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. 2023 Apr 15:2023:9969532.
doi: 10.1155/2023/9969532. eCollection 2023.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

Cristian Ramos-Vera  1 , Antonio Serpa Barrientos  2 , José Vallejos-Saldarriaga  1 , Yaquelin E Calizaya-Milla  3 , Jacksaint Saintila  3
Affiliations

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

Cristian Ramos-Vera et al. Depress Res Treat. .

Abstract

Background: People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear.

Objective: The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder.

Methods: A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships.

Results: The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality.

Conclusion: Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
EGA of comorbidity patterns. Note: the greater the intensity of the connection, the greater the positive association. The thickness of the line is equivalent to the magnitude of the ratio. (1) Obesity, (2) cancer, (3) high blood pressure, (4) high blood cholesterol, (5) heart attack, (6) coronary heart disease, (7) stroke, (8) asthma, (9) respiratory diseases, (10) arthritis, (11) kidney disease, and (12) diabetes.
Figure 2
Figure 2
Centrality indexes of chronic conditions. Note: numbers refer to a chronic condition identified in Table 2. (1) Obesity, (2) cancer, (3) high blood pressure, (4) high blood cholesterol, (5) heart attack, (6) coronary heart disease, (7) stroke, (8) asthma, (9) respiratory diseases, (10) arthritis, (11) kidney disease, and (12) diabetes. This figure refers to the Bootstrap difference test for node strength, where gray boxes indicate nonsignificant differences and black boxes indicate significant differences.
Figure 3
Figure 3
Stability of the 12 chronic conditions of the EGA. Note: in the EGA, the nodes correspond to each instance of the item (comorbidity) in the initial dimension identified by the analysis. (1) Obesity, (2) cancer, (3) high blood pressure, (4) high blood cholesterol, (5) heart attack, (6) coronary heart disease, (7) stroke, (8) asthma, (9) respiratory diseases, (10) arthritis, (11) kidney disease, and (12) diabetes.
See this image and copyright information in PMC

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