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J Gerontol A Biol Sci Med Sci. 2016 Feb;71(2):205-14. doi: 10.1093/gerona/glv128. Epub 2015 Sep 29.

Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study.

Author information

1
Pharmacy Department, Hospital de la Santa Creu i Sant Pau, Institut d'Investigacions Biomèdiques Sant Pau (IIB Sant Pau), Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain. Research and Development Unit, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Spain. Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain.
2
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Spain. Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain.
3
Department of Health Statistics and Information Systems, World Health Organization, Geneva, Switzerland.
4
Neurology, Public Health and Disability Unit, Neurological Institute Carlo Besta IRCCS Foundation, Milan, Italy.
5
National Institute for Health and Welfare, Helsinki, Finland.
6
Department of Epidemiology, Jagiellonian University Medical College, Krakow, Poland.
7
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain. Department of Psychiatry, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IP), Madrid, Spain. Department of Psychiatry, Universidad Autónoma de Madrid, Spain.
8
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Spain. Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain. jmharo@pssjd.org.

Abstract

BACKGROUND:

Population ageing challenges health care systems due to the high prevalence and impact of multimorbidity in older adults. However, little is known about how chronic conditions present in certain multimorbidity patterns, which could have great impact on public health at several levels. The aim of our study was to identify and describe multimorbidity patterns in low-, middle-, and high-income countries.

METHODS:

We analyzed data from the Collaborative Research on Ageing in Europe project (Finland, Poland, and Spain) and the World Health Organization's Study on Global Ageing and Adult Health (China, Ghana, India, Mexico, Russia, and South Africa). These cross-sectional studies obtained data from 41,909 noninstitutionalized adults older than 50 years. Exploratory factor analysis was performed to detect multimorbidity patterns. Additional adjusted binary logistic regressions were performed to identify associations between sociodemographic factors and multimorbidity.

RESULTS:

Overall multimorbidity prevalence was high across countries. Hypertension, cataract, and arthritis were the most prevalent comorbid conditions. Two or three multimorbidity patterns were found per country. Several patterns were identified across several countries: "cardio-respiratory" (angina, asthma, and chronic obstructive pulmonary disease), "metabolic" (diabetes, obesity, and hypertension), and "mental-articular" (arthritis and depression).

CONCLUSIONS:

A high prevalence of multimorbidity occurs in older adults across countries, with low- and middle-income countries gradually approaching the figures of richer countries. Certain multimorbidity patterns are present in several countries, which suggest that common underlying etiopathogenic factors may play a role. Deeper understanding of these patterns may lead to the development of preventive actions to diminish their prevalence and also give rise to new, comprehensive approaches for the management of these co-occurring conditions.

KEYWORDS:

Epidemiology; Health disparities; Morbidity; Multimorbidities; Public health; Socioeconomic issues

PMID:
26419978
PMCID:
PMC5864156
DOI:
10.1093/gerona/glv128
[Indexed for MEDLINE]
Free PMC Article

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