Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies

Abstract

Objective(1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence.MethodsIn this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics.Results13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%).ConclusionsThe pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics.PROSPERO registration numberCRD42020172409.

A systematic method for comparing multimorbidity in national surveys

“…Two key findings were that multimorbidity was consistently higher among women compared to men; and that multimorbidity increased in older age groups. Although female sex has inconsistently been linked to higher levels of multimorbidity in South Africa [ 5 ], the findings on age and sex [ 17 ] are consistent with much of the international literature [ 21 ]. Rising multimorbidity in aging populations has implications for healthcare costs and service utilisation in a country such as South Africa with an ageing population [ 22 ].…”

“…We included self-reported disease conditions which may underestimate the prevalence as people may have been unaware that they have the disease. However, a recent systematic review indicated no significant difference in the prevalence of multimorbidity when self-report versus clinic/administrative data were used [ 21 ]. Where self-reported disease conditions were included, the way in which the question was asked at times differed.…”

Measuring multimorbidity in research: Delphi consensus study

“…For studies of prevalence, we recommend that researchers also report age and sex stratified prevalence based on the "always include" and "always or usually include" lists to improve comparability of studies. 10 More generally, although not the focus of this study, multimorbidity measures are often poorly reported, and clarity about choices made and their rationale is critical (figure 3). 19 We recommend that selection of other long term conditions in measures should take account of the criteria agreed as important by panellists in this study (figure 2), and that researchers explicitly report why and how they make decisions on condition and measurement choice (figure 3).…”

“…9 The high level of heterogeneity in multimorbidity prevalence has been found to be mainly attributed to age and inconsistent multimorbidity measurement. 10 The estimated pooled prevalence was 68.7% for an oldest population (aged ≥74 years), 26.3% for a younger population (aged >55 years), 29.3% for a measure including fewer than nine conditions, and 87.6% for a measure including 44 or more conditions. 10 Previous studies have synthesised existing evidence on multimorbidity measures, 8 11 12 compared the performance of different measures in predicting selected outcomes, 13 14 and adapted existing measures to meet the professionally perceived needs of specific regions or populations.…”

“…10 The estimated pooled prevalence was 68.7% for an oldest population (aged ≥74 years), 26.3% for a younger population (aged >55 years), 29.3% for a measure including fewer than nine conditions, and 87.6% for a measure including 44 or more conditions. 10 Previous studies have synthesised existing evidence on multimorbidity measures, 8 11 12 compared the performance of different measures in predicting selected outcomes, 13 14 and adapted existing measures to meet the professionally perceived needs of specific regions or populations. 15 16 These studies have identified heterogeneity in the definition and measurement of multimorbidity as a key issue or limitation, and demonstrate the need for shared approaches to definition to improve comparability and reproducibility.…”