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Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts

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: T...

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Autores principales: Bayes-Marin, Ivet, Sanchez-Niubo, Albert, Egea-Cortés, Laia, Nguyen, Hai, Prina, Matthew, Fernández, Daniel, Haro, Josep Maria, Olaya, Beatriz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371222/
https://www.ncbi.nlm.nih.gov/pubmed/32690500
http://dx.doi.org/10.1136/bmjopen-2019-034441
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author Bayes-Marin, Ivet
Sanchez-Niubo, Albert
Egea-Cortés, Laia
Nguyen, Hai
Prina, Matthew
Fernández, Daniel
Haro, Josep Maria
Olaya, Beatriz
author_facet Bayes-Marin, Ivet
Sanchez-Niubo, Albert
Egea-Cortés, Laia
Nguyen, Hai
Prina, Matthew
Fernández, Daniel
Haro, Josep Maria
Olaya, Beatriz
author_sort Bayes-Marin, Ivet
collection PubMed
description 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.
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spelling pubmed-73712222020-07-22 Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts Bayes-Marin, Ivet Sanchez-Niubo, Albert Egea-Cortés, Laia Nguyen, Hai Prina, Matthew Fernández, Daniel Haro, Josep Maria Olaya, Beatriz BMJ Open Epidemiology 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. BMJ Publishing Group 2020-07-19 /pmc/articles/PMC7371222/ /pubmed/32690500 http://dx.doi.org/10.1136/bmjopen-2019-034441 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology
Bayes-Marin, Ivet
Sanchez-Niubo, Albert
Egea-Cortés, Laia
Nguyen, Hai
Prina, Matthew
Fernández, Daniel
Haro, Josep Maria
Olaya, Beatriz
Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title_full Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title_fullStr Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title_full_unstemmed Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title_short Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
title_sort multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using athlos harmonised cohorts
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371222/
https://www.ncbi.nlm.nih.gov/pubmed/32690500
http://dx.doi.org/10.1136/bmjopen-2019-034441
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