Multimorbidity Patterns in Older Adults: the Role of Social Variables and Lifestyle Behaviors
Entity
UAM. Departamento de Medicina Preventiva y Salud Pública y MicrobiologíaPublisher
Karger PublishersDate
2023-06-01Citation
10.1159/000529406
Gerontology 69.6 (2023):716-727
ISSN
0304-324X (online); 1423-0003 (print)DOI
10.1159/000529406Funded by
This study has been funded by Instituto de Salud Carlos III through the FIS projects 19/319, 19/665 and 20/01040 (Instituto de Salud Carlos III, State Secretary of R+D+I), and co-funded by a European Regional Development Fund, “A way of shaping Europe”. The funding agencies had no role in study design, data analysis, interpretation of results, manuscript preparation or in the decision to submit this manuscript for publication.Project
Gobierno de España. FIS projects 19/319; Gobierno de España. FIS projects 19/665Editor's Version
https://doi.org/10.1159/000529406Subjects
chronic disease categories; exploratory factor analysis; lifestyle behaviors; multimorbidity patterns; social variables; MedicinaRights
© 2023 Karger PublishersAbstract
Introduction: While some condition clusters represent the chance co-occurrence of common individual conditions, others may represent shared causal factors. The aims of this study were to identify multimorbidity patterns in older adults and to explore the relationship between social variables, lifestyle behaviors, and the multimorbidity patterns identified. Methods: This was a cross-sectional design. Data came from 3,273 individuals aged ≥65 from the Seniors-ENRICA-2 cohort; information on 60 chronic disease categories, categorized according to the 2nd edition of the International Classification of Primary Care and the 10th edition of the International Classification of Diseases, was obtained from clinical record linkage. To identify multimorbidity patterns, an exploratory factor analysis was conducted over chronic disease categories with a prevalence >5%, using Oblimin rotation and Kaiser's eigenvalues-greater-than-one rule. The association between multimorbidity patterns and their potential determinants was assessed with multivariable linear regression. Results: The three-factor solution (Musculoskeletal diseases and mental disorders, Cardiometabolic diseases, and Cardiopulmonary diseases) explained 64.5% of the total variance. Being older, lower occupational category, higher levels of loneliness, lower levels of physical activity, and higher body mass index were associated with higher scores in the multimorbidity patterns identified. Female sex was linked to the Musculoskeletal diseases and mental disorders pattern, while being male was revealed to the two remaining multimorbidity patterns. A high diet quality was inversely related to Cardiometabolic diseases, while optimal sleep duration was inversely related to Cardiopulmonary diseases. Conclusion: Three multimorbidity patterns were identified in older adults. Multimorbidity patterns were differently associated with social variables and lifestyles behavioral factors
Files in this item
Google Scholar:Caballero Díaz, Francisco Félix
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Lana, Alberto
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Struijk, Ellen A.
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Arias-Fernández, Lucía
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Cárdenas-Valladolid, Juan
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Salinero-Fort, Miguel Ángel
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Banegas Banegas, José Ramón
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Rodríguez Artalejo, Fernando
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López García, Esther
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