ResearchPublications

Identifying symptom clusters among people living with HIV on antiretroviral therapy in China: A network analysis
Abstract

CONTEXT: There exists a research interest shift from separate symptoms to symptom clusters among people living with HIV (PLWH), which may provide a better understanding of symptom management in HIV/AIDS care. However, the symptom clusters among Chinese PLWH are still unknown.

OBJECTIVES: The aim of our study was to identify symptom clusters and to examine demographic and health-related factors associated with these symptom clusters among PLWH prescribing antiretroviral therapy (ART) in China.

METHODS: From April to September 2017, we recruited 1116 participants through a convenience sampling in five HIV/AIDS designated facilities in the eastern, middle, and southwest regions of China. The principal component analysis was used to identify the symptom clusters. Association network was adopted to describe the relationships among symptoms and clusters. A multiple linear model was used to investigate the associated factors for the severity of overall symptoms and the prevalence of each symptom clusters.

RESULTS: Five symptom clusters were identified, including cognitive dysfunction, mood disturbance, wasting syndrome, dizziness/headache, and skin-muscle-joint disorder. Cognitive dysfunction was the most central symptom cluster. Variables including primary caregiver during ART treatment, years of HIV diagnosis and ART use, having comorbidity, self-rated health, and quality of life were associated with the prevalence of these five symptom clusters.

CONCLUSION: Our study suggests that there is a need to evaluate symptom clusters for the improvement of symptom management among PLWH. It is particularly important to include assessment and treatment of cognitive symptoms as an essential component of the HIV care.

Full citation:
Zhu Z, Hu Y, Xing W, Guo M, Zhao R, Han S, Wu B (2019).
Identifying symptom clusters among people living with HIV on antiretroviral therapy in China: A network analysis
, 57 (3), 617-626. doi: 10.1016/j.jpainsymman.2018.11.011.