Multimorbidity clusters: Clustering binary data from a large administrative medical database.

Cornell J, Pugh JA, Williams JW Jr., Kazis L, Lee A, Parchman ML, Zeber JE, Pederson T, Montgomery KA, Noel PH. Multimorbidity clusters: Clustering binary data from a large administrative medical database. Applied Multivariate Research. 2008 Sep 1; 12(3):163-182.

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• IIR 01-110 – The Prevalence and Needs of Veterans with Multiple Chronic Illnesses

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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. 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.