Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system

BackgroundPeople with cognitive impairment may be exposed to an increased risk of comorbidities; however, the clustering of comorbidity patterns in these patients is unclear.ObjectiveTo explore the network structure of chronic comorbidity in a U.S. national sample spanning all 50 U.S. states with mo...

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Main Authors: Cristian Ramos-Vera (Author), Jacksaint Saintila (Author), Angel García O'Diana (Author), Yaquelin E. Calizaya-Milla (Author)
Format: Book
Published: Frontiers Media S.A., 2022-09-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Cristian Ramos-Vera  |e author 
700 1 0 |a Jacksaint Saintila  |e author 
700 1 0 |a Angel García O'Diana  |e author 
700 1 0 |a Yaquelin E. Calizaya-Milla  |e author 
245 0 0 |a Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system 
260 |b Frontiers Media S.A.,   |c 2022-09-01T00:00:00Z. 
500 |a 2296-2565 
500 |a 10.3389/fpubh.2022.981944 
520 |a BackgroundPeople with cognitive impairment may be exposed to an increased risk of comorbidities; however, the clustering of comorbidity patterns in these patients is unclear.ObjectiveTo explore the network structure of chronic comorbidity in a U.S. national sample spanning all 50 U.S. states with more than 170,000 participants reporting perceived cognitive impairment.MethodsThis is a cross-sectional study conducted using Behavioral Risk Factor Surveillance System (BRFSS) secondary data collected in 2019 and covering 49 U.S. states, the District of Columbia, Guam, and the Commonwealth of Puerto Rico. A total of 15,621 non-institutionalized U.S. adult participants who reported "yes" to the subjective cognitive impairment question were considered, of whom 7,045 were men and 8,576 were women. All participants were aged 45 years or older. A statistical graphical model was used that included clustering algorithms and factorization of variables in a multivariate network relationship system [exploratory graphical analysis (EGA)].ResultsThe results of the EGA show associations between the comorbid conditions evaluated. These associations favored the clustering of various comorbidity patterns. In fact, three patterns of comorbidities have been identified: (1) arthritis, asthma, respiratory diseases, and depression, (2) obesity, diabetes, blood pressure high, and blood cholesterol high, and (3) heart attack, coronary heart disease, stroke, and kidney disease.ConclusionThese results suggest the development of interdisciplinary treatment strategies in patients with perceived cognitive impairment, which could help to design an integrated prevention and management of the disease and other related health problems, such as Alzheimer's disease and related dementias. 
546 |a EN 
690 |a cognitive impairment 
690 |a multimorbidity 
690 |a comorbidity 
690 |a chronic disease 
690 |a cluster analysis 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Frontiers in Public Health, Vol 10 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fpubh.2022.981944/full 
787 0 |n https://doaj.org/toc/2296-2565 
856 4 1 |u https://doaj.org/article/36e8a1e590bb4fdd87baf0d714b581ff  |z Connect to this object online.