You are here

Article Text

Article menu
Download PDFPDF
Original research
A model to predict adherence to antiretroviral therapy among people living with HIV
  1. Hui Chen1,
  2. Rusi Long1,
  3. Tian Hu2,
  4. Yaqi Chen2,
  5. Rongxi Wang1,
  6. Yujie Liu1,
  7. Shangbin Liu1,
  8. Chen Xu1,
  9. Xiaoyue Yu1,
  10. Ruijie Chang1,
  11. Huwen Wang1,3,
  12. Kechun Zhang2,
  13. Fan Hu1,
  14. Yong Cai1
  1. 1 School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  2. 2 Shenzhen Longhua District Center for Disease Control and Prevention, Shenzhen, China
  3. 3 The Chinese University of Hong Kong The Jockey Club School of Public Health and Primary Care, Hong Kong Special Administrative Region, China, Hong Kong
  1. Correspondence to Kechun Zhang; 398081363{at}qq.com; Fan Hu; joyking2003{at}163.com; Dr Yong Cai; caiyong202028{at}hotmail.com

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.

  • China
  • treatment adherence and compliance
  • antiretroviral agents
  • HIV

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

https://doi.org/10.1136/sextrans-2021-055222

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplemental information.

    View Full Text

    Footnotes

    • Handling editor Joseph D Tucker

    • HC, RL and TH contributed equally.

    • Contributors All authors contributed to the design of this research. HC and RL drafted the manuscript and performed statistical analyses. TH and YC was involved in the compilation of the questionnaire. YC, CX, XY, RW, YL, SL, RC and HW played a major role in the field survey. KZ, FH and YC made a substantial contribution to the interpretation of the data and were involved in revision of the manuscript through all stages. All authors read and approved the final manuscript. YC is the guarantor of this study.

    • Funding This work was support by Shanghai Three-year Action Plan for Public Health under Grant (GWV 10.1-XK15, GWV-10.2-XD13, GWV-10.1-XK18); the Project from Longhua Technology and Innovation Bureau (2020207); the High-Level Project of Medicine in Longhua, Shenzhen (HLPM201907020105); Strategic collaborative innovation team (SSMU-ZLCX20180601).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.