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. 2017 Mar 7;15(1):9.
doi: 10.1186/s12963-017-0126-4.

Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates

Barbara M Holzer  1   2   3 , Klarissa Siebenhuener  4   5 , Matthias Bopp  6   5 , Christoph E Minder  7
Affiliations

Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates

Barbara M Holzer et al. Popul Health Metr. .

Abstract

Background: In aging populations, multimorbidity causes a disease burden of growing importance and cost. However, estimates of the prevalence of multimorbidity (prevMM) vary widely across studies, impeding valid comparisons and interpretation of differences. With this study we pursued two research objectives: (1) to identify a set of study design and demographic factors related to prevMM, and (2) based on (1), to formulate design recommendations for future studies with improved comparability of prevalence estimates.

Methods: Study data were obtained through systematic review of the literature. UsingPubMed/MEDLINE, Embase, CINAHL, Web of Science, BIOSIS, and Google Scholar, we looked for articles with the terms "multimorbidity," "comorbidity," "polymorbidity," and variations of these published in English or German in the years 1990 to 2011. We selected quantitative studies of the prevalence of multimorbidity (two or more chronic medical conditions) with a minimum sample size of 50 and a study population with a majority of Caucasians. Our database consisted of prevalence estimates in 108 age groups taken from 45 studies. To assess the effects of study design variables, we used meta regression models.

Results: In 58% of the studies, there was only one age group, i.e., no stratification by age. The number of persons per age group ranged from 136 to 5.6 million. Our analyses identified the following variables as highly significant: "mean age," "number of age groups", and "data reporting quality" (all p < 0.0001). "Setting," "disease classification," and "number of diseases in the classification" were significant (0.01 < p ≤ 0.03), and "data collection period" and "data source" were non-significant. A separate analysis showed that prevMM was significantly higher in women than men (sign test, p = 0.0015).

Conclusions: Comparable prevalence estimates are urgently needed for realistic description of the magnitude of the problem of multimorbidity. Based on the results of our analyses of variables affecting prevMM, we make some design recommendations. Our suggestions were guided by a pragmatic approach and aimed at facilitating the implementation of a uniform methodology. This should aid progress towards a more uniform operationalization of multimorbidity.

Keywords: Age; Gender; Multiple chronic conditions; Study design variables; Systematic review.

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Figures

Fig. 1
Fig. 1
PRISMA flow diagram
Fig. 2
Fig. 2
Observed versus predicted percentage of multimorbidity P2+ in a scatter plot
Fig. 3
Fig. 3
Effect of the number of age groups used in a study on multimorbidity P2+. Category “one age group” is the reference
Fig. 4
Fig. 4
Effect of the number of diseases used in a study on multimorbidity P2+. Category “fewer than 10 diseases on list” is the reference
See this image and copyright information in PMC

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