Association of Area Deprivation Index with Prostate Cancer-Specific Mortality in a Contemporary North American Statewide Cohort

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This study evaluates the impact of the Area Deprivation Index (ADI) on prostate cancer-specific mortality (PCSM) in a North American statewide cohort. Methods: Using data from the Michigan Department of Health and Human Services (MDHHS), we included men aged 18–74 with histologically confirmed PCa between 2004 and 2022. An ADI score, based on residential census block group and ranked nationally by deprivation percentile, was assigned to each patient. Individuals were grouped into quartiles, with the fourth (ADI 75–100) representing the most deprived. PCSM incidence was estimated after stratification by ADI quartiles using the competing-risk method. A competing-risk regression model, adjusting for covariates, assessed ADI’s impact on PCSM. Results: We included 53,532 patients, 18.4% of whom were NHB (Non H ispanic Black). Median (IQR) age and year at diagnosis were 64 (58–69) years and 2010 (2008–2013), respectively. In the fourth ADI quartile, median diagnosis age was 64 (58–69) vs 63 (58–68) in the first quartile. At 15 years after diagnosis, PCSM cumulative incidence was 3.6%, 5.2%, 5.4%, and 6.9% across increasing ADI quartiles (p < 0.0001). Competing-risk regression showed ADI was significantly associated with higher PCSM hazard. Patients in the fourth quartile had a 1.54-fold (CI: 1.28–1.85; p < .001) higher hazard compared to those in the first. Conclusions: Individuals in the most deprived areas had higher PCSM than those in more advantaged areas, underscoring the impact of socioeconomic factors on cancer outcomes and the need for targeted equity-focused interventions. Prostate Cancer Prostatic neoplasms Socioeconomic status Social deprivation Healthcare disparities Cancer Mortality Figures Figure 1 Figure 2 Introduction Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men in the United States. 1 Numerous studies have identified disparities in PCa incidence and mortality associated with race and ethnicity 2 , 3 , as well as with socioeconomic factors. 4 , 5 Within this context, neighborhood-level measures—which reflect unique economic, physical, and social characteristics—have been shown to significantly impact both community and individual health. Specifically, individuals residing in socioeconomically deprived neighborhoods are more likely to experience higher lethal disease rates compared to those in less deprived areas, especially among African American men. 6 , 7 Evidence suggests that disparities in cancer-specific mortality are largely driven by differences in diagnosis and treatment accessibility based on socioeconomic status. 8 Recently, the Area Deprivation Index (ADI), a numerical measure derived from publicly available data on income, education, employment, and housing quality at the census block level, has been proposed as a predictor of cancer control outcomes. 9 Neighborhoods are ranked by comparing their ADI to national and state benchmarks, with higher ADI scores indicating greater socioeconomic disadvantage. Despite its potential importance, no study to date has examined the relationship between ADI and prostate cancer-specific mortality (PCSM) in a large population as the one represented in this study and most importantly with this long-term follow-up time. 10 Socioeconomic status and geographical location are known to contribute to disparities, but this gap in the literature remains unexplored. 11 To address this, we analyzed the association between ADI and PCa outcomes, hypothesizing that ADI could predict variations in disease progression and outcomes among patients, statewide. Materials & Methods Data Sources Data for this study were abstracted from the Michigan Department of Health and Human Services (MDHHS), which included all men aged between 18 and 74 with histologically confirmed PCa between 2004 and 2022. Our population's social and ethnic diversity provides an ideal context for examining the influence of socio-cultural, economic, environmental, and biological factors on cancer control outcomes. We included only patients who have been followed up in and have known Area Deprivation Index (ADI) data or geographic/residential data. Our selection criteria, as detailed in Fig. 1 , yielded a final cohort of 53,532 eligible patients stratified by ADI quartile. Variables The following variables were extracted for all patients: age, year of diagnosis, race (White, Black, Other), insurance (Not Insured, Private Insurance, Medicaid, Medicare, Other/Unknown), and county type (Non-Metropolitan Counties, Metropolitan Counties). At the time of diagnosis, the following data were extracted: PSA, clinical T stage, pathological Grade, nodal status, and metastasis. Our main variable of interest was ADI, which is a numeric index calculated based on census tract that includes education level, home value, income, and employment. 9 This numerical index was assigned to each census block, with a score of 1 representing the least deprived neighborhoods and 100 indicating the most deprived areas. The 5-digit ZIP codes obtained from patient addresses were paired with their corresponding plus-4 codes using the United States Postal Service ZIP code database. These 9-digit zip codes were used to match patients within their census block. 12 Based on previous methodology 10 , 13 , 14 , 15 , individuals were further categorized based on national quartiles, where the fourth one (ADI 75–100) represented those living in the most deprived areas. Endpoints Our main endpoint was Prostate cancer-specific mortality. Death due to a cause other than PCa was considered a competing event. Follow-up time was calculated from the time of diagnosis to the last available follow-up. Statistical Analysis Descriptive statistics included the median and interquartile range (IQR) for continuous variables, while categorical variables were reported as frequencies with percentages. Kruskal-Wallis and Chi-Square tests were performed for continuous and categorical variables, respectively. The Benjamini-Hochberg correction was applied for multiple comparisons. Our analysis was based on multiple steps. First, we calculated the estimated 15-year PCSM cumulative incidence for the entire cohort of patients, stratified according to ADI quartiles. Second, multivariable competing risk regression analyses were tested to further analyze the impact of ADI on PCSM. ADI was calculated by units of 10 rather than 1 relative to the national level 11 to make interpretation easier and more meaningful. The significant p-value (alpha) was set at 0.05. This study was deemed exempt from review by the Henry Ford Institutional Review Board (IRB), as all MDHHS data are fully de-identified and do not require individual patient informed consent. The data were analyzed using SAS Studio Version 3.81 (SAS Institute, Cary, NC). Results Demographic information and clinical variables for the entire cohort of patients, stratified by ADI quartiles are summarized in Table 1 . Our cohort consisted of 53,532 patients, 18.4% (9,828) of whom were NHB. The median (IQR) age at diagnosis and median (IQR) year of diagnosis were 64 (58 – 69) years and 2010 (2008-2013), respectively. Median age at diagnosis (IQR) in the fourth quartile was 64 (58- 69) vs 63 (58-68), in the first ADI quartile. Within the NHB population, the majority lived in the most deprived neighborhoods, compared to patients living in the most affluent areas. Focusing on the clinical and pathological variables, patients in the most disadvantaged quartile were diagnosed with PCa with more aggressive features. Patients in the fourth-quartile were more likely to present with PSA > 10 ng/mL (9.3% vs. 7.1%), ISUP Grade III PCa (52.0% vs. 50.1%), cT3-4 PCa (4.1% vs. 2.8%), and metastasis (3.0% vs. 1.4%) than first-quartile counterparts. Cancer-specific mortality The 15-year PCSM cumulative incidence estimates were 3.6%, 5.2%, 5.4%, and 6.9% for patients in the first, second, third, and fourth ADI quartiles, respectively (p < 0.0001) ( Figure 2 ). In competing-risk regression analysis ( Table 2 ), ADI was significantly associated with a higher hazard of PCSM. Specifically, patients in the fourth ADI quartile had a 1.54-fold (CI:1.03-1.49; p-value <.001) higher hazard of PCSM, compared to those included in the first ADI quartile. As expected, patients with more advanced disease were associated with higher risk of PCSM. Patients who were diagnosed with PSA > 20 ng/mL (HR: 2.98; CI: 2.63-3.37; p-value<0.001), ISUP Grade IV PCa (HR: 4.93; CI: 2.64-9.19; p-value<0.001), cT4 PCa (HR: 2.260; CI: 1.81-2.82; p-value<0.001), N+ PCa (HR: 2.03; CI: 1.74-2.38; p-value<0.001) and metastatic PCa (HR: 8.76; CI: 7.61-10.10; p-value<0.001), had higher hazard of PCSM. Discussion Socioeconomic disparities significantly affect PCa outcomes, urging further investigation. Specifically, men who live in the most deprived areas seem to have a higher incidence and worse oncological PCa outcomes than those living in wealthier neighborhoods. 8 However, it is noteworthy that very few of the previously published studies on the topic relied on solid as well as validated indexes for an objective assessment of socioeconomic factors 16 . To circumvent this limitation, we used the ADI score to comprehensively measure neighbourhood deprivation. The latter is calculated using 17 different indicators, including income level, income disparity, educational attainment, employment, home values, and quality of life. These indicators are weighted to create an underlying deprivation score 9 . Since it utilizes census block groups, it provides greater geographic precision and greater reliability for community-level interventions than census tracts, which are instead used in other Socioeconomic Status scores (SES). 17 Furthermore, ADI is nationally standardized, while the other tools can differ based on the region, data sources, and weighting methods 18 . We tested the hypothesis that ADI is a significant predictor of PCSM in Michigan patients. Our findings highlight several key points. First, patients living in the most deprived neighbourhoods were predominantly NHB and were diagnosed with a more aggressive PCa profile. Specifically, the higher the ADI, the greater the association of higher PSA, high ISUP Grade, and more advanced clinical status at diagnosis. Second, socioeconomic deprivation was found to be significantly associated with worse survival outcomes. Specifically, 15-year PCSM were 3.6%, 5.2%, 5.4%, and 6.9% in the first, second, third, and fourth ADI quartiles, respectively. This was also confirmed in multivariable analysis, where ADI was an independent predictor of PCSM. Specifically, patients living in the most deprived areas (Q4) had a 1.54-fold higher PCSM hazard compared to those living in the least deprived neighborhoods (Q1). Before specifically analysing the impact of ADI on PCa outcomes, it should be emphasized that ADI has been used to assess the impact of neighbourhood-level socioeconomic disadvantage on cancer-specific mortality for various solid tumors. For instance, a study based in Georgia evaluated the relationship between ADI and overall survival in breast cancer. 15 The authors observed that living in more deprived areas was associated with a 1.33-fold increase in overall mortality risk. Only a handful of groups have investigated the relationship between socioeconomic status and PCSM utilizing large national cancer databases, mostly focusing on short or medium-term survival outcomes 19 . To circumvent these limitations, we set to examine for the first time the impact of ADI on long-term PCSM risk using a statewide cohort. Indeed, our study has the longest median follow-up of 10.8 years (7.8–13.4) time, evaluating the impact of socioeconomic deprivation on long-term survival outcomes. Despite the growing body of literature investigating the influence of socioeconomic disparities on oncological outcomes, the existing literature on PCa and its association with ADI is quite controversial and relying on small cohorts 19 , 20 . For example, Madhav et al. reported a study including 2,113 men from the North Carolina–Louisiana Cancer Project, stratifying them by ADI quintiles. They observed that individuals in the most socioeconomically disadvantaged regions, as measured by ADI, had almost 2-fold higher PCSM hazard compared to residents in the least deprived areas. Additionally, meeting abstracts have explored ADI’s impact on PCa mortality. However, the major limitation is that they were not expanded into full manuscripts, preventing them from undergoing peer review. For instance, a brief report by Cullen et al. included 112,023 men who were diagnosed with PCa between 1996 and 2016 in order to assess the impact of ADI on the probability of harboring metastatic PCa at presentation 14 . Similarly to what we have observed, the authors reported that higher ADI values were associated with a higher risk of metastatic PCa at diagnosis. This corroborates previous findings highlighting that individuals from low-income areas may be less likely to receive timely screening and medical care for early cancer symptoms, thus facing a greater risk of adverse oncological outcomes 1 . On the other hand, another meeting abstract that is noteworthy to mention by Duran et al. included 25,222 men diagnosed with PCa between 2012 and 2015 within the Veterans Health Administration (VHA). The authors tested the impact of socioeconomic factors, as measured by ADI, showing different results from ours 19 . Specifically, ADI didn’t show a significant association with percentile and PSA values, ISUP GG, and the presence of metastasis at diagnosis 19 . Nonetheless, it should be emphasized that the specific inclusion of patients who were diagnosed and/or treated within the Veterans Health System, which is known to provide free healthcare services to all Veterans, makes the setting of this study significantly different from ours and from the other abstract by Cullen et al. where patients have different levels of access to health insurance and thus healthcare. Furthermore, the interesting results of Duran et al. convey a message that is as intuitive as it is relevant, highlighting that equal access to healthcare services can diminish the impact of living in a disadvantaged neighborhood on PCa cancer control outcomes. To the best of our knowledge, this is the first study that evaluated the impact of socioeconomic deprivation, as objectively measured by a composite and robust index (ADI), on the long-term risk of PCSM relying on a statewide cohort of patients and a significantly long median follow-up time (10.8 years). However, several limitations must be addressed within this study. First, the retrospective nature of this study might have generated some bias in the data collection which must be considered. Nonetheless, it should be emphasized that randomized studies examining ADI impact on oncological outcomes are unpractical and extremely difficult to perform. Second, although ADI offers a good and comprehensive measurement of socio-economic and demographic factors, we were able to record only data at the time of entrance to the study. Third, despite being a robust index of socioeconomic disadvantage, ADI may not fully capture all relevant deprivation factors or health determinants across racial groups. Area-level measures like ADI may overlook other neighborhood characteristics that impact health outcomes, such as walkability, access to healthy food, healthcare availability, residential segregation, and crime rate. Conclusion In summary, people living in the most deprived areas had less favorable long-term PCSM rates than their counterparts living in the most advantageous areas. This implies that the socioeconomic factors captured by ADI have an important impact on cancer control outcomes. Our study suggests that ADI should be used as a solid index for further healthcare disparity research outcomes, and additional national funding should be allocated to support research in this context. Declarations Data Availability: Data from the Michigan Department of Health and Human Services (MDHHS) Database will be made available upon request in compliance with our Institution and IRB regulations. Funding: The Vattikuti Urology Institute Center for Outcomes Research, Analysis, and Evaluation is supported by a fund, which was started by a contribution from the Menon Foundation and the Vattikuti Foundation. Conflicts of interest: None of the authors have any relevant disclosures, and none of the authors have any financial or non-financial interests that may be relevant to the submitted work. Acknowledgments: The funder did not play a role in any part or phase of the study. References DeSantis CE, Miller KD, Goding Sauer A, Jemal A, Siegel RL. Cancer statistics for African Americans, 2019. CA A Cancer J Clinicians . 2019;69(3):211-233. doi:10.3322/caac.21555 Deka R, Courtney PT, Parsons JK, et al. Association Between African American Race and Clinical Outcomes in Men Treated for Low-Risk Prostate Cancer With Active Surveillance. JAMA . 2020;324(17):1747. doi:10.1001/jama.2020.17020 Vince RA, Jamieson S, Mahal B, Underwood W. Examining the Racial Disparities in Prostate Cancer. Urology . 2022;163:107-111. doi:10.1016/j.urology.2021.08.004 Williams VL, Awasthi S, Fink AK, et al. African‐American men and prostate cancer‐specific mortality: a competing risk analysis of a large institutional cohort, 1989–2015. Cancer Medicine . 2018;7(5):2160-2171. doi:10.1002/cam4.1451 Salmon C, Quesnel-Vallée A, Barnett TA, et al. Neighbourhood social deprivation and risk of prostate cancer. Br J Cancer . 2023;129(2):335-345. doi:10.1038/s41416-023-02299-7 Finati M, Stephens A, Cirulli GO, et al. Association of race and area of deprivation index with prostate cancer incidence and lethality: results from a contemporary North American cohort. JNCI Cancer Spectrum . 2024;8(6):pkae112. doi:10.1093/jncics/pkae112 Pichardo MS, Minas TZ, Pichardo CM, et al. Association of Neighborhood Deprivation With Prostate Cancer and Immune Markers in African American and European American Men. JAMA Netw Open . 2023;6(1):e2251745. doi:10.1001/jamanetworkopen.2022.51745 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 Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas. N Engl J Med . 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313 K. C. M, Oral E, Rung AL, et al. Neighborhood deprivation and risk of mortality among men with prostate cancer: Findings from a long‐term follow‐up study. The Prostate . 2022;82(7):783-792. doi:10.1002/pros.24320 Kumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. Front Oncol . 2024;14:1343070. doi:10.3389/fonc.2024.1343070 Center for Health Disparities Research, University of Wisconsin School of Medicine Public Health. 2015 Area Deprivation Index version 2.0. August 1, 2021. Accessed October 11, 2023. https:// www.neighborhoodatlas.medicine.wisc.edu. Hu J, Kind AJH, Nerenz D. Area Deprivation Index Predicts Readmission Risk at an Urban Teaching Hospital. Am J Med Qual . 2018;33(5):493-501. doi:10.1177/1062860617753063 Cullen J, Payne JY, Rhodes SP, Shoag JE. Twenty-year patterns in area deprivation index and risk of metastatic prostate cancer at initial diagnosis among men in Ohio. JCO . 2023;41(16_suppl):10526-10526. doi:10.1200/JCO.2023.41.16_suppl.10526 Luningham JM, Seth G, Saini G, et al. Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia. JAMA Netw Open . 2022;5(10):e2238183. doi:10.1001/jamanetworkopen.2022.38183 Kumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. Front Oncol . 2024;14:1343070. doi:10.3389/fonc.2024.1343070 Tomic K, Ventimiglia E, Robinson D, Häggström C, Lambe M, Stattin P. Socioeconomic status and diagnosis, treatment, and mortality in men with prostate cancer. Nationwide population‐based study. Intl Journal of Cancer . 2018;142(12):2478-2484. doi:10.1002/ijc.31272 Gopal K. Singh, PhD, MS, MSc. Area Deprivation and Widening Inequalities in US Mortality, 1969–1998. Published online July 2003. https://pmc.ncbi.nlm.nih.gov/articles/PMC1447923/pdf/0931137.pdf Duran EAM, Morgan KM, Deshler LN, et al. Association between National Area Deprivation Index Rank on Disease Characteristics in Prostate Cancer. International Journal of Radiation Oncology*Biology*Physics . 2023;117(2):e380. doi:10.1016/j.ijrobp.2023.06.2490 Bai J, Pugh SL, Eldridge R, et al. Neighborhood Deprivation and Rurality Associated With Patient-Reported Outcomes and Survival in Men With Prostate Cancer in NRG Oncology RTOG 0415. International Journal of Radiation Oncology*Biology*Physics . 2023;116(1):39-49. doi:10.1016/j.ijrobp.2023.01.035 Tables Table 1. Descriptive statistic of the entire cohort of patients, as categorized into ADI quartiles. ADI Quartile 1 (N=5567) 2 (N=14732) 3 (N=16199) 4 (N=17034) Total (N=53532) P-value Age (Years) <.0001 1 Median (IQR) 63 (58, 68) 64 (58, 68) 64 (59, 69) 64 (58, 69) 64 (58, 69) Year of Diagnosis <.0001 1 Median (IQR) 2010 (2008, 2013) 2010 (2008, 2013) 2010 (2008, 2013) 2010 (2007, 2013) 2010 (2008, 2013) Race , n (%) <.0001 2 White 5054 (90.8%) 13470 (91.4%) 14547 (89.8%) 9768 (57.3%) 42839 (80.0%) Black 349 (6.3%) 997 (6.8%) 1425 (8.8%) 7057 (41.4%) 9828 (18.4%) Other 164 (2.9%) 265 (1.8%) 227 (1.4%) 209 (1.2%) 865 (1.6%) Insurance , n (%) <.0001 2 Not Insured 38 (0.7%) 126 (0.9%) 141 (0.9%) 230 (1.4%) 535 (1.0%) Private Insurance 1751 (31.5%) 5849 (39.7%) 6761 (41.7%) 5098 (29.9%) 19459 (36.4%) Medicaid 14 (0.3%) 83 (0.6%) 191 (1.2%) 344 (2.0%) 632 (1.2%) Medicare 512 (9.2%) 2538 (17.2%) 3840 (23.7%) 3390 (19.9%) 10280 (19.2%) Other/Unknown 3252 (58.4%) 6136 (41.7%) 5266 (32.5%) 7972 (46.8%) 22626 (42.3%) County Type , n (%) <.0001 2 Non-Metropolitan Counties 146 (2.6%) 1420 (9.6%) 3971 (24.5%) 3311 (19.4%) 8848 (16.5%) Metropolitan Counties 5421 (97.4%) 13312 (90.4%) 12228 (75.5%) 13723 (80.6%) 44684 (83.5%) PSA (ng/mL) , n (%) <.0001 2 20 ng/mL 236 (4.2%) 885 (6.0%) 1302 (8.0%) 1755 (10.3%) 4178 (7.8%) Unknown 738 (13.3%) 1877 (12.7%) 2106 (13.0%) 2538 (14.9%) 7259 (13.6%) Grade , n (%) <.0001 2 Grade I (Gleason 7) 2789 (50.1%) 7419 (50.4%) 8490 (52.4%) 9156 (53.8%) 27854 (52.0%) Grade IV (Anaplastic) 13 (0.2%) 40 (0.3%) 65 (0.4%) 58 (0.3%) 176 (0.3%) Clinical T Stage, n (%) <.00012 cT1 3557 (63.9%) 9033 (61.3%) 9512 (58.7%) 9706 (57.0%) 31808 (59.4%) cT2 1855 (33.3%) 5123 (34.8%) 6012 (37.1%) 6629 (38.9%) 19619 (36.6%) cT3 141 (2.5%) 482 (3.3%) 577 (3.6%) 574 (3.4%) 1774 (3.3%) cT4 14 (0.3%) 94 (0.6%) 98 (0.6%) 125 (0.7%) 331 (0.6%) Nodal Status, n (%) 0.52 Negative 5451 (97.9%) 14375 (97.6%) 15816 (97.6%) 16628 (97.6%) 52270 (97.6%) Positive 116 (2.1%) 357 (2.4%) 383 (2.4%) 406 (2.4%) 1262 (2.4%) Metastasis, n (%) <.00012 No 5489 (98.6%) 14457 (98.1%) 15906 (98.2%) 16523 (97.0%) 52375 (97.8%) Yes 78 (1.4%) 275 (1.9%) 293 (1.8%) 511 (3.0%) 1157 (2.2%) Follow-up (Years) N/A Median (IQR) 11.3 (8.5, 13.7) 11.0 (8.0, 13.6) 10.8 (7.8, 13.4) 10.1 (7.1, 13.0) 10.8 (7.8, 13.4) Status, n (%) N/A Alive 4695 (84.3%) 11791 (80.0%) 12186 (75.2%) 11443 (67.2%) 40115 (74.9%) Prostate Cancer-Specific Mortality 169 (3.0%) 623 (4.2%) 728 (4.5%) 1010 (5.9%) 2530 (4.7%) Other Cause Mortality 703 (12.6%) 2318 (15.7%) 3285 (20.3%) 4581 (26.9%) 10887 (20.3%) Overall Status, n (%) N/A Alive 4695 (84.3%) 11791 (80.0%) 12186 (75.2%) 11443 (67.2%) 40115 (74.9%) All-Cause Mortality 872 (15.7%) 2941 (20.0%) 4013 (24.8%) 5591 (32.8%) 13417 (25.1%) 1 Kruskal-Wallis p-value; 2 Chi-Square p-value; Table 2. Competing-risk regression analysis testing the impact of ADI on PCSM, after accounting for available covariates, with other-cause mortality as a competing risk. Follow-up (Years) ---------------------------------------- Covariate Level Hazard Ratio HR P-value Type3 P-value ADI Quartile 2 1.31 (1.08-1.57) 0.007 <.001 3 1.31 (1.09-1.58) 0.005 4 1.54 (1.28-1.85) <.001 1 - - Age (Years) 1.01 (1.01-1.02) <.001 <.001 Year of Diagnosis 0.92 (0.91-0.94) <.001 <.001 Race Black 0.94 (0.83-1.06) 0.4 0.6 Other 0.86 (0.59-1.25) 0.5 White - - Insurance Medicaid 1.11 (0.79-1.58) 0.6 0.1 Medicare 1.00 (0.88-1.12) 0.9 Not Insured 0.85 (0.60-1.19) 0.4 Other/Unknown 0.88 (0.79-0.98) 0.022 Private Insurance - - County Type Metropolitan Counties 0.99 (0.88-1.11) 0.9 0.9 Non-Metropolitan Counties - - PSA (ng/mL) 10 to 20 ng/mL 1.95 (1.72-2.22) <.001 20 ng/mL 2.98 (2.63-3.37) <.001 Unknown 1.63 (1.43-1.85) <.001 < 10 ng/mL - - Grade Grade II (Gleason 7) 0.93 (0.59-1.46) 0.8 7) 3.57 (2.29-5.58) <.001 Grade IV (Anaplastic) 4.93 (2.64-9.19) <.001 Grade I (Gleason < 7) - - Clinical T Stage cT2 1.33 (1.21-1.46) <.001 <.001 cT3 1.80 (1.53-2.13) <.001 cT4 2.26 (1.81-2.82) <.001 cT1 - - Nodal Status Positive 2.03 (1.74-2.38) <.001 <.001 Negative - - Metastasis Yes 8.76 (7.61-10.10) <.001 <.001 No - - * Number of observations in the original data set = 53532. Number of observations used = 53532. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7947530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574107685,"identity":"f616ee86-2d87-4ffa-84ae-ab774470dc9a","order_by":0,"name":"Antonio Perri","email":"","orcid":"","institution":"IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Perri","suffix":""},{"id":574107686,"identity":"3d72a45a-b1d6-47b6-9d14-7c2899698872","order_by":1,"name":"Tylecki Anna","email":"","orcid":"","institution":"Henry Ford Health 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Abdollah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBAC+wM8DBIMBRY8/BKMz4jTYsAA0mIgISM5g9mMNC02BjeI1sJ+9uCNDwYSPMa3m9ke/mCwyZd3IKDFnicv2XIGUIvZncPsxjwMaZYbDxB0WI6ZNA9Iy438Y9IMDIcNDBsIaeF/A9FiPCOZTfIHUVokoLYYSCSzSfAAtcgT0AHU8g7iF4kbyWxAvWkGBgS18OcCQ6zCxp4f7LAKGwN5Qg5DNwGIDpCmBQhItWUUjIJRMAqGPwAAUxMyh+eLml4AAAAASUVORK5CYII=","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":true,"prefix":"","firstName":"Firas","middleName":"","lastName":"Abdollah","suffix":""}],"badges":[],"createdAt":"2025-10-27 10:51:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7947530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7947530/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100400394,"identity":"31fd9d02-477b-458e-9224-839d348757e0","added_by":"auto","created_at":"2026-01-16 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11:57:53","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181060,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7947530/v1/5ef0a712b58adfaffda4277c.html"},{"id":100399432,"identity":"02b00559-667f-47b6-85a7-cb34817c08c5","added_by":"auto","created_at":"2026-01-16 11:56:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort selection flowchart: \u003c/strong\u003eDetailed breakdown of the number of participants included in this study based on the inclusion and exclusion criteria used for this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7947530/v1/c2314b7f7d5433ee769613fc.png"},{"id":100400250,"identity":"395b65aa-543a-4e97-b1ea-67a641c209fe","added_by":"auto","created_at":"2026-01-16 11:58:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84719,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence Function for Prostate Cancer-Specific Mortality (ADI).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7947530/v1/c1ab80272ea1b37e6b72339d.png"},{"id":106813179,"identity":"b5abe542-5a21-4b31-8408-c319ee2e18e0","added_by":"auto","created_at":"2026-04-13 16:41:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7947530/v1/13e94015-4d5f-465b-a665-38a4493ef896.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Area Deprivation Index with Prostate Cancer-Specific Mortality in a Contemporary North American Statewide Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is the second leading cause of cancer-related mortality among men in the United States.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Numerous studies have identified disparities in PCa incidence and mortality associated with race and ethnicity \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, as well as with socioeconomic factors.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Within this context, neighborhood-level measures\u0026mdash;which reflect unique economic, physical, and social characteristics\u0026mdash;have been shown to significantly impact both community and individual health. Specifically, individuals residing in socioeconomically deprived neighborhoods are more likely to experience higher lethal disease rates compared to those in less deprived areas, especially among African American men.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Evidence suggests that disparities in cancer-specific mortality are largely driven by differences in diagnosis and treatment accessibility based on socioeconomic status.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Recently, the Area Deprivation Index (ADI), a numerical measure derived from publicly available data on income, education, employment, and housing quality at the census block level, has been proposed as a predictor of cancer control outcomes.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Neighborhoods are ranked by comparing their ADI to national and state benchmarks, with higher ADI scores indicating greater socioeconomic disadvantage. Despite its potential importance, no study to date has examined the relationship between ADI and prostate cancer-specific mortality (PCSM) in a large population as the one represented in this study and most importantly with this long-term follow-up time.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Socioeconomic status and geographical location are known to contribute to disparities, but this gap in the literature remains unexplored.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e To address this, we analyzed the association between ADI and PCa outcomes, hypothesizing that ADI could predict variations in disease progression and outcomes among patients, statewide.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eData for this study were abstracted from the Michigan Department of Health and Human Services (MDHHS), which included all men aged between 18 and 74 with histologically confirmed PCa between 2004 and 2022. Our population's social and ethnic diversity provides an ideal context for examining the influence of socio-cultural, economic, environmental, and biological factors on cancer control outcomes. We included only patients who have been followed up in and have known Area Deprivation Index (ADI) data or geographic/residential data. Our selection criteria, as detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, yielded a final cohort of 53,532 eligible patients stratified by ADI quartile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe following variables were extracted for all patients: age, year of diagnosis, race (White, Black, Other), insurance (Not Insured, Private Insurance, Medicaid, Medicare, Other/Unknown), and county type (Non-Metropolitan Counties, Metropolitan Counties). At the time of diagnosis, the following data were extracted: PSA, clinical T stage, pathological Grade, nodal status, and metastasis. Our main variable of interest was ADI, which is a numeric index calculated based on census tract that includes education level, home value, income, and employment.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e This numerical index was assigned to each census block, with a score of 1 representing the least deprived neighborhoods and 100 indicating the most deprived areas. The 5-digit ZIP codes obtained from patient addresses were paired with their corresponding plus-4 codes using the United States Postal Service ZIP code database. These 9-digit zip codes were used to match patients within their census block. \u003csup\u003e12\u003c/sup\u003e Based on previous methodology \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, individuals were further categorized based on national quartiles, where the fourth one (ADI 75\u0026ndash;100) represented those living in the most deprived areas.\u003c/p\u003e\n\u003ch3\u003eEndpoints\u003c/h3\u003e\n\u003cp\u003eOur main endpoint was Prostate cancer-specific mortality. Death due to a cause other than PCa was considered a competing event. Follow-up time was calculated from the time of diagnosis to the last available follow-up.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics included the median and interquartile range (IQR) for continuous variables, while categorical variables were reported as frequencies with percentages. Kruskal-Wallis and Chi-Square tests were performed for continuous and categorical variables, respectively. The Benjamini-Hochberg correction was applied for multiple comparisons. Our analysis was based on multiple steps. First, we calculated the estimated 15-year PCSM cumulative incidence for the entire cohort of patients, stratified according to ADI quartiles. Second, multivariable competing risk regression analyses were tested to further analyze the impact of ADI on PCSM. ADI was calculated by units of 10 rather than 1 relative to the national level \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e to make interpretation easier and more meaningful. The significant p-value (alpha) was set at 0.05. This study was deemed exempt from review by the Henry Ford Institutional Review Board (IRB), as all MDHHS data are fully de-identified and do not require individual patient informed consent. The data were analyzed using SAS Studio Version 3.81 (SAS Institute, Cary, NC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic information and clinical variables for the entire cohort of patients, stratified by ADI quartiles are summarized in \u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e. Our cohort consisted of 53,532 patients, 18.4% (9,828) of whom were NHB. The median (IQR) age at diagnosis and median (IQR) year of diagnosis were 64 (58 – 69) years and 2010 (2008-2013), respectively. Median age at diagnosis (IQR) in the fourth quartile was 64 (58- 69) vs 63 (58-68), in the first ADI quartile. Within the NHB population, the majority lived in the most deprived neighborhoods, compared to patients living in the most affluent areas. Focusing on the clinical and pathological variables, patients in the most disadvantaged quartile were diagnosed with PCa with more aggressive features. Patients in the fourth-quartile were more likely to present with PSA \u0026gt; 10 ng/mL (9.3% vs. 7.1%), ISUP Grade III PCa (52.0% vs. 50.1%), cT3-4 PCa (4.1% vs. 2.8%), and metastasis (3.0% vs. 1.4%) than first-quartile counterparts.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCancer-specific mortality\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 15-year PCSM cumulative incidence estimates were\u0026nbsp;3.6%, 5.2%, 5.4%, and 6.9% for patients in the first, second, third, and fourth ADI quartiles, respectively (p \u0026lt; 0.0001) (\u003cstrong\u003e\u003cem\u003eFigure 2\u003c/em\u003e\u003c/strong\u003e). In competing-risk regression analysis (\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e), ADI was significantly associated with a higher hazard of PCSM. Specifically, patients in the fourth ADI quartile had a 1.54-fold (CI:1.03-1.49; p-value \u0026lt;.001) higher hazard of PCSM, compared to those included in the first ADI quartile. As expected, patients with more advanced disease were associated with higher risk of PCSM. Patients who were diagnosed with PSA \u0026gt; 20 ng/mL (HR: 2.98; CI: 2.63-3.37; p-value\u0026lt;0.001), ISUP Grade IV PCa (HR: 4.93; CI: 2.64-9.19; p-value\u0026lt;0.001), cT4 PCa (HR: 2.260; CI: 1.81-2.82; p-value\u0026lt;0.001), N+ PCa (HR: 2.03; CI: 1.74-2.38; p-value\u0026lt;0.001) and metastatic PCa (HR: 8.76; CI: 7.61-10.10; p-value\u0026lt;0.001), had higher hazard of PCSM.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSocioeconomic disparities significantly affect PCa outcomes, urging further investigation. Specifically, men who live in the most deprived areas seem to have a higher incidence and worse oncological PCa outcomes than those living in wealthier neighborhoods.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, it is noteworthy that very few of the previously published studies on the topic relied on solid as well as validated indexes for an objective assessment of socioeconomic factors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. To circumvent this limitation, we used the ADI score to comprehensively measure neighbourhood deprivation. The latter is calculated using 17 different indicators, including income level, income disparity, educational attainment, employment, home values, and quality of life. These indicators are weighted to create an underlying deprivation score \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Since it utilizes census block groups, it provides greater geographic precision and greater reliability for community-level interventions than census tracts, which are instead used in other Socioeconomic Status scores (SES).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Furthermore, ADI is nationally standardized, while the other tools can differ based on the region, data sources, and weighting methods\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. We tested the hypothesis that ADI is a significant predictor of PCSM in Michigan patients.\u003c/p\u003e \u003cp\u003eOur findings highlight several key points. First, patients living in the most deprived neighbourhoods were predominantly NHB and were diagnosed with a more aggressive PCa profile. Specifically, the higher the ADI, the greater the association of higher PSA, high ISUP Grade, and more advanced clinical status at diagnosis. Second, socioeconomic deprivation was found to be significantly associated with worse survival outcomes. Specifically, 15-year PCSM were 3.6%, 5.2%, 5.4%, and 6.9% in the first, second, third, and fourth ADI quartiles, respectively. This was also confirmed in multivariable analysis, where ADI was an independent predictor of PCSM. Specifically, patients living in the most deprived areas (Q4) had a 1.54-fold higher PCSM hazard compared to those living in the least deprived neighborhoods (Q1). Before specifically analysing the impact of ADI on PCa outcomes, it should be emphasized that ADI has been used to assess the impact of neighbourhood-level socioeconomic disadvantage on cancer-specific mortality for various solid tumors. For instance, a study based in Georgia evaluated the relationship between ADI and overall survival in breast cancer.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The authors observed that living in more deprived areas was associated with a 1.33-fold increase in overall mortality risk. Only a handful of groups have investigated the relationship between socioeconomic status and PCSM utilizing large national cancer databases, mostly focusing on short or medium-term survival outcomes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To circumvent these limitations, we set to examine for the first time the impact of ADI on long-term PCSM risk using a statewide cohort. Indeed, our study has the longest median follow-up of 10.8 years (7.8\u0026ndash;13.4) time, evaluating the impact of socioeconomic deprivation on long-term survival outcomes. Despite the growing body of literature investigating the influence of socioeconomic disparities on oncological outcomes, the existing literature on PCa and its association with ADI is quite controversial and relying on small cohorts\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, Madhav et al. reported a study including 2,113 men from the North Carolina\u0026ndash;Louisiana Cancer Project, stratifying them by ADI quintiles. They observed that individuals in the most socioeconomically disadvantaged regions, as measured by ADI, had almost 2-fold higher PCSM hazard compared to residents in the least deprived areas. Additionally, meeting abstracts have explored ADI\u0026rsquo;s impact on PCa mortality. However, the major limitation is that they were not expanded into full manuscripts, preventing them from undergoing peer review. For instance, a brief report by Cullen et al. included 112,023 men who were diagnosed with PCa between 1996 and 2016 in order to assess the impact of ADI on the probability of harboring metastatic PCa at presentation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Similarly to what we have observed, the authors reported that higher ADI values were associated with a higher risk of metastatic PCa at diagnosis. This corroborates previous findings highlighting that individuals from low-income areas may be less likely to receive timely screening and medical care for early cancer symptoms, thus facing a greater risk of adverse oncological outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. On the other hand, another meeting abstract that is noteworthy to mention by Duran et al. included 25,222 men diagnosed with PCa between 2012 and 2015 within the Veterans Health Administration (VHA). The authors tested the impact of socioeconomic factors, as measured by ADI, showing different results from ours\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Specifically, ADI didn\u0026rsquo;t show a significant association with percentile and PSA values, ISUP GG, and the presence of metastasis at diagnosis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Nonetheless, it should be emphasized that the specific inclusion of patients who were diagnosed and/or treated within the Veterans Health System, which is known to provide free healthcare services to all Veterans, makes the setting of this study significantly different from ours and from the other abstract by Cullen et al. where patients have different levels of access to health insurance and thus healthcare. Furthermore, the interesting results of Duran et al. convey a message that is as intuitive as it is relevant, highlighting that equal access to healthcare services can diminish the impact of living in a disadvantaged neighborhood on PCa cancer control outcomes.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study that evaluated the impact of socioeconomic deprivation, as objectively measured by a composite and robust index (ADI), on the long-term risk of PCSM relying on a statewide cohort of patients and a significantly long median follow-up time (10.8 years). However, several limitations must be addressed within this study. First, the retrospective nature of this study might have generated some bias in the data collection which must be considered. Nonetheless, it should be emphasized that randomized studies examining ADI impact on oncological outcomes are unpractical and extremely difficult to perform. Second, although ADI offers a good and comprehensive measurement of socio-economic and demographic factors, we were able to record only data at the time of entrance to the study. Third, despite being a robust index of socioeconomic disadvantage, ADI may not fully capture all relevant deprivation factors or health determinants across racial groups. Area-level measures like ADI may overlook other neighborhood characteristics that impact health outcomes, such as walkability, access to healthy food, healthcare availability, residential segregation, and crime rate.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, people living in the most deprived areas had less favorable long-term PCSM rates than their counterparts living in the most advantageous areas. This implies that the socioeconomic factors captured by ADI have an important impact on cancer control outcomes. Our study suggests that ADI should be used as a solid index for further healthcare disparity research outcomes, and additional national funding should be allocated to support research in this context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eData from the Michigan Department of Health and Human Services (MDHHS) Database will be made available upon request in compliance with our Institution and IRB regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe Vattikuti Urology Institute Center for Outcomes Research, Analysis, and Evaluation is supported by a fund, which was started by a contribution from the Menon Foundation and the Vattikuti Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eNone of the authors have any relevant disclosures, and none of the authors have any financial or non-financial interests that may be relevant to the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe funder did not play a role in any part or phase of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeSantis CE, Miller KD, Goding Sauer A, Jemal A, Siegel RL. Cancer statistics for African Americans, 2019. \u003cem\u003eCA A Cancer J Clinicians\u003c/em\u003e. 2019;69(3):211-233. doi:10.3322/caac.21555\u003c/li\u003e\n\u003cli\u003eDeka R, Courtney PT, Parsons JK, et al. Association Between African American Race and Clinical Outcomes in Men Treated for Low-Risk Prostate Cancer With Active Surveillance. \u003cem\u003eJAMA\u003c/em\u003e. 2020;324(17):1747. doi:10.1001/jama.2020.17020\u003c/li\u003e\n\u003cli\u003eVince RA, Jamieson S, Mahal B, Underwood W. Examining the Racial Disparities in Prostate Cancer. \u003cem\u003eUrology\u003c/em\u003e. 2022;163:107-111. doi:10.1016/j.urology.2021.08.004\u003c/li\u003e\n\u003cli\u003eWilliams VL, Awasthi S, Fink AK, et al. African‐American men and prostate cancer‐specific mortality: a competing risk analysis of a large institutional cohort, 1989\u0026ndash;2015. \u003cem\u003eCancer Medicine\u003c/em\u003e. 2018;7(5):2160-2171. doi:10.1002/cam4.1451\u003c/li\u003e\n\u003cli\u003eSalmon C, Quesnel-Vall\u0026eacute;e A, Barnett TA, et al. Neighbourhood social deprivation and risk of prostate cancer. \u003cem\u003eBr J Cancer\u003c/em\u003e. 2023;129(2):335-345. doi:10.1038/s41416-023-02299-7\u003c/li\u003e\n\u003cli\u003eFinati M, Stephens A, Cirulli GO, et al. Association of race and area of deprivation index with prostate cancer incidence and lethality: results from a contemporary North American cohort. \u003cem\u003eJNCI Cancer Spectrum\u003c/em\u003e. 2024;8(6):pkae112. doi:10.1093/jncics/pkae112\u003c/li\u003e\n\u003cli\u003ePichardo MS, Minas TZ, Pichardo CM, et al. Association of Neighborhood Deprivation With Prostate Cancer and Immune Markers in African American and European American Men. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2023;6(1):e2251745. doi:10.1001/jamanetworkopen.2022.51745\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\u003eKind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible \u0026mdash; The Neighborhood Atlas. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313\u003c/li\u003e\n\u003cli\u003eK. C. M, Oral E, Rung AL, et al. Neighborhood deprivation and risk of mortality among men with prostate cancer: Findings from a long‐term follow‐up study. \u003cem\u003eThe Prostate\u003c/em\u003e. 2022;82(7):783-792. doi:10.1002/pros.24320\u003c/li\u003e\n\u003cli\u003eKumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. \u003cem\u003eFront Oncol\u003c/em\u003e. 2024;14:1343070. doi:10.3389/fonc.2024.1343070\u003c/li\u003e\n\u003cli\u003eCenter for Health Disparities Research, University of Wisconsin School of Medicine Public Health. 2015 Area Deprivation Index version 2.0. August 1, 2021. Accessed October 11, 2023. https:// www.neighborhoodatlas.medicine.wisc.edu.\u003c/li\u003e\n\u003cli\u003eHu J, Kind AJH, Nerenz D. Area Deprivation Index Predicts Readmission Risk at an Urban Teaching Hospital. \u003cem\u003eAm J Med Qual\u003c/em\u003e. 2018;33(5):493-501. doi:10.1177/1062860617753063\u003c/li\u003e\n\u003cli\u003eCullen J, Payne JY, Rhodes SP, Shoag JE. Twenty-year patterns in area deprivation index and risk of metastatic prostate cancer at initial diagnosis among men in Ohio. \u003cem\u003eJCO\u003c/em\u003e. 2023;41(16_suppl):10526-10526. doi:10.1200/JCO.2023.41.16_suppl.10526\u003c/li\u003e\n\u003cli\u003eLuningham JM, Seth G, Saini G, et al. Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2022;5(10):e2238183. doi:10.1001/jamanetworkopen.2022.38183\u003c/li\u003e\n\u003cli\u003eKumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. \u003cem\u003eFront Oncol\u003c/em\u003e. 2024;14:1343070. doi:10.3389/fonc.2024.1343070\u003c/li\u003e\n\u003cli\u003eTomic K, Ventimiglia E, Robinson D, H\u0026auml;ggstr\u0026ouml;m C, Lambe M, Stattin P. Socioeconomic status and diagnosis, treatment, and mortality in men with prostate cancer. Nationwide population‐based study. \u003cem\u003eIntl Journal of Cancer\u003c/em\u003e. 2018;142(12):2478-2484. doi:10.1002/ijc.31272\u003c/li\u003e\n\u003cli\u003eGopal K. Singh, PhD, MS, MSc. Area Deprivation and Widening Inequalities in US Mortality, 1969\u0026ndash;1998. Published online July 2003. https://pmc.ncbi.nlm.nih.gov/articles/PMC1447923/pdf/0931137.pdf\u003c/li\u003e\n\u003cli\u003eDuran EAM, Morgan KM, Deshler LN, et al. Association between National Area Deprivation Index Rank on Disease Characteristics in Prostate Cancer. \u003cem\u003eInternational Journal of Radiation Oncology*Biology*Physics\u003c/em\u003e. 2023;117(2):e380. doi:10.1016/j.ijrobp.2023.06.2490\u003c/li\u003e\n\u003cli\u003eBai J, Pugh SL, Eldridge R, et al. Neighborhood Deprivation and Rurality Associated With Patient-Reported Outcomes and Survival in Men With Prostate Cancer in NRG Oncology RTOG 0415. \u003cem\u003eInternational Journal of Radiation Oncology*Biology*Physics\u003c/em\u003e. 2023;116(1):39-49. doi:10.1016/j.ijrobp.2023.01.035\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Descriptive statistic of the entire cohort of patients, as categorized into ADI quartiles.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 425px;\"\u003e\n \u003cp\u003eADI Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003cbr\u003e\u0026nbsp;(N=5567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003cbr\u003e\u0026nbsp;(N=14732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003cbr\u003e\u0026nbsp;(N=16199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4\u003cbr\u003e\u0026nbsp;(N=17034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eTotal\u003cbr\u003e\u0026nbsp;(N=53532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eP-value\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003csup\u003e1\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: 216px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e63 (58, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64 (58, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64 (59, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64 (58, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64 (58, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003csup\u003e1\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: 216px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2010 (2008, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2010 (2008, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2010 (2008, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2010 (2007, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2010 (2008, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\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: 216px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5054 (90.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13470 (91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14547 (89.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9768 (57.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e42839 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e349 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e997 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1425 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7057 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9828 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e164 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e265 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e227 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e209 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e865 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\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: 216px;\"\u003e\n \u003cp\u003eNot Insured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e38 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e126 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e141 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e230 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e535 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1751 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5849 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6761 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5098 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e19459 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e83 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e191 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e344 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e632 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e512 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2538 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3840 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3390 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10280 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eOther/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3252 (58.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6136 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5266 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7972 (46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e22626 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCounty Type\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\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: 216px;\"\u003e\n \u003cp\u003eNon-Metropolitan Counties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e146 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1420 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3971 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3311 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8848 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eMetropolitan Counties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5421 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13312 (90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12228 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13723 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e44684 (83.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA (ng/mL)\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\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: 216px;\"\u003e\n \u003cp\u003e\u0026lt; 10 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4196 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10752 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11222 (69.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10929 (64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e37099 (69.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e10 to 20 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e397 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1218 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1569 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1812 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4996 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026gt; 20 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e236 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e885 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1302 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1755 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4178 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e738 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1877 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2106 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2538 (14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7259 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\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: 216px;\"\u003e\n \u003cp\u003eGrade I (Gleason \u0026lt; 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e323 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e895 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e908 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e871 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2997 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eGrade II (Gleason 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2442 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6378 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6736 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6949 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e22505 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eGrade III (Gleason \u0026gt; 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2789 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7419 (50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8490 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9156 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e27854 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eGrade IV (Anaplastic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e40 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e65 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e58 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e176 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eClinical T Stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;.00012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003ecT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3557 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9033 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9512 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9706 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e31808 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003ecT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1855 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5123 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6012 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6629 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e19619 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003ecT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e141 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e482 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e577 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e574 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1774 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003ecT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e94 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e98 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e125 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e331 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eNodal Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5451 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14375 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e15816 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e16628 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e52270 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e116 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e357 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e383 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e406 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1262 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eMetastasis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;.00012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5489 (98.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14457 (98.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e15906 (98.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e16523 (97.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e52375 (97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e78 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e275 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e293 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e511 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1157 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eFollow-up (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11.3 (8.5, 13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11.0 (8.0, 13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.8 (7.8, 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.1 (7.1, 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.8 (7.8, 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eStatus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4695 (84.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11791 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12186 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11443 (67.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e40115 (74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eProstate Cancer-Specific Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e169 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e623 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e728 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1010 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2530 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eOther Cause Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e703 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2318 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3285 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4581 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10887 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eOverall Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4695 (84.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11791 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12186 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11443 (67.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e40115 (74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\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: 216px;\"\u003e\n \u003cp\u003eAll-Cause Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e872 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2941 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4013 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5591 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13417 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 820px;\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eKruskal-Wallis p-value; \u003csup\u003e2\u003c/sup\u003eChi-Square p-value;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eCompeting-risk regression analysis testing the impact of ADI on PCSM, after accounting for available covariates, with other-cause mortality as a competing risk.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e----------------------------------------\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType3 P-value\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 rowspan=\"4\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eADI Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.31 (1.08-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.31 (1.09-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.54 (1.28-1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\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: 192px;\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.01 (1.01-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\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: 192px;\"\u003e\n \u003cp\u003eYear of Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.92 (0.91-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.94 (0.83-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.86 (0.59-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.11 (0.79-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.00 (0.88-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNot Insured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.85 (0.60-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eOther/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.88 (0.79-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eCounty Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMetropolitan Counties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.99 (0.88-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNon-Metropolitan Counties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePSA (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e10 to 20 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.95 (1.72-2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026gt; 20 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.98 (2.63-3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.63 (1.43-1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026lt; 10 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGrade II (Gleason 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.93 (0.59-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGrade III (Gleason \u0026gt; 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3.57 (2.29-5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGrade IV (Anaplastic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4.93 (2.64-9.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGrade I (Gleason \u0026lt; 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eClinical T Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ecT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.33 (1.21-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ecT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.80 (1.53-2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ecT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.26 (1.81-2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ecT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eNodal Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.03 (1.74-2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMetastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e8.76 (7.61-10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 599px;\"\u003e\n \u003cp\u003e* \u0026nbsp;Number of observations in the original data set = 53532. Number of observations used = 53532.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate Cancer, Prostatic neoplasms, Socioeconomic status, Social deprivation, Healthcare disparities, Cancer Mortality ","lastPublishedDoi":"10.21203/rs.3.rs-7947530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7947530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSocio-economic status and geographic location contribute to disparities in prostate cancer (PCa) outcomes. This study evaluates the impact of the Area Deprivation Index (ADI) on prostate cancer-specific mortality (PCSM) in a North American statewide cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing data from the Michigan Department of Health and Human Services (MDHHS), we included men aged 18–74 with histologically confirmed PCa between 2004 and 2022. An ADI score, based on residential census block group and ranked nationally by deprivation percentile, was assigned to each patient. Individuals were grouped into quartiles, with the fourth (ADI 75–100) representing the most deprived. PCSM incidence was estimated after stratification by ADI quartiles using the competing-risk method. A competing-risk regression model, adjusting for covariates, assessed ADI’s impact on PCSM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe included 53,532 patients, 18.4% of whom were NHB (Non H ispanic Black). Median (IQR) age and year at diagnosis were 64 (58–69) years and 2010 (2008–2013), respectively. In the fourth ADI quartile, median diagnosis age was 64 (58–69) vs 63 (58–68) in the first quartile. At 15 years after diagnosis, PCSM cumulative incidence was 3.6%, 5.2%, 5.4%, and 6.9% across increasing ADI quartiles (p \u0026lt; 0.0001). Competing-risk regression showed ADI was significantly associated with higher PCSM hazard. Patients in the fourth quartile had a 1.54-fold (CI: 1.28–1.85; p \u0026lt; .001) higher hazard compared to those in the first.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIndividuals in the most deprived areas had higher PCSM than those in more advantaged areas, underscoring the impact of socioeconomic factors on cancer outcomes and the need for targeted equity-focused interventions.\u003c/p\u003e","manuscriptTitle":"Association of Area Deprivation Index with Prostate Cancer-Specific Mortality in a Contemporary North American Statewide Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:57:18","doi":"10.21203/rs.3.rs-7947530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba3d0479-bc17-4c2b-bdea-69a238a3a46b","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:39:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:57:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7947530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7947530","identity":"rs-7947530","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}