www.neighborhoodatlas.medicine.wisc.edu/ Cheng E, Soulos PR, Irwin ML, et al. Neighborhood and Individual Socioeconomic Disadvantage and Survival Among Patients With Nonmetastatic Common Cancers. JAMA Netw Open . 2021;4(12):e2139593. doi:10.1001/jamanetworkopen.2021.39593 Unger JM, Moseley AB, Cheung CK, et al. Persistent Disparity: Socioeconomic Deprivation and Cancer Outcomes in Patients Treated in Clinical Trials. J Clin Oncol . 2021;39(12):1339-1348. doi:10.1200/JCO.20.02602 CDC. Social Vulnerability Index. Place and Health - Geospatial Research, Analysis, and Services Program (GRASP). October 22, 2024. Accessed October 26, 2025. https://www.atsdr.cdc.gov/place-health/php/svi/index.html The Healthy Places Index (HPI). Public Health Institute. Accessed October 26, 2025. https://www.phi.org/thought-leadership/the-california-healthy-places-index/ NCI Comorbidity Index Overview. Accessed January 18, 2026. https://healthcaredelivery.cancer.gov/seermedicare/considerations/comorbidity.html CDC. U.S. Cancer Statistics Lung Cancer Stat Bite. United States Cancer Statistics. June 10, 2025. Accessed October 27, 2025. https://www.cdc.gov/united-states-cancer-statistics/publications/lung-cancer-stat-bite.html CDC. U.S. Cancer Statistics Colorectal Cancer Stat Bite. United States Cancer Statistics. June 10, 2025. Accessed October 28, 2025. https://www.cdc.gov/united-states-cancer-statistics/publications/colorectal-cancer-stat-bite.html Cowan R, Baker E, Saleem M, et al. Association Between Area Deprivation Index and Melanoma Stage at Presentation. Cancers . 2025;17(17):2772. doi:10.3390/cancers17172772 Tables Table 1. Baseline Characteristics of the Study Population Characteristic Overall (N=27,064) Total N 27,064 Age at diagnosis, years Mean ± SD 61.1± 13.1 Gender, n (%) Female 21,713 (80.2%) Male 5,329 (19.7%) Unknown 19 (0.1%) Other 3 (0.0%) Race, n (%) White 15,103 (55.8%) Asian 3,895 (14.4%) Other 4,026 (14.9%) Black 1,073 (4.0%) Pacific Islander 156 (0.6%) American Indian or Alaska Native 88 (0.6%) Unknown 2,723 (10.1%) Charlson Comorbidity Index Mean ± SD 0.2 ± 0.7 Area Deprivation Index Mean ± SD 4.1 ± 2.7 Social Vulnerability Index Mean ± SD 3.0 ± 1.1 Healthy Places Index Mean ± SD 3.1 ± 1.0 Cancer Type, n (%) Breast Cancer 17,030 (62.9%) Lung and Bronchus Cancer 4,734 (17.5%) Colon and Rectum Cancer 5,300 (19.6%) Cancer Staging, n (%) Early Stages 22,300 (82.4%) Late Stages 4,764 (17.6%) Insurance, n (%) Private Insurance 13,790 (51.0%) Medicaid 5,074 (18.7%) Other/Unknown 4,680 (17.3%) Medicare Advantage 1,750 (6.5%) Medicare 1,378 (5.1%) Veteran Affairs 391 (1.4%) Caption: Baseline demographic, clinical, insurance, and neighborhood-level socioeconomic characteristics of 27,064 adult patients diagnosed with breast, colorectal, or lung and bronchus cancer across six University of California academic medical centers between 2015 and 2025. Continuous variables are presented as mean ± standard deviation, and categorical variables are presented as number (percentage). Neighborhood socioeconomic context was assessed using the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI). Cancer stage at diagnosis was categorized using AJCC criteria, with stages 0–II classified as early-stage and stages III–IV classified as late-stage disease. Table 2. Multivariable Logistic Regression Results (Model 1): Demographic, Clinical, Insurance, and Comorbidity Factors Associated With Late-Stage Cancer Diagnosis Variable OR 95% CI P-Value American Indian or Alaska Native 0.855 (0.453, 1.612) 0.628 Asian 1.109 (1.004, 1.226) 0.042 Black 1.128 (0.949, 1.341) 0.172 Pacific Islander 1.069 (0.678, 1.684) 0.774 Other Race 1.089 (0.982, 1.207) 0.105 Unknown Race 1.155 (1.024, 1.303) 0.019 White – – – Male 1.090 (1.004, 1.183) 0.039 Other Sex 0.000 (0.000, Inf) 1.000 Unknown Sex 3.238 (1.049, 9.998) 0.041 Female – – – Colorectal Cancer 8.394 (7.619, 9.248) <0.001 Lung Cancer 13.893 (12.548, 15.383) <0.001 Breast Cancer – – – Veteran Affairs 0.763 (0.579, 1.005) 0.055 Medicaid 1.160 (1.055, 1.275) 0.002 Medicare Advantage 1.085 (0.939, 1.255) 0.269 Medicare 1.113 (0.948, 1.306) 0.190 Other/Unknown Insurance 1.386 (1.261, 1.523) <0.001 Private Insurance – – – Age at Diagnosis 0.986 (0.983, 0.989) <0.001 Charlson Comorbidity Index 0.833 (0.794, 0.873) <0.001 Caption: Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression examining associations between patient demographics (race and sex), cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index with odds of late-stage cancer diagnosis (AJCC stage III/IV). White race, female sex, breast cancer, and private insurance served as reference categories. All covariates were included simultaneously in the model. An OR greater than 1 indicates higher odds of late-stage diagnosis, while an OR less than 1 indicates lower odds. Table 3. Multivariable Logistic Regression Results (Model 2): Model 1 Plus Area Deprivation Index (ADI) Tertiles Variable OR 95% CI P-Value American Indian or Alaska Native 0.803 (0.417, 1.548) 0.513 Asian 1.169 (1.055, 1.295) 0.003 Black 1.149 (0.963, 1.371) 0.124 Pacific Islander 1.015 (0.631, 1.632) 0.953 Other Race 1.075 (0.965, 1.196) 0.188 Unknown Race 1.177 (1.039, 1.332) 0.010 White – – – Male 1.107 (1.017, 1.205) 0.018 Other Sex 0.000 (0.000, Inf) 0.996 Unknown Sex 3.187 (0.876, 11.599) 0.079 Female – – – Colorectal Cancer 7.854 (7.103, 8.684) <0.001 Lung Cancer 13.651 (12.297, 15.156) <0.001 Breast Cancer – – – Veteran Affairs 0.718 (0.537, 0.958) 0.025 Medicaid 1.165 (1.056, 1.285) 0.002 Medicare Advantage 1.084 (0.935, 1.258) 0.284 Medicare 1.095 (0.929, 1.291) 0.279 Other/Unknown Insurance 1.313 (1.189, 1.451) <0.001 Private Insurance – – – ADI – High Tertile 1.101 (1.008, 1.202) 0.032 ADI – Medium Tertile 1.214 (1.107, 1.331) <0.001 ADI – Low Tertile – – – Age at Diagnosis 0.988 (0.985, 0.991) <0.001 Charlson Comorbidity Index 0.832 (0.793, 0.874) <0.001 Caption: Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression evaluating the association between neighborhood-level socioeconomic deprivation and late-stage cancer diagnosis. Area Deprivation Index (ADI) State Rank was categorized into tertiles representing low, medium, and high neighborhood deprivation, with the lowest tertile serving as the reference group. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. Reduced sample size reflects exclusion of patients with missing neighborhood-level socioeconomic data. Table 4. Multivariable Logistic Regression Results (Model 3A): Model 1 Plus Social Vulnerability Index (SVI) Variable OR 95% CI P-Value American Indian or Alaska Native 0.914 (0.483, 1.729) 0.782 Asian 1.150 (1.039, 1.273) 0.007 Black 1.151 (0.964, 1.373) 0.119 Pacific Islander 1.014 (0.630, 1.630) 0.955 Other Race 1.120 (1.007, 1.246) 0.038 Unknown Race 1.182 (1.039, 1.338) 0.008 White – – – Male 1.105 (1.016, 1.203) 0.020 Other Sex 0.000 (0.000, Inf) 1.000 Unknown Sex 3.219 (0.880, 11.776) 0.077 Female – – – Colorectal Cancer 7.911 (7.157, 8.743) <0.001 Lung Cancer 13.655 (12.304, 15.154) <0.001 Breast Cancer – – – Veteran Affairs 0.738 (0.554, 0.984) 0.039 Medicaid 1.190 (1.078, 1.314) <0.001 Medicare Advantage 1.103 (0.951, 1.279) 0.193 Medicare 1.125 (0.955, 1.324) 0.159 Other/Unknown Insurance 1.345 (1.218, 1.485) <0.001 Private Insurance – – – Social Vulnerability Index 1.001 (0.966, 1.037) 0.964 Age at Diagnosis 0.988 (0.985, 0.990) <0.001 Charlson Comorbidity Index 0.834 (0.794, 0.875) <0.001 Caption: Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression assessing the association between the Social Vulnerability Index (SVI) and late-stage cancer diagnosis. SVI was modeled as a continuous variable, with higher values indicating greater neighborhood-level social vulnerability. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. An OR of 1.00 indicates no association between SVI and stage at diagnosis after adjustment. Table 5. Multivariable Logistic Regression Results (Model 3B): Model 1 Plus Healthy Places Index (HPI) Variable OR 95% CI P-Value American Indian or Alaska Native 0.828 (0.429, 1.600) 0.575 Asian 1.163 (1.050, 1.288) 0.004 Black 1.117 (0.936, 1.334) 0.220 Pacific Islander 1.018 (0.633, 1.637) 0.942 Other Race 1.092 (0.981, 1.216) 0.108 Unknown Race 1.175 (1.038, 1.330) 0.011 White – – – Male 1.101 (1.012, 1.198) 0.026 Other Sex 0.000 (0.000, Inf) 0.999 Unknown Sex 3.161 (0.856, 11.678) 0.084 Female – – – Colorectal Cancer 7.877 (7.125, 8.709) <0.001 Lung Cancer 13.613 (12.263, 15.111) <0.001 Breast Cancer – – – Veteran Affairs 0.733 (0.549, 0.979) 0.035 Medicaid 1.155 (1.047, 1.276) 0.004 Medicare Advantage 1.091 (0.941, 1.266) 0.250 Medicare 1.117 (0.948, 1.317) 0.185 Other/Unknown Insurance 1.331 (1.205, 1.470) <0.001 Private Insurance – – – Healthy Places Index 0.957 (0.922, 0.993) 0.019 Age at Diagnosis 0.988 (0.985, 0.991) <0.001 Charlson Comorbidity Index 0.832 (0.793, 0.874) <0.001 Caption: Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression evaluating the association between the Healthy Places Index (HPI) and late-stage cancer diagnosis. HPI was modeled as a continuous variable, with higher scores reflecting more favorable neighborhood health conditions and greater access to community resources. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. An OR less than 1 indicates lower odds of late-stage diagnosis associated with more favorable neighborhood conditions. Table 6. Model Fit and Performance Comparison Across Sequential Multivariable Logistic Regression Models Model N AIC Pseudo R 2 1: Demographics + Insurance + Comorbidity 27,063 20559.73 0.1872 2: Model 1 + ADI 25,780 19372.54 0.1857 3A: Model 1 + SVI 25,940 19533.48 0.1846 3B: Model 1 + HPI 25,784 19392.23 0.1847 Caption: Comparison of model performance metrics across sequential multivariable logistic regression models, including sample size (N), Akaike Information Criterion (AIC), and pseudo-R². Model 1 included demographic, clinical, insurance, and comorbidity variables; Model 2 additionally included Area Deprivation Index (ADI) tertiles; Model 3A included Social Vulnerability Index (SVI); and Model 3B included Healthy Places Index (HPI). Differences in sample size across models reflect missing neighborhood-level socioeconomic data. Lower AIC values indicate improved model fit, though comparisons should be interpreted cautiously due to differing sample sizes. Additional Declarations No competing interests reported. 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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-8767825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627866506,"identity":"4a911597-1d0b-4ed3-b1fd-9f41eeef20e9","order_by":0,"name":"Vivian Hoang Tran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACZgaGAwk/bEBMAwYGNqK0MDM+eNiTRooWoB7DB2yHSdBicJz/mEQCz/nE/v7DGxg+lB0mQsthZjaJBIvbiTNupBUwzjhHhBbJZpAWntuJGyR4DJh524jWwnYucQP/GQPmv8Ro4WdmZjZIYDuQuIEhx4CZkUgthg8Se5KNQX452HMunbAWNv6DDw7++GEnCwyxjQ9+lFkT1oICDpCofhSMglEwCkYBLgAA9VA4tLdNC/UAAAAASUVORK5CYII=","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"prefix":"","firstName":"Vivian","middleName":"Hoang","lastName":"Tran","suffix":""},{"id":627866507,"identity":"fd509242-8da5-4a32-b437-19e7cc607037","order_by":1,"name":"Suraj Manohar Rajan","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Suraj","middleName":"Manohar","lastName":"Rajan","suffix":""},{"id":627866508,"identity":"0a0997d4-47fa-48b0-8d0f-868745412cb2","order_by":2,"name":"Carol Y. Ochoa Dominguez","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"Y. Ochoa","lastName":"Dominguez","suffix":""},{"id":627866510,"identity":"c373f24f-34db-452a-839b-898e7598fd53","order_by":3,"name":"Joshua Demb","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Demb","suffix":""},{"id":627866511,"identity":"1822fbec-9456-4569-b438-43efb8b3901b","order_by":4,"name":"Humberto Parada","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Humberto","middleName":"","lastName":"Parada","suffix":""},{"id":627866515,"identity":"0916e19a-155f-4dd4-bbb8-56744287b9c8","order_by":5,"name":"Elena Martinez","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Martinez","suffix":""},{"id":627866518,"identity":"448a9c80-82b2-4705-b274-18c5bf2184b5","order_by":6,"name":"Corinne McDaniels","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Corinne","middleName":"","lastName":"McDaniels","suffix":""},{"id":627866520,"identity":"28e6ab79-8f7c-4662-b2b3-e1e5a0c29d7b","order_by":7,"name":"Noe Crespo","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Noe","middleName":"","lastName":"Crespo","suffix":""},{"id":627866522,"identity":"e4c4b51d-e367-4b08-a1f0-be8df2a030d8","order_by":8,"name":"Melody K. Schiaffino","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Melody","middleName":"K.","lastName":"Schiaffino","suffix":""},{"id":627866524,"identity":"b60a2550-b4e2-406d-ab67-366bede5b6fe","order_by":9,"name":"Winta Mehtsun","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Winta","middleName":"","lastName":"Mehtsun","suffix":""},{"id":627866525,"identity":"bbfbb66d-0a76-4c38-bcc9-b00e747ec576","order_by":10,"name":"James Murphy","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Murphy","suffix":""},{"id":627866526,"identity":"85f9b39a-304f-4df3-9b7d-55f748fd2544","order_by":11,"name":"Brenton Rose","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Brenton","middleName":"","lastName":"Rose","suffix":""},{"id":627866527,"identity":"73af13bf-9fca-4ce4-8d5e-1f104c263031","order_by":12,"name":"Matthew P. Banegas","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"P.","lastName":"Banegas","suffix":""}],"badges":[],"createdAt":"2026-02-02 17:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8767825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8767825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107667973,"identity":"6cc21722-18c1-48c2-ab53-5fb7669d1808","added_by":"auto","created_at":"2026-04-23 19:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":514840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8767825/v1/f660ae9a-38f6-40a7-996f-8c10297d587b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disparities in Timeliness of Cancer Diagnosis Across a Multi-Site Academic Health System","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eContext\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly cancer detection is a fundamental public health strategy recognized by the World Health Organization (WHO) for reducing global cancer morbidity and mortality.\u003csup\u003e1,2\u003c/sup\u003e Early-stage diagnosis is consistently associated with improved survival, reduced treatment intensity, and better quality of life across cancer types.\u003csup\u003e2,3\u003c/sup\u003e In the United States (US), sustained declines in cancer mortality since the early 1990s have been attributed in part to advances in screening and early detection, with an estimated 3.8 million cancer-related deaths averted during this period.\u003csup\u003e3\u003c/sup\u003e Despite these gains, a substantial proportion of cancers continue to be diagnosed at advanced stages, underscoring persistent barriers to timely diagnosis and access to care.\u003csup\u003e3,4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eStage-specific survival data reveal that early diagnosis is critical for improving patient outcomes.\u003csup\u003e2\u003c/sup\u003e For localized cancer, when the disease is confined to its primary site, the 5-year relative survival rates are significantly higher compared to distant (metastatic) disease across the majority of cancer types.\u003csup\u003e5\u003c/sup\u003e For instance, with colorectal cancer, survival falls from 91% for localized disease to 13% for distant disease, and with lung and bronchus cancers survival declines from 64.7% (localized) to 9.7% (distant).\u003csup\u003e6,7\u003c/sup\u003e Despite the proven efficacy of early-stage screening programs, a substantial proportion of late-stage cancer diagnoses persist nationally, underscoring significant systemic barriers in the timely delivery of cancer care.\u003csup\u003e3,4\u003c/sup\u003e The burden of advanced disease varies significantly by cancer type: lung and bronchus cancer patients comprising the highest proportion, with 45.1% of diagnoses occurring at the distant stage, compared to 23.4% for colon cancer, 18.9% for rectum cancer, and 31.0% for female breast cancer (where late-stage includes regional and distant disease).\u003csup\u003e4\u003c/sup\u003e The continued diagnosis of late-stage cancers, particularly for cancers with well-established screening guidelines (e.g. colorectal, breast), reveals persistent disparities in access to and utilization of these life-saving technologies across the nation.\u003csup\u003e4,8\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eLate-stage cancer diagnoses are closely associated with patient demographics and systemic socioeconomic inequities.\u003csup\u003e9,10\u003c/sup\u003e Men and women living in low-income neighborhoods face a 10%-point lower 5-year cancer survival rate than their more affluent counterparts, underscoring the influence of area-level socioeconomic factors on cancer outcomes.\u003csup\u003e11\u003c/sup\u003e Lower rates of screening adherence among economically vulnerable groups have also been documented.\u003csup\u003e3\u003c/sup\u003e Colorectal cancer screening rates are substantially lower among uninsured persons (21%) and recent immigrants (29%) and breast cancer screening rates were the lowest among uninsured women (29%) and recent immigrants (37%).\u003csup\u003e3\u003c/sup\u003e Racial and ethnic disparities in cancer survival also persist, indicating that economic factors alone do not fully account for inequities in cancer survival.\u003csup\u003e9,11\u003c/sup\u003e Even after adjusting for poverty levels, African American, American Indian, and Alaskan Native men and women exhibited lower 5-year survival rates than non-Hispanic Whites.\u003csup\u003e11\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eTo obtain a comprehensive understanding of cancer stage disparities, the effect of the social environment also needs to be assessed, rather than relying solely on patient-level data. The Area Deprivation Index (ADI) is a validated composite measure that quantifies neighborhood-level socioeconomic disadvantage across factors associated with income, employment, education level, and housing quality and is typically aggregated to the census block group level.\u003csup\u003e12\u003c/sup\u003e The ADI, often represented as a State Rank from 1 (least deprived) to 10 (most deprived) on a local scale or 1 (most affluent) to 100 (most deprived) on a national scale, ranks areas by disadvantage such that higher values reflect greater structural disadvantage across these domains.\u003csup\u003e12\u003c/sup\u003e For cancer research, neighborhood disadvantage as measured by the ADI has been shown to be significantly associated with advanced-stage cancer diagnosis and subsequently worse survival, often independent of individual demographic and socioeconomic factors.\u003csup\u003e13,14\u003c/sup\u003e However, much of the existing evidence is derived from population-based registries or single-site studies, with limited evaluation of how neighborhood-level disadvantage interacts with individual clinical and access-related factors within large, multi-site academic health systems.\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to quantify the independent associations between patient demographics, cancer type, insurance status, comorbidity burden, and neighborhood-level socioeconomic disadvantage with the likelihood of a late-stage (AJCC stage III/IV) cancer diagnosis among patients with breast, colorectal, and lung and bronchus cancer treated within a large, multi-site academic health system. Specifically, we evaluated whether neighborhood-level socioeconomic measures, including the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI), were associated with stage at diagnosis after adjustment for individual-level demographic and clinical factors. We hypothesized that patients with lung and colorectal cancers would have substantially higher odds of late-stage diagnosis compared to those with breast cancer and that residence in more socioeconomically disadvantaged neighborhoods would be associated with higher odds of late-stage diagnosis.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a retrospective cohort design utilizing patient data extracted from the University of California Data Discovery Platform (UCDDP), a HIPAA-compliant repository of standardized electronic health records from all six UC Health academic medical centers (Davis, Irvine, Los Angeles, Riverside, San Diego, and San Francisco). The UCDDP employs the Observational Medical Outcomes Partnership (OMOP) Common Data Model version 5.4, which enables standardized querying and analysis of healthcare data across multiple institutions. Data extraction occurred in October 2025. Under IRB protocol 1604619-1, the University of California Health System has granted this study an exemption from human subjects protection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final study cohort included patients over the age of 18 diagnosed with breast, colorectal, or lung and bronchus cancers that were diagnosed from January 1, 2015 to October 1, 2025. Age at diagnosis was recorded for all patients and modeled as a continuous variable in regression analyses. Cancer diagnoses were identified using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes mapped to standardized OMOP concept identifiers. The cancer types included breast cancer (ICD-10-CM codes C50.x), colorectal cancer (C18.x-C21.x), and lung and bronchus cancer (C34.x). Patients were excluded for having an unknown cancer type, unknown staging information, or if their residential address could not be linked to geospatial socioeconomic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary exposure variables focused on neighborhood-level socioeconomic disadvantage, linked to each patient\u0026rsquo;s residential address at diagnosis using three validated indices. The Area Deprivation Index (ADI) State Rank is a composite measure of socioeconomic disadvantage based on 17 factors across income, employment, education, and housing quality, which ranks neighborhoods within a state such that a higher rank indicates greater deprivation.\u003csup\u003e12\u003c/sup\u003e To improve interpretability and facilitate comparison across levels of disadvantage, ADI State Rank was categorized into tertiles representing low, medium, and high neighborhood deprivation based on the distribution of ADI values within the study population, with higher tertiles indicating greater deprivation. ADI tertiles were used as the primary neighborhood exposure. \u003c/p\u003e\n\u003cp\u003eIn secondary analyses, the neighborhood socioeconomic context was further evaluated using two additional, validated indices. The Social Vulnerability Index (SVI) is a composite measure developed by the Centers for Disease Control and Prevention (CDC) that captures vulnerability related to socioeconomic status, household composition, minority status, and housing and transportation, with higher scores associated with greater social vulnerability.\u003csup\u003e15\u003c/sup\u003e The Healthy Places Index (HPI) measures neighborhood conditions that support health, including economic opportunity, education, transportation access, housing, and environmental quality, with higher scores reflecting more favorable neighborhood health conditions and greater access to community resources.\u003csup\u003e16\u003c/sup\u003e SVI and HPI were modeled as continuous variables in regression analyses to assess whether alternative dimensions of neighborhood context were independently associated with stage at diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the stage at diagnosis, represented as a binary outcome based on the American Joint Committee on Cancer (AJCC) staging system. Stage 0, I, and II were categorized as early-stage disease (n = 10,927; 85.9%) and served as the reference group, while stages III and IV were categorized as late-stage disease (n = 1,794; 14.1%). This outcome was selected given its established association with cancer survival and sensitivity to delays in screening, diagnostic evaluation, and care initiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecondary analyses compared neighborhood-level socioeconomic characteristics between patients diagnosed at early versus late-stages. Mean values of ADI, SVI, and HPI were compared across stage groups using bivariate statistical tests to characterize unadjusted differences in neighborhood context by stage at diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and demographic covariates were selected based on existing literature and clinical relevance. Covariates included cancer type (categorical: breast, colorectal, and lung and bronchus, with breast cancer as the reference group), age at diagnosis (modeled as a continuous variable in years), sex (categorical: female or male, with female as the reference group), race (categorical: White, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, Multiracial, Other race, and Unknown, with White as the reference group), and ethnicity (categorical: Not Hispanic or Latino, Hispanic or Latino, and Unknown).\u003c/p\u003e\n\u003cp\u003eInsurance status and comorbidity burden were also included as key covariates given their relevance to healthcare access and utilization. Comorbidity burden was measured using the Charlson Comorbidity Index (CCI), a validated weighted index that summarizes the presence of chronic conditions associated with mortality risk based on diagnosis codes, and was modeled as a continuous variable.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics for the cohort were calculated with continuous variables (i.e. age, ADI, SVI, and HPI) summarized using means \u0026plusmn; standard deviations (SD), and categorical variables (i.e. gender, race, cancer type) reported as frequencies and percentages (n (%)). \u003c/p\u003e\n\u003cp\u003eBivariate analyses compared patient characteristics between the early-stage and late-stage diagnosis groups using chi-square tests for categorical variables and independent samples t-tests for continuous variables. As this was a retrospective observational study, participants were not randomized to exposure groups. Blinding was not applicable, as all analyses were conducted on pre-existing, de-identified electronic health record data.\u003c/p\u003e\n\u003cp\u003eTo determine adjusted associations with late-stage diagnosis, a series of sequential multivariable logistic regression models were constructed with results presented as odds ratios (OR) and 95% confidence intervals (CI). A formal a priori power calculation was not performed, as the study included all eligible patients meeting inclusion criteria during the study period. The base model (Model 1) included patient-level demographic and clinical covariates, including age at diagnosis, sex, race, ethnicity, cancer type, insurance status, and the CCI. Breast cancer, female sex, White race, and private insurance served as reference groups. Subsequent models evaluated the independent contribution of neighborhood-level socioeconomic factors. Model 2 added ADI tertiles to the base model. Model 3A added SVI as a continuous variable to the base model and Model 3B also added HPI as a continuous variable to the base model. This approach allowed for the direct comparison of neighborhood indices while maintaining a consistent set of individual-level covariates across models. Model fit and performance were assessed using Akaike Information Criterion (AIC) and pseudo-R\u0026sup2; statistics. All analyses were performed using Python (version 3.14) with the statsmodels package (version 0.14.6) and PySpark for data management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the University of California Data Discovery Platform (UCDDP), a HIPAA-limited dataset comprising patient records from the six UC Health academic medical centers. To protect patient confidentiality, the data are not publicly accessible. The data used in this study are not publicly available due to HIPAA and institutional data use agreements but may be accessed through the University of California Data Discovery Platform following appropriate approvals.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final analytic cohort consisted of 27,064 adult patients diagnosed with breast, colorectal, or lung and bronchus cancer between January 1, 2015 and October 1, 2025 across six University of California academic medical centers (Table 1). The mean age at diagnosis was 61.1 \u0026plusmn; 13.1 years, and the cohort was predominantly female (80.2%), reflecting the large proportion of breast cancer cases. More than half of patients identified as White (55.8%), followed by Asian patients (14.4%), Black or African American patients (4.0%), and small proportions of other racial groups. Breast cancer accounted for 62.9% of diagnoses, while colorectal and lung and bronchus cancers represented 19.6% and 17.5% of cases, respectively.\u003c/p\u003e\n\u003cp\u003eOverall, 17.6% of patients were diagnosed at a late stage (AJCC stage III/IV). Insurance coverage varied substantially across the cohort. Over half of patients were covered by private insurance, nearly one-fifth were insured through Medicaid, and a notable proportion had other or unknown insurance coverage (Table 1). On average, patients resided in neighborhoods characterized by moderate socioeconomic disadvantage, as indicated by mean ADI, SVI, and HPI scores. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic, Clinical, and Insurance Factors Associated with Late-Stage Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable analyses adjusting for demographic, clinical, insurance, and comorbidity factors, cancer type emerged as the most influential predictor of late-stage diagnosis (Table 2). Compared with breast cancer, lung and bronchus cancer was associated with nearly fourteen-fold higher odds of late-stage diagnosis (OR = 13.89; 95% CI: 12.55\u0026ndash;15.38), while colorectal cancer was associated with approximately eight-fold higher odds (OR = 8.39; 95% CI: 7.62\u0026ndash;9.25).\u003c/p\u003e\n\u003cp\u003eBeyond cancer type, several individual-level characteristics were independently associated with stage at diagnosis. Male sex was associated with modestly higher odds of late-stage diagnosis compared with female sex (OR = 1.09; 95% CI: 1.00\u0026ndash;1.18). Insurance status demonstrated a significant association with diagnostic stage. Patients insured through Medicaid (OR = 1.16; 95% CI: 1.06\u0026ndash;1.28) and those with other or unknown insurance (OR = 1.39; 95% CI: 1.26\u0026ndash;1.52) had significantly higher odds of late-stage diagnosis compared with privately insured patients. In contrast, patients receiving care through the Veterans Affairs system experienced lower odds of late-stage diagnosis (OR = 0.76; 95% CI: 0.58\u0026ndash;1.01).\u003c/p\u003e\n\u003cp\u003eIncreasing age at diagnosis (OR = 0.99 per year; 95% CI: 0.98\u0026ndash;0.99) and higher Charlson Comorbidity Index scores (OR = 0.83; 95% CI: 0.79\u0026ndash;0.87) were associated with lower odds of late-stage diagnosis. This inverse association likely reflects more frequent healthcare contact, monitoring, and opportunities for cancer detection among older individuals and those with greater comorbidity burden rather than a protective effect of comorbidity itself (Table 2). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood Context and Late-Stage Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter incorporating neighborhood-level socioeconomic context, residence in more deprived neighborhoods remained independently associated with late-stage diagnosis (Table 3). Relative to patients residing in neighborhoods in the lowest ADI tertile, those in the medium deprivation tertile had approximately 21% higher odds of late-stage diagnosis (95% CI), while those in the highest tertile had approximately 10% higher odds (95% CI). The stronger association observed in the medium tertile suggests a non-linear relationship between neighborhood deprivation and diagnostic delays.\u003c/p\u003e\n\u003cp\u003eEvaluation of alternative neighborhood indices yielded mixed findings. The Social Vulnerability Index was not independently associated with late-stage diagnosis after adjustment for individual-level factors (Table 4). In contrast, higher Healthy Places Index scores were significantly associated with lower odds of late-stage diagnosis (Table 5). These findings suggest that neighborhood characteristics reflecting access to health-promoting resources may support earlier detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Performance and Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel performance metrics were similar across sequential multivariable logistic regression models, with comparable pseudo-R\u0026sup2; values indicating similar overall explanatory power (Table 6). Although models incorporating neighborhood-level indices demonstrated slightly lower Akaike Information Criterion values, these differences should be interpreted cautiously due to reduced sample sizes resulting from missing neighborhood-level data. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, multi-site academic health system, we found that stage at cancer diagnosis was shaped by a combination of clinical, individual-level, and neighborhood-level factors, with cancer type emerging as the most influential determinant of late-stage presentation. Lung and colorectal cancers were associated with substantially higher odds of late-stage diagnosis compared with breast cancer. These findings reinforce the central role of cancer-specific biology, symptom presentation, and screening access in determining stage at diagnosis.\u003c/p\u003e\n\u003cp\u003eThe significantly higher odds of late-stage diagnosis observed for lung and colorectal cancers are consistent with prior evidence demonstrating substantial variation in stage distribution, access to, and utilization of recommended screening across cancer types.\u003csup\u003e18,19\u003c/sup\u003e Lung cancer, in particular, is frequently diagnosed at advanced stages due to non-specific early symptoms and historically limited screening uptake, while colorectal cancer continues to experience gaps in screening participation despite well-established guidelines.\u003csup\u003e18,19\u003c/sup\u003e These persistent disparities highlight the need for enhanced early detection strategies, particularly for cancers with high late-stage burden.\u003c/p\u003e\n\u003cp\u003eBeyond cancer type, individual-level access-related factors played an important role in diagnostic timeliness. Insurance status was a strong and consistent predictor of late-stage diagnosis, with Medicaid-insured patients and those with other or unknown insurance experiencing higher odds of advanced disease at presentation compared with privately insured patients. These findings align with prior literature demonstrating association between insurance coverage, access to preventative services, and stage at diagnosis.\u003csup\u003e8,10\u003c/sup\u003e In contrast, patients receiving care through the Veterans Affairs system experienced lower odds of late-stage diagnosis, suggesting that integrated healthcare delivery models with coordinated preventive and diagnostic services may mitigate delays in cancer detection. Age at diagnosis and comorbidity burden were inversely associated with late-stage diagnosis, likely reflecting greater healthcare engagement among older individuals and patients with higher comorbidity burden, who may have more frequent clinical encounters and opportunities for cancer screening or incidental detection.\u003c/p\u003e\n\u003cp\u003eResidence in more socioeconomically deprived neighborhoods, as measured by ADI tertiles, was independently associated with higher odds of late-stage diagnosis. This finding is consistent with prior studies demonstrating that neighborhood disadvantage is associated with advanced-stage cancer presentation and worse outcomes, often independent of individual socioeconomic characteristics.\u003csup\u003e13,20\u003c/sup\u003e These findings suggest that structural barriers within disadvantaged environments such as transportation challenges, competing social demands, and limited access to preventative services continue to influence diagnostic timeliness. \u003c/p\u003e\n\u003cp\u003eAlternatively, individuals in the most deprived neighborhoods may be more likely to qualify for targeted public health programs or safety-net services that can mediate barriers to care. These findings highlight the complexity of neighborhood effects and suggest that deprivation does not operate uniformly across contexts.\u003c/p\u003e\n\u003cp\u003eIn contrast to ADI, the Social Vulnerability Index was not independently associated with stage at diagnosis after adjustment for individual-level factors. While SVI captures aspects of vulnerability relevant to emergency preparedness and population-level risk, it may be less sensitive to barriers specific to cancer screening and diagnostic pathways within academic health systems.\u003csup\u003e15\u003c/sup\u003e Conversely, the Healthy Places Index demonstrated a protective association, indicating that neighborhood characteristics reflecting access to health-promoting resources and infrastructure may facilitate earlier cancer detection.\u003csup\u003e16\u003c/sup\u003e These findings suggest that the relevance of neighborhood indices may vary depending on the specific outcome of interest and the healthcare context in which they are applied. Across all models, the inclusion of neighborhood-level measures resulted in relatively minimal improvements in model performance compared with models including demographic, clinical, and insurance variables alone. This suggests that within a large academic health system, individual-level access and clinical factors explain most of the variation in stage at diagnosis, while neighborhood context provides additional context. \u003c/p\u003e\n\u003cp\u003eThe strengths of the study include its large, diverse cohort drawn from multiple academic medical centers and the use of standardized electronic health record data to ensure consistently measured exposures and outcomes. Additionally, the evaluation of multiple neighborhood-level indices allowed for comparison of different dimensions of socioeconomic context. Limitations include restriction to a single academic health system, potential misclassification of neighborhood exposure based on residential address at diagnosis, missing neighborhood-level data for a subset of patients and the impact of unmeasured factors such as individual screening history, health literacy, or primary care access.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFindings from this large, multi-site study confirm that the risk of a late-stage cancer diagnosis is not distributed randomly but is shaped by a combination of clinical, individual-level, and structural factors. The persistence of neighborhood-level associations after adjustment for individual demographic, insurance, and comorbidity factors suggest that the physical and social environment acts as an important structural determinant of diagnostic timeliness, underscoring the need for interventions that target community-level infrastructure and access to care.\u003c/p\u003e \u003cp\u003eTogether, these findings suggest that efforts to reduce late-stage cancer diagnosis should prioritize cancer-specific early detection strategies, particularly for lung and colorectal cancers, while also addressing structural barriers related to insurance coverage and neighborhood disadvantage. Leveraging geospatial tools such as the ADI may help health systems and public health agencies identify communities at elevated risk for delayed diagnosis and guide targeted outreach, screening, and patient navigation efforts to advance equity in cancer outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eThis study was conducted using de-identified, retrospective electronic health record data from the University of California Data Discovery Platform. The study protocol was reviewed and deemed exempt from human subjects research by the University of California Health Institutional Review Board (IRB protocol 1604619-1). The requirement for informed consent was waived.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding Statement\u003c/h2\u003e\n\u003cp\u003eThis research was conducted without external funding. Individual author funding is as follows:\u003c/p\u003e\n\u003cp\u003eVHT: None; SMR: None; CYOD: National Institutes of Health/National Cancer Institute under Award Number K00CA264294 (P.I.: C.Y.O.-D.) and the Burroughs Wellcome Fund Postdoctoral Enrichment Program (Award #1057518); JD: National Institutes of Health/National Cancer Institute (R00CA267181; Principal Investigator); HPJ: National Cancer Institute (K01CA234317), the SDSU/UCSD Cancer Center Comprehensive Partnership (U54CA285117 and U54CA285115), and the Alzheimer\u0026rsquo;s Disease Resource Center for Advancing Minority Aging Research at the University of California San Diego (P30AG059299); EM: None; CM: None; NC: University Grants Program, San Diego State University; American Heart Association Scientist Development Grant; National Institutes of Health/National Cancer Institute (R01; Co-Investigator); MS: National Institutes of Health/National Institute on Minority Health and Health Disparities (L32MD013114) and National Institutes of Health/National Institute on Aging (K01AG068592); WM: None; JM: National Institutes of Health/National Cancer Institute Cancer Research and Education to Advance HealTh Equity (CREATE) Partnership (U54CA285115 and U54CA285117; Multiple Principal Investigator), National Institutes of Health/National Cancer Institute (R25CA274175; Principal Investigator), and California Institute for Regenerative Medicine (EDUC3-13126; Principal Investigator); BR: None; MPB: National Institutes of Health/National Cancer Institute (1R01CA298021; Heintzman/Banegas, MPI).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eVT and SR contributed to the conceptualization of the study. MPB and CYOD provided mentorship and guidance during the study ideation and outlining process. VT developed the study outline and led manuscript drafting. SR conducted all data analyses and generated the tables. HPJ reviewed and provided feedback on the study outline. All authors contributed to critical revision of the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThis study utilized data from the University of California Data Discovery Platform (UCDDP), a HIPAA-limited dataset comprising patient records from the six UC Health academic medical centers. To protect patient confidentiality, the data are not publicly accessible. The data used in this study are not publicly available due to HIPAA and institutional data use agreements but may be accessed through the University of California Data Discovery Platform following appropriate approvals.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePromoting cancer early diagnosis. Accessed October 26, 2025. https://www.who.int/activities/promoting-cancer-early-diagnosis \u003c/li\u003e\n\u003cli\u003eCrosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. \u003cem\u003eScience\u003c/em\u003e. 2022;375(6586):eaay9040. doi:10.1126/science.aay9040 \u003c/li\u003e\n\u003cli\u003eCancer Prevention and Early Detection Facts \u0026amp; Figures 2023-2024. Accessed October 26, 2025. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/cancer-prevention-and-early-detection-facts-and-figures/2024-cped-files/cped-2024-cff.pdf \u003c/li\u003e\n\u003cli\u003eStage at Diagnosis. Accessed October 26, 2025. https://progressreport.cancer.gov/diagnosis/stage \u003c/li\u003e\n\u003cli\u003eMiller KD, Nogueira L, Devasia T, et al. Cancer treatment and survivorship statistics, 2022. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2022;72(5):409-436. doi:10.3322/caac.21731 \u003c/li\u003e\n\u003cli\u003eColorectal Cancer Survival Rates | Colorectal Cancer Prognosis. Accessed October 26, 2025. https://www.cancer.org/cancer/types/colon-rectal-cancer/detection-diagnosis-staging/survival-rates.html \u003c/li\u003e\n\u003cli\u003eCancer of the Lung and Bronchus - Cancer Stat Facts. SEER. Accessed October 26, 2025. https://seer.cancer.gov/statfacts/html/lungb.html \u003c/li\u003e\n\u003cli\u003eDisparities in Cancer Screening for Early Detection - CDPR24. Cancer Progress Report. Accessed October 26, 2025. https://cancerprogressreport.aacr.org/disparities/cdpr24-contents/cdpr24-disparities-in-cancer-screening-for-early-detection/ \u003c/li\u003e\n\u003cli\u003eIslami F, Kahn AR, Bickell NA, Schymura MJ, Boffetta P. Disentangling the effects of race/ethnicity and socioeconomic status of neighborhood in cancer stage distribution in New York City. \u003cem\u003eCancer Causes Control CCC\u003c/em\u003e. 2013;24(6):1069-1078. doi:10.1007/s10552-013-0184-2 \u003c/li\u003e\n\u003cli\u003eWang F, Luo L, McLafferty S. Healthcare access, socioeconomic factors and late-stage cancer diagnosis: an exploratory spatial analysis and public policy implication. \u003cem\u003eInt J Public Policy\u003c/em\u003e. 2010;5(2-3):237-258. doi:10.1504/IJPP.2010.030606 \u003c/li\u003e\n\u003cli\u003eWard E, Jemal A, Cokkinides V, et al. Cancer Disparities by Race/Ethnicity and Socioeconomic Status. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2004;54(2):78-93. doi:10.3322/canjclin.54.2.78 \u003c/li\u003e\n\u003cli\u003eAbout the Neighborhood Atlas\u0026reg; and Area Deprivation Index (ADI). \u003cem\u003eCent Health Disparities Res Univ Wis Sch Med Public Health\u003c/em\u003e. https://www.neighborhoodatlas.medicine.wisc.edu/ \u003c/li\u003e\n\u003cli\u003eCheng E, Soulos PR, Irwin ML, et al. Neighborhood and Individual Socioeconomic Disadvantage and Survival Among Patients With Nonmetastatic Common Cancers. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2021;4(12):e2139593. doi:10.1001/jamanetworkopen.2021.39593 \u003c/li\u003e\n\u003cli\u003eUnger JM, Moseley AB, Cheung CK, et al. Persistent Disparity: Socioeconomic Deprivation and Cancer Outcomes in Patients Treated in Clinical Trials. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. 2021;39(12):1339-1348. doi:10.1200/JCO.20.02602 \u003c/li\u003e\n\u003cli\u003eCDC. Social Vulnerability Index. Place and Health - Geospatial Research, Analysis, and Services Program (GRASP). October 22, 2024. Accessed October 26, 2025. https://www.atsdr.cdc.gov/place-health/php/svi/index.html \u003c/li\u003e\n\u003cli\u003eThe Healthy Places Index (HPI). Public Health Institute. Accessed October 26, 2025. https://www.phi.org/thought-leadership/the-california-healthy-places-index/ \u003c/li\u003e\n\u003cli\u003eNCI Comorbidity Index Overview. Accessed January 18, 2026. https://healthcaredelivery.cancer.gov/seermedicare/considerations/comorbidity.html \u003c/li\u003e\n\u003cli\u003eCDC. U.S. Cancer Statistics Lung Cancer Stat Bite. United States Cancer Statistics. June 10, 2025. Accessed October 27, 2025. https://www.cdc.gov/united-states-cancer-statistics/publications/lung-cancer-stat-bite.html \u003c/li\u003e\n\u003cli\u003eCDC. U.S. Cancer Statistics Colorectal Cancer Stat Bite. United States Cancer Statistics. June 10, 2025. Accessed October 28, 2025. https://www.cdc.gov/united-states-cancer-statistics/publications/colorectal-cancer-stat-bite.html \u003c/li\u003e\n\u003cli\u003eCowan R, Baker E, Saleem M, et al. Association Between Area Deprivation Index and Melanoma Stage at Presentation. \u003cem\u003eCancers\u003c/em\u003e. 2025;17(17):2772. doi:10.3390/cancers17172772 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline Characteristics of the Study Population\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"507\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=27,064)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eTotal N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e27,064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at diagnosis, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e61.1\u0026plusmn; 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e21,713 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e5,329 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e19 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e15,103 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3,895 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4,026 (14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1,073 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e156 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e88 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2,723 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson Comorbidity Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.2 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Deprivation Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Vulnerability Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Places Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Type, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e17,030 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLung and Bronchus Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4,734 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eColon and Rectum Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e5,300 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Staging, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eEarly Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e22,300 (82.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLate Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4,764 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e13,790 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e5,074 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eOther/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4,680 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1,750 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1,378 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVeteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e391 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u003c/em\u003e Baseline demographic, clinical, insurance, and neighborhood-level socioeconomic characteristics of 27,064 adult patients diagnosed with breast, colorectal, or lung and bronchus cancer across six University of California academic medical centers between 2015 and 2025. Continuous variables are presented as mean \u0026plusmn; standard deviation, and categorical variables are presented as number (percentage). Neighborhood socioeconomic context was assessed using the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI). Cancer stage at diagnosis was categorized using AJCC criteria, with stages 0\u0026ndash;II classified as early-stage and stages III\u0026ndash;IV classified as late-stage disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMultivariable Logistic Regression Results (Model 1): Demographic, Clinical, Insurance, and Comorbidity Factors Associated With Late-Stage Cancer Diagnosis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.453, 1.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.004, 1.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.949, 1.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.678, 1.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.982, 1.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eUnknown Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.024, 1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.004, 1.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOther Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.000, Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eUnknown Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e3.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.049, 9.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e8.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(7.619, 9.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eLung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e13.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(12.548, 15.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eVeteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.579, 1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.055, 1.275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.939, 1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.948, 1.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOther/Unknown Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(1.261, 1.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAge at Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.983, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(0.794, 0.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u003c/em\u003e Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression examining associations between patient demographics (race and sex), cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index with odds of late-stage cancer diagnosis (AJCC stage III/IV). White race, female sex, breast cancer, and private insurance served as reference categories. All covariates were included simultaneously in the model. An OR greater than 1 indicates higher odds of late-stage diagnosis, while an OR less than 1 indicates lower odds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultivariable Logistic Regression Results (Model 2): Model 1 Plus Area Deprivation Index (ADI) Tertiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.417, 1.548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.055, 1.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.963, 1.371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.631, 1.632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.965, 1.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.039, 1.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.017, 1.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.000, Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.876, 11.599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(7.103, 8.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eLung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(12.297, 15.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eVeteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.537, 0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.056, 1.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.935, 1.258)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.929, 1.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther/Unknown Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.189, 1.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eADI \u0026ndash; High Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.008, 1.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eADI \u0026ndash; Medium Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.107, 1.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eADI \u0026ndash; Low Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAge at Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.985, 0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.793, 0.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eAdjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression evaluating the association between neighborhood-level socioeconomic deprivation and late-stage cancer diagnosis. Area Deprivation Index (ADI) State Rank was categorized into tertiles representing low, medium, and high neighborhood deprivation, with the lowest tertile serving as the reference group. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. Reduced sample size reflects exclusion of patients with missing neighborhood-level socioeconomic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eMultivariable Logistic Regression Results (Model 3A): Model 1 Plus Social Vulnerability Index (SVI)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.483, 1.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.039, 1.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.964, 1.373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.630, 1.630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.007, 1.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.039, 1.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.016, 1.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.000, Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.880, 11.776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(7.157, 8.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eLung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(12.304, 15.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eVeteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.554, 0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.078, 1.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.951, 1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.955, 1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther/Unknown Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.218, 1.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSocial Vulnerability Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.966, 1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAge at Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.985, 0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.794, 0.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eAdjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression assessing the association between the Social Vulnerability Index (SVI) and late-stage cancer diagnosis. SVI was modeled as a continuous variable, with higher values indicating greater neighborhood-level social vulnerability. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. An OR of 1.00 indicates no association between SVI and stage at diagnosis after adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eMultivariable Logistic Regression Results (Model 3B): Model 1 Plus Healthy Places Index (HPI)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.429, 1.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.050, 1.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.936, 1.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.633, 1.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.981, 1.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.038, 1.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.012, 1.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.000, Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eUnknown Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.856, 11.678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(7.125, 8.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eLung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(12.263, 15.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eVeteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.549, 0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.047, 1.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.941, 1.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.948, 1.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOther/Unknown Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(1.205, 1.470)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eHealthy Places Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.922, 0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAge at Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.985, 0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.793, 0.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eAdjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression evaluating the association between the Healthy Places Index (HPI) and late-stage cancer diagnosis. HPI was modeled as a continuous variable, with higher scores reflecting more favorable neighborhood health conditions and greater access to community resources. Models were adjusted for demographic characteristics, cancer type, insurance status, age at diagnosis, and Charlson Comorbidity Index. An OR less than 1 indicates lower odds of late-stage diagnosis associated with more favorable neighborhood conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eModel Fit and Performance Comparison Across Sequential Multivariable Logistic Regression Models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e1: Demographics + Insurance + Comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e27,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e20559.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e2: Model 1 + ADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25,780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19372.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e3A: Model 1 + SVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25,940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19533.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e3B: Model 1 + HPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19392.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCaption:\u003c/em\u003e Comparison of model performance metrics across sequential multivariable logistic regression models, including sample size (N), Akaike Information Criterion (AIC), and pseudo-R\u0026sup2;. Model 1 included demographic, clinical, insurance, and comorbidity variables; Model 2 additionally included Area Deprivation Index (ADI) tertiles; Model 3A included Social Vulnerability Index (SVI); and Model 3B included Healthy Places Index (HPI). Differences in sample size across models reflect missing neighborhood-level socioeconomic data. Lower AIC values indicate improved model fit, though comparisons should be interpreted cautiously due to differing sample sizes.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8767825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8767825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly cancer diagnosis improves survival and quality of life, yet disparities in stage at diagnosis persist. This study evaluates demographic, clinical, insurance, and neighborhood-level socioeconomic factors associated with late-stage cancer diagnosis within an academic health system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study of 27,064 adults diagnosed with breast, colorectal, or lung and bronchus cancer between 2015 and 2025 in the University of California Health System. Late-stage disease was defined as AJCC stage III/IV. Multivariable logistic regression examined associations between late-stage diagnosis and patient characteristics, insurance status, comorbidity burden, and neighborhood socioeconomic measures, including the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Healthy Places Index (HPI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e17.6% of patients were diagnosed at a late stage. Cancer type was the strongest predictor, with lung (aOR\u0026thinsp;\u0026asymp;\u0026thinsp;13\u0026ndash;14) and colorectal cancer (aOR\u0026thinsp;\u0026asymp;\u0026thinsp;8) associated with higher odds of late-stage diagnosis compared with breast cancer. Residence in medium and high ADI tertiles and Medicaid insurance (OR\u0026thinsp;=\u0026thinsp;1.16; 95% CI: 1.06\u0026ndash;1.28) were associated with higher odds of late-stage diagnosis, while Veterans Affairs coverage was associated with lower odds (OR\u0026thinsp;=\u0026thinsp;0.76; 95% CI: 0.58\u0026ndash;1.01). SVI was not associated with stage at diagnosis, whereas higher HPI scores were modestly protective.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLate-stage cancer diagnosis is driven primarily by cancer type and insurance status, with additional contributions from neighborhood disadvantage.\u003c/p\u003e","manuscriptTitle":"Disparities in Timeliness of Cancer Diagnosis Across a Multi-Site Academic Health System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 19:39:00","doi":"10.21203/rs.3.rs-8767825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:47:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198555951978687854667753290132487912467","date":"2026-04-27T18:30:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T16:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104184590710820035252488944092701878081","date":"2026-04-24T12:33:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15767240563171364094928712074501809389","date":"2026-04-15T15:17:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T13:47:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T08:48:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T03:56:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T03:55:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-02-02T17:32:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7078956-9948-4a03-9e83-00ea6c2fd84d","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:47:02+00:00","index":78,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T19:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 19:39:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8767825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8767825","identity":"rs-8767825","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}