Evidence map›Paper›PMID 31624969›Full record

SynthesisEuropean journal of epidemiology2019

Do replicable profiles of multimorbidity exist? Systematic review and synthesis.

Ljoudmila Busija, Karen Lim, Cassandra Szoeke, Kerrie M Sanders, Marita P McCabe

Open access · hybridAbstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
130citing papers in PubMed, 6 pooled it
8.6field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

130 citing papers in PubMed, 6 syntheses or guidelines pooled it, 203 citations in OpenAlex.

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70 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Ljoudmila BusijaBiostatistics Consulting Platform, Research Methodology Division, School of Public Health and Preventive Medicine, Monash University, Level 4, 553 St Kilda Road, Melbourne, VIC, 3004, Australia. lucy.busija@monash.edu.ORCID http://orcid.org/0000-0001-7464-9089
Karen LimMary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.ORCID http://orcid.org/0000-0002-2767-1651
Cassandra SzoekeSchool of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia.ORCID http://orcid.org/0000-0002-3698-2590
Kerrie M SandersDepartment of Medicine - Western Health, Melbourne Medical School, The University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0002-2718-6592
Marita P McCabeHealth and Ageing Research Group, Swinburne University of Technology, Hawthorn, Australia.ORCID http://orcid.org/0000-0002-4989-8582
Australian Catholic University · AUMonash Health · AUSwinburne University of Technology · AUThe University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

MultimorbidityChronic DiseaseCluster AnalysisFactor Analysis, StatisticalHumansLatent Class AnalysisReproducibility of ResultsCluster analysisExploratory factor analysisLatent class analysisMultidimensional scalingMultimorbidity

Identifiers

PMID31624969
OpenAlexW2980845325

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.