The multimorbidity cluster analysis tool: identifying combinations and permutations of multiple chronic diseases using a record-level computational analysis. Issue 4 (1st October 2017) - Nicholson, Kathryn; Bauer, Michael; Terry, Amanda L.; Fortin, Martin; Williamson, Tyler; Thind, Amardeep
Abstract : Introduction: Multimorbidity, or the co-occurrence of multiple chronic health conditions within an individual, is an increasingly dominant presence and burden in modern health care systems. To fully capture its complexity, further research is needed to uncover the patterns and consequences of these co-occurring health states. As such, the Multimorbidity Cluster Analysis Tool and the accompanying Multimorbidity Cluster Analysis Toolkit have been created to allow researchers to identify distinct clusters that exist within a sample of participants or patients living with multimorbidity. Development: The tool and toolkit were developed at Western University in London, ON, Canada. This open-access computational program (JAVA code and executable file) was developed and tested to support an analysis of thousands of individual records and up to 100 disease diagnoses or categories. Application: The computational program can be adapted to the methodological elements of a research project, including type of data, type of chronic disease reporting, measurement of multimorbidity, sample size and research setting. The computational program will identify all existing, and mutually exclusive, combinations and permutations within the dataset. An application of this computational program is provided as an example, in which more than 75, 000 individual records and 20 chronic disease categories resulted in the detection of 10, 411 unique combinations and 24, 647 unique permutations among female and male patients. Discussion: The tool and toolkit are now available for use by researchers interested in exploring the complexities of multimorbidity. Its careful use, and the comparison between results, will be valuable additions to the nuanced understanding of multimorbidity.