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Algorithmic hospital catchment area estimation using label propagation.

Robert J Challen1,2, Gareth J Griffith3,4, Lucas Lacasa5,6

  • 1Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK. rc538@exeter.ac.uk.

BMC Health Services Research
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

A new algorithm estimates hospital catchment areas using hospital capacity and population demographics, without needing patient activity data. This method accurately maps populations to hospitals, proving useful for disease outbreak planning.

Keywords:
Catchment areaCovid-19

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Area of Science:

  • Public Health
  • Health Services Research
  • Computational Epidemiology

Background:

  • Hospital catchment areas define the patient population served by a healthcare facility.
  • Accurate catchment area delineation is crucial for assessing hospital demand, particularly during public health emergencies like disease outbreaks.
  • Traditional methods often rely on patient activity data, which may not be available in real-time.

Purpose of the Study:

  • To introduce a novel algorithm for estimating hospital catchment areas.
  • To develop a method that does not require hospital activity data for catchment area estimation.
  • To provide a tool for understanding population distribution relative to healthcare resources.

Main Methods:

  • The study presents a new algorithm based on label propagation.
  • The algorithm utilizes hospital capacity and nearby population demographics as inputs.
  • It maps fine-grained geographic regions to larger-scale hospital catchment areas.

Main Results:

  • The algorithm generates contiguous and realistic catchment area subdivisions.
  • Validation against activity-based methods during the UK's COVID-19 outbreak showed high agreement and comparable accuracy.
  • The method successfully estimated catchment areas in scenarios lacking activity data.

Conclusions:

  • The label propagation algorithm offers a viable alternative for estimating hospital catchment areas.
  • This approach is particularly valuable for situations where real-time activity data is unavailable, such as during the initial phases of an outbreak.
  • The algorithm enhances public health preparedness by enabling timely assessment of healthcare access and demand.