School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shenzhen Longhua District Center for Disease Control and Prevention, Shenzhen, China.
Sex Transm Infect. 2022 Sep;98(6):438-444. doi: 10.1136/sextrans-2021-055222. Epub 2021 Dec 6.
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.
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.
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).
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.
抗逆转录病毒疗法（ART）的依从性不理想极大地阻碍了实现艾滋病署艾滋病毒治疗目标。本研究旨在根据中国的数据，建立一个基于理论的预测模型，以预测 ART 依从性。
2020 年 12 月在中国深圳进行了一项横断面研究。通过滚雪球抽样招募参与者，完成了一项包括社会人口统计学特征、艾滋病毒临床信息、信息-动机-行为技能（IMB）结构和 ART 依从性的调查。CD4 计数和 HIV 病毒载量从病历中提取。通过多变量逻辑回归建立了一个预测 ART 依从性的模型，使用最小绝对收缩和选择算子（LASSO）回归选择有意义的预测因子。为了评估模型的性能，我们使用一致性指数（C 指数）测试了判别能力，并使用 Hosmer 和 Lemeshow 检验测试了校准准确性。
在训练组的 651 名艾滋病毒感染者（PLHIV）中，平均年龄为 34.1±8.4 岁，有 20.1%报告依从性不理想。在验证组的 276 名 PLHIV 中，平均年龄为 33.9±8.2 岁，不良依从的患病率为 22.1%。该模型包含五个预测因子：教育水平、饮酒、副作用、客观能力和自我效能。该模型由这些预测因子构建，内部验证的 C 指数为 0.739（95%可信区间为 0.703 至 0.772），通过 bootstrap 验证确认 C 指数为 0.717，在时间验证中保持适度（C 指数为 0.676）。在训练组和验证组中，校准能力均可以接受（p＞0.05）。
我们的模型准确估计了 ART 依从行为。该预测工具可以帮助识别依从性较差的高风险个体，并指导量身定制的干预措施以优化依从性。
