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Lookup NU author(s): Professor Matthew Prina
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.Objectives Our aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates. Design The total sample was 72 140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50-64 and 65+ years old). Results Three clusters were identified: € cardio-metabolic' (8.93% in participants aged 50-64 years and 27.22% in those aged 65+ years), € respiratory-mental-articular' (3.91% and 5.27%) and € healthy' (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the € cardio-metabolic' group (18.8%) and England the € respiratory-mental-articular' (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the € healthy' class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the € respiratory-mental-articular'. Conclusion Multimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity.
Author(s): Bayes-Marin I, Sanchez-Niubo A, Egea-Cortes L, Nguyen H, Prina M, Fernandez D, Haro JM, Olaya B
Publication type: Article
Publication status: Published
Journal: BMJ Open
Year: 2020
Volume: 10
Issue: 7
Print publication date: 01/07/2020
Online publication date: 19/07/2020
Acceptance date: 27/05/2020
Date deposited: 21/08/2023
ISSN (electronic): 2044-6055
Publisher: BMJ Publishing Group
URL: https://doi.org/10.1136/bmjopen-2019-034441
DOI: 10.1136/bmjopen-2019-034441
Data Access Statement: Data are available upon reasonable request. Data may be obtained from a third party and are not publicly available. The original studies data are available on their respective websites: the Study on Global Ageing and Adult Health—SAGE (https://www.who.int/healthinfo/sage/en/), the English Longitudinal Study of Ageing—ELSA (https://www.elsa-project.ac.uk/), and the Survey of Health, Ageing and Retirement in Europe—SHARE (http://www.share-project.org/home0.html). R codes for harmonising the included variables, as well as the STATA codes for the performed analysis are available on https://github.com/athlosproject/athlos-project.github.io.
PubMed id: 32690500
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