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

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

Joseph M. Woodside (Cleveland State University, USA) and Iftikhar U. Sikder (Cleveland State University, USA)
DOI: 10.4018/jhisi.2009071005

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.
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Introduction

The increasing demand for health data analysis in spatial and temporal scale has made emerging technologies such as Geographic Information Systems (GIS) an essential tool for healthcare information systems. In healthcare settings application of such new technology are proving useful in the analysis of health data and planning of healthcare services (Pfeiffer, D. U., Robinson, T. P., Stevenson, M., Stevens, K. B., Rogers, D. J., & Clements, A. C. A., 2008). The ability of GIS to manage and retrieve georeference data has demonstrated its value in the integration of complex epidemiological models through visualization of spatial and temporal relationships. This has been recognized by the World Health Organization (WHO):

“Geographical information systems (GIS) provide ideal platforms for the convergence of disease-specific information and their analyses in relation to population settlements, surrounding social and health services and the natural environment. They are highly suitable for analyzing epidemiological data, revealing trends and interrelationships that would be difficult to discover in tabular format. Moreover GIS allows policy makers to easily visualize problems in relation to existing health and social services and the natural environment and so more effectively target resources”(World Health Organization, 2008)

Geographical analysis is not only important for the identification of patterns of healthcare outcomes it also offers insight into understanding the association or linkage to political processes and policy makers (Cromley, E. K., 2002; Gatrell, A., 2002). Health data from managed health care organizations offers the opportunity to analyze unusual geographical patterns of disease. Routine, aggregated healthcare data stored in health systems can be utilized to identify disease clusters or utilization patterns. Recently methods have been sought to further improve identification within case and disease management programs.

The real world clinical service data stored in healthcare information systems provides opportunity to analyze spatio-temporal patterns at finer granularity. The investigation of space and space-time epidemiological patterns often gives rise to the explanation of factors that might create an adverse health condition. This study uses routine, aggregated service data to find excess space and time variations in rendered services where the primary diagnosis was lung cancer. From the health care management point of view, if clusters are detected and explanatory factors linked, this understanding allows for better patient care, i.e. serving a particular population with targeted specialists, and preventing spread of disease amongst populations. This research aims to study different clustering methods of the spatial and spatio-temporal patterns of lung cancer particularly for routine clinical service data collected by a Northeast Ohio healthcare organization (Kaiser Foundation Health Plan of Ohio) over a period from 1994 – 2006.

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