Network Optimization Approach to Delineating Health Care Service Areas: Spatially Constrained Louvain and Leiden Algorithms.

Abstract

Constructing service areas is an important task for evaluating geographic variation of health care markets. This study uses cancer care as an example to illustrate the methodology, with the nine-state Northeast Region of the U.S. as the study area. Two recent algorithms of network community detection are implemented to account for additional constraints such as spatial connectivity and threshold region size. The refined methods are termed "spatially-constrained Louvain (ScLouvain)" and "spatially-constrained Leiden (ScLeiden)" algorithms, corresponding to their predecessors Louvain and Leiden algorithms, respectively. Both are network optimization methods that maximize flows within delineated communities while minimizing inter-community flows. The service areas derived by the methods, termed "Cancer Service Areas (CSAs)", are more favorable than the commonly used comparable unit, Hospital Referral Regions (HRRs) for evaluating cancer-specific variation in care. Between the two, the ScLeiden performs better than ScLouvain in modularity, localization index and computational efficiency, and thus is recommended as an effective and efficient approach for defining functional regions.