Network Analysis: A Novel Method for Mapping Neonatal Acute Transport Patterns in California

Abstract

Objective: To use network analysis to describe the pattern of neonatal transfers in California, to compare empirical sub-networks with established referral regions, and to determine factors associated with transport outside the originating sub-network. Study Design This cross-sectional database study included 6546 infants <28 days old transported within California in 2012. After generating a graph representing acute transfers between hospitals (n=6696), we used community detection techniques to identify more tightly connected sub-networks. These empirically-derived sub-networks were compared to state-defined regional referral networks. Reasons for transfer between empirical sub-networks were assessed using logistic regression. Results: Empirical sub-networks showed significant overlap with regulatory regions (p <0.001). Transfer outside the empirical sub-network was associated with major congenital anomalies (p<0.001), need for surgery (p=0.01), and insurance as the reason for transfer (p<0.001). Conclusion: Network analysis accurately reflected empirical neonatal transfer patterns, potentially facilitating quantitative, rather than qualitative, analysis of regionalized health care delivery systems.