Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study.
Por:
Olaya B, Moneta MV, Caballero FF, Tyrovolas S, Bayes I, Ayuso-Mateos JL and Haro JM
Publicada:
18 ago 2017
Ahead of Print:
18 ago 2017
Categoría:
Geriatrics and gerontology
Resumen:
BACKGROUND: This study sought to identify multimorbidity patterns and determine the association between these latent classes with several outcomes, including health, functioning, disability, quality of life and use of services, at baseline and after 3 years of follow-up. METHODS: We analyzed data from a representative Spanish cohort of 3541 non-institutionalized people aged 50 years old and over. Measures were taken at baseline and after 3 years of follow-up. Latent Class Analysis (LCA) was conducted using eleven common chronic conditions. Generalized linear models were conducted to determine the adjusted association of multimorbidity latent classes with several outcomes. RESULTS: 63.8% of participants were assigned to the "healthy" class, with minimum disease, 30% were classified under the "metabolic/stroke" class and 6% were assigned to the "cardiorespiratory/mental/arthritis" class. Significant cross-sectional associations were found between membership of both multimorbidity classes and poorer memory, quality of life, greater burden and more use of services. After 3 years of follow-up, the "metabolic/stroke" class was a significant predictor of lower levels of verbal fluency while the two multimorbidity classes predicted poor quality of life, problems in independent living, higher risk of hospitalization and greater use of health services. CONCLUSIONS: Common chronic conditions in older people cluster together in broad categories. These broad clusters are qualitatively distinct and are important predictors of several health and functioning outcomes. Future studies are needed to understand underlying mechanisms and common risk factors for patterns of multimorbidity and to propose more effective treatments.
Filiaciones:
Olaya B:
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
Moneta MV:
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
Caballero FF:
Department of Psychiatry, Instituto de Investigación Sanitaria Princesa (IP), Hospital Universitario de La Princesa, Madrid, Spain
Tyrovolas S:
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
Bayes I:
Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Fundació Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain
Ayuso-Mateos JL:
Department of Psychiatry, Instituto de Investigación Sanitaria Princesa (IP), Hospital Universitario de La Princesa, Madrid, Spain
Haro JM:
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
Open Access
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