|Title||Network analysis: a novel method for mapping neonatal acute transport patterns in California.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Kunz SN, Zupancic JAF, Rigdon J, Phibbs CS, Lee HC, Gould JB, Leskovec J, Profit J|
|Date Published||2017 06|
|Keywords||California, 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 Journal||J Perinatol|
|PubMed Central ID||PMC5446293|
|Grant List||R01 HD083368 / HD / NICHD NIH HHS / United States |
T32 HD075727 / HD / NICHD NIH HHS / United States
U54 EB020405 / EB / NIBIB NIH HHS / United States