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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, Iftikhar U. Sikder
Copyright: © 2009 |Volume: 4 |Issue: 4 |Pages: 14
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781616920623|EISSN: 1555-340X|DOI: 10.4018/jhisi.2009071005
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MLA

Woodside, Joseph M., and Iftikhar U. Sikder. "Space-Time Cluster Analysis: Application of Healthcare Service Data in Epidemiological Studies." IJHISI vol.4, no.4 2009: pp.69-82. http://doi.org/10.4018/jhisi.2009071005

APA

Woodside, J. M. & Sikder, I. U. (2009). Space-Time Cluster Analysis: Application of Healthcare Service Data in Epidemiological Studies. International Journal of Healthcare Information Systems and Informatics (IJHISI), 4(4), 69-82. http://doi.org/10.4018/jhisi.2009071005

Chicago

Woodside, Joseph M., and Iftikhar U. Sikder. "Space-Time Cluster Analysis: Application of Healthcare Service Data in Epidemiological Studies," International Journal of Healthcare Information Systems and Informatics (IJHISI) 4, no.4: 69-82. http://doi.org/10.4018/jhisi.2009071005

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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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