Comorbidity patterns among people living with HIV: a hierarchical clustering approach through integrated electronic health records data in South Carolina

Abstract

ABSTRACT Comorbidity among people living with HIV (PLWH) is understudied although identifying its patterns and socio-demographic predictors would be beneficial for comorbidity management. Using electronic health records (EHR) data, 8,490 PLWH diagnosed between January 2005 and December 2016 in South Carolina were included in the current study. An initial list of 86 individual diagnoses of chronic conditions was extracted in the EHR data. After grouping individual diagnoses with a pathophysiological similarity, 24 diagnosis groups were generated. Hierarchical cluster analysis was applied to these 24 diagnosis groups and yielded four comorbidity clusters: “substance use and mental disorder” (e.g., alcohol use, depression, and illicit drug use); “metabolic disorder” (e.g., hypothyroidism, diabetes, hypertension, and chronic kidney disease); “liver disease and cancer” (e.g., hepatitis B, chronic liver disease, and non-AIDS defining cancers); and “cerebrovascular disease” (e.g., stroke and dementia). Multivariable logistic regression was conducted to investigate the association between socio-demographic factors and multimorbidity (defined as concurrence of ≥ 2 comorbidity clusters). The multivariable logistic regression showed that age, gender, transmission risk, race, initial CD4 counts, and viral load were significant factors associated with multimorbidity. The results suggested the importance of integrated clinical care that addresses the complexities of multiple, and potentially interacting comorbidities among PLWH.