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A Health Service Area (HSA) is a group of geographic regions served bysimilar health care facilities. The delineation of HSAs plays a pivotal role inthe characterization of health care services available in an area, enabling abetter planning and regulation of health care services. Though Dartmouth HSAshave been the standard delineation for decades, previous work has recentlyshown an improved HSA delineation using a network-based approach, in which HSAsare the communities extracted by the Louvain algorithm in hospital-patientdischarge networks. Given the existent heterogeneity of communities extractedby different community detection algorithms, a comparative analysis ofcommunity detection algorithms for optimal HSA delineation is lacking. In thiswork, we compared HSA delineations produced by community detection algorithmsusing a large-scale dataset containing different types of hospital-patientdischarges spanning a 7-year period in US. Our results replicated theheterogeneity among community detection algorithms found in previous works, theimproved HSA delineation obtained by a network-based, and suggested thatInfomap may be a more suitable community detection for HSA delineation since itfinds a high number of HSAs with high localization index and a low networkconductance.

Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms