Contemporary neighborhood redlining and racial mortgage lending bias and disparities in prostate cancer survival.

Contemporary Neighborhood Redlining and Racial Mortgage Lending Bias and Disparities in Prostate Cancer Survival - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Cancer . Author manuscript; available in PMC: 2026 Apr 15. Published in final edited form as: Cancer. 2025 Apr 15;131(8):e35850. doi: 10.1002/cncr.35850 Search in PMC Search in PubMed View in NLM Catalog Add to search Contemporary Neighborhood Redlining and Racial Mortgage Lending Bias and Disparities in Prostate Cancer Survival Wayne R Lawrence Wayne R Lawrence , DrPH 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Wayne R Lawrence 1 , Neal D Freedman Neal D Freedman , PhD 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. 2 Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Neal D Freedman 1, 2 , Jennifer K McGee-Avila Jennifer K McGee-Avila , PhD 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. 2 Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Jennifer K McGee-Avila 1, 2 , Hyokyoung G Hong Hyokyoung G Hong , PhD 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Hyokyoung G Hong 1 , Scarlett L Gomez Scarlett L Gomez , PhD 3 Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA. 4 Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. Find articles by Scarlett L Gomez 3, 4 , Andrew P Loehrer Andrew P Loehrer , MD 5 Geisel School of Medicine at Dartmouth, Hanover, NH, USA. 6 Dartmouth Cancer Center, Lebanon, NH, USA. 7 Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA. 8 The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA. Find articles by Andrew P Loehrer 5, 6, 7, 8 , Kirsten M M Beyer Kirsten M M Beyer , PhD 9 Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA. Find articles by Kirsten M M Beyer 9 , Yuhong Zhou Yuhong Zhou , PhD 9 Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA. Find articles by Yuhong Zhou 9 , Jared W Magnani Jared W Magnani , MD 10 Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 11 Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA 12 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. Find articles by Jared W Magnani 10, 11, 12 , Harold W Neighbors Harold W Neighbors , PhD 13 Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, USA. Find articles by Harold W Neighbors 13 , Jarrett A Johnson Jarrett A Johnson , DrPH 14 Division of Community Health and Population Science, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA. Find articles by Jarrett A Johnson 14 , Ziqiang Lin Ziqiang Lin , PhD 15 Department of Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China. Find articles by Ziqiang Lin 15 , Amy J Davidoff Amy J Davidoff , PhD 2 Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Amy J Davidoff 2 , Adana A M Llanos Adana A M Llanos , PhD 16 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA. 17 Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA. Find articles by Adana A M Llanos 16, 17 , Meredith S Shiels Meredith S Shiels , PhD 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. Find articles by Meredith S Shiels 1 Author information Copyright and License information 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. 2 Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. 3 Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA. 4 Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. 5 Geisel School of Medicine at Dartmouth, Hanover, NH, USA. 6 Dartmouth Cancer Center, Lebanon, NH, USA. 7 Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA. 8 The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA. 9 Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA. 10 Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 11 Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA 12 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 13 Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, USA. 14 Division of Community Health and Population Science, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA. 15 Department of Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China. 16 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA. 17 Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA. Author Contributions: Concept and design: W.R.L, N.D.F, J.K.M, K.M.M.B. H.G.H, and M.S.S, Acquisition, analysis, or interpretation of data: W.R.L, N.D.F, J.K.M, A.J.D, K.M.M.B, Z.L., H.G.H, J.W.M, S.L.G, A.P.L, J.A.J, H.W.N, E.T.W, and M.S.S. Drafting of the manuscript: W.R.L. Critical revision of the manuscript for important intellectual content: W.R.L, N.D.F, J.K.M, A.J.D, K.M.M.B, Z.L., H.G.H, J.W.M, S.L.G, A.P.L, Y.Z, J.A.J, H.W.N, E.T.W, A.A.M.L, and M.S.S. Statistical analysis: W.R.L Obtained funding: W.R.L., M.S.S., and N.D.F Administrative, technical, or material support: M.S.S. and N.D.F Supervision: M.S.S. and N.D.F. ✉ Corresponding Author: Dr. Wayne R. Lawrence, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20850. ( wayne.lawrence@nih.gov ). PMC Copyright notice PMCID: PMC12001745  NIHMSID: NIHMS2072539  PMID: 40233138 The publisher's version of this article is available at Cancer Abstract Introduction: Mortgage lending bias is a critical driver of residential segregation and may contribute to disparities in cancer survival. We investigated the association between contemporary redlining and racial lending bias and prostate cancer survival. Methods: This cohort study used SEER-Medicare database that included 34,163 Black and White men diagnosed with prostate cancer between 2010–2013. Home Mortgage Disclosure Act data were used to calculate census-tract redlining index (systematic denial of mortgages based on property location) and racial lending bias index (systematic denial of mortgage application to a Black applicant compared with White applicant in the local area). Both indices were assessed continuously and categorically (low, moderate, or high). Multivariable-adjusted Cox models were used to estimate hazard ratios for prostate cancer-specific and all-cause mortality. Results: Overall, as the redlining index increased, men experienced poorer prostate cancer survival. Compared to men residing in low redlined neighborhoods, those in high redlined neighborhoods had an increased risk of prostate cancer-specific (HR=1.21; 95%CI 1.03–1.42) and all-cause (HR=1.25; 95%CI 1.17–1.34) mortality. Similar results were observed for redlining in race-stratified analysis among Black and White men. Among White men, compared with those residing in low racial lending bias neighborhoods, those in high racial lending bias neighborhoods had an increased all-cause mortality risk (HR=1.11; 95%CI 1.03–1.21). Conclusion: Contemporary redlining was associated with poorer prostate cancer survival in the overall population. However, association between racial lending bias and elevated mortality was only observed among White men. Findings suggest mortgage lending discrimination may contribute to disparities in prostate cancer survival. Keywords: Economic Disparities in Health, Structural Racism, Health Inequities, Cancer Survivorship, Social Determinants of Health, Residential Segregation, Institutional Racism Précis: Residing in a more contemporary redlined neighborhood (location-based mortgage discrimination) was associated with increased risk of all-cause mortality among Black and White men diagnosed with prostate cancer, whereas among White men, residing in a neighborhood with greater contemporary racial mortgage lending bias (race-based mortgage discrimination) was associated with increased risk of prostate cancer-specific and all-cause mortality. Contemporary mortgage lending discrimination may contribute to inequities in prostate cancer survival. Introduction In the United States (U.S.), disparities in prostate cancer survival are well documented and characterized by inequities in access to care across the cancer control continuum. 1 – 4 Men of lower socioeconomic position are less likely to have been screened for prostate cancer, more likely to be diagnosed at later stages, and experience greater delay in initiating treatment, all associated with poorer survivorship. 4 – 8 Additionally, Black men diagnosed with prostate cancer are less likely to receive guideline-concordant care and have a 2-fold higher prostate cancer-specific mortality compared with their White counterparts. 1 , 3 , 4 , 7 A growing body of evidence has implicated neighborhood socioeconomic attributes as contributors to disparities in prostate cancer survival, including access to medical care and health-promoting resources. 4 , 7 , 9 Strategies to reduce disparities in prostate cancer mortality must therefore begin with understanding how upstream factors, including institutional practices, shape neighborhood health conditions that affect survival, especially among structurally marginalized populations. Neighborhoods in the U.S. are often patterned by the perceived social value of their residents, largely based on racial and socioeconomic composition. 10 – 13 Previous studies have shown an association between residential racial and economic segregation and disparities in access to cancer care and mortality. 14 – 16 Housing discrimination via mortgage lending prospectively reinforces patterns of residential segregation by systemically influencing the flow of financing into a neighborhood. 17 – 20 Despite the Fair Housing Act of 1968’s prohibition of discrimination in housing, ample data demonstrate that discriminatory practices persist today in the form of mortgage denial based on property location and applicant’s race. 17 , 18 , 21 Conceptually, bias in mortgage lending practices may affect the distribution and quality of neighborhood resources (e.g., access to medical care), influencing prostate cancer survival, and thus contribute to inequities in prostate cancer mortality. 10 – 12 , 15 , 20 In this study, we investigated whether housing discrimination via contemporary redlining (mortgage lending bias on the basis of property location) and racial lending bias (mortgage lending bias on the basis of applicant’s race) are associated with prostate cancer survival among Medicare-enrolled men diagnosed with prostate cancer. Methods The study population included all metropolitan statistical areas (MSAs) within the National Cancer Institute’s (NCI) SEER program areas in which individuals resided at time of prostate cancer diagnosis ( Supplemental Figure 1 ). Since prior studies have reported contemporary patterns of residential segregation in metropolitan areas, focusing on MSAs allows us to analyze local mortgage lending practices. 20 – 22 Men diagnosed with prostate cancer were identified from the SEER program and linked with Medicare claims from the Centers for Medicare and Medicaid Services’ database. The initial cohort included 40,407 men newly diagnosed with prostate cancer in 2010–2013 aged 66–90 years enrolled in Medicare Parts A and B for at least 12 months before diagnosis and had known stage at diagnosis. We excluded individuals who were other than non-Hispanic Black (Black) or White (White) (n=4,992), had no information on comorbidity a year prior to diagnosis (n=772), died the same month as diagnosis (n=116), and enrolled in a Medicare Advantage plan (n=364) ( Supplemental Figure 2 ). We focused on Black and White Medicare beneficiaries because of the persistent large racial disparities in prostate cancer mortality between these racial groups and Black individuals’ experience of structural racism exceeding that of other racial and ethnic groups. 3 , 7 , 23 , 24 This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. 25 Redlining and Racial Lending Bias We examined contemporary redlining and racial lending bias separately because census tracts affected by greater redlining may differ from those affected by racial lending bias and can have differing influence on prostate cancer survival. We defined redlining as the systematic denial of mortgages based on property location. 21 , 26 The redlining index measures the odds ratio of denial of a mortgage comparing local area properties with all properties within the same MSA. Details on this index have been described in detail previously. 21 Briefly, the index identifies census tracts within MSAs containing properties that are less likely to secure mortgages; and was estimated using logistic regression in an adaptive spatial filtering framework based on Home Mortgage Disclosure Act (HMDA) data in 2010–2017 at the census tract-level. In the creation of the index, each adaptive spatial filter expanded until it identified at least four Black applicants and four White applicants. The index centers around an odds ratio value of 1, which corresponds to a locality that received the same rate of mortgage approvals compared with the wider MSA, values >1 indicate higher odds of denial of mortgage applications locally, and values <1 indicate lower odds of denial of mortgage applications locally. The redlining index was estimated across the MSAs included in the study, and then surfaces (raster pixel values) were averaged within each census tract boundary to create a census tract-level measure. This tract measure was then linked to the residential census tract at the time of prostate cancer diagnosis. We assessed the redlining index continuously and categorically using specific break points along the distribution ( Supplemental Figure 3 ). We categorized redlining as low (<1), moderate (1–<2), or high (≥2). We defined racial lending bias as the systematic denial of mortgage application to a Black applicant compared with White applicant in the local area. 21 Therefore, the racial lending bias index was estimated as the adjusted odds ratio of denial of a mortgage application for a Black applicant compared with denial of a White applicant for a property in the census tract, controlling for applicant sex and the ratio of the loan amount to applicant’s reported annual income. 21 A value of 1 indicates that a Black and White applicant have equal probability of being denied a mortgage application in the census tract of interest. The racial lending bias index was measured continuously and categorically as low (<1), moderate (1–<2), or high (≥2). Outcome The two outcomes were prostate cancer-specific mortality and all-cause mortality. These outcomes were assessed through December 31, 2019. Survival time was calculated using date of death from the Medicare enrollment file and prostate cancer-specific mortality was identified using cause of death information from SEER registry (ICD-10: C61). Covariates Individual-level covariates were identified based on evidence from prior studies examining prostate cancer survival. Patient age and race and ethnicity reported in SEER were abstracted from medical records. NCI Comorbidity Index score was calculated using Medicare claims during the twelve months before prostate cancer diagnosis. 27 , 28 Tumor stage at diagnosis was classified using SEER Summary Stage. We further identified whether individuals were dual-Medicaid enrolled (“yes” or “no”). Medicaid is a joint state-federal program that provides additional health insurance to disabled and low-income Medicare beneficiaries with limited financial assets. Census tract of residents living below poverty was obtained from the 2012 American Community Survey 5-year estimate and categorized into distribution-based quantiles, where higher quantiles represented greater neighborhood percentage of residents below poverty. Neighborhood socioeconomic attributes and cancer treatment are not included in our model, as these factors were conceptualized to be on the causal pathway between the exposure (redlining, racial lending bias) and prostate cancer survival. 7 , 20 , 26 Statistical Analysis Descriptive analysis was performed to summarize baseline characteristics by their frequencies and distributions. Kaplan-Meier survival curves were created to visualize the relationship between the exposure (redlining, racial lending bias) and outcome (prostate cancer-specific and all-cause mortality). Cox proportional hazard regression models were estimated with time from diagnosis to survival/event as the timescale and the redlining and racial lending bias indices (continuous and categorical) as the key predictor for both prostate cancer–specific and all-cause mortality. Since death dates obtained from the Social Security Administration are available for all deceased patients before the end of the study, censoring only occurred at the end of the study observation period for all-cause mortality; and at death from other causes, and at end of study for prostate cancer-specific mortality. The proportionality assumption of the hazard models was tested, and violation occurred for stage at diagnosis. In analysis examining prostate cancer-specific and all-cause mortality, we stratified by stage at diagnosis, adjusting for age, race, NCI comorbidity index, with standard error adjusted for MSA clusters. We further included a quadratic term for redlining and racial lending bias to assess for potential nonlinearity for both outcomes. Additionally, interactions were tested for race and dual-Medicaid enrollment status. Results A total of 34,163 Medicare-insured men diagnosed with prostate cancer were included in the study. The mean age of the population was 73.3 (±5.6) years and majority of prostate cancer diagnoses were localized stage (84.3%) ( Table 1 ). The study population was comprised of 87% White and 13% Black men. Black men were more likely to be dual-Medicaid enrolled (13.4% vs. 3.1% of White men) and live in high redlined neighborhoods (44.6% vs. 7.4% of White men). The racial distribution of residing in neighborhoods with high racial lending bias was similar between Black (60.3%) and White (63.7%) men. Additionally, neighborhoods with the highest percentage of residents living below poverty had the largest proportion of residents living in high redline neighborhoods (High Redlining: Quantile 5=32.6% vs Quantile 1=1.6%) ( Supplemental Table 1 ). High redlined neighborhoods had a higher distribution of distant stage at diagnosis (7.3%) compared with low redlined neighborhoods (5.6%) ( Supplemental Table 2 ). The median follow-up was 87 months, and at end of follow-up 31.9% of the cohort was deceased, with 25.8% having died from prostate cancer (24.4% Black and 26.1% White). Table 1. Study Baseline Characteristics of Medicare-Insured Men Diagnosed with Prostate Cancer by Racial Group (2010–2013) Overall (n=34,163) Non-Hispanic Black (n=4,439) Non-Hispanic White (n=29,724) Age, mean ±SD 73.3 ±5.6 72.4 ±5.2 73.5 ±5.7 NCI Comorbidity Index, mean ±SD a 0.3 ±0.4 0.4 ±0.5 0.3 ±0.4 Dual-Medicaid Enrolled Yes 1,510 (4.4) 596 (13.4) 914 (3.1) No 32,653 (95.6) 3,843 (86.6) 2,8810 (96.9) Stage at diagnosis Localized, n (%) 28,801 (84.3) 3,817 (86.0) 24,984 (84.1) Regional, n (%) 3,311 (9.7) 303 (6.8) 3,008 (10.1) Distant, n (%) 2,051 (6.0) 319 (7.2) 1,732 (5.8) Tumor Grade Grade I 576 (1.7) 62 (1.4) 514 (1.7) Grade II 11,620 (34.0) 1,404 (31.6) 10,216 (34.4) Grade III 19,747 (57.8) 2,672 (60.2) 17,075 (57.4) Grade IV 94 (0.3) 18 (0.4) 76 (0.3) Unknown 2,126 (6.2) 283 (6.4) 1,843 (6.2) Redlining Index b Continuous, mean ±SD 1.3 ±1.1 2.5 ±2.4 1.1 ±0.7 Low, n (%) 16,755 (49.0) 924 (20.8) 15,831 (53.3) Moderate, n (%) 13,215 (38.7) 1,536 (34.6) 11,679 (39.3) High, n (%) 4,193 (12.3) 1,979 (44.6) 2,214 (7.4) Racial Lending Bias Index c Continuous, mean ±SD 3.1 ±2.7 3.2 ±4.5 3.0 ±2.3 Low, n (%) 2,635 (7.7) 484 (10.9) 2,151 (7.2) Moderate, n (%) 9,907 (29.0) 1,277 (28.8) 8,630 (29.0) High, n (%) 21,621 (63.3) 2,678 (60.3) 18,943 (63.7) Vital status Alive, n (%) 23,264 (68.1) 2,770 (62.4) 20,494 (69.0) Deceased, n (%) 10,899 (31.9) 1,669 (37.6) 9,230 (31.0) Cause of Death Prostate Cancer 2,820 (25.8) 408 (24.4) 2,412 (26.1) Other Causes 8,079 (74.2) 1,261 (75.6) 6,818 (73.9) Open in a new tab a Score calculated using the National Cancer Institute comorbidity index, range 0 to 3.8 b Redlining index categories: 0 – <1 (low), 1.0 – < 2.0 (moderate), and ≥2.0 (high). c Racial Lending Bias index categories: 0 – <1 (low), 1.0 – < 2.0 (moderate), and ≥2.0 (high). Redlining Kaplan-Meier survival curves for both prostate cancer-specific and all-cause mortality by redlining are shown in Figure 1 . Men who resided in highly redlined neighborhoods experienced poorer prostate cancer-specific and all-cause survival. Adjusting for confounders, men residing in more redlined neighborhoods had 3% per unit increased prostate cancer-specific mortality (HR=1.03; 95%CI 1.00–1.05) and 4% per unit increased all-cause mortality (1.04; 95% 1.02–1.06) ( Table 2 ). When including the quadratic term for redlining, results were significant, suggesting the relation with redlining was nonlinear only for all-cause mortality, though increased risk remained (HR Quadratic =1.08; 95%CI 1.04–1.12) ( Supplemental Table 3 ). In race-stratified analysis, residing in a more redlined neighborhood was associated with increased all-cause mortality among White (HR=1.10 per unit increase; 95%CI 1.06–1.15) and Black men (HR=1.02; 95%CI 1.01–1.03; P interaction <0.001). No significant association was observed for prostate cancer-specific mortality in race-specific models. Additionally, residing in a more redlined neighborhood was associated with increased all-cause mortality among non-dual-Medicaid enrolled individuals (HR=1.05; 95%CI 1.02–1.07; P interaction <0.001). Figure 1. Open in a new tab Kaplan-Meier Estimates of (A) Prostate Cancer-Specific Mortality; and (B) All-Cause Mortality by tract redlining index category: 0 – <1 (least), 1.0 – < 2.0 (moderate), and ≥2.0 (high). Table 2. Cox Proportional Hazards Regression Model Relating the Redlining Index (Continuous) to Prostate Cancer-Specific and All-Cause Mortality Overall and Stratified by Racial Group and Dual Medicaid Enrolled Status Among SEER-Medicare Prostate Cancer Cohort Redlining Index a Prostate Cancer-Specific Mortality All-Cause Mortality Number of events HR (95% CI) b P P interaction Number of events HR (95% CI) b P P interaction Redlining 2,820 1.03 (1.00–1.05) 0.022 10,899 1.04 (1.02–1.06) <0.001 By Racial group 0.572 <0.001 Non-Hispanic White 2,412 1.07 (1.00–1.14) 0.059 9,230 1.10 (1.06–1.15) <0.001 Non-Hispanic Black 408 1.02 (1.00–1.04) 0.128 1,699 1.02 (1.01–1.03) 0.007 By Dual-Medicaid Enrolled 0.031 <0.001 No 2,580 1.03 (1.00–1.06) 0.059 10,097 1.05 (1.02–1.07) <0.001 Yes 240 1.02 (0.95–1.10) 0.637 802 0.99 (0.94–1.04) 0.605 Open in a new tab a Redlining index measured as a continuous variable b Adjusted for age, racial group, NCI comorbidity index; stratified by stage at diagnosis, and account for clustering by metropolitan statistical area. Abbreviations: HR, Hazard Ratio; 95% CI, 95% confidence interval When modeled categorically, compared to men residing in low redlined neighborhoods, those in high redlined neighborhoods had a 21% (HR=1.21;95%CI 1.03–1.42) and 25% (HR=1.25; 95%CI 1.17–1.34) increased prostate cancer-specific and all-cause mortality, respectively ( Table 3 ). In race-stratified analysis, residing in high redlined neighborhood was associated with increased prostate cancer-specific mortality among only White men (HR=1.28; 95%CI 1.07–1.54; P interaction =0.062). However, among both White (HR=1.30; 95%CI 1.19, 1.43) and Black men (HR=1.14; 95%CI 1.03, 1.26), residing in a high redlined neighborhood was associated with increased risk of all-cause mortality (P interaction <0.001). Similar increased risks were observed for non-dual-Medicaid enrolled individuals for prostate cancer-specific (HR=1.26; 95%CI 1.06–1.50; P interaction <0.001) and all-cause mortality (HR=1.29; 95%CI 1.19–1.38; P interaction <0.001). In contrast, for dual-Medicaid enrolled individuals, residing in a high redlined neighborhood was associated with reduced all-cause mortality risk (HR=0.84; 95%CI 0.70–0.99). Across all categories of redlining, Black and dual-Medicaid enrolled men had a greater risk of all-cause mortality compared with their White and non-dual enrolled counterparts ( Supplemental Table 4 ). Table 3. Cox Proportional Hazards Regression Model Relating the Redlining Index (Categorical) to Prostate Cancer-Specific and All-Cause Mortality Overall and by Racial Group and Dual Medicaid Enrolled Status Among SEER-Medicare Prostate Cancer Cohort Overall Racial Group Dual-Medicaid Enrolled Non-Hispanic White Non-Hispanic Black Yes No Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Prostate Cancer-Specific Mortality Redlining Index b Low 1,276 Reference 1,203 Reference 73 Reference 76 Reference 1,200 Reference Moderate 1,135 1.11 (1.00–1.22) 992 1.12 (1.01–1.24) 143 1.03 (0.74–1.43) 98 0.71 (0.47–1.08) 1,037 1.13 (1.03–1.23) High 409 1.21 (1.03–1.42) 217 1.28 (1.07–1.54) 192 1.04 (0.74–1.44) 66 0.72 (0.48–1.09) 343 1.26 (1.06–1.50) P interaction 0.062 <0.001 All-Cause Mortality Redlining Index b Low 4,939 Reference 4,633 Reference 306 Reference 238 Reference 4,701 Reference Moderate 4,305 1.08 (1.03–1.14) 3,742 1.09 (1.02–1.16) 563 1.03 (0.92–1.15) 347 0.77 (0.67–0.88) 3,958 1.09 (1.04–1.16) High 1,655 1.25 (1.17–1.34) 855 1.30 (1.19–1.43) 800 1.14 (1.03–1.26) 217 0.84 (0.70–0.99) 1,438 1.29 (1.19–1.38) P interaction <0.001 <0.001 Open in a new tab a Adjusted for age, racial group, NCI comorbidity index; stratified by stage at diagnosis, and account for clustering by metropolitan statistical area. Abbreviations: HR, Hazard Ratio; 95% CI, 95% confidence interval b Redlining index categories: low (0 – <1), moderate (1 – <2), and high (≥2). Racial Lending Bias The Kaplan-Meier survival curves for prostate cancer-specific mortality showed men residing in neighborhoods with low racial lending bias experienced poorer survival ( Figure 2 ). However, after adjusting for confounders, no association was observed between increased racial lending bias and prostate cancer-specific and all-cause mortality ( Supplemental Table 5 ). When including the quadratic term, results were significant suggesting that the relation with racial lending bias was nonlinear, showing increased risk for prostate cancer-specific mortality (HR Quadratic =1.03; 95%CI 1.01–1.06) and all-cause mortality (HR Quadratic =1.02; 95%CI 1.01–1.03) ( Supplemental Table 6 ). Among White men, residing in a neighborhood with greater racial lending bias was associated with increased prostate cancer-specific mortality (HR=1.02 per unit increase; 95%CI 1.00–1.03; P interaction =0.013) and all-cause mortality (HR=1.01; 95%CI 1.00–1.02; P interaction =0.034). No significant associations were observed among Black men. In analysis by dual-Medicaid enrollment status, an association was only observed for prostate cancer-specific mortality among non-dual-enrolled men (HR=1.01; 95%CI 1.00–1.02; P interaction =0.018). Figure 2. Open in a new tab Kaplan-Meier Estimates of (A) Prostate Cancer-Specific Mortality; and (B) All-Cause Mortality by tract racial lending bias index category: 0 – <1 (least), 1.0 – < 2.0 (moderate), and ≥2.0 (high). When modeled categorically, compared to Black men residing in neighborhoods with low racial lending bias, those residing in moderate racial lending bias neighborhoods had a reduced risk of prostate cancer-specific mortality (HR=0.69; 95%CI 0.49–0.97; P interaction <0.001) ( Table 4 ). No significant association was observed among White men. Compared to low racial lending bias neighborhoods, risk of all-cause mortality was elevated among White men residing in neighborhoods with high racial lending bias (HR=1.11; 95%CI 1.03–1.21), and reduced among Black men residing in neighborhoods with high racial lending bias, though not statistically significant (HR=0.91; 95%CI 0.80–1.05; P interaction <0.001). Reduced risk was also observed for moderate racial lending bias among Black men (HR=0.85; 95%CI 0.75–0.97). Across all categories of racial lending bias, Black men and dual-Medicaid enrolled men had a greater risk of prostate cancer-specific and all-cause mortality compared with White and non-dual enrolled men ( Supplemental Table 7 ). Table 4. Cox Proportional Hazards Regression Model Relating the Racial Lending Bias Index (Categorical) to Prostate Cancer-Specific and All-Cause Mortality Overall and by Racial Group and Dual Medicaid Enrolled Status Among SEER-Medicare Prostate Cancer Cohort Overall Racial Group Dual-Medicaid Enrolled White Black Yes No Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Number of events HR (95% CI) a Prostate Cancer-Specific Mortality Racial Lending Bias Index b Low 243 Reference 189 Reference 54 Reference 29 Reference 214 Reference Moderate 772 0.92 (0.80–1.05) 666 0.99 (0.85–1.15) 106 0.69 (0.49–0.97) 76 1.11 (0.71–1.73) 696 0.91 (0.79–1.05) High 1,805 1.01 (0.88–1.17) 1,557 1.09 (0.94–1.28) 248 0.75 (0.54–1.04) 135 0.88 (0.57–1.35) 1,670 1.04 (0.91–1.19) P interaction <0.001 <0.001 All-Cause Mortality Racial Lending Bias Index b Low 845 Reference 657 Reference 188 Reference 80 Reference 765 Reference Moderate 3,108 1.01 (0.93–1.11) 2,657 1.06 (0.97–1.16) 451 0.85 (0.75–0.97) 232 1.00 (0.76–1.31) 2,876 1.02 (0.93–1.12) High 6,946 1.06 (0.99–1.15) 5,916 1.11 (1.03–1.21) 1030 0.91 (0.80–1.05) 490 1.04 (0.85–1.27) 6,456 1.08 (0.99–1.16) P interaction <0.001 <0.001 Open in a new tab a Adjusted for age, racial group, NCI comorbidity index; stratified by stage at diagnosis, and account for clustering by metropolitan statistical area. Abbreviations: HR, Hazard Ratio; 95% CI, 95% confidence interval b Racial Lending Bias index categories: low (0 – <1), moderate (1 – <2), and high (≥2). Discussion In this analysis of Medicare-insured men diagnosed with prostate cancer, men residing in more heavily redlined neighborhoods experienced higher prostate cancer-specific and all-cause mortality. The association between residing in a more redlined neighborhood and all-cause mortality was consistent among Black and White men, although of greater magnitude for White men. We also observed differences in risk by dual-Medicaid enrollment status, where the association with redlining was greater for non-dual-Medicaid enrolled individuals. Additionally, compared to White men residing in neighborhoods with low racial lending bias, those residing in neighborhoods with high racial lending bias had an increased risk of all-cause mortality. Studies have demonstrated an association between historical redlining, based on The Home Owners’ Loan Corporation maps, and cancer outcomes. 15 , 29 – 31 Our work complements such prior research by using administrative data demonstrating contemporary redlining is associated with poorer prostate cancer survival. Previous studies have suggested that discriminatory mortgage lending practices may manifest in poor health through adverse neighborhood exposures, including lower investment in community infrastructure, housing instability, and health-promoting resources. 17 , 32 For instance, high-redlined neighborhoods are more likely to be under-resourced due to lower tax base (e.g., lower property value, high percentage of renters, lower household income) and less political capital. 10 , 18 , 19 This results in high redlined areas having limited access to medical specialists and employment opportunities, as well as greater exposure to adverse environmental conditions. 15 , 17 , 19 Consistent with prior research, in the present study high redlined neighborhoods, where cancer prevention and detection may be challenged by limited health care resources, had a greater proportion of distant stage disease at diagnosis. 15 , 33 , 34 Our results showed that the association between residing in a more redlined neighborhood and prostate cancer-specific and all-cause mortality was more pronounced among White men. In this study, Black men disproportionately resided in high redlined neighborhoods (44.6%) compared with their White counterparts (7.4%), therefore, these findings may suggest lower utilization of community health resources among White men in neighborhoods with higher redlining. 20 , 35 Also, stage at diagnosis by level of redlining was similar between Black and White men, indicating that there are other factors likely contributing to this racial difference in mortality. Interestingly, among dual-Medicaid enrolled individuals, those residing in high redlined neighborhoods had reduced all-cause mortality compared to their counterparts in low redlined neighborhoods. We hypothesize that high redlined neighborhoods may offer greater community protective effects for the dual-Medicaid enrolled, such as greater local affordability and easier ability to utilize government assistance programs (i.e., assistance for housing and food). 36 – 38 Still, across all categories of redlining, prostate cancer-specific and all-cause mortality were greater among dual-Medicaid enrolled men compared with non-dual-Medicaid enrolled men. Our findings showed that contemporary neighborhood racial lending bias was not associated with prostate cancer survival in the overall population. In race-stratified analysis, we observed that compared to neighborhoods with low racial lending bias, Black individuals residing in neighborhoods with more racial lending bias had a reduced risk of prostate cancer-specific and all-cause mortality. These findings are similar to a prior study that reported racial lending bias was associated with reduced risk of breast cancer mortality. 20 Previous studies have suggested that communities with more racial lending bias are more likely to be of higher socioeconomic status, therefore Black men able to avoid residential exclusion may be of higher income and benefited from greater access to health-promoting resources. 1 , 20 , 39 In contrast, we observed an increased risk of all-cause mortality among White men residing in a neighborhood with greater racial lending bias compared to their counterparts in low racial lending bias neighborhoods. We hypothesize that since neighborhoods with high racial lending bias are more likely to be socioeconomically advantaged, these communities may have high levels of socioeconomic-related exclusionary practices which may disadvantage White individuals of lower income, especially as White individuals are more likely to obtain a mortgage in higher socioeconomic status neighborhoods than Black applicants with similar or slightly higher incomes. 40 – 42 However, regardless of neighborhood racial lending bias severity, prostate cancer survival remained poorer among Black men, aligning with extant research. 1 , 3 Mortgage lending discrimination does not only occur at the individual lender-level but is embedded within institution lending practices through algorithmic bias, disproportionately impacting Black applicants. 42 , 43 As a result, mortgage discrimination often limits structurally marginalized applicants to underinvested neighborhoods that are often lacking or limited in access to quality health care services. 18 This form of housing discrimination can adversely impact cancer survivorship among residents residing in low socioeconomic neighborhoods through reduced accessibility to cancer prevention and treatment. 44 , 45 Policy interventions may consequently consider not only identifying discriminatory lending practices (algorithmic and human bias) but equitable investment in community infrastructure, including access to health-promoting resources. Several limitations must be noted. First, redlining and racial lending bias indices were calculated using HMDA data, which does not provide information on applicant’s creditworthiness and employment information, which is likely an important indicator for mortgage denial. Second, this study focused on MSAs, limiting generalizability of our findings to less urban areas. Third, we did not account for reverse redlining, wherein mortgage lenders predatorily target high interest (i.e., sub-prime) loans to neighborhoods deemed high-risk instead of denying loans outright. 17 , 19 , 46 Fourth, we restricted our analysis to Black and White individuals, while prior studies have reported housing discrimination among other groups including Latine and Asian Americans. 47 – 49 However, we were limited by small sample size for individuals that were neither Black nor White. Fifth, the study population was restricted to men 66 years of age and older, therefore, may not be generalizable to men diagnosed at younger ages, especially Black men, as they are more likely to be diagnosed before age 65 than the general population. 50 , 51 Sixth, this study did not account for changes in neighborhood mortgage-lending bias over time. Finally, the cohort did not include men enrolled in Medicare Advantage plans, potentially hindering generalizability. Conclusion In this cohort of men diagnosed with prostate cancer, residing in a more contemporary redlined neighborhood was associated with poorer overall and prostate cancer-specific survival. Our results suggest differential effects of greater neighborhood racial lending bias by race — namely, poorer overall survival among White men but not among Black men. This study highlights how mortgage lending discriminatory practices may influence neighborhood health conditions, thereby contributing to disparities in prostate cancer survival. Consequently, place-based interventions should target discrimination in the housing sector to mitigate negative upstream determinants of health that contribute to inequities in prostate cancer survival. Supplementary Material Supinfo NIHMS2072539-supplement-Supinfo.docx (561.1KB, docx) Acknowledgement This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results Program tumor registries in the creation of the SEER-Medicare database. The collection of cancer data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s (CDC) National Program of Cancer Registries, under cooperative agreement 1NU58DP007156; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. Funding Statement: This work was supported by the National Institutes of Health Intramural Research Program of the National Cancer Institute (Z99 CA999999). Andrew P. Loehrer – time supported through the National Cancer Institute career development award (NCI K08CA263546) Jared W. Magnani – time supported through the National Heart, Lung, and Blood Institute (NHLBI K24HL160527) Role of the Funder/Sponsor: The National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Footnotes Conflict of Interest Disclosures: The authors declare no conflicts of interest. Disclaimer The ideas and opinions expressed herein are those of the authors and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, the Centers for Disease Control and Prevention, U.S. Department of Health and Human Services, or their Contractors and Subcontractors. 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