Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic

被引:3
作者
Zhang, Jiajia [1 ,2 ]
Yang, Xueying [3 ]
Weissman, Sharon [4 ]
Li, Xiaoming [2 ,3 ]
Olatosi, Bankole [2 ,5 ]
机构
[1] Univ South Carolina, Arnold Sch Publ Hlth, Dept Epidemiol & Biostat, Columbia, SC USA
[2] Univ South Carolina, South Carolina SmartState Ctr Healthcare Qual, Arnold Sch Publ Hlth, Columbia, SC 29208 USA
[3] Univ South Carolina, Hlth Promot Educ & Behav, Arnold Sch Publ Hlth, Columbia, SC USA
[4] Univ South Carolina, Sch Med, Dept Internal Med, Columbia, SC USA
[5] Univ South Carolina, Hlth Serv Policy & Management, Arnold Sch Publ Hlth, Columbia, SC 29208 USA
来源
BMJ OPEN | 2023年 / 13卷 / 05期
基金
美国国家卫生研究院;
关键词
COVID-19; HIV & AIDS; PUBLIC HEALTH; STATISTICS & RESEARCH METHODS;
D O I
10.1136/bmjopen-2022-070869
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the 'Ending the HIV Epidemic' federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data. Methods and analysis This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited similar to 4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naive Bayes and long short-term memory. Ethics and dissemination The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.
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页数:9
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