Circles of Care: a geospatial analysis of computed tomography (CT) imaging access and socioeconomic vulnerability in Houston, Texas

BackgroundComputed tomography (CT) is a cornerstone of timely diagnosis for stroke, trauma, and oncologic conditions, and delays in access are associated with worsened outcomes. Although Houston, Texas, is home to one of the worlds largest medical complexes, the geographic distribution of CT imaging infrastructure has not been systematically examined against neighborhood-level measures of socioeconomic vulnerability. MethodsWe conducted a cross-sectional geospatial analysis of CT imaging facilities across the Greater Houston metropolitan area. Facility locations -- including hospital-based scanners, independent imaging centers, and freestanding emergency facilities -- were compiled from publicly available imaging directories, Texas Department of State Health Services (DSHS) facility listings, Centers for Medicare & Medicaid Services (CMS) provider data, and CT location data contributed by MD Anderson Cancer Center. Census tract-level indicators (median household income, percent uninsured, poverty rate) were obtained from the U.S. Census Bureau American Community Survey. Facility locations were geocoded and overlaid on census-tract choropleths in ArcGIS Online and ArcGIS StoryMaps to identify tracts with elevated socioeconomic vulnerability and limited proximity to CT infrastructure. ResultsCT imaging facilities were markedly clustered in the central urban core and in higher-income corridors, with hospital-based and independent scanners concentrated in census tracts with lower poverty rates, higher median household income (>$119,300), and higher insurance coverage. Conversely, peripheral and southeastern tracts with elevated poverty (>24%), median household income below $37,800, and uninsured rates exceeding 16% contained comparatively sparse CT infrastructure, generating spatial "gaps" in advanced diagnostic capacity. The pattern persisted across facility type: freestanding emergency and independent imaging centers did not meaningfully compensate for the undersupply of hospital-based scanners in vulnerable communities. ConclusionsIn Houston, the spatial distribution of CT imaging resources mirrors rather than offsets underlying socioeconomic inequality. Neighborhoods with higher poverty and uninsured rates face compounded barriers of distance and coverage. Citywide spatial analysis renders these inequities visible in ways individual clinical encounters cannot, and supports equity-informed health-system planning, targeted investment in underserved catchments, and policies linking imaging-capacity expansion to measurable community need.