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Do replicable profiles of multimorbidity exist? Systematic review and synthesis
Busija, Ljoudmila Lim, Karen Szoeke, Cassandra Sanders, Kerrie M. McCabe, Marita P.
Busija, Ljoudmila
Lim, Karen
Szoeke, Cassandra
Sanders, Kerrie M.
McCabe, Marita P.
Abstract
This systematic review aimed to synthesise multimorbidity profiling literature to identify replicable and clinically meaningful groupings of multimorbidity. We searched six electronic databases (Medline, EMBASE, PsycINFO, CINAHL, Scopus, and Web of Science) for articles reporting multimorbidity profiles. The identified profiles were synthesised with multidimensional scaling, stratified by type of statistical analysis used in the derivation of profiles. The 51 studies that met inclusion criteria reported results of 98 separate analyses of multimorbidity profiling, with a total of 407 multimorbidity profiles identified. The statistical techniques used to identify multimorbidity profiles were exploratory factor analysis, cluster analysis of diseases, cluster analysis of people, and latent class analysis. Reporting of methodological details of statistical methods was often incomplete. The discernible groupings of multimorbidity took the form of both discrete categories and continuous dimensions. Mental health conditions and cardio-metabolic conditions grouped along identifiable continua in the synthesised results of all four methods. Discrete groupings of chronic obstructive pulmonary disease with asthma, falls and fractures with sensory deficits and of Parkinson’s disease and cognitive decline where partially replicable (identifiable in the results of more than one method), while clustering of musculoskeletal conditions and clustering of reproductive systems were each observed only in one statistical approach. The two most replicable multimorbidity profiles were mental health conditions and cardio-metabolic conditions. Further studies are needed to understand aetiology and evolution of these multimorbidity groupings. Guidelines for strengthening the reporting of multimorbidity profiling studies are proposed.
Keywords
multimorbidity, exploratory factor analysis, cluster analysis, latent class analysis, multidimensional scaling
Date
2019
Type
Journal article
Journal
European Journal of Epidemiology
Book
Volume
34
Issue
Page Range
1025-1053
Article Number
ACU Department
School of Behavioural and Health Sciences
Faculty of Health Sciences
Faculty of Health Sciences
Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled