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Title: Modelling neonatal care pathways for babies born preterm: an application of multistate modelling
Authors: Seaton, Sarah E.
Barker, Lisa
Draper, Elizabeth S.
Abrams, Keith R.
Modi, N.
Manktelow, Bradley N.
UK Neonatal Collaborative
First Published: 20-Oct-2016
Publisher: Public Library of Science
Citation: PLoS One 11(10): e0165202.
Abstract: Modelling length of stay in neonatal care is vital to inform service planning and the counselling of parents. Preterm babies, at the highest risk of mortality, can have long stays in neonatal care and require high resource use. Previous work has incorporated babies that die into length of stay estimates, but this still overlooks the levels of care required during their stay. This work incorporates all babies, and the levels of care they require, into length of stay estimates. Data were obtained from the National Neonatal Research Database for singleton babies born at 24–31 weeks gestational age discharged from a neonatal unit in England from 2011 to 2014. A Cox multistate model, adjusted for gestational age, was used to consider a baby’s two competing outcomes: death or discharge from neonatal care, whilst also considering the different levels of care required: intensive care; high dependency care and special care. The probabilities of receiving each of the levels of care, or having died or been discharged from neonatal care are presented graphically overall and adjusted for gestational age. Stacked predicted probabilities produced for each week of gestational age provide a useful tool for clinicians when counselling parents about length of stay and for commissioners when considering allocation of resources. Multistate modelling provides a useful method for describing the entire neonatal care pathway, where rates of in-unit mortality can be high. For a healthcare service focussed on costs, it is important to consider all babies that contribute towards workload, and the levels of care they require.
DOI Link: 10.1371/journal.pone.0165202
ISSN: 1932-6203
eISSN: 1932-6203
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © 2016 Seaton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: The anonymised data used to produce this manuscript were obtained from a third party. Readers can request access to the data by contacting the Neonatal Data Analysis Unit (
Appears in Collections:Published Articles, Dept. of Health Sciences

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