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...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/328892 |
| Acceso en línea: | https://hdl.handle.net/2117/328892 https://dx.doi.org/10.1136/bmjopen-2019-034441 |
| Access Level: | acceso abierto |
| Palabra clave: | Diseases--Social aspects Comorbidity Social classes Multimorbidity Non-communicable diseases (NCDs) Low- and middleincome countries (LMICs) High-income country (HICs) Latent class analysis (LCA) Malalties -- Aspectes socials Comorbiditat Classes socials Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Sumario: | 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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