Abstract
Compared to White individuals, SARS-CoV-2 infection, hospitalization, and death is higher among Black/African-American, Hispanic, and American Indian or Alaska Native people. This study used sociocentric network visualizations to 1) identify racial and ethnic disparities associated with cancer, hypertension, diabetes, dyslipidemia, kidney disease, and liver disease during the SARS-CoV-2 pandemic, and 2) determine whether comorbidity network structures of these illnesses differ by race, ethnicity, age, and gender during this time. The study used cross-sectional data from the MACS/WIHS Combined Cohort Study collected from October 2020 to September 2021 during visit 101. The analysis included Fisher exact test and bivariate logistic regression using R studio, social network centrality measurements using UCINET6, and sociocentric network visualizations using NetDraw 2.182. In all sociocentric network visualizations, the prevalence of dyslipidemia was the highest among all illnesses included in the study. Social network visualizations for all participants showed that dyslipidemia was the illness with the highest prevalence, followed by hypertension, diabetes, and kidney disease. The network visualization for non-Hispanics showed a higher density than for Hispanics. For all illnesses except diabetes and hypertension, centrality measurements were higher for males than females. Overall, centrality was higher for illnesses among adults <65 years old. All illnesses had comorbidities in the network visualizations for Black/African-Americans and Whites, with hypertension having higher centrality in Whites. Social network visualization is a promising tool to guide interventions and policies tailored to provide insight into marginalized groups' disease comorbidity. Innovative research in understanding and addressing the complexity of comorbidities is essential in managing unique chronic comorbid conditions and diseases faced by underserved populations during the SARS-CoV-2 pandemic.