  A Health Service Area (HSA) is a group of geographic regions served by
similar health care facilities. The delineation of HSAs plays a pivotal role in
the characterization of health care services available in an area, enabling a
better planning and regulation of health care services. Though Dartmouth HSAs
have been the standard delineation for decades, previous work has recently
shown an improved HSA delineation using a network-based approach, in which HSAs
are the communities extracted by the Louvain algorithm in hospital-patient
discharge networks. Given the existent heterogeneity of communities extracted
by different community detection algorithms, a comparative analysis of
community detection algorithms for optimal HSA delineation is lacking. In this
work, we compared HSA delineations produced by community detection algorithms
using a large-scale dataset containing different types of hospital-patient
discharges spanning a 7-year period in US. Our results replicated the
heterogeneity among community detection algorithms found in previous works, the
improved HSA delineation obtained by a network-based, and suggested that
Infomap may be a more suitable community detection for HSA delineation since it
finds a high number of HSAs with high localization index and a low network
conductance.
