Identification of hospital catchment areas using clustering: an example from the NHS

Health Serv Res. 2010 Apr;45(2):497-513. doi: 10.1111/j.1475-6773.2009.01069.x. Epub 2009 Dec 30.


Objective: To develop a method of hospital market area identification using multivariate data, and compare it with existing standard methods.

Data sources: Hospital Episode Statistics, a secondary dataset of admissions data from all hospitals in England, between April 2005 and March 2006.

Study design: Seven criteria for catchment area definition were proposed. K-means clustering was used on several variables describing the relationship between hospitals and local authority districts (LADs) to enable the placement of every LAD into or out of the catchment area for every hospital. Principal component analysis confirmed the statistical robustness of the method, and the method was compared against existing methods using the seven criteria.

Principal findings: Existing methods for identifying catchment areas do not capture desirable properties of a hospital market area. Catchment areas identified using K-means clustering are superior to those identified using existing Marginal methods against these criteria and are also statistically robust.

Conclusions: K-means clustering uses multivariate data on the relationship between hospitals and geographical units to define catchment areas that are both statistically robust and more informative than those obtained from existing methods.

MeSH terms

  • Catchment Area, Health* / statistics & numerical data
  • Cluster Analysis
  • England
  • Hospitals, Public*
  • Humans
  • State Medicine / organization & administration*
  • United Kingdom