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European Journal of Epidemiology

, Volume 34, Issue 11, pp 1025–1053 | Cite as

Do replicable profiles of multimorbidity exist? Systematic review and synthesis

  • Ljoudmila BusijaEmail author
  • Karen Lim
  • Cassandra Szoeke
  • Kerrie M. Sanders
  • Marita P. McCabe
REVIEW

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 

Notes

Acknowledgements

This study was partially funded by a seeding grant from the Office of Deputy of Vice-Chancellor (Research), Australian Catholic University. We thank Kathryn Duncan, research librarian at Australian Catholic University, who provided assistance with developing the search strategy.

Compliance with ethical standards

Conflict of interest

Monash University received consultancy fees from Charité – Universitätsmedizin Berlin and Jesuit Social Services (Richmond, Victoria, Australia) for the statistical consultancy work undertaken by LB. The consultancy fees were unrelated to this project. The remaining authors have no conflict of interest to declare.

Supplementary material

10654_2019_568_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 19 kb)
10654_2019_568_MOESM2_ESM.docx (32 kb)
Supplementary material 2 (DOCX 32 kb)
10654_2019_568_MOESM3_ESM.xlsx (33 kb)
Supplementary material 3 (XLSX 32 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Biostatistics Consulting Platform, Research Methodology Division, School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
  2. 2.Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneAustralia
  3. 3.School of Behavioural and Health Sciences, Faculty of Health SciencesAustralian Catholic UniversityMelbourneAustralia
  4. 4.Department of Medicine - Western Health, Melbourne Medical SchoolThe University of MelbourneMelbourneAustralia
  5. 5.Health and Ageing Research GroupSwinburne University of TechnologyHawthornAustralia

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