Space-Time Cluster Analysis: Application of Healthcare Service Data in Epidemiological Studies

Abstract

Spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decision making. This article illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization (Kaiser Foundation Health Plan of Ohio) over a period 1994—2006. The objective is to find excess space and space - time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski, & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.