Identifying multimorbidity clusters among Brazilian older adults using network analysis: Findings and perspectives

Abstract

In aging populations, multimorbidity (MM) is a significant challenge for health systems, however there are scarce evidence available in Low- and Middle-Income Countries, particularly in Brazil. A national cross-sectional study was conducted with 11,177 Brazilian older adults to evaluate the occurrence of MM and related clusters in Brazilians aged ≥ 60 years old. MM was assessed by a list of 16 physical and mental morbidities and it was defined considering ≥ 2 morbidities. The frequencies of MM and its associated factors were analyzed. After this initial approach, a network analysis was performed to verify the occurrence of clusters of MM and the network of interactions between coexisting morbidities. The occurrence of MM was 58.6% (95% confidence interval [CI]: 57.0-60.2). Hypertension (50.6%) was the most frequent morbidity and it was present all combinations of morbidities. Network analysis has demonstrated 4 MM clusters: 1) cardiometabolic; 2) respiratory + cancer; 3) musculoskeletal; and 4) a mixed mental illness + other diseases. Depression was the most central morbidity in the model according to nodes' centrality measures (strength, closeness, and betweenness) followed by heart disease, and low back pain. Similarity in male and female networks was observed with a conformation of four clusters of MM and cancer as an isolated morbidity. The prevalence of MM in the older Brazilians was high, especially in female sex and persons living in the South region of Brazil. Use of network analysis could be an important tool for identifying MM clusters and address the appropriate health care, research, and medical education for older adults in Brazil.

Fig 1. Morbidity network in Brazilian older adults.

National Health Survey (PNS-Brazil, 2013). (A) The graph depicts the analysis network. The nodes represent each morbidity in the model, and the edges connecting the nodes represent the effect size for the association between nodes. Green and red edges represent positive and negative connections, respectively. The colors of the nodes correspond to the clusters detected in the network. (B) Network centrality measurements (degree, betweenness, closeness). Morbidities: 1- arthritis/rheumatism; 2- asthma/bronchitis, 3- back pain; 4- cancer; 5- heart disease (myocardial infarction, heart failure, or cardiac arrhythmias), 6- hypercholesterolemia, 7- chronic obstructive pulmonary disease (COPD); 8- depression, 9- diabetes; 10- hypertension; 11- kidney disease; 12- obesity; 13- other chronic diseases; 14- mental illnesses other than depression; 15- stroke; 16- work-related musculoskeletal disorders.

Fig 2. Morbidity network in Brazilian older adults by sex.

National Health Survey (PNS-Brazil, 2013). (A) The graph depicts morbidity network for Brazilian older men, n = 4,555. (B) Network centrality measurements (degree, betweenness, closeness) for Brazilian older men. (C) The graph depicts morbidity network for Brazilian older women, n = 6,662. (D) Network centrality measurements (degree, betweenness, closeness) for Brazilian older women. Morbidities: 1- arthritis/rheumatism; 2- asthma/bronchitis, 3- back pain; 4- cancer; 5- heart disease (myocardial infarction, heart failure, or cardiac arrhythmias), 6- hypercholesterolemia, 7- chronic obstructive pulmonary disease (COPD); 8- depression, 9- diabetes; 10- hypertension; 11- kidney disease; 12- obesity; 13- other chronic diseases; 14- mental illnesses other than depression; 15- stroke; 16- work-related musculoskeletal disorders.