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J Perinatol. 2017 Jun;37(6):702-708. doi: 10.1038/jp.2017.20. Epub 2017 Mar 23.

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

Author information

1
Division of Newborn Medicine, Harvard Medical School, Boston, MA, USA.
2
Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
3
Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.
4
Department of Pediatrics-Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
5
Health Economics Resource Center, Veterans Affairs Palo Alto Healthcare System, Menlo Park, CA, USA.
6
California Perinatal Quality Care Collaborative, Stanford, CA, USA.
7
Department of Computer Science, Stanford University, Stanford, CA, USA.

Abstract

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.

PMID:
28333155
PMCID:
PMC5446293
DOI:
10.1038/jp.2017.20
[Indexed for MEDLINE]
Free PMC Article

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