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

TL;DR

This study compares various community detection algorithms to improve the delineation of Health Service Areas using hospital-patient discharge networks, highlighting Infomap as a particularly effective method.

Contribution

It provides a comprehensive comparison of community detection algorithms for HSA delineation, identifying Infomap as a promising approach based on large-scale US healthcare data.

Findings

Infomap yields high localization index HSAs

Community detection algorithms show heterogeneity in results

Network-based delineation improves HSA accuracy

Abstract

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.