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Chen H, Long R, Hu T, Chen Y, Wang R, Liu Y, Liu S, Xu C, Yu X, Chang R, Wang H, Zhang K, Hu F, Cai Y. A model to predict adherence to antiretroviral therapy among people living with HIV. Sex Transm Infect 2021; 98:438-444. [PMID: 34873028 DOI: 10.1136/sextrans-2021-055222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/08/2021] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Suboptimal adherence to antiretroviral therapy (ART) dramatically hampers the achievement of the UNAIDS HIV treatment targets. This study aimed to develop a theory-informed predictive model for ART adherence based on data from Chinese. METHODS A cross-sectional study was conducted in Shenzhen, China, in December 2020. Participants were recruited through snowball sampling, completing a survey that included sociodemographic characteristics, HIV clinical information, Information-Motivation-Behavioural Skills (IMB) constructs and adherence to ART. CD4 counts and HIV viral load were extracted from medical records. A model to predict ART adherence was developed from a multivariable logistic regression with significant predictors selected by Least Absolute Shrinkage and Selection Operator (LASSO) regression. To evaluate the performance of the model, we tested the discriminatory capacity using the concordance index (C-index) and calibration accuracy using the Hosmer and Lemeshow test. RESULTS The average age of the 651 people living with HIV (PLHIV) in the training group was 34.1±8.4 years, with 20.1% reporting suboptimal adherence. The mean age of the 276 PLHIV in the validation group was 33.9±8.2 years, and the prevalence of poor adherence was 22.1%. The suboptimal adherence model incorporates five predictors: education level, alcohol use, side effects, objective abilities and self-efficacy. Constructed by those predictors, the model showed a C-index of 0.739 (95% CI 0.703 to 0.772) in internal validation, which was confirmed be 0.717 via bootstrapping validation and remained modest in temporal validation (C-index 0.676). The calibration capacity was acceptable both in the training and in the validation groups (p>0.05). CONCLUSIONS Our model accurately estimates ART adherence behaviours. The prediction tool can help identify individuals at greater risk for poor adherence and guide tailored interventions to optimise adherence.
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Affiliation(s)
- Hui Chen
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rusi Long
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tian Hu
- Shenzhen Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Yaqi Chen
- Shenzhen Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Rongxi Wang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yujie Liu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shangbin Liu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Xu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyue Yu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruijie Chang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huwen Wang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,The Chinese University of Hong Kong The Jockey Club School of Public Health and Primary Care, Hong Kong Special Administrative Region, China, Hong Kong
| | - Kechun Zhang
- Shenzhen Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Fan Hu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Cai
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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