Algorithmic hospital catchment area estimation using label propagation

Robert J Challen*, Gareth Griffith, Lucas Lacasa, Krasimira T Tsaneva-Atanasova

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
110 Downloads (Pure)

Abstract

Background
Hospital catchment areas define the primary population of a hospital and are central to assessing the potential demand on that hospital, for example, due to infectious disease outbreaks.

Methods
We present a novel algorithm, based on label propagation, for estimating hospital catchment areas, from the capacity of the hospital and demographics of the nearby population, and without requiring any data on hospital activity.

Results
The algorithm is demonstrated to produce a mapping from fine grained geographic regions to larger scale catchment areas, providing contiguous and realistic subdivisions of geographies relating to a single hospital or to a group of hospitals. In validation against an alternative approach predicated on activity data gathered during the COVID-19 outbreak in the UK, the label propagation algorithm is found to have a high level of agreement and perform at a similar level of accuracy.

Results
The algorithm can be used to make estimates of hospital catchment areas in new situations where activity data is not yet available, such as in the early stages of a infections disease outbreak.
Original languageEnglish
Article number828
JournalBMC Health Services Research
Volume22
Issue number1
DOIs
Publication statusPublished - 27 Jun 2022

Bibliographical note

Funding Information:
Many thanks to TJ McKinley at the University of Exeter, for feedback on an early draft of the paper and testing of the implementation of the algorithm.

Funding Information:
RC and KTA gratefully acknowledge the financial support of the EPSRC via grant EP/N014391/1. KTA gratefully acknowledge the financial support of The Alan Turing Institute under the EPSRC grant EP/N510129/1 and EP/T017856/1. RC is supported by NHS England, Global Digital Exemplar programme and the MRC (MC/PC/19067). LL acknowledges the financial support of the EPSRC via Early Career Fellowship EP/P01660X/1. GJG is supported by an ESRC postdoctoral fellowship ES/T009101/1.

Publisher Copyright:
© 2022, The Author(s).

Cite this