The Association of Neighborhood-Level Deprivation with Glioblastoma Outcomes: A Single Center Cohort Study

The Association of Neighborhood-Level Deprivation with Glioblastoma Outcomes: A Single Center Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report The Association of Neighborhood-Level Deprivation with Glioblastoma Outcomes: A Single Center Cohort Study Yifei Sun, Dagoberto Estevez-Ordonez, Travis J Atchley, Burt Nabors, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5913656/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Journal of Neuro-Oncology → Version 1 posted 16 You are reading this latest preprint version Abstract Purpose Glioblastoma is the most common primary brain malignancy. Though literature has suggested the association of glioblastoma outcomes and socioeconomic status, there is limited evidence regarding the association of neighborhood-level socioeconomic deprivation on glioblastoma outcomes. The aim of this study was to assess the impact of neighborhood-level socioeconomic deprivation on glioblastoma survival. Methods We retrospectively reviewed all adult glioblastoma patients seen at a single institution from 2008 to 2023. Neighborhood deprivation was assessed via Area Deprivation Index (ADI), with higher ADI indicating greater neighborhood socioeconomic deprivation. Log-rank tests and multivariate cox regression was used to assess the effect of ADI and other socioeconomic variables while controlling for a priori selected clinical variables with known relevance to survival. Results In total, 1464 patients met inclusion criteria. The average age at diagnosis was 60 ± 14 years with a median overall survival of 13.8 months (IQR 13-14.8). The median ADI of the cohort was 66(IQR 46-84). Patients with high ADI had worse overall survival compared to patients with low ADI (11.7 vs 14.8 months, p=.001). In the multivariable model, patients with high ADI had worse overall survival (HR 1.25, 95%CI 1.09-1.43). To account for changes in WHO guidelines, we implemented the model on patients diagnosed between 2017-2023 and findings were consistent (HR 1.26,95%CI 1.01-1.56). Conclusion We report the first study demonstrating glioblastoma patients with higher neighborhood deprivation have worse survival after controlling for other socioeconomic and biomolecular markers. Neighborhood socioeconomic status may be a prognostic marker for glioblastoma survival. Area deprivation Glioblastoma neighborhood socioeconomic status survival Figures Figure 1 Figure 2 INTRODUCTION Glioblastoma is the most common primary brain malignancy, comprising around half of all primary brain tumors [1]. Despite recent progress in treatments, prognosis for patients remain poor, with median survival around 15 months [2]. Thus, it is of interest to better understand the risk factors that are associated with worsened survival. Recent literature has identified a connection between socioeconomic disparity and worsened outcomes in patients with glioblastoma [3, 4]. Socioeconomic factors are complex and can differentially affect outcomes. Recent studies have identified racial and income-related disparities in surgical care, chemotherapy, and radiotherapy for patients in glioblastoma [4, 5]. Other studies have also identified disparities in glioblastoma outcomes on the basis of race, insurance status, age, and educational status, as well as inequalities in timeliness of care as well [6]. However, the effect of neighborhood-level SES status on glioblastoma survival remains unclear. Much of socioeconomic literature data at the ZIP code level, which has been shown to be poor proxies of socioeconomic status [7]. Furthermore, currently commonly utilized measures of SES are often poorly generalizable and subject to regional bias [8, 9]. Area Deprivation Index (ADI) is a tool developed by the Health Resources and Services Administration (HRSA) that measures neighborhood-level disadvantage by taking into account 17 measures of socioeconomic disparity in 4 main domains: education, income/employment, housing, and household characteristics [10]. Amongst the most studied area-level measures of socioeconomic disadvantage and independently validated by studies across many domains of health outcomes research, neighborhood deprivation has been used to link socioeconomic disparity to poor patient outcomes in diabetes research, cardiovascular research, and other surgical specialties [11-13]. Neighborhood deprivation has also gained increased attention due its inclusion in incentives programs and reimbursement adjustment calculations by the Center for Medicare/Medicaid (CMS) [14]. Despite its widespread adoption in other fields, there is little evidence regarding the association of neighborhood-level socioeconomic status on the overall survival (OS) of patients with glioblastoma. There is also little literature that examine the effect of these socioeconomic factors on glioblastoma outcomes in the context of clinically important molecular markers such as MGMT methylation and IDH wild-type status. The aim of this study was to assess the impact of neighborhood-level socioeconomic status on glioblastoma survival in the largest cohort to date and to better understand how socioeconomic status affects outcomes for patients with glioblastoma. To our knowledge, this is the first report to describe the association of neighborhood level socioeconomic deprivation with OS in glioblastoma. METHODS We performed a single center retrospective review with approval from the Institutional Review Board (IRB- 300011516). This manuscript was written in compliance with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [15]. Participants and Data Collection We retrospectively identified all adult patients, 18 years or older, with histopathological new glioblastoma diagnosis seen at our institution between January 1 st , 2008 and December 31 st , 2023. In total, 1493 patients met inclusion criteria. The electronic medical record (EMR) was reviewed for variables on patient demographics, socioeconomic background, geography, and treatment characteristics. Due to the retrospective nature of this study, patient consent was not needed. Defining Variables Variables were defined a priori with advice from the senior authors. Study variables included were age at diagnosis, race, gender, marital status, extent of surgical resection, IDH status MGMT methylation status, history of chemotherapy and history of radiotherapy. Patient addresses were extracted from the EMR and were geocoded using ArcGIS software. Federal Information Processing System (FIPS) codes were extracted and correlated to its individual Area Deprivation Index (ADI), with higher ADI relating to more socioeconomic deprivation. ADI was retrieved from the Neighborhood Atlas dataset produced by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health. 4 High ADI was designated patients in the top national quartile according to previous literature [16]. Statistical Analysis Univariable analysis including Student’s t-test, one-way analysis of variance (ANOVA), Chi squared test, and Wilcoxon rank sum test were used to compare incidences of chemotherapy, radiotherapy extent of resection, and other demographic variables between patients with high and low ADI. Kaplan Meier curves were used to investigate differences in survival between groups of interest, and log rank tests were used to assess differences in survival. Sensitivity analysis was conducted by performing multiple methods of imputation for missing data as well as replicating the model in patients diagnosed and treated after the WHO guidelines in 2016. All statistical analyses were performed using R studio (version 4.3.1) [17]. Further details on statistical analysis can be found in the supplementary content (Supplementary Content, Table 1S – 5S, Figure 1S-2S). RESULTS In total, 1464 patients met inclusion criteria. The mean age at diagnosis was 60 ± 14 years. Of these patients, 155 (11%) were African American (AA) and 816 (56%) were male. At time of censoring, 249 (17%) were alive. Of these patients, 671(46%) received complete resection, 1235 (84%) received radiotherapy and 1219 (83%) received chemotherapy. The median ADI was 66 (IQR 46-84). Ninety-two (6.3%) of the patients had IDH mutations and 344 (23%) of the patients were of MGMT-methylated status. The median OS (mOS) of the cohort was 13.7 months (IQR 12.99-14.16). Further details on patient demographics and characteristics can be found in the Table 1 and supplement (Supplementary Content, Table 6S). Table 1. Patient Characteristics and Demographics High ADI * No , N = 912 1 Yes , N = 552 1 p-value 2 Age (years) 0.8 < 45 124 (14%) 87 (16%) 45-54 142 (16%) 89 (16%) 55-64 248 (27%) 144 (26%) 65-74 281 (31%) 164 (30%) ≥75 117 (13%) 68 (12%) Sex 0.5 Female 398 (44%) 250 (45%) Male 514 (56%) 302 (55%) Race <0.001 Black 60 (6.6%) 95 (17%) Other 62 (6.8%) 23 (4.2%) White 790 (87%) 434 (79%) Married 682 (75%) 351 (64%) <0.001 Insurance <0.001 Indigent/Self Pay 28 (3.1%) 23 (4.2%) Medicaid 51 (5.6%) 60 (11%) Medicare 365 (40%) 225 (41%) Private 468 (51%) 244 (44%) Median Household Income (USD) 55,681 (47,276, 68,608) 42,116 (37,433, 48,371) <0.001 Vital Status 0.12 Alive 166 (18%) 83 (15%) Deceased 746 (82%) 469 (85%) IDH status 0.4 IDH-Mut 55 (6.0%) 37 (6.7%) IDH-WT 566 (62%) 324 (59%) Unknown 291 (32%) 191 (35%) MGMT Status 0.037 Methylated 211 (23%) 133 (24%) Unknown 340 (37%) 236 (43%) Unmethylated 361 (40%) 183 (33%) Extent of Resection 0.021 Biopsy 247 (27%) 183 (33%) Complete resection 441 (48%) 230 (42%) Partial Resection 224 (25%) 139 (25%) Received Radiotherapy 782 (86%) 453 (82%) 0.06 Received Chemotherapy 770 (84%) 449 (81%) 0.12 ADI 52 (36, 64) 87 (82, 92) <0.001 RUCA <0.001 Metropolitan 753 (83%) 309 (56%) Micropolitan 111 (12%) 114 (21%) Rural 8 (0.9%) 43 (7.8%) Small Town 40 (4.4%) 86 (16%) Distance from Institution (miles) <0.001 75 Univariable Comparison Analysis Patients in the highest quartile of ADI were more likely to be AA (17% vs 6.6%, p<.001), higher rates of Medicaid/Medicare (52% vs 45.6%, p<.001), more likely to be MGMT unmethylated (33% vs 40%, p=.037), more likely to live greater than 60 miles from the institution, and more likely to live in a rural region (7.8% vs 0.9% p<.001). These patients were also less likely to undergo complete resection compared to those with lower ADI (42% vs 48%, p=.021). The results of this analysis can be found in Table 1. Univariable Survival Comparison Analysis In univariable survival comparison, AA patients had longer mOS (15.2 months, 95%CI 10.6–18.0) compared to Caucasian patients (13.5 months 95%CI 12.6 – 14.5). Patients with high ADI had lower mOS (11.67 months, 95% CI 13.8–15.8) compared to those with low ADI (14.83 months, 95%CI 10.2–13.4). Patients who were privately insured had longer mOS (15.4 months, 95%CI 14.5 – 16.5) compared to those who were publicly insured (11.5 months, 95%CI 10.0–12.9) and those who were uninsured (12.4 months, 95%CI 7.3–21.2). Patients who received chemotherapy had longer mOS (15.4 months, 95%CI 14.6 – 16.1) than those who did not (3.7 months, 95%CI 3.1–4.7). Patients who received radiotherapy had longer mOS (15.6 months, 95%CI 14.5–16.5) compared to those who did not (3.2 months, 95%CI 3.0–4.0). The results of this analysis can be found in Table 2. Kaplan Meier survival curves stratifying for ADI are presented in Figure 1. Table 2. Univariable Survival Analysis Level Median survival CI lower CI upper p-value 1 Age < 45 26.27 22.03 32.12 <.001 45-54 17.69 15.85 20.51 55-64 14.66 13.08 16.18 65-74 9.86 8.38 11.28 ≥75 6.12 4.67 7.53 Sex Female 14.10 12.53 15.48 0.25 Male 13.51 12.79 14.83 Income Class* Lower 13.84 13.02 15.06 0.51 Middle 13.51 11.24 15.09 Upper 10.32 1.12 NA ADI** Low ADI 14.83 13.81 15.78 <.001 High ADI 11.67 10.22 13.38 IDH status IDH-Mut 33.57 27.32 41.79 <.001 IDH-WT 13.08 12.09 14.14 Unknown 13.12 11.80 14.89 MGMT Status Methylated 21.14 18.35 23.08 <.001 Unmethylated 12.99 11.74 14.10 Unknown 12.26 10.49 13.48 Extent of Resection Biopsy 7.43 5.85 8.84 <.001 Complete resection 17.03 16.14 19.04 Partial Resection 14.01 12.33 15.58 Chemotherapy No 3.68 3.12 4.67 <.001 Yes 15.42 14.63 16.14 Radiotherapy No 3.22 3.02 4.04 <.001 Yes 15.55 14.70 16.21 Insurance Category Private 15.39 14.53 16.50 <.001 Public 11.51 9.99 12.85 Self-Pay/Indigent 12.36 7.27 21.17 RUCA*** Metropolitan 14.24 13.12 15.16 0.18 Micropolitan 13.71 11.74 15.75 Rural 11.57 9.04 19.96 Small Town 11.97 9.50 15.52 Race Black 15.19 10.62 17.98 0.13 White 13.51 12.59 14.53 Other 18.94 13.58 23.80 1 log-rank test; * Income categories determined according to Pew Research Reports 2022;**ADI- Area Deprivation Index;***RUCA-Rural Urban Communicating Area Multivariate cox proportional hazards Using multivariate cox proportional hazards analysis, we identified that patients with high ADI had worse survival (HR 1.26, 95%CI 1.11-1.43, p<.001) compared to patients with low ADI. These findings were replicated in both types of imputation, complete case analysis, and in subgroup analysis of patients diagnosed after the 2016 WHO CNS Guidelines. (Supplementary Content, Table 7S) When compared to patients who were in the youngest age group at time of diagnosis (<45 years), all other age groups had worse survival. Patients who underwent gross total resection had better survival (HR 0.65, 95%CI 0.56–0.74, p <.001) compared to patients who underwent partial resections or biopsy. Patients with public insurance had better survival (HR 0.81, 95%CI 0.70-0.93, p=.004) compared to patients with private insurance. Patients with IDH mutation had better survival (HR 0.65, 95%CI 0.56–0.74, p<.001) compared to those with wild-type IDH. Patients with methylated MGMT had better survival (HR 0.53, 95%CI 0.47-0.61, p<.001) when compared to patients with unmethylated MGMT. In all three multivariate models, AA race was not found to be associated with worsened survival. Chemotherapy was found to be associated with improved survival in only the MICE model (HR 0.74, 95%CI 0.57–0.95, p=.02). Forest plots for this analysis can be found in Figure 2. DISCUSSION Here we report the first analysis of the effects of neighborhood level socioeconomic status on glioblastoma survival after adjusting for other socioeconomic, clinical, and molecular factors. Our findings strongly suggest that neighborhood deprivation independently predicts survival in patients with glioblastoma and is a potential prognostic marker for patients with glioblastoma. The role of socioeconomic status in cancer is well known, with many studies highlighting the effect of socioeconomic status on survival of cancer patients. Studies have suggested that income, insurance status, and various other commonly used measures are imperfect proxies of socioeconomic status, varying by region and race. Studies on the association of race and other socioeconomic markers are conflicted as well. Commonly utilized measures of socioeconomic disadvantage in literature can be imperfect proxies for SES [8, 9]. Additionally, social determinants of health play complex roles in determining the health outcomes of patients, and findings may be difficult to generalize findings on a national level. Thus, the utilization of ADI represents an advancement in understanding the prognostic effect of patient SES on glioblastoma survival by utilizing a nationally standardized, multifactorial measure of patient socioeconomic disadvantage. Here, we report the effect of ADI, an accurate and well-validated measure of neighborhood-level socioeconomic status, with survival in patients with glioblastoma in the largest single institution cohort to date. ADI has emerged as a preferred metric for capturing socioeconomic status in many fields of medicine [12, 18, 19]. However, it has been under-utilized in neuro-oncology and neurosurgery. We observed that high ADI is independently associated with worse survival compared to patients with lower ADI (HR 1.25, 95% CI 1.09- 1.43, p<.001) after adjusting for age, race, income, insurance status, IDH status, MGMT methylation, rurality, extent of resection, history of chemotherapy, and history of radiotherapy. There are many potential mechanisms for this observation. Access to Care Patients with high ADI reflect high degrees of socioeconomic disparity, which has been associated with decreased neurosurgical coverage [20]. This is reinforced in our findings, in which we observed that patients with high ADI were less likely to have undergone gross total resection compared to patients with high ADI (Table 2). Analysis by Perla et al.[21] found that patients with higher ADI had lower access to post-operative care in glioblastoma patients, reflecting a disparity in access to neurosurgical care. Similarly, a study conducted by Guidry et al.[22] found that patients with high ADI patients were more likely to be lost to follow-up and have an unplanned readmission following emergent surgery for acute subdural hematoma. Barriers in accessing to care would contribute to a worse prognosis for glioblastoma patients, whether due to socioeconomic or geographic disparities. The association of transportation difficulties would potentially be associated with poor glioblastoma outcomes as well, though we adjusted for rurality, and by proxy, distance in our analysis. Adherence to Treatment High ADI is associated with difficulties in adhering to treatment regimens. An analysis conducted by Brown et al. [23] found that patients with high ADI had increased rates of no-shows in scheduled telehealth visits. A study by Hensley et al.[24] suggested that patients of low socioeconomic status had lower rates of medication adherence. Similar results were found by Wadhwania et al.[25] in children following liver transplantation, where patients with higher neighborhood-level socioeconomic deprivation had lower levels of medication adherence. Neighborhood-level socioeconomic deprivation may capture barriers in patient education, access to care, or a socioeconomic environment that may present difficulties in adhering to treatment regiments [26]. This association in the context of glioblastoma treatment should be further explored in future studies. Clinical Comorbidities High ADI has also been shown to be associated with comorbidities such as uncontrolled diabetes and cardiovascular health. Durfey et al. [27] reported that patients with high ADI had more and worse controlled chronic comorbidities. Similarly, Kurani et al. [28] identified that diabetes patients with high ADI received lower-quality care, leading lower significantly lower likelihood of acceptable HbA1C levels, blood pressure, and lipid levels. Additionally, they identified that increased ADI was also associated with smoking. Lindner et al.[29] also found that ADI was associated with lower glycemic control in patients with Type I Diabetes Mellites (DMI) and higher risk of diabetic ketoacidosis. This was also found by Rodriguez et al.[30], who identified an increased incidence of cardiovascular comorbidities in patients with high ADI. Worsened comorbidity status could result in worse tolerance of the challenging and taxing course of treatment that is the current standard of care for glioblastoma patients, leading to earlier mortality, which is supported in analysis by Carr et al.[31] Health Literacy High ADI has also been associated with lower levels of health literacy as well. Knighton et al. [32] found that low health literacy was significantly associated with a higher ADI. The relationship between low health literacy and poor health outcomes is well-established in the literature. Poor health literacy may contribute to delays in presentation, lower rates of follow-up care, and lower rates of adherence to treatment regimens [33, 34]. However, the association of health literacy and glioblastoma survival should be investigated in future studies to better understand this mechanism. Delayed Care High ADI could result in delayed initiation of care of glioblastoma, leading to delayed detection and a worse prognosis. Areas of high ADI generally have lower primary care coverage, and these socioeconomically disadvantaged patients are less likely to be insured and have regular primary care providers (PCPs); this would lead to delays in diagnosis and treatment of glioblastoma in patients with high ADI and potentially lead to worse outcomes [35]. The association of low socioeconomic and lack of oncological and neurosurgical care is also well established [20, 36, 37]. This is supported by analysis by Aguirre et al. [38] who reported that patients with high ADI had higher WHO tumor grades at presentation. Delay in cancer care for socioeconomically disadvantaged patients has also been identified in previous literature as well. In an analysis conducted by Ahmad et al. [39], patients of public insurance, and racially minoritized patients were more likely to encounter delays in initiation of treatment for anal squamous cell carcinoma. Interventions Utilization of neighborhood-level deprivation offers several avenues for systematic interventions to ameliorate disparities in survival. ADI captures patients who may have lower levels of education, income and employment, issues with housing, and disparate housing conditions such as lack of access to internet or phones. Thus, interventions may be designed to address each aspect of neighborhood deprivation captured by ADI (Supplementary Content, Table 8S). To address patients with low educational attainment and health literacy, increased outreach and public education programs should be put in place in communities with high neighborhood deprivation to allow for potentially earlier detection, improved decision-making, and better overall management of disease progression [40]. Assistance with access to primary care and preventative services, enrollment in public food assistance programs and other community programs have also been shown to be of use in improving health outcomes and may be an effective intervention for patients with high neighborhood deprivation [41, 42]. To address the patients with disadvantaged housing characteristics, implementation of medical-legal collaborations and assistance in accessing public housing unites may help address inequitable housing conditions and improve outcomes for patients as well [43, 44]. Assistance through low-cost/free internet access programs, as well as transportation assistance programs may help decrease rates of follow-up loss and improve survival in patients who struggle with low household characteristics [45] , [46-48]. Patients with high ADI may encounter delays of care for glioblastoma, contributing to a worse overall prognosis. Future studies should investigate the relationship between neighborhood socioeconomic status and time to presentation or degree of glioblastoma progression at initial evaluation to better understand this mechanism. Limitations Our study is limited by its retrospective design. New revised 2021 WHO Central Nervous System (CNS) Tumor guidelines categorize IDH mutant, grade IV astrocytomas as a separate entity from glioblastoma. All IDH-mutant tumors were still included in this cohort to understand the socioeconomic disparities that exist in high grade glioma care. However, we controlled for IDH status in our analysis. There was missing data for IDH status and MGMT methylation status in our cohort, largely due to changing patterns of practice and the diagnosis and treatment of patients prior to the adoption of 2016 WHO CNS tumor guidelines. Because of this, we can reasonably suspected that missing data patterns likely met criteria for missing-at-random (MAR), thus justifying the usage of multiple imputations even at higher proportions [49, 50]. Furthermore, two different methods of imputation, complete case analysis, and a separate analysis using only patients diagnosed after the 2016 WHO CNS guidelines were consistent, reinforcing the robustness of our findings. Our study also does not consider quality of life and functional metrics, which may be associated with survival and may be considered as future avenues of study. Though ADI is a highly validated measure of socioeconomic status in medicine, it may fail to completely capture other aspects of a patient’s socioeconomic status. Despite larger numbers, single study design may suggest possible selection bias for patients seen at a high-volume academic center. CONCLUSION In this study, we have validated ADI as a potential prognostic marker for overall survival in glioblastoma. Utilization of ADI allows more nuanced and granular understanding of socioeconomic status in patients with glioblastoma. Patients with high ADI should be considered at higher risk of poor outcomes and received additional counseling by an interdisciplinary team. Future studies should seek to further validate the effect of ADI on glioblastoma outcomes in a multi-center cohort and to identify interventions to remedy this disparity. Abbreviations ADI-Area Deprivation Index; ERS-Economic Research Service; FIPS-Federal Information Processing Standards; HRSA-Health Resources and Services Administration Declarations Funding: This project is supported in part by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R25NS079188 (DEO). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. DEO is also a Cornwall Clinical Scholar supported by the University of Alabama at Birmingham. Disclosures: The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. Previous Presentations This work was previously presented at the 2024 Congress of Neurological Surgeons in Houston, Texas and received the 2024 CNS Foundation Diversity, Equity, and Inclusion Abstract Award. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YS and DEO. All figures, and tables were prepared by YS and all authors reviewed. The first draft of the manuscript, was prepared by YS and all authors reviewed and commented on previous versions of the manuscript. All authors read and approved the final manuscript Data Availability Data is available upon reasonable request. References Grochans S, Cybulska AM, Simińska D, Korbecki J, Kojder K, Chlubek D, Baranowska-Bosiacka I (2022) Epidemiology of Glioblastoma Multiforme-Literature Review. Cancers (Basel) 14 doi:10.3390/cancers14102412 Hanif F, Muzaffar K, Perveen K, Malhi SM, Simjee Sh U (2017) Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pac J Cancer Prev 18: 3-9 doi:10.22034/APJCP.2017.18.1.3 Estevez-Ordonez D, Abdelrashid M, Coffee E, Laskay NMB, Atchley TJ, Chkheidze R, Fiveash JB, Markert JM, Lobbous M, Maveal BM, Burt Nabors L (2023) Racial and socioeconomic disparities in glioblastoma outcomes: A single-center, retrospective cohort study. Cancer 129: 3010-3022 doi:https://doi.org/10.1002/cncr.34881 Goyal A, Zreik J, Brown DA, Kerezoudis P, Habermann EB, Chaichana KL, Chen CC, Bydon M, Parney IF (2021) Disparities in access to surgery for glioblastoma multiforme at high-volume Commission on Cancer-accredited hospitals in the United States. J Neurosurg: 1-10 doi:10.3171/2021.7.Jns211307 Gorenflo MP, Shen A, Murphy ES, Cullen J, Yu JS (2023) Area-level socioeconomic status is positively correlated with glioblastoma incidence and prognosis in the United States. Front Oncol 13 doi:10.3389/fonc.2023.1110473 Wang T, Pham A, Yoo S, Attenello FJ, Jennelle R, Wagle N, Chang EL, Zada G (2020) Identifying Disparities in Care in Treating Glioblastoma: A Retrospective Cohort Study of Patients Treated at a Safety-net Versus Private Hospital Setting. World Neurosurgery 137: e213-e220 doi:https://doi.org/10.1016/j.wneu.2020.01.133 Kind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas. New England Journal of Medicine 378: 2456-2458 doi:10.1056/NEJMp1802313 Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA (2020) Indicators of Socioeconomic Status for Individuals, Census Tracts, and Counties: How Well Do Measures Align for Demographic Subgroups? J Registry Manag 47: 4-12 Krieger N, Waterman P, Chen JT, Soobader MJ, Subramanian SV, Carson R (2002) Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project. Am J Public Health 92: 1100-1102 doi:10.2105/ajph.92.7.1100 Kind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med 378: 2456-2458 doi:10.1056/NEJMp1802313 Johnson AE, Zhu J, Garrard W, Thoma FW, Mulukutla S, Kershaw KN, Magnani JW (2021) Area Deprivation Index and Cardiac Readmissions: Evaluating Risk-Prediction in an Electronic Health Record. J Am Heart Assoc 10: e020466 doi:10.1161/jaha.120.020466 Berman AN, Biery DW, Ginder C, Singh A, Baek J, Wadhera RK, Wu WY, Divakaran S, DeFilippis EM, Hainer J, Cannon CP, Plutzky J, Polk DM, Nasir K, Di Carli MF, Ash AS, Bhatt DL, Blankstein R (2021) Association of Socioeconomic Disadvantage With Long-term Mortality After Myocardial Infarction: The Mass General Brigham YOUNG-MI Registry. JAMA Cardiology 6: 880-888 doi:10.1001/jamacardio.2021.0487 Hannan EL, Wu Y, Cozzens K, Friedrich M, Chikwe J, Adams DH, Sundt TM, 3rd, Girardi L, Smith CR, Lahey SJ, Gold JP, Wechsler A (2022) The Association of Socioeconomic Factors With Outcomes for Coronary Artery Bypass Surgery. Ann Thorac Surg 114: 1318-1325 doi:10.1016/j.athoracsur.2021.10.006 Milliman (2023) Area Deprivation Index: Medicare Advantage VBID Model CY 2025. https://www.milliman.com/en/insight/area-deprivation-index-medicare-advantage-vbid-model-cy-2025. 2024 Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, Poole C, Schlesselman JJ, Egger M, Initiative S (2014) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 12: 1500-1524 doi:10.1016/j.ijsu.2014.07.014 Luningham JM, Seth G, Saini G, Bhattarai S, Awan S, Collin LJ, Swahn MH, Dai D, Gogineni K, Subhedar P, Mishra P, Aneja R (2022) Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia. JAMA Network Open 5: e2238183-e2238183 doi:10.1001/jamanetworkopen.2022.38183 Team RC (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria Baidya J, Gordon AM, Nian PP, Schwartz J, Golub IJ, Abdelgawad AA, Kang KK (2023) Social determinants of health in patients undergoing hemiarthroplasty: are they associated with medical complications, healthcare utilization, and payments for care? Arch Orthop Trauma Surg 143: 7073-7080 doi:10.1007/s00402-023-05045-z Cheng E, Soulos PR, Irwin ML, Cespedes Feliciano EM, Presley CJ, Fuchs CS, Meyerhardt JA, Gross CP (2021) Neighborhood and Individual Socioeconomic Disadvantage and Survival Among Patients With Nonmetastatic Common Cancers. JAMA Network Open 4: e2139593-e2139593 doi:10.1001/jamanetworkopen.2021.39593 Perera S, Hervey-Jumper SL, Mummaneni PV, Barthélemy EJ, Haddad AF, Marotta DA, Burke JF, Chan AK, Manley GT, Tarapore PE, Huang MC, Dhall SS, Chou D, Orrico KO, DiGiorgio AM (2022) Do social determinants of health impact access to neurosurgical care in the United States? A workforce perspective. J Neurosurg: 1-10 doi:10.3171/2021.10.Jns211330 Rivera Perla KM, Tang OY, Durfey SNM, Vivas-Buitrago T, Sherman WJ, Parney I, Uhm JH, Porter AB, Elinzano H, Toms SA, Quiñones-Hinojosa A (2022) Predicting access to postoperative treatment after glioblastoma resection: an analysis of neighborhood-level disadvantage using the Area Deprivation Index (ADI). J Neurooncol 158: 349-357 doi:10.1007/s11060-022-04020-9 Guidry BS, Tang AR, Thomas H, Thakkar R, Sermarini A, Dambrino RJIV, Yengo-Kahn A, Chambless LB, Morone P, Chotai S (2022) Loss to Follow-up and Unplanned Readmission After Emergent Surgery for Acute Subdural Hematoma. Neurosurgery 91 Brown SH, Griffith ML, Kripalani S, Horst SN (2022) Association of Health Literacy and Area Deprivation With Initiation and Completion of Telehealth Visits in Adult Medicine Clinics Across a Large Health Care System. JAMA Network Open 5: e2223571-e2223571 doi:10.1001/jamanetworkopen.2022.23571 Hensley C, Heaton PC, Kahn RS, Luder HR, Frede SM, Beck AF (2018) Poverty, transportation access, and medication nonadherence. Pediatrics 141 Wadhwani SI, Bucuvalas JC, Brokamp C, Anand R, Gupta A, Taylor S, Shemesh E, Beck AF (2020) Association Between Neighborhood-level Socioeconomic Deprivation and the Medication Level Variability Index for Children Following Liver Transplantation. Transplantation 104: 2346-2353 doi:10.1097/tp.0000000000003157 Hiscock R, Pearce J, Blakely T, Witten K (2008) Is Neighborhood Access to Health Care Provision Associated with Individual-Level Utilization and Satisfaction? Health Services Research 43: 2183-2200 doi:https://doi.org/10.1111/j.1475-6773.2008.00877.x Durfey SNM, Kind AJH, Buckingham WR, DuGoff EH, Trivedi AN (2019) Neighborhood disadvantage and chronic disease management. Health Serv Res 54 Suppl 1: 206-216 doi:10.1111/1475-6773.13092 Kurani SS, Heien HC, Sangaralingham LR, Inselman JW, Shah ND, Golden SH, McCoy RG (2022) Association of Area-Level Socioeconomic Deprivation With Hypoglycemic and Hyperglycemic Crises in US Adults With Diabetes. JAMA Netw Open 5: e2143597 doi:10.1001/jamanetworkopen.2021.43597 Lindner A, Rass V, Ianosi BA, Schiefecker AJ, Kofler M, Gaasch M, Addis A, Rhomberg P, Pfausler B, Beer R, Schmutzhard E, Thomé C, Helbok R (2021) Individualized blood pressure targets in the postoperative care of patients with intracerebral hemorrhage. J Neurosurg 135: 1656-1665 doi:10.3171/2020.9.Jns201024 Pujades-Rodriguez M, Timmis A, Stogiannis D, Rapsomaniki E, Denaxas S, Shah A, Feder G, Kivimaki M, Hemingway H (2014) Socioeconomic Deprivation and the Incidence of 12 Cardiovascular Diseases in 1.9 Million Women and Men: Implications for Risk Prediction and Prevention. PLOS ONE 9: e104671 doi:10.1371/journal.pone.0104671 Carr MT, Hochheimer CJ, Rock AK, Dincer A, Ravindra L, Zhang FL, Opalak CF, Poulos N, Sima AP, Broaddus WC (2019) Comorbid Medical Conditions as Predictors of Overall Survival in Glioblastoma Patients. Scientific Reports 9: 20018 doi:10.1038/s41598-019-56574-w Knighton AJ, Brunisholz KD, Savitz ST (2017) Detecting Risk of Low Health Literacy in Disadvantaged Populations Using Area-based Measures. EGEMS (Wash DC) 5: 7 doi:10.5334/egems.191 Coughlin SS, Vernon M, Hatzigeorgiou C, George V (2020) Health Literacy, Social Determinants of Health, and Disease Prevention and Control. J Environ Health Sci 6 Levy H, Janke A (2016) Health Literacy and Access to Care. J Health Commun 21 Suppl 1: 43-50 doi:10.1080/10810730.2015.1131776 Morenz AM, Liao JM, Au DH, Hayes SA (2024) Area-Level Socioeconomic Disadvantage and Health Care Spending: A Systematic Review. JAMA Netw Open 7: e2356121 doi:10.1001/jamanetworkopen.2023.56121 Malik NH, Montero M, Chen JJ, Sinha S, Yom SS, Chan JW (2024) Higher area deprivation index is associated with poorer local control and overall survival in non-metastatic nasopharyngeal carcinoma. Head Neck doi:10.1002/hed.27751 Sawaf T, Virgen CG, Renslo B, Farrokhian N, Yu KM, Somani SN, Bur AM, Kakarala K, Shnayder Y, Gan GN, Graboyes EM, Sykes KJ (2023) Association of Social-Ecological Factors With Delay in Time to Initiation of Postoperative Radiation Therapy: A Prospective Cohort Study. JAMA Otolaryngol Head Neck Surg 149: 477-484 doi:10.1001/jamaoto.2023.0308 Aguirre AO, Lim J, Baig AA, Ruggiero N, Siddiqi M, Recker MJ, Li V, Reynolds RM (2024) Association of area deprivation index (ADI) with demographics and postoperative outcomes in pediatric brain tumor patients. Child's Nervous System 40: 79-86 doi:10.1007/s00381-023-06098-6 Ahmad TR, Susko M, Lindquist K, Anwar M (2019) Socioeconomic disparities in timeliness of care and outcomes for anal cancer patients. Cancer Med 8: 7186-7196 doi:10.1002/cam4.2595 Stormacq C, Wosinski J, Boillat E, Van den Broucke S (2020) Effects of health literacy interventions on health-related outcomes in socioeconomically disadvantaged adults living in the community: a systematic review. JBI Evidence Synthesis 18: 1389-1469 doi:10.11124/jbisrir-d-18-00023 Keith-Jennings B, Llobrera J, Dean S (2019) Links of the Supplemental Nutrition Assistance Program With Food Insecurity, Poverty, and Health: Evidence and Potential. American Journal of Public Health 109: 1636-1640 doi:10.2105/AJPH.2019.305325 Katayama ES, Thammachack R, Woldesenbet S, Khalil M, Munir MM, Tsilimigras D, Pawlik TM (2024) The Association of Established Primary Care with Postoperative Outcomes Among Medicare Patients with Digestive Tract Cancer. Annals of Surgical Oncology 31: 8170-8178 doi:10.1245/s10434-024-16042-w Cohen E, Fullerton DF, Retkin R, Weintraub D, Tames P, Brandfield J, Sandel M (2010) Medical-Legal Partnership: Collaborating with Lawyers to Identify and Address Health Disparities. Journal of General Internal Medicine 25: 136-139 doi:10.1007/s11606-009-1239-7 Asthana S, Gago L, Garcia J, Beestrum M, Pollack T, Post L, Barnard C, Goel MS (2024) Housing Instability Screening and Referral Programs: A Scoping Review. The Joint Commission Journal on Quality and Patient Safety doi:https://doi.org/10.1016/j.jcjq.2024.08.007 Demiralp B, Speelman JS, Cook CM, Pierotti D, Steele-Adjognon M, Hudak N, Neuman MP, Juliano I, Harder S, Koenig L (2021) Incomplete Home Health Care Referral After Hospitalization Among Medicare Beneficiaries. J Am Med Dir Assoc 22: 1022-1028.e1021 doi:10.1016/j.jamda.2020.11.039 Graboyes EM, Chaiyachati KH, Sisto Gall J, Johnson W, Krishnan JA, McManus SS, Thompson L, Shulman LN, Yabroff KR (2022) Addressing Transportation Insecurity Among Patients With Cancer. JNCI: Journal of the National Cancer Institute 114: 1593-1600 doi:10.1093/jnci/djac134 O’Shea AMJ, Baum A, Haraldsson B, Shahnazi A, Augustine MR, Mulligan K, Kaboli PJ (2022) Association of Adequacy of Broadband Internet Service With Access to Primary Care in the Veterans Health Administration Before and During the COVID-19 Pandemic. JAMA Network Open 5: e2236524-e2236524 doi:10.1001/jamanetworkopen.2022.36524 Saharkhiz M, Rao T, Parker-Lue S, Borelli S, Johnson K, Cataife G (2024) Telehealth Expansion and Medicare Beneficiaries’ Care Quality and Access. JAMA Network Open 7: e2411006-e2411006 doi:10.1001/jamanetworkopen.2024.11006 Lee JH, Huber JC, Jr. (2021) Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much? Iran J Public Health 50: 1372-1380 doi:10.18502/ijph.v50i7.6626 Madley-Dowd P, Hughes R, Tilling K, Heron J (2019) The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology 110: 63-73 doi:https://doi.org/10.1016/j.jclinepi.2019.02.016 Additional Declarations No competing interests reported. Supplementary Files NSGYGBMADISupplementaryContent.docx SUPPLEMENTAL DIGITAL CONTENT LEGENDS Supplemental Digital Content 1. Table 1S. Schoenfeld’s residuals for Multivariate Cox Regression Models, Table 2S.Pooled Cox Regression for Random Forest Imputation, Table 3S. Pooled Cox Regression for MICE, Table 4S. Random Forest Imputation Diagnostics, Table 5S. MICE Diagnostics, Table 6S. Patient Demographics, Table 7S.Results of Multivariate Cox Regression Analysis, Table 8S. Domains of ADI Used in Calculation, Figure 1S. Schoenfeld’s Residuals for A.Random Forest imputed B. MICE imputed C. Complete Case D.Post-2016 WHO Guidelines cohorts, Figure 2S. Convergence Plots for A.Random Forest imputation B. MICE Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Journal of Neuro-Oncology → Version 1 posted Editorial decision: Revision requested 14 Feb, 2025 Reviews received at journal 09 Feb, 2025 Reviews received at journal 09 Feb, 2025 Reviews received at journal 08 Feb, 2025 Reviews received at journal 06 Feb, 2025 Reviewers agreed at journal 03 Feb, 2025 Reviewers agreed at journal 02 Feb, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers invited by journal 28 Jan, 2025 Editor assigned by journal 28 Jan, 2025 Submission checks completed at journal 28 Jan, 2025 First submitted to journal 27 Jan, 2025 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-5913656","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":408715455,"identity":"5a4fa25b-9a41-4f97-8780-30cb2f3f2c6a","order_by":0,"name":"Yifei Sun","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Sun","suffix":""},{"id":408715457,"identity":"5e254089-4f08-401f-b979-a1c962bb6e11","order_by":1,"name":"Dagoberto Estevez-Ordonez","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Dagoberto","middleName":"","lastName":"Estevez-Ordonez","suffix":""},{"id":408715459,"identity":"dff915be-3894-49cc-b8dd-24cd48e589e1","order_by":2,"name":"Travis J Atchley","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Travis","middleName":"J","lastName":"Atchley","suffix":""},{"id":408715460,"identity":"1cccaeb1-bc59-4971-9736-7a8300fc8194","order_by":3,"name":"Burt Nabors","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Burt","middleName":"","lastName":"Nabors","suffix":""},{"id":408715461,"identity":"8a3cdfd2-3a51-4873-bd09-b5ac621d9162","order_by":4,"name":"James Markert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACxgYIZuBnYGCDCB0gUouEZAOxWmDaJAwOEKuFedrhZx9nVNyrM76R/OzRjRoGOb4bCQRsmJ1mPHPDmWIJsxtp5sY5xxiMJQlrSTBmfNiWANSSYCad28CQuIGwlvTPjA//JUgYz0j/BtJST4SWHGPGjQ0JEgYSOWBbEgyI0FLMOONYguSMM2/KgX6RMJx55gF+LYaz0zcz9tQk8PO3p297nFNjI893nIAthg2ofAn8ykFAnrCSUTAKRsEoGPEAAGStRn+hzZxiAAAAAElFTkSuQmCC","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"","lastName":"Markert","suffix":""}],"badges":[],"createdAt":"2025-01-27 16:11:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5913656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5913656/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11060-025-05002-3","type":"published","date":"2025-04-07T16:05:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75085979,"identity":"fccb02f0-0f09-4c2d-a8cf-fcb2edf20e92","added_by":"auto","created_at":"2025-01-30 10:07:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168771,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier Survival Plot stratified by ADI\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5913656/v1/b63328e4c8936d535ec30162.png"},{"id":75085981,"identity":"762fc984-4c10-47f3-9c03-e6ad8fda67f7","added_by":"auto","created_at":"2025-01-30 10:07:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178625,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Multivariate Cox Regression Analysis for Overall Survival \u003cstrong\u003eA.\u003c/strong\u003eImputed cohort \u003cstrong\u003eB. \u003c/strong\u003eComplete Case cohort \u003cstrong\u003eC.\u003c/strong\u003e Post-2016 WHO guidelines cohort\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5913656/v1/f90b478a951a1e7fef4945e7.png"},{"id":80558582,"identity":"60a9b74d-e594-42c0-bc4e-0768a7b246ef","added_by":"auto","created_at":"2025-04-14 16:14:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5913656/v1/c5177a00-8c72-443b-a783-112b0bce4d50.pdf"},{"id":75086777,"identity":"bc99ae45-7bca-452a-a45a-dd73b3697d7e","added_by":"auto","created_at":"2025-01-30 10:15:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":506036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSUPPLEMENTAL DIGITAL CONTENT LEGENDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Digital Content 1. Table 1S\u003c/strong\u003e. Schoenfeld’s residuals for Multivariate Cox Regression Models, \u003cstrong\u003eTable 2S.\u003c/strong\u003ePooled Cox Regression for Random Forest Imputation, \u003cstrong\u003eTable 3S.\u003c/strong\u003e Pooled Cox Regression for MICE, \u003cstrong\u003eTable 4S.\u003c/strong\u003e Random Forest Imputation Diagnostics, \u003cstrong\u003eTable 5S.\u003c/strong\u003e MICE Diagnostics, \u003cstrong\u003eTable 6S.\u003c/strong\u003e Patient Demographics, \u003cstrong\u003eTable 7S.\u003c/strong\u003eResults of Multivariate Cox Regression Analysis, \u003cstrong\u003eTable 8S.\u003c/strong\u003e Domains of ADI Used in Calculation,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1S.\u003c/strong\u003e Schoenfeld’s Residuals for \u003cstrong\u003eA.\u003c/strong\u003eRandom Forest imputed \u003cstrong\u003eB.\u003c/strong\u003e MICE imputed \u003cstrong\u003eC.\u003c/strong\u003e Complete Case \u003cstrong\u003eD.\u003c/strong\u003ePost-2016 WHO Guidelines cohorts, \u003cstrong\u003eFigure 2S.\u003c/strong\u003e Convergence Plots for \u003cstrong\u003eA.\u003c/strong\u003eRandom Forest imputation \u003cstrong\u003eB.\u003c/strong\u003e MICE\u003c/p\u003e","description":"","filename":"NSGYGBMADISupplementaryContent.docx","url":"https://assets-eu.researchsquare.com/files/rs-5913656/v1/429329142e6c308b60e5e8e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association of Neighborhood-Level Deprivation with Glioblastoma Outcomes: A Single Center Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlioblastoma is the most common primary brain malignancy, comprising around half of all primary brain tumors [1]. \u0026nbsp; Despite recent progress in treatments, prognosis for patients remain poor, with median survival around 15 months [2]. Thus, it is of interest to better understand the risk factors that are associated with worsened survival. Recent literature has identified a connection between socioeconomic disparity and worsened outcomes in patients with glioblastoma [3, 4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocioeconomic factors are complex and can differentially affect outcomes. Recent studies have identified racial and income-related disparities in surgical care, chemotherapy, and radiotherapy for patients in glioblastoma [4, 5]. Other studies have also identified disparities in glioblastoma outcomes on the basis of race, insurance status, age, and educational status, as well as inequalities in timeliness of care as well [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the effect of neighborhood-level SES status on glioblastoma survival remains unclear. Much of socioeconomic literature data at the ZIP code level, which has been shown to be poor proxies of socioeconomic status [7]. Furthermore, currently commonly utilized measures of SES are often poorly generalizable and subject to regional bias [8, 9]. Area Deprivation Index \u0026nbsp;(ADI) is a tool developed by the\u0026nbsp;Health Resources and Services Administration (HRSA) that\u0026nbsp;measures neighborhood-level disadvantage by taking into account 17 measures of socioeconomic disparity in 4 main domains: education, income/employment, housing, and household characteristics\u0026nbsp;[10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Amongst the most studied area-level measures of socioeconomic disadvantage and independently validated by studies across many domains of health outcomes research, neighborhood deprivation has been used to link socioeconomic disparity to poor patient outcomes in diabetes research, cardiovascular research, and other surgical specialties [11-13]. Neighborhood deprivation has also gained increased attention due its inclusion in incentives programs and reimbursement adjustment calculations by the Center for Medicare/Medicaid (CMS) [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite its widespread adoption in other fields, there is little evidence regarding the association of neighborhood-level socioeconomic status on the overall survival (OS) of patients with glioblastoma. There is also little literature that examine the effect of these socioeconomic factors on glioblastoma outcomes in the context of clinically important molecular markers such as MGMT methylation and IDH wild-type status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The aim of this study was to assess the impact of neighborhood-level socioeconomic status on glioblastoma survival in the largest cohort to date and to better understand how socioeconomic status affects outcomes for patients with glioblastoma. To our knowledge, this is the first report to describe the association of neighborhood level socioeconomic deprivation with OS in glioblastoma. \u0026nbsp;\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe performed a single center retrospective review with approval from the Institutional Review Board (IRB- 300011516). This manuscript was written in compliance with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eParticipants and Data Collection\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively identified all adult patients, 18 years or older, with histopathological new glioblastoma diagnosis seen at our institution between January 1\u003csup\u003est\u003c/sup\u003e, 2008 and December 31\u003csup\u003est\u003c/sup\u003e, 2023. In total, 1493 patients met inclusion criteria. The electronic medical record (EMR) was reviewed for variables on patient demographics, socioeconomic background, geography, and treatment characteristics. Due to the retrospective nature of this study, patient consent was not needed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDefining Variables\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eVariables were defined \u003cem\u003ea priori\u0026nbsp;\u003c/em\u003ewith advice from the senior authors. Study variables included were age at diagnosis, race, gender, marital status, extent of surgical resection, IDH status MGMT methylation status, history of chemotherapy and history of radiotherapy. Patient addresses were extracted from the EMR and were geocoded using ArcGIS software. Federal Information Processing System (FIPS) codes were extracted and correlated\u0026nbsp;to its individual Area Deprivation Index (ADI), with higher ADI relating to more socioeconomic deprivation. ADI was retrieved from the Neighborhood Atlas dataset produced by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health.\u003csup\u003e4\u003c/sup\u003e High ADI was designated patients in the top national quartile according to previous literature [16]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable analysis including\u0026nbsp;Student\u0026rsquo;s t-test, one-way analysis of variance (ANOVA), Chi squared test, and Wilcoxon rank sum test were used to compare incidences of chemotherapy, radiotherapy extent of resection, and other demographic variables between patients with high and low ADI. Kaplan Meier curves were used to investigate differences in survival between groups of interest, and log rank tests were used to assess differences in survival.\u003c/p\u003e\n\u003cp\u003eSensitivity analysis was conducted by performing multiple methods of imputation for missing data as well as replicating the model in patients diagnosed and treated after the WHO guidelines in 2016. All statistical analyses were performed using R studio (version 4.3.1) [17]. Further details on statistical analysis can be found in the supplementary content (Supplementary Content, Table 1S \u0026ndash; 5S, Figure 1S-2S).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn total, 1464 patients met inclusion criteria. The mean age at diagnosis was 60 \u0026plusmn; 14 years. Of these patients, 155 (11%) were African American (AA) and 816 (56%) were male. At time of censoring, 249 (17%) were alive. Of these patients, 671(46%) received complete resection, 1235 (84%) received radiotherapy and 1219 (83%) received chemotherapy. The median ADI was 66 (IQR 46-84). Ninety-two (6.3%) of the patients had IDH mutations and 344 (23%) of the patients were of MGMT-methylated status. The median OS (mOS) of the cohort was 13.7 months (IQR 12.99-14.16). Further details on patient demographics and characteristics can be found in the Table 1 and supplement (Supplementary Content, Table 6S).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Patient Characteristics and Demographics\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh ADI *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN = 912\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN = 552\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e124 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e87 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e142 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e89 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e248 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e144 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e281 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e164 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e117 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e68 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e398 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e250 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e514 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e302 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e60 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e95 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e62 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e23 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e790 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e434 (79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e682 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e351 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Indigent/Self Pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e28 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e23 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e51 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e60 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Medicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e365 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e225 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e468 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e244 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eMedian Household Income (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e55,681 (47,276, 68,608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e42,116 (37,433, 48,371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eVital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Alive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e166 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e83 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Deceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e746 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e469 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eIDH status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; IDH-Mut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e55 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; IDH-WT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e566 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e324 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e291 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e191 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eMGMT Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Methylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e211 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e133 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e340 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e236 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unmethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e361 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e183 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eExtent of Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e247 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e183 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Complete resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e441 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e230 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Partial Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e224 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e139 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eReceived Radiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e782 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e453 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eReceived Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e770 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e449 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e52 (36, 64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e87 (82, 92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eRUCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e753 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e309 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Micropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e111 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e114 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e8 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e43 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Small Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e40 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e86 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eDistance from Institution (miles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e426 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e191 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 60-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e362 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e336 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e124 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e25 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e n (%); Median (IQR)\u003cem\u003e\u003csup\u003e\u0026nbsp;2\u003c/sup\u003e\u003c/em\u003e Pearson\u0026rsquo;s Chi-squared test; Wilcoxon rank sum test. * ADI-Area Deprivation Index, High ADI-ADI\u0026gt;75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnivariable Comparison Analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients in the highest quartile of ADI were more likely to be AA (17% vs 6.6%, p\u0026lt;.001), higher rates of Medicaid/Medicare (52% vs 45.6%, p\u0026lt;.001), more likely to be MGMT unmethylated (33% vs 40%, p=.037), more likely to live greater than 60 miles from the institution, and more likely to live in a rural region (7.8% vs 0.9% p\u0026lt;.001). These patients were also less likely to undergo complete resection compared to those with lower ADI (42% vs 48%, p=.021). The results of this analysis can be found in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnivariable Survival Comparison Analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn univariable survival comparison, AA patients had longer mOS (15.2 months, 95%CI 10.6\u0026ndash;18.0) compared to Caucasian patients (13.5 months 95%CI 12.6 \u0026ndash; 14.5). Patients with high ADI had lower mOS (11.67 months, 95% CI 13.8\u0026ndash;15.8) compared to those with low ADI (14.83 months, 95%CI 10.2\u0026ndash;13.4). Patients who were privately insured had longer mOS (15.4 months, 95%CI 14.5 \u0026ndash; 16.5) compared to those who were publicly insured (11.5 months, 95%CI 10.0\u0026ndash;12.9) and those who were uninsured (12.4 months, 95%CI 7.3\u0026ndash;21.2). Patients who received chemotherapy had longer mOS (15.4 months, 95%CI 14.6 \u0026ndash; 16.1) than those who did not (3.7 months, 95%CI 3.1\u0026ndash;4.7). Patients who received radiotherapy had longer mOS (15.6 months, 95%CI 14.5\u0026ndash;16.5) compared to those who did not (3.2 months, 95%CI 3.0\u0026ndash;4.0). The results of this analysis can be found in Table 2. Kaplan Meier survival curves stratifying for ADI are presented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Univariable Survival Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 18px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e22.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e32.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e17.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e20.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e16.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e9.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eIncome Class*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eADI**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eLow ADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHigh ADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eIDH status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eIDH-Mut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e33.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e41.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eIDH-WT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMGMT Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e18.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e23.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eUnmethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e12.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e12.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eExtent of Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eBiopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eComplete resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e17.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e16.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePartial Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e16.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRadiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eInsurance Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e11.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSelf-Pay/Indigent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e12.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e21.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRUCA***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMicropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSmall Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e11.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e17.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003elog-rank test; * Income categories determined according to Pew Research Reports 2022;**ADI- Area Deprivation Index;***RUCA-Rural Urban Communicating Area\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMultivariate cox proportional hazards\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing multivariate cox proportional hazards analysis, we identified that patients with high ADI had worse survival (HR 1.26, 95%CI 1.11-1.43, p\u0026lt;.001) compared to patients with low ADI. These findings were replicated in both types of imputation, complete case analysis, and in subgroup analysis of patients diagnosed after the 2016 WHO CNS Guidelines. (Supplementary Content, Table 7S) When compared to patients who were in the youngest age group at time of diagnosis (\u0026lt;45 years), all other age groups had worse survival. Patients who underwent gross total resection had better survival (HR 0.65, 95%CI 0.56\u0026ndash;0.74, p \u0026lt;.001) compared to patients who underwent partial resections or biopsy. Patients with public insurance had better survival (HR 0.81, 95%CI 0.70-0.93, p=.004) compared to patients with private insurance. Patients with IDH mutation had better survival (HR 0.65, 95%CI 0.56\u0026ndash;0.74, p\u0026lt;.001) compared to those with wild-type IDH. Patients with methylated MGMT had better survival (HR 0.53, 95%CI 0.47-0.61, p\u0026lt;.001) when compared to patients with unmethylated MGMT. In all three multivariate models, AA race was not found to be associated with worsened survival. Chemotherapy was found to be associated with improved survival in only the MICE model (HR 0.74, 95%CI 0.57\u0026ndash;0.95, p=.02). Forest plots for this analysis can be found in Figure 2.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHere we report the first analysis of the effects of neighborhood level socioeconomic status on glioblastoma survival after adjusting for other socioeconomic, clinical, and molecular factors. Our findings strongly suggest that neighborhood deprivation independently predicts survival in patients with glioblastoma and is a potential prognostic marker for patients with glioblastoma.\u003c/p\u003e\n\u003cp\u003eThe role of socioeconomic status in cancer is well known, with many studies highlighting the effect of socioeconomic status on survival of cancer patients. Studies have suggested that income, insurance status, and various other commonly used measures are imperfect proxies of socioeconomic status, varying by region and race. Studies on the association of race and other socioeconomic markers are conflicted as well. Commonly utilized measures of socioeconomic disadvantage in literature can be imperfect proxies for SES [8, 9]. Additionally, social determinants of health play complex roles in determining the health outcomes of patients, and findings may be difficult to generalize findings on a national level. Thus, the utilization of ADI represents an advancement in understanding the prognostic effect of patient SES on glioblastoma survival by utilizing a nationally standardized, multifactorial measure of patient socioeconomic disadvantage.\u003c/p\u003e\n\u003cp\u003eHere, we report the effect of ADI, an accurate and well-validated measure of neighborhood-level socioeconomic status, with survival in patients with glioblastoma in the largest single institution cohort to date. ADI has emerged as a preferred metric for capturing socioeconomic status in many fields of medicine [12, 18, 19]. However, it has been under-utilized in neuro-oncology and neurosurgery. We observed that high ADI is independently associated with worse survival compared to patients with lower ADI (HR 1.25, 95% CI 1.09- 1.43, p\u0026lt;.001) after adjusting for age, race, income, insurance status, IDH status, MGMT methylation, rurality, extent of resection, history of chemotherapy, and history of radiotherapy. There are many potential mechanisms for this observation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAccess to Care\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients with high ADI reflect high degrees of socioeconomic disparity, which has been associated with decreased neurosurgical coverage [20]. This is reinforced in our findings, in which we observed that patients with high ADI were less likely to have undergone gross total resection compared to patients with high ADI (Table 2). Analysis by Perla et al.[21] found that patients with higher ADI had lower access to post-operative care in glioblastoma patients, reflecting a disparity in access to neurosurgical care. Similarly, a study conducted by Guidry et al.[22] found that patients with high ADI patients were more likely to be lost to follow-up and have an unplanned readmission following emergent surgery for acute subdural hematoma. Barriers in accessing to care would contribute to a worse prognosis for glioblastoma patients, whether due to socioeconomic or geographic disparities. The association of transportation difficulties would potentially be associated with poor glioblastoma outcomes as well, though we adjusted for rurality, and by proxy, distance in our analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAdherence to Treatment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh ADI is associated with difficulties in adhering to treatment regimens. An analysis conducted by Brown et al. [23] found that patients with high ADI had increased rates of no-shows in scheduled telehealth visits. A study by Hensley et al.[24] suggested that patients of low socioeconomic status had lower rates of medication adherence. Similar results were found by Wadhwania et al.[25] in children following liver transplantation, where patients with higher neighborhood-level socioeconomic deprivation had lower levels of medication adherence. Neighborhood-level socioeconomic deprivation may capture barriers in patient education, access to care, or a socioeconomic environment that may present difficulties in adhering to treatment regiments [26]. This association in the context of glioblastoma treatment should be further explored in future studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eClinical Comorbidities\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh ADI has also been shown to be associated with comorbidities such as uncontrolled diabetes and cardiovascular health. Durfey et al. [27] reported that patients with high ADI had more and worse controlled chronic comorbidities. Similarly, Kurani et al. [28] identified that diabetes patients with high ADI received lower-quality care, leading lower significantly lower likelihood of acceptable HbA1C levels, blood pressure, and lipid levels. Additionally, they identified that increased ADI was also associated with smoking. Lindner et al.[29] also found that ADI was associated with lower glycemic control in patients with Type I Diabetes Mellites (DMI) and higher risk of diabetic ketoacidosis. This was also found by Rodriguez et al.[30], who identified an increased incidence of cardiovascular comorbidities in patients with high ADI. Worsened comorbidity status could result in worse tolerance of the challenging and taxing course of treatment that is the current standard of care for glioblastoma patients, leading to earlier mortality, which is supported in analysis by Carr et al.[31]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHealth Literacy \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh ADI has also been associated with lower levels of health literacy as well. Knighton et al. [32] found that low health literacy was significantly associated with a higher ADI. The relationship between low health literacy and poor health outcomes is well-established in the literature. Poor health literacy may contribute to delays in presentation, lower rates of follow-up care, and lower rates of adherence to treatment regimens [33, 34]. However, the association of health literacy and glioblastoma survival should be investigated in future studies to better understand this mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDelayed Care\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh ADI could result in delayed initiation of care of glioblastoma, leading to delayed detection and a worse prognosis. Areas of high ADI generally have lower primary care coverage, and these socioeconomically disadvantaged patients are less likely to be insured and have regular primary care providers (PCPs); this would lead to delays in diagnosis and treatment of glioblastoma in patients with high ADI and potentially lead to worse outcomes [35]. The association of low socioeconomic and lack of oncological and neurosurgical care is also well established [20, 36, 37]. This is supported by analysis by Aguirre et al. [38] who reported that patients with high ADI had higher WHO tumor grades at presentation. Delay in cancer care for socioeconomically disadvantaged patients has also been identified in previous literature as well. In an analysis conducted by Ahmad et al. [39], patients of public insurance, and racially minoritized patients were more likely to encounter delays in initiation of treatment for anal squamous cell carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUtilization of neighborhood-level deprivation offers several avenues for systematic interventions to ameliorate disparities in survival. ADI captures patients who may have lower levels of education, income and employment, issues with housing, and disparate housing conditions such as lack of access to internet or phones. Thus, interventions may be designed to address each aspect of neighborhood deprivation captured by ADI (Supplementary Content, Table 8S).\u003c/p\u003e\n\u003cp\u003eTo address patients with low educational attainment and health literacy, increased outreach and public education programs should be put in place in communities with high neighborhood deprivation to allow for potentially earlier detection, improved decision-making, and better overall management of disease progression [40]. Assistance with access to primary care and preventative services, enrollment in public food assistance programs and other community programs have also been shown to be of use in improving health outcomes and may be an effective intervention for patients with high neighborhood deprivation [41, 42]. To address the patients with disadvantaged housing characteristics, implementation of medical-legal collaborations and assistance in accessing public housing unites may help address inequitable housing conditions and improve outcomes for patients as well [43, 44]. Assistance through low-cost/free internet access programs, as well as transportation assistance programs may help decrease rates of follow-up loss and improve survival in patients who struggle with low household characteristics [45]\u003csup\u003e,\u003c/sup\u003e[46-48].\u003c/p\u003e\n\u003cp\u003ePatients with high ADI may encounter delays of care for glioblastoma, contributing to a worse overall prognosis. Future studies should investigate the relationship between neighborhood socioeconomic status and time to presentation or degree of glioblastoma progression at initial evaluation to better understand this mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur study is limited by its retrospective design. New revised 2021 WHO Central Nervous System (CNS) Tumor guidelines categorize IDH mutant, grade IV astrocytomas as a separate entity from glioblastoma. All IDH-mutant tumors were still included in this cohort to understand the socioeconomic disparities that exist in high grade glioma care. However, we controlled for IDH status in our analysis. There was missing data for IDH status and MGMT methylation status in our cohort, largely due to changing patterns of practice and the diagnosis and treatment of patients prior to the adoption of 2016 WHO CNS tumor guidelines. Because of this, we can reasonably suspected that missing data patterns likely met criteria for missing-at-random (MAR), thus justifying the usage of multiple imputations even at higher proportions [49, 50]. Furthermore, two different methods of imputation, complete case analysis, and a separate analysis using only patients diagnosed after the 2016 WHO CNS guidelines were consistent, reinforcing the robustness of our findings. Our study also does not consider quality of life and functional metrics, which may be associated with survival and may be considered as future avenues of study. Though ADI is a highly validated measure of socioeconomic status in medicine, it may fail to completely capture other aspects of a patient\u0026rsquo;s socioeconomic status. Despite larger numbers, single study design may suggest possible selection bias for patients seen at a high-volume academic center.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we have validated ADI as a potential prognostic marker for overall survival in glioblastoma. Utilization of ADI allows more nuanced and granular understanding of socioeconomic status in patients with glioblastoma. Patients with high ADI should be considered at higher risk of poor outcomes and received additional counseling by an interdisciplinary team. Future studies should seek to further validate the effect of ADI on glioblastoma outcomes in a multi-center cohort and to identify interventions to remedy this disparity.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADI-Area Deprivation Index; ERS-Economic Research Service; \u0026nbsp;FIPS-Federal Information Processing Standards; HRSA-Health Resources and Services Administration\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is supported in part by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R25NS079188 (DEO). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. DEO is also a Cornwall Clinical Scholar supported by the University of Alabama at Birmingham.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious Presentations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was previously presented at the 2024 Congress of Neurological Surgeons in Houston, Texas and received the 2024 CNS Foundation Diversity, Equity, and Inclusion Abstract Award.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YS and DEO. All figures, and tables were prepared by YS and all authors reviewed. The first draft of the manuscript, was prepared by YS and all authors reviewed and commented on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrochans S, Cybulska AM, Simińska D, Korbecki J, Kojder K, Chlubek D, Baranowska-Bosiacka I (2022) Epidemiology of Glioblastoma Multiforme-Literature Review. Cancers (Basel) 14 doi:10.3390/cancers14102412\u003c/li\u003e\n\u003cli\u003eHanif F, Muzaffar K, Perveen K, Malhi SM, Simjee Sh U (2017) Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pac J Cancer Prev 18: 3-9 doi:10.22034/APJCP.2017.18.1.3\u003c/li\u003e\n\u003cli\u003eEstevez-Ordonez D, Abdelrashid M, Coffee E, Laskay NMB, Atchley TJ, Chkheidze R, Fiveash JB, Markert JM, Lobbous M, Maveal BM, Burt Nabors L (2023) Racial and socioeconomic disparities in glioblastoma outcomes: A single-center, retrospective cohort study. Cancer 129: 3010-3022 doi:https://doi.org/10.1002/cncr.34881\u003c/li\u003e\n\u003cli\u003eGoyal A, Zreik J, Brown DA, Kerezoudis P, Habermann EB, Chaichana KL, Chen CC, Bydon M, Parney IF (2021) Disparities in access to surgery for glioblastoma multiforme at high-volume Commission on Cancer-accredited hospitals in the United States. J Neurosurg: 1-10 doi:10.3171/2021.7.Jns211307\u003c/li\u003e\n\u003cli\u003eGorenflo MP, Shen A, Murphy ES, Cullen J, Yu JS (2023) Area-level socioeconomic status is positively correlated with glioblastoma incidence and prognosis in the United States. Front Oncol 13 doi:10.3389/fonc.2023.1110473\u003c/li\u003e\n\u003cli\u003eWang T, Pham A, Yoo S, Attenello FJ, Jennelle R, Wagle N, Chang EL, Zada G (2020) Identifying Disparities in Care in Treating Glioblastoma: A Retrospective Cohort Study of Patients Treated at a Safety-net Versus Private Hospital Setting. World Neurosurgery 137: e213-e220 doi:https://doi.org/10.1016/j.wneu.2020.01.133\u003c/li\u003e\n\u003cli\u003eKind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible \u0026mdash; The Neighborhood Atlas. New England Journal of Medicine 378: 2456-2458 doi:10.1056/NEJMp1802313\u003c/li\u003e\n\u003cli\u003eMoss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA (2020) Indicators of Socioeconomic Status for Individuals, Census Tracts, and Counties: How Well Do Measures Align for Demographic Subgroups? J Registry Manag 47: 4-12 \u003c/li\u003e\n\u003cli\u003eKrieger N, Waterman P, Chen JT, Soobader MJ, Subramanian SV, Carson R (2002) Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project. Am J Public Health 92: 1100-1102 doi:10.2105/ajph.92.7.1100\u003c/li\u003e\n\u003cli\u003eKind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med 378: 2456-2458 doi:10.1056/NEJMp1802313\u003c/li\u003e\n\u003cli\u003eJohnson AE, Zhu J, Garrard W, Thoma FW, Mulukutla S, Kershaw KN, Magnani JW (2021) Area Deprivation Index and Cardiac Readmissions: Evaluating Risk-Prediction in an Electronic Health Record. J Am Heart Assoc 10: e020466 doi:10.1161/jaha.120.020466\u003c/li\u003e\n\u003cli\u003eBerman AN, Biery DW, Ginder C, Singh A, Baek J, Wadhera RK, Wu WY, Divakaran S, DeFilippis EM, Hainer J, Cannon CP, Plutzky J, Polk DM, Nasir K, Di Carli MF, Ash AS, Bhatt DL, Blankstein R (2021) Association of Socioeconomic Disadvantage With Long-term Mortality After Myocardial Infarction: The Mass General Brigham YOUNG-MI Registry. JAMA Cardiology 6: 880-888 doi:10.1001/jamacardio.2021.0487\u003c/li\u003e\n\u003cli\u003eHannan EL, Wu Y, Cozzens K, Friedrich M, Chikwe J, Adams DH, Sundt TM, 3rd, Girardi L, Smith CR, Lahey SJ, Gold JP, Wechsler A (2022) The Association of Socioeconomic Factors With Outcomes for Coronary Artery Bypass Surgery. Ann Thorac Surg 114: 1318-1325 doi:10.1016/j.athoracsur.2021.10.006\u003c/li\u003e\n\u003cli\u003eMilliman (2023) Area Deprivation Index: Medicare Advantage VBID Model CY 2025. https://www.milliman.com/en/insight/area-deprivation-index-medicare-advantage-vbid-model-cy-2025. 2024\u003c/li\u003e\n\u003cli\u003eVandenbroucke JP, von Elm E, Altman DG, G\u0026oslash;tzsche PC, Mulrow CD, Pocock SJ, Poole C, Schlesselman JJ, Egger M, Initiative S (2014) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 12: 1500-1524 doi:10.1016/j.ijsu.2014.07.014\u003c/li\u003e\n\u003cli\u003eLuningham JM, Seth G, Saini G, Bhattarai S, Awan S, Collin LJ, Swahn MH, Dai D, Gogineni K, Subhedar P, Mishra P, Aneja R (2022) Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia. JAMA Network Open 5: e2238183-e2238183 doi:10.1001/jamanetworkopen.2022.38183\u003c/li\u003e\n\u003cli\u003eTeam RC (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria\u003c/li\u003e\n\u003cli\u003eBaidya J, Gordon AM, Nian PP, Schwartz J, Golub IJ, Abdelgawad AA, Kang KK (2023) Social determinants of health in patients undergoing hemiarthroplasty: are they associated with medical complications, healthcare utilization, and payments for care? Arch Orthop Trauma Surg 143: 7073-7080 doi:10.1007/s00402-023-05045-z\u003c/li\u003e\n\u003cli\u003eCheng E, Soulos PR, Irwin ML, Cespedes Feliciano EM, Presley CJ, Fuchs CS, Meyerhardt JA, Gross CP (2021) Neighborhood and Individual Socioeconomic Disadvantage and Survival Among Patients With Nonmetastatic Common Cancers. JAMA Network Open 4: e2139593-e2139593 doi:10.1001/jamanetworkopen.2021.39593\u003c/li\u003e\n\u003cli\u003ePerera S, Hervey-Jumper SL, Mummaneni PV, Barth\u0026eacute;lemy EJ, Haddad AF, Marotta DA, Burke JF, Chan AK, Manley GT, Tarapore PE, Huang MC, Dhall SS, Chou D, Orrico KO, DiGiorgio AM (2022) Do social determinants of health impact access to neurosurgical care in the United States? A workforce perspective. J Neurosurg: 1-10 doi:10.3171/2021.10.Jns211330\u003c/li\u003e\n\u003cli\u003eRivera Perla KM, Tang OY, Durfey SNM, Vivas-Buitrago T, Sherman WJ, Parney I, Uhm JH, Porter AB, Elinzano H, Toms SA, Qui\u0026ntilde;ones-Hinojosa A (2022) Predicting access to postoperative treatment after glioblastoma resection: an analysis of neighborhood-level disadvantage using the Area Deprivation Index (ADI). J Neurooncol 158: 349-357 doi:10.1007/s11060-022-04020-9\u003c/li\u003e\n\u003cli\u003eGuidry BS, Tang AR, Thomas H, Thakkar R, Sermarini A, Dambrino RJIV, Yengo-Kahn A, Chambless LB, Morone P, Chotai S (2022) Loss to Follow-up and Unplanned Readmission After Emergent Surgery for Acute Subdural Hematoma. Neurosurgery 91 \u003c/li\u003e\n\u003cli\u003eBrown SH, Griffith ML, Kripalani S, Horst SN (2022) Association of Health Literacy and Area Deprivation With Initiation and Completion of Telehealth Visits in Adult Medicine Clinics Across a Large Health Care System. JAMA Network Open 5: e2223571-e2223571 doi:10.1001/jamanetworkopen.2022.23571\u003c/li\u003e\n\u003cli\u003eHensley C, Heaton PC, Kahn RS, Luder HR, Frede SM, Beck AF (2018) Poverty, transportation access, and medication nonadherence. Pediatrics 141 \u003c/li\u003e\n\u003cli\u003eWadhwani SI, Bucuvalas JC, Brokamp C, Anand R, Gupta A, Taylor S, Shemesh E, Beck AF (2020) Association Between Neighborhood-level Socioeconomic Deprivation and the Medication Level Variability Index for Children Following Liver Transplantation. Transplantation 104: 2346-2353 doi:10.1097/tp.0000000000003157\u003c/li\u003e\n\u003cli\u003eHiscock R, Pearce J, Blakely T, Witten K (2008) Is Neighborhood Access to Health Care Provision Associated with Individual-Level Utilization and Satisfaction? Health Services Research 43: 2183-2200 doi:https://doi.org/10.1111/j.1475-6773.2008.00877.x\u003c/li\u003e\n\u003cli\u003eDurfey SNM, Kind AJH, Buckingham WR, DuGoff EH, Trivedi AN (2019) Neighborhood disadvantage and chronic disease management. Health Serv Res 54 Suppl 1: 206-216 doi:10.1111/1475-6773.13092\u003c/li\u003e\n\u003cli\u003eKurani SS, Heien HC, Sangaralingham LR, Inselman JW, Shah ND, Golden SH, McCoy RG (2022) Association of Area-Level Socioeconomic Deprivation With Hypoglycemic and Hyperglycemic Crises in US Adults With Diabetes. JAMA Netw Open 5: e2143597 doi:10.1001/jamanetworkopen.2021.43597\u003c/li\u003e\n\u003cli\u003eLindner A, Rass V, Ianosi BA, Schiefecker AJ, Kofler M, Gaasch M, Addis A, Rhomberg P, Pfausler B, Beer R, Schmutzhard E, Thom\u0026eacute; C, Helbok R (2021) Individualized blood pressure targets in the postoperative care of patients with intracerebral hemorrhage. J Neurosurg 135: 1656-1665 doi:10.3171/2020.9.Jns201024\u003c/li\u003e\n\u003cli\u003ePujades-Rodriguez M, Timmis A, Stogiannis D, Rapsomaniki E, Denaxas S, Shah A, Feder G, Kivimaki M, Hemingway H (2014) Socioeconomic Deprivation and the Incidence of 12 Cardiovascular Diseases in 1.9 Million Women and Men: Implications for Risk Prediction and Prevention. PLOS ONE 9: e104671 doi:10.1371/journal.pone.0104671\u003c/li\u003e\n\u003cli\u003eCarr MT, Hochheimer CJ, Rock AK, Dincer A, Ravindra L, Zhang FL, Opalak CF, Poulos N, Sima AP, Broaddus WC (2019) Comorbid Medical Conditions as Predictors of Overall Survival in Glioblastoma Patients. Scientific Reports 9: 20018 doi:10.1038/s41598-019-56574-w\u003c/li\u003e\n\u003cli\u003eKnighton AJ, Brunisholz KD, Savitz ST (2017) Detecting Risk of Low Health Literacy in Disadvantaged Populations Using Area-based Measures. EGEMS (Wash DC) 5: 7 doi:10.5334/egems.191\u003c/li\u003e\n\u003cli\u003eCoughlin SS, Vernon M, Hatzigeorgiou C, George V (2020) Health Literacy, Social Determinants of Health, and Disease Prevention and Control. J Environ Health Sci 6 \u003c/li\u003e\n\u003cli\u003eLevy H, Janke A (2016) Health Literacy and Access to Care. J Health Commun 21 Suppl 1: 43-50 doi:10.1080/10810730.2015.1131776\u003c/li\u003e\n\u003cli\u003eMorenz AM, Liao JM, Au DH, Hayes SA (2024) Area-Level Socioeconomic Disadvantage and Health Care Spending: A Systematic Review. JAMA Netw Open 7: e2356121 doi:10.1001/jamanetworkopen.2023.56121\u003c/li\u003e\n\u003cli\u003eMalik NH, Montero M, Chen JJ, Sinha S, Yom SS, Chan JW (2024) Higher area deprivation index is associated with poorer local control and overall survival in non-metastatic nasopharyngeal carcinoma. Head Neck doi:10.1002/hed.27751\u003c/li\u003e\n\u003cli\u003eSawaf T, Virgen CG, Renslo B, Farrokhian N, Yu KM, Somani SN, Bur AM, Kakarala K, Shnayder Y, Gan GN, Graboyes EM, Sykes KJ (2023) Association of Social-Ecological Factors With Delay in Time to Initiation of Postoperative Radiation Therapy: A Prospective Cohort Study. JAMA Otolaryngol Head Neck Surg 149: 477-484 doi:10.1001/jamaoto.2023.0308\u003c/li\u003e\n\u003cli\u003eAguirre AO, Lim J, Baig AA, Ruggiero N, Siddiqi M, Recker MJ, Li V, Reynolds RM (2024) Association of area deprivation index (ADI) with demographics and postoperative outcomes in pediatric brain tumor patients. Child\u0026apos;s Nervous System 40: 79-86 doi:10.1007/s00381-023-06098-6\u003c/li\u003e\n\u003cli\u003eAhmad TR, Susko M, Lindquist K, Anwar M (2019) Socioeconomic disparities in timeliness of care and outcomes for anal cancer patients. Cancer Med 8: 7186-7196 doi:10.1002/cam4.2595\u003c/li\u003e\n\u003cli\u003eStormacq C, Wosinski J, Boillat E, Van den Broucke S (2020) Effects of health literacy interventions on health-related outcomes in socioeconomically disadvantaged adults living in the community: a systematic review. JBI Evidence Synthesis 18: 1389-1469 doi:10.11124/jbisrir-d-18-00023\u003c/li\u003e\n\u003cli\u003eKeith-Jennings B, Llobrera J, Dean S (2019) Links of the Supplemental Nutrition Assistance Program With Food Insecurity, Poverty, and Health: Evidence and Potential. American Journal of Public Health 109: 1636-1640 doi:10.2105/AJPH.2019.305325\u003c/li\u003e\n\u003cli\u003eKatayama ES, Thammachack R, Woldesenbet S, Khalil M, Munir MM, Tsilimigras D, Pawlik TM (2024) The Association of Established Primary Care with Postoperative Outcomes Among Medicare Patients with Digestive Tract Cancer. Annals of Surgical Oncology 31: 8170-8178 doi:10.1245/s10434-024-16042-w\u003c/li\u003e\n\u003cli\u003eCohen E, Fullerton DF, Retkin R, Weintraub D, Tames P, Brandfield J, Sandel M (2010) Medical-Legal Partnership: Collaborating with Lawyers to Identify and Address Health Disparities. Journal of General Internal Medicine 25: 136-139 doi:10.1007/s11606-009-1239-7\u003c/li\u003e\n\u003cli\u003eAsthana S, Gago L, Garcia J, Beestrum M, Pollack T, Post L, Barnard C, Goel MS (2024) Housing Instability Screening and Referral Programs: A Scoping Review. The Joint Commission Journal on Quality and Patient Safety doi:https://doi.org/10.1016/j.jcjq.2024.08.007\u003c/li\u003e\n\u003cli\u003eDemiralp B, Speelman JS, Cook CM, Pierotti D, Steele-Adjognon M, Hudak N, Neuman MP, Juliano I, Harder S, Koenig L (2021) Incomplete Home Health Care Referral After Hospitalization Among Medicare Beneficiaries. J Am Med Dir Assoc 22: 1022-1028.e1021 doi:10.1016/j.jamda.2020.11.039\u003c/li\u003e\n\u003cli\u003eGraboyes EM, Chaiyachati KH, Sisto Gall J, Johnson W, Krishnan JA, McManus SS, Thompson L, Shulman LN, Yabroff KR (2022) Addressing Transportation Insecurity Among Patients With Cancer. JNCI: Journal of the National Cancer Institute 114: 1593-1600 doi:10.1093/jnci/djac134\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Shea AMJ, Baum A, Haraldsson B, Shahnazi A, Augustine MR, Mulligan K, Kaboli PJ (2022) Association of Adequacy of Broadband Internet Service With Access to Primary Care in the Veterans Health Administration Before and During the COVID-19 Pandemic. JAMA Network Open 5: e2236524-e2236524 doi:10.1001/jamanetworkopen.2022.36524\u003c/li\u003e\n\u003cli\u003eSaharkhiz M, Rao T, Parker-Lue S, Borelli S, Johnson K, Cataife G (2024) Telehealth Expansion and Medicare Beneficiaries\u0026rsquo; Care Quality and Access. JAMA Network Open 7: e2411006-e2411006 doi:10.1001/jamanetworkopen.2024.11006\u003c/li\u003e\n\u003cli\u003eLee JH, Huber JC, Jr. (2021) Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much? Iran J Public Health 50: 1372-1380 doi:10.18502/ijph.v50i7.6626\u003c/li\u003e\n\u003cli\u003eMadley-Dowd P, Hughes R, Tilling K, Heron J (2019) The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology 110: 63-73 doi:https://doi.org/10.1016/j.jclinepi.2019.02.016\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Area deprivation, Glioblastoma, neighborhood socioeconomic status, survival","lastPublishedDoi":"10.21203/rs.3.rs-5913656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5913656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlioblastoma is the most common primary brain malignancy. Though literature has suggested the association of glioblastoma outcomes and socioeconomic status, there is limited evidence regarding the association of neighborhood-level socioeconomic deprivation on glioblastoma outcomes. The aim of this study was to assess the impact of neighborhood-level socioeconomic deprivation on glioblastoma survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively reviewed all adult glioblastoma patients seen at a single institution from 2008 to 2023. Neighborhood deprivation was assessed via Area Deprivation Index (ADI), with higher ADI indicating greater neighborhood socioeconomic deprivation. Log-rank tests and multivariate cox regression was used to assess the effect of ADI and other socioeconomic variables while controlling for \u003cem\u003ea priori\u003c/em\u003e selected clinical variables with known relevance to survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 1464 patients met inclusion criteria. The average age at diagnosis was 60 ± 14 years with a median overall survival of 13.8 months (IQR 13-14.8). The median ADI of the cohort was 66(IQR 46-84). Patients with high ADI had worse overall survival compared to patients with low ADI (11.7 vs 14.8 months, p=.001). In the multivariable model, patients with high ADI had worse overall survival (HR 1.25, 95%CI 1.09-1.43). To account for changes in WHO guidelines, we implemented the model on patients diagnosed between 2017-2023 and findings were consistent (HR 1.26,95%CI 1.01-1.56).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe report the first study demonstrating glioblastoma patients with higher neighborhood deprivation have worse survival after controlling for other socioeconomic and biomolecular markers. Neighborhood socioeconomic status may be a prognostic marker for glioblastoma survival.\u003c/p\u003e","manuscriptTitle":"The Association of Neighborhood-Level Deprivation with Glioblastoma Outcomes: A Single Center Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 10:07:05","doi":"10.21203/rs.3.rs-5913656/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-15T01:45:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-10T04:43:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-09T18:35:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-08T20:31:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-06T15:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46255419807810608489311185981881725168","date":"2025-02-03T13:02:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164920864262425207485415233530239931260","date":"2025-02-02T16:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279845496762070835539313331860641150868","date":"2025-02-01T01:20:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177865941058942018478938077866615205770","date":"2025-01-31T15:23:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127484213106544563161841619154961443657","date":"2025-01-31T14:46:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140249263678569722009657142633997999536","date":"2025-01-31T12:51:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305231354143658674248074994851478400945","date":"2025-01-29T14:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-28T11:34:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-28T10:39:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-28T10:38:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2025-01-27T15:55:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"75897e13-2dbf-4106-a202-4cd190d6abdc","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-5913656","link":"https://doi.org/10.1007/s11060-025-05002-3","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2025-04-07 16:05:15","publishedOnDateReadable":"April 7th, 2025"},"versionCreatedAt":"2025-01-30 10:07:05","video":"","vorDoi":"10.1007/s11060-025-05002-3","vorDoiUrl":"https://doi.org/10.1007/s11060-025-05002-3","workflowStages":[]},"version":"v1","identity":"rs-5913656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5913656","identity":"rs-5913656","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}