/VON_NICU_2018

Code for The Interhospital Transfer Network for Very Low Birth Weight Infants in the United States

Primary LanguageRMIT LicenseMIT

Code for The Interhospital Transfer Network for Very Low Birth Weight Infants in the United States

Citation

Shrestha, M., Scarpino, S.V., Edwards, E.M., Greenberg, L.T. and Horbar, J.D., 2018. The Interhospital Transfer Network for Very Low Birth Weight Infants in the United States. EPJ Data Science 20187:27

Notes on the code

The file community-entropy-and-gini.ipynb contains a definition for calculating the regionalization index. The .R files contain the code to recreate the figures and main statistical analyses. Because of patient privacy, we are unable to make the data publicly available. Please see below for information on requesting access to VON data.

Data

The infant-level data, including data on transfers, is protected by U.S. privacy laws and legal membership agreement. Individuals interested in using VON data are asked to follow our Policy and Guidelines for Collaborative Research using the Vermont Oxford Network Databases, available here.

Study Abstract

Very low birth weight (VLBW) infants require specialized care in neonatal intensive care units. In the United States (U.S.), such infants frequently are transferred between hospitals. Although these neonatal transfer networks are important, both economically and for infant morbidity and mortality, the national-level pattern of neonatal transfers is largely unknown. Using data from Vermont Oxford Network on 44,753 births, 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We find that transfers are organized around regional communities, but that despite being largely within state boundaries, most communities often contain at least two hospitals in different states. To classify the structural variation in transfer pattern amongst these communities, we applied a spectral measure for regionalization and found a positive correlation between a community's degree of regionalization and their infant transfer rate, which was not utilized in detecting communities. We also demonstrate that the established measures of network centrality and hierarchy, e.g., the community-wide entropy in PageRank or Betweenness centrality and number of distinct 'layers' within a community, correlate weakly with our regionalization index and were not significantly associated with metrics on infant transfer rate. Our results suggest that the regionalization index captures novel information about the structural properties of VLBW infant transfer networks, have the practical implication of characterizing neonatal care in the U.S., and may apply more broadly to the role of centralizing forces in organizing complex adaptive systems.