Multimorbidity Cluster Analysis Toolkit
In examining the issue of multimorbidity, previous literature has focused on the descriptive counting of individual diseases or the simplistic link between co-occurring pairs of diseases. However, the analysis of cumulative interactions and non-random associations between disease diagnoses will lead to a deeper understanding of the multidimensional burden of multimorbidity (Garin et al., 2014; Sinnige et al., 2013; Prados-Torres et al., 2012). This burden and impact of multimorbidity can be assessed from the patient, caregiver, health care provider and health system perspective. A computational cluster analysis can explore the distinct clinical profiles that exist within a sample of individuals living with multimorbidity.
This toolkit is designed to allow researchers to identify the distinct clusters or clinical profiles that exist within a sample of patients or individuals with multimorbidity. This toolkit can be adapted for use with varying diagnostic systems, multimorbidity definitions, sample sizes, target populations and settings. Its intent is to create a consistent approach to identify subgroups of patients or individuals with multimorbidity, based on co-existing conditions or diseases. This information is driven by the data, and the results should be assessed carefully. In fact, it is most ideal to incorporate clinical and contextual insight for interpretation. This information can be a helpful resource for research, clinical care and health policy decisions.
Garin N, Olaya B, Perales J, Moneta MV, Miret M, Ayuso-Mateos JL, et al. Multimorbidity patterns in a national representative sample of the Spanish adult population. PLoS ONE. 2014;9(1):e84794-803.
Prados-Torres A, Poblador-Plou B, Calderon-Larranaga A, Gimeno-Feliu LA, Gonzalez-Rubio F, Poncel-Falco A, et al. Multimorbidity patterns in primary care: Interactions among chronic diseases using factor analysis. PLoS ONE. 2012;7(2):e32190-202.
Sinnige J, Braspenning J, Schellevis F, Stirbu-Wagner I, Westert G, Korevaar J. The prevalence of disease clusters in older adults with multiple chronic diseases: a systematic literature review. PLoS ONE. 2013;8(11):e79641-53.
Development of This Toolkit
toolkit was developed by a research team at Western University from the
Departments of Epidemiology & Biostatistics and Computer Science. It was
developed using data from the Canadian Primary Care Sentinel Surveillance
Network (CPCSSN), which is based at Queen’s University and is funded by the Public
Health Agency of Canada under a contribution agreement with the College of
Family Physicians of Canada. The views expressed herein do not necessarily
represent the views of the Public Health Agency of Canada. This toolkit is
available to all academic researchers interested in exploring multimorbidity. When utilized in research projects, it is
requested that acknowledgement (below) is made in any publications.
Bauer M & Nicholson K. Multimorbidity Cluster Analysis Toolkit. 2016.
The above background, a description of the Toolkit, along with details on data formats (both input and output) and on how to use the software can be found the document: Multimorbidity Cluster Analysis Toolkit.
The Multimorbidity Cluster Analysis Tool can be downloaded here: mm cluster tool
A file with sample input data is found here.
If you have follow-up questions or comments about the Multimorbidity Cluster Analysis Tool and/or Toolkit, you can direct them to: firstname.lastname@example.org .