4.1 Article

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

出版社

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/09540121.2020.1844864

关键词

HIV; AIDS; comorbidity patterns; hierarchical cluster analysis; electronic health records; South Carolina

资金

  1. National Institute of Allergy and Infectious Diseases [R01AI127203]

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This study analyzed electronic health records data of 8,490 PLWH in South Carolina and identified four comorbidity clusters: substance use and mental disorder, metabolic disorder, liver disease and cancer, cerebrovascular disease. Socio-demographic factors such as age, gender, race were found to be associated with multimorbidity. Integrated clinical care is important for managing the complexities of comorbidities among PLWH.
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.

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