Network analysis: a novel method for mapping neonatal acute transport patterns in California

Objective: The objectives of this study are 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 with 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.