Evidence map›Paper›PMID 35761225›Full record

ArticleBMC health services research2022

Algorithmic hospital catchment area estimation using label propagation.

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

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Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Robert J ChallenHub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK. rc538@exeter.ac.uk.
Gareth J GriffithBristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
Lucas LacasaSchool of Mathematical Sciences, Queen Mary University of London, London, UK.
Krasimira Tsaneva-AtanasovaHub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK.

Funding

Engineering and Physical Sciences Research Council EP/N014391/1Engineering and Physical Sciences Research Council EP/P01660X/1Engineering and Physical Sciences Research Council EP/T017856/1Engineering and Physical Sciences Research Council ES/T009101/1Medical Research Council MC/PC/19067
6 · The paper itself

Abstract

backgroundHospital 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.

methodsWe 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.

resultsThe 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.

resultsThe 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.

Indexed as

COVID-19Catchment Area, HealthDelivery of Health CareDisease OutbreaksHospitalsHumansCatchment areaCovid-19

Identifiers

PMID35761225
PMCPMC9235278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.