Abstract A21: Using spatial analysis to identify the impact of medically underserved areas within and beyond the catchment area of a Comprehensive Cancer Center
Authors: Marx Christine M. , Thein Jessica L. , Colditz Graham A. 
Published: February 2019

Abstract
Objective: To develop a spatial analysis-driven enhanced description of the patients treated at the Alvin J. Siteman Cancer Center (SCC) at Washington University School of Medicine, to better address cancer disparities impacting our community by tailoring the resources offered at SCC to the needs of our patients.
Background: The race and ethnicity breakdown of the self-defined catchment area (CA) at NCI-designated Comprehensive Cancer Centers serves as a benchmark for the demographic proportion seeking treatment at the institution as well as clinical trial (CT) enrollment. To meet the goals set by this benchmark, institutions must have a thorough understanding of the population treated at their institution. Using Geographic Information Systems (GIS) to explore patient data provides additional context beyond the traditionally reported race and ethnic descriptors. Further, incorporating Medically Underserved Areas (MUAs), as identified by the Health Resources and Services Administration (HRSA), and rural status into the GIS is an important and comprehensive method to provide a richer understanding of the patient population we serve. This understanding can be used to tailor resources offered by the center, establish a more appropriate CT portfolio, and may provide insight into disparate trends not readily apparent.
Methods: The database of patients (n=8,691) was obtained from the SCC cancer registry and includes those seen at SCC for the first time in 2015 who met the NCI's Data Table Three reporting criteria. Spatial data on MUAs were downloaded from HRSA website for use in the GIS at SCC. Rural status was defined using Census-designated, ZIP code-level rural-urban commuting area (RUCA) codes, where rural &gt;= 7. The patient addresses were geocoded and spatial analyses performed using ArcGIS Desktop.
Results: Of the patients in the database, 84.8% were geocoded to address-level specificity. Of those, 67% live in catchment area, and 25.9% live in an MUA. Exploring the percent of total patients by race and MUA, 4.1% are African American patients living in an MUA and 21.4% are white patients living in an MUA. A significantly higher proportion of patients who are African American live in an MUA (31.3%), compared to white patients who live in an MUA (25.2%, p&lt;0.001). 12.7% of SCC patients live in a rural zip code; of these, 48.5% live in an MUA.
Conclusions: As health disparities for rural patients continue to be revealed, including and beyond proximity to adequate health care, we have greater responsibility to understand this aspect of our patient population and surrounding communities, to minimize the impact of these disparities. White patients living in an MUA have not previously been represented in Comprehensive Cancer Center data reporting as having health disparities. This spatial analysis approach acknowledges this disparity and ensures that health disparities of this group will be represented in future policy priorities and CT portfolio decisions. Additionally, these results highlight the burden of intersectionality of race and geographic disparities on health, particularly in our African American patients.
We continue to refine GIS approaches to expand on these results. Future applications of spatial analysis to understand patient populations and health disparities include: inform policy decisions, outreach, and clinical trial portfolio, explore known cancer disparities as it relates to MUAs (such as prostate cancer in African American men, by MUA status), review staging at diagnosis by MUA status, and possibly include in the electronic health record for use by clinical staff. Understanding the MUA and rural status of Comprehensive Cancer Center patients provides a richer description that will benefit both the patient and the community as a whole, and subsequently the potential impact on reducing cancer disparities.
Citation Format: Christine M. Marx, Jessica L. Thein, Graham A. Colditz. Using spatial analysis to identify the impact of medically underserved areas within and beyond the catchment area of a Comprehensive Cancer Center [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A21.