Identification of Hospital Catchment Areas Using Clustering: An Example from the NHS
Identification of Hospital Catchment Areas Using Clustering: An Example from the NHS
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DOI：
10.1111/j.1475-6773.2009.01069.x
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发表时间：
2010-04-01
影响因子：
3.4
通讯作者：
Gilmour, Stuart John
中科院分区：
文献类型：
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作者：
Gilmour, Stuart John
目的建立一种基于多变量数据的医院市场区域识别方法，并与现有标准方法进行比较。数据来源医院事件统计，这是2005年4月至2006年3月间英格兰所有医院入院数据的二级数据集。研究设计提出了七个集水区界定标准。k -均值聚类用于描述医院和地方当局地区（LADs）之间关系的几个变量，以便将每个LAD放置在每家医院的集水区内或区外。主成分分析证实了该方法的统计稳健性，并使用七个标准将该方法与现有方法进行了比较。现有的确定集水区的方法没有捕捉到医院市场区域的理想属性。根据这些标准，使用K-means聚类确定的集水区优于使用现有边际方法确定的集水区，并且在统计上也具有稳健性。结论：sk -means聚类使用医院与地理单位之间关系的多变量数据来定义集水区，与现有方法相比，该方法在统计上具有稳健性，而且信息量更大。
ObjectiveTo develop a method of hospital market area identification using multivariate data, and compare it with existing standard methods.Data SourcesHospital Episode Statistics, a secondary dataset of admissions data from all hospitals in England, between April 2005 and March 2006.Study DesignSeven 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 FindingsExisting 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.ConclusionsK-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.