Analysis and stress-test of the spatial accessibility to German radiation oncology centers

Radiation therapy (RT) is a crucial component of cancer care. However, access to RT services varies significantly across regions. This study aims to assess and stress-test the geospatial distribution of radiation oncology centers (ROCs) in Germany.

Geospatial analysis was used to calculate travel times and distances and to calculate regional RT demand. A regional network analysis was also conducted. Simulation of local ROC closure and recalculation was conducted to stress-test the remaining RT capacities, resulting in a redistribution network analysis (ReDNA).

While some regions have a higher concentration of ROCs than others, the average ROC usually serves a population of approximately 238,185 inhabitants. In urban regions, the average travel time to the nearest ROC was 7.9 min, compared to 11.4 min in rural regions. However, the heterogeneity increases when considering the second- or third-closest ROC, especially in rural regions. The regional ReDNA identified regions with high vulnerability to local ROC disruption. In these areas, the closure of a single ROC probably leads to significant increases in travel times for patients. Inhabitants of cities/towns in rural regions are specifically dependent on the serviceability of their ROC, with a relative increase in travel time by 171.9% if the currently nearest ROC is closed.

For the first time, this study investigates the supply of RT services on a national level. The spatial accessibility of RT services is analyzed by regional types as well as by federal states. The novel ReDNA approach allows regions with potential vulnerabilities to be identified, resulting in a framework for stress-testing RT supply on a national level.

This research provides valuable insights into the spatial accessibility and vulnerability of radiation oncology services in Germany. Additionally, the methodology can also serve as a stress test and be applied to other regions and healthcare specialties to improve healthcare planning and patient outcomes.