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

Overview of attention for article published in Population Health Metrics, March 2017
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Title
Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates
Published in
Population Health Metrics, March 2017
DOI 10.1186/s12963-017-0126-4
Pubmed ID
Authors

Barbara M. Holzer, Klarissa Siebenhuener, Matthias Bopp, Christoph E. Minder

Abstract

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. 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. 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). 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 1%
Unknown 77 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 15%
Student > Master 12 15%
Student > Postgraduate 8 10%
Researcher 7 9%
Student > Doctoral Student 5 6%
Other 18 23%
Unknown 16 21%
Readers by discipline Count As %
Medicine and Dentistry 25 32%
Nursing and Health Professions 11 14%
Agricultural and Biological Sciences 4 5%
Social Sciences 3 4%
Mathematics 2 3%
Other 8 10%
Unknown 25 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 March 2017.
All research outputs
#9,073,450
of 11,333,579 outputs
Outputs from Population Health Metrics
#213
of 263 outputs
Outputs of similar age
#188,046
of 257,244 outputs
Outputs of similar age from Population Health Metrics
#10
of 12 outputs
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