Alessandra Marengoni1,2, Albert Roso-Llorach3,4, Davide L Vetrano1,5,6, Sergio Fernández-Bertolín3,4, Marina Guisado-Clavero3,4, Concepción Violán3,4, Amaia Calderón-Larrañaga1. 1. Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Sweden. 2. Department of Clinical and Experimental Sciences, University of Brescia, Italy. 3. Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Spain. 4. Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain. 5. Department of Geriatrics, Catholic University of Rome, vItaly. 6. Centro di Medicina dell'Invecchiamento, Fondazione Policlinico "A. Gemelli," Scientific Institute for Research and Healthcare (IRCCS), Rome, Italy.
Abstract
BACKGROUND: The aim of this study is to identify clusters of older persons based on their multimorbidity patterns and to analyze differences among clusters according to sociodemographic, lifestyle, clinical, and functional characteristics. METHODS: We analyzed data from the Swedish National Study on Aging and Care in Kungsholmen on 2,931 participants aged 60 years and older who had at least two chronic diseases. Participants were clustered by the fuzzy c-means cluster algorithm. A disease was considered to be associated with a given cluster when the observed/expected ratio was ≥2 or the exclusivity was ≥25%. RESULTS: Around half of the participants could be classified into five clinically meaningful clusters: respiratory and musculoskeletal diseases (RESP-MSK) 15.7%, eye diseases and cancer (EYE-CANCER) 10.7%, cognitive and sensory impairment (CNS-IMP) 10.6%, heart diseases (HEART) 9.3%, and psychiatric and respiratory diseases (PSY-RESP) 5.4%. Individuals in the CNS-IMP cluster were the oldest, with the worst function and more likely to live in a nursing home; those in the HEART cluster had the highest number of co-occurring diseases and drugs, and they exhibited the highest mean values of serum creatinine and C-reactive protein. The PSY-RESP cluster was associated with higher levels of alcoholism and neuroticism. The other half of the cohort was grouped in an unspecific cluster, which was characterized by gathering the youngest individuals, with the lowest number of co-occurring diseases, and the best functional and cognitive status. CONCLUSIONS: The identified multimorbidity patterns provide insight for setting targets for secondary and tertiary preventative interventions and for designing care pathways for multimorbid older people.
BACKGROUND: The aim of this study is to identify clusters of older persons based on their multimorbidity patterns and to analyze differences among clusters according to sociodemographic, lifestyle, clinical, and functional characteristics. METHODS: We analyzed data from the Swedish National Study on Aging and Care in Kungsholmen on 2,931 participants aged 60 years and older who had at least two chronic diseases. Participants were clustered by the fuzzy c-means cluster algorithm. A disease was considered to be associated with a given cluster when the observed/expected ratio was ≥2 or the exclusivity was ≥25%. RESULTS: Around half of the participants could be classified into five clinically meaningful clusters: respiratory and musculoskeletal diseases (RESP-MSK) 15.7%, eye diseases and cancer (EYE-CANCER) 10.7%, cognitive and sensory impairment (CNS-IMP) 10.6%, heart diseases (HEART) 9.3%, and psychiatric and respiratory diseases (PSY-RESP) 5.4%. Individuals in the CNS-IMP cluster were the oldest, with the worst function and more likely to live in a nursing home; those in the HEART cluster had the highest number of co-occurring diseases and drugs, and they exhibited the highest mean values of serum creatinine and C-reactive protein. The PSY-RESP cluster was associated with higher levels of alcoholism and neuroticism. The other half of the cohort was grouped in an unspecific cluster, which was characterized by gathering the youngest individuals, with the lowest number of co-occurring diseases, and the best functional and cognitive status. CONCLUSIONS: The identified multimorbidity patterns provide insight for setting targets for secondary and tertiary preventative interventions and for designing care pathways for multimorbid older people.
Authors: Marisa Baré; Susana Herranz; Rosa Jordana; Maria Queralt Gorgas; Sara Ortonobes; Daniel Sevilla; Elisabet De Jaime; Olatz Ibarra; Candelaria Martín Journal: BMJ Open Date: 2020-01-26 Impact factor: 2.692