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Quantifying the variation in neonatal transport referral patterns using network analysis

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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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Fig. 1: Representative graphs of the networks defined by medical and surgical transfers.
Fig. 2: Network metrics calculated for each group of patient characteristics.

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Funding

SNK was supported by Grant K08 HS025749 from the Agency for Healthcare Research and Quality. JP and DH were supported by the Stanford Maternal Child Health Research Institute (MCHRI) Grant Program.

Author information

Author notes

  1. These authors contributed equally: John A. F. Zupancic, Jochen Profit

Authors and Affiliations

  1. Division of Newborn Medicine, Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Sarah N. Kunz & John A. F. Zupancic

  2. Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Sarah N. Kunz & John A. F. Zupancic

  3. Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA

    Daniel Helkey, Ciaran S. Phibbs & Jochen Profit

  4. California Perinatal Quality Care Collaborative, Palo Alto, CA, USA

    Daniel Helkey & Jochen Profit

  5. Department of Biomedical Informatics, Harvard University, Boston, MA, USA

    Marinka Zitnik

  6. Health Economics Resource Center, Veterans Affairs Palo Alto Healthcare System, Menlo Park, CA, USA

    Ciaran S. Phibbs

  7. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Joseph Rigdon

Authors
  1. Sarah N. Kunz
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  2. Daniel Helkey
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  4. Ciaran S. Phibbs
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  5. Joseph Rigdon
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  6. John A. F. Zupancic
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  7. Jochen Profit
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Contributions

SNK conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript. JAFZ and JP conceptualized and designed the study, and reviewed and revised the manuscript. MZ, JR, and DH carried out the analyses, and reviewed and revised the manuscript. CSP conceptualized and designed the study, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Supplementary information is available at JPER’s website.

Corresponding author

Correspondence to Sarah N. Kunz.

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Conflict of interest

The authors declare no competing interests.

Ethical approval

The study was approved by the institutional review board at Stanford University (protocol #50047). The study was submitted to the institutional review board at Beth Israel Deaconess Medical Center and deemed not human subject research, as all analysis was performed at Stanford University.

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Kunz, S.N., Helkey, D., Zitnik, M. et al. Quantifying the variation in neonatal transport referral patterns using network analysis. J Perinatol 41, 2795–2803 (2021). https://doi.org/10.1038/s41372-021-01091-w

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