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Multimorbidity clusters: Clustering binary data from multimorbidity clusters: Clustering binary data from a large administrative medical database
Cornell, John E. Pugh, Jacqueline A. Williams, John W. Kazis, Lewis Lee, Austin F. S. Parchman, Michael L. Zeber, John Pederson, Thomas Montgomery, Kelly A. Hitchcock Noël, Polly
Cornell, John E.
Pugh, Jacqueline A.
Williams, John W.
Kazis, Lewis
Lee, Austin F. S.
Parchman, Michael L.
Zeber, John
Pederson, Thomas
Montgomery, Kelly A.
Hitchcock Noël, Polly
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Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2008
Type
Article
Genre
Keywords
Clinical psychology,Multivariate analysis,Personality
Subjects (LCSH)
Citation
Cornell, J. E., Pugh, J. A., Williams, J. W., Kazis, L., Lee, A. F. S., Parchman, M. L., Zeber, J., Pederson, T., Montgomery, K. A., Hitchcock Noël, P. (2008). Multimorbidity Clusters: Clustering Binary Data from Multimorbidity Clusters: Clustering Binary Data from a Large Administrative Medical Database. Applied Multivariate Research, 12(3), 163-182. https://doi.org/10.22329/amr.v12i3.658
Abstract
Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Multimorbidity is the co-occurrence of 2 or more illnesses within a single person, which raises the question whether consistent, clinically useful multimorbidity groups exist among sets of chronic illnesses. Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Application of cluster analysis involves a sequence of critical methodological and analytic decisions that influence the quality and meaning of the clusters produced. We illustrate the application of cluster analysis to identify multimorbidity clusters in a set of 45 chronic illnesses in primary care patients (N = 1,327,328), with 2 or more chronic conditions, served by the Veterans Health Administration. Six clinically useful multimorbidity clusters were identified: a Metabolic Cluster, an Obesity Cluster, a Liver Cluster, a Neurovascular Cluster, a Stress Cluster and a Dual Diagnosis Cluster. Cluster analysis appears to be a useful technique for identifying multiple disease clusters and patterns of multimorbidity.
Table of Contents
Description
Publisher
Wichita State University, Department of Psychology
Journal
Book Title
Series
Applied Multivariate Research
v.12 no.3
v.12 no.3
Digital Collection
Finding Aid URL
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Archival Collection
PubMed ID
ISSN
1918-1108