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

CPQCC Publication
TitleNetwork analysis: a novel method for mapping neonatal acute transport patterns in California.
Publication TypeJournal Article
Year of Publication2017
AuthorsKunz SN, Zupancic JAF, Rigdon J, Phibbs CS, Lee HC, Gould JB, Leskovec J, Profit J
JournalJ Perinatol
Date Published2017 06
KeywordsCalifornia, Cross-Sectional Studies, Humans, Infant, Newborn, Intensive Care Units, Neonatal, Logistic Models, Models, Statistical, Patient Transfer

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.

Alternate JournalJ Perinatol
PubMed ID28333155
PubMed Central IDPMC5446293
Grant ListR01 HD083368 / HD / NICHD NIH HHS / United States
T32 HD075727 / HD / NICHD NIH HHS / United States
U54 EB020405 / EB / NIBIB NIH HHS / United States