title: Modelling Neonatal Care Pathways for Babies Born Preterm: An Application of Multistate Modelling.
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.
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
authors.forename: Sarah E
authors.identifier: http://orcid.org/0000-0001-8711-4817
authors.initials: SE
authors.lastname: Seaton
authors.affiliation: Leicester Neonatal Unit, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom.
authors.forename: Lisa
authors.initials: L
authors.lastname: Barker
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Draper
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
authors.forename: Keith R
authors.initials: KR
authors.lastname: Abrams
authors.affiliation: Neonatal Data Analysis Unit, Section of Neonatal Medicine, Department of Medicine, Imperial College, London, United Kingdom.
authors.forename: Neena
authors.initials: N
authors.lastname: Modi
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
authors.forename: Bradley N
authors.initials: BN
authors.lastname: Manktelow
mesh_terms: D016208:Databases, Factual; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D015931:Intensive Care, Neonatal; D008297:Male; D008962:Models, Theoretical; D016016:Proportional Hazards Models