Objectives: To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters.
Study design and setting: Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians.
Results: Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity.
Conclusion: These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.
Keywords: CLSA; Chronic conditions; Clustering algorithms; Disease clusters; Multimorbidity; Network analysis.
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