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. 2021 Dec;41(12):2795-2803.
doi: 10.1038/s41372-021-01091-w. Epub 2021 May 25.

Quantifying the variation in neonatal transport referral patterns using network analysis

Sarah N Kunz  1   2 , Daniel Helkey  3   4 , Marinka Zitnik  5 , Ciaran S Phibbs  3   6 , Joseph Rigdon  7 , John A F Zupancic #  8   9 , Jochen Profit #  3   4
Affiliations

Quantifying the variation in neonatal transport referral patterns using network analysis

Sarah N Kunz et al. J Perinatol. 2021 Dec.

Abstract

Objective: Regionalized care reduces neonatal morbidity and mortality. This study evaluated the association of patient characteristics with quantitative differences in neonatal transport networks.

Study design: We retrospectively analyzed prospectively collected data for infants <28 days of age acutely transported within California from 2008 to 2012. We generated graphs representing bidirectional transfers between hospitals, stratified by patient attribute, and compared standard network analysis metrics.

Result: We analyzed 34,708 acute transfers, representing 1594 unique transfer routes between 271 hospitals. Density, centralization, efficiency, and modularity differed significantly among networks drawn based on different infant attributes. Compared to term infants and to those transported for medical reasons, network metrics identify greater degrees of regionalization for preterm and surgical patients (more centralized and less dense, respectively [p < 0.001]).

Conclusion: Neonatal interhospital transport networks differ by patient attributes as reflected by differences in network metrics, suggesting that regionalization should be considered in the context of a multidimensional system.

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Conflict of interest statement

Conflict of Interest

The authors have no competing interests relevant to this article to disclose.

Figures

Figure 1
Figure 1. Representative graphs of the networks defined by medical and surgical transfers.
Circular nodes represent hospitals, with edges between nodes representing acute transports. Node size is proportional to the total number of infants transported into the hospital. Node color denotes California Children’s Services (CCS) level of care, and edge color indicates CCS level of the destination hospital.
Figure 2
Figure 2. Network metrics calculated for each group of patient characteristics.
Network metrics (centralization, density, efficiency, and modularity) with associated 95% confidence intervals for the entire transport network and all networks based on infant attributes. *Differences significant at p<0.05 level for that attribute **Differences significant at p<0.001 level for that attribute Centralization: measure of “hub-and-spoke” organization around important hospitals. Density: proportion of all possible transfer routes between hospitals that were actually used. Efficiency: measure of the shortest (non-geographical) path between any two random hospitals in the network based on existing transfer routes. Modularity: tendency of the network to break into discrete communities of hospitals.
See this image and copyright information in PMC

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