A model to predict adherence to antiretroviral therapy among people living with HIV

Author:

Chen Hui,Long Rusi,Hu Tian,Chen Yaqi,Wang Rongxi,Liu Yujie,Liu Shangbin,Xu Chen,Yu Xiaoyue,Chang Ruijie,Wang Huwen,Zhang Kechun,Hu Fan,Cai YongORCID

Abstract

ObjectivesSuboptimal 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.MethodsA 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.ResultsThe 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).ConclusionsOur 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.

Funder

the Project from Longhua Technology and Innovation Bureau

the High-Level Project of Medicine in Longhua, Shenzhen

Shanghai Three-year Action Plan for Public Health under Grant

Strategic collaborative innovation team

Publisher

BMJ

Subject

Infectious Diseases,Dermatology

Reference29 articles.

1. Hiv/Aids TJUNPo . 90–90-90 an ambitious treatment target to help end the AIDS epidemic 2014. Available: http://www.unaids.org/sites/default/files/media_asset/90-90- 90_en_0.pdf. JC2684

2. Can the UNAIDS 90-90-90 target be achieved? A systematic analysis of national HIV treatment cascades

3. Update on the AIDS/STD epdemic in China and the third quarter of 2018 (in Chinese);NCAIDS;China Journal of AIDS/STD,2018

4. Non-planning impulsivity but not behavioral impulsivity is associated with HIV medication Non-adherence;Dunne;AIDS Behav,2019

5. Analysis of factors associated with dropping-out from HIV antiretroviral therapy in Kunming City, China;Liao;BMC Infect Dis,2019

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3