Skip to main content
main-content

01.12.2017 | Research | Ausgabe 1/2017 Open Access

Population Health Metrics 1/2017

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

Zeitschrift:
Population Health Metrics > Ausgabe 1/2017
Autoren:
Barbara M. Holzer, Klarissa Siebenhuener, Matthias Bopp, Christoph E. Minder
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12963-017-0126-4) contains supplementary material, which is available to authorized users.

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. Using PubMed/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.
Zusatzmaterial
Additional file 1: Studies included in the analyses (N = 45) with complete reference information for the studies listed in the table. (PDF 62 kb)
12963_2017_126_MOESM1_ESM.pdf
Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 1/2017

Population Health Metrics 1/2017 Zur Ausgabe