A Geospatial Analysis of Factors Affecting Access to CT Facilities: Implications for Lung Cancer Screening

Objective: The association between access to CT facilities for lung cancer screening and population characteristics is understudied. We aimed to determine the relationship between census tract–level socioeconomic characteristics (SEC) and driving distance to an ACR-accredited CT facility. Methods: Census tract–level SEC were determined from the US Census Bureau. Distance to nearest ACR-accredited CT facility was derived at the census tract level. Census tract–level multivariable regression modeling was used to determine the relationship between driving distance to a CT facility and census tract SEC, including population density (a marker of rural versus urban), gender, race, insurance status or type, and education level. Results: In an adjusted multivariable model, census tract–level population density was the greatest relative determinant of distance to a CT facility. Namely, rural census tracts had relatively longer distances to CT facilities than urban census tracts (P < .001). Census tracts with higher uninsured, Medicaid, undereducated (less <high school degree) populations had relatively greater distances to CT facilities (p<0.001), whereas those with higher non-White, female, and Medicare populations had shorter distances (p<0.001). Discussion: Rural populations have relatively less geographic access to CT facilities. Furthermore, other vulnerable populations, such as the uninsured, those on Medicaid, and the undereducated, may also have relatively less access to CT imaging facilities. These variations in access to CT may affect the uptake and utilization of lung cancer screening.