Evidence map›Paper›PMID 37188476›Full record

ArticleBMJ open2023

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

Jiajia Zhang, Xueying Yang, Sharon Weissman, Xiaoming Li, Bankole Olatosi

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Jiajia ZhangDepartment of Epidemiology and Biostatistics, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA.
Xueying YangHealth Promotion Education and Behavior, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA.ORCID 0000-0001-6788-0688
Sharon WeissmanDepartment of Internal Medicine, School of Medicine, University of South Carolina, Columbia, South Carolina, USA.
Xiaoming LiSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA.
Bankole OlatosiSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA olatosi@mailbox.sc.edu.ORCID 0000-0002-8295-8735
University of South Carolina · US

Funding

Patterns and predictors of viral suppression: A Big Data approachR01AI164947 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI OLATOSI, BANKOLE, ZHANG, JIAJIA · 2021 to 2025
$4.4M
NIAID NIH HHS R01 AI164947
6 · The paper itself

Abstract

introductionSustained 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 ~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, naïve 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.

Indexed as

COVID-19HIV InfectionsPopulation HealthBayes TheoremCohort StudiesEthnicityHumansMinority GroupsPandemicsCOVID-19HIV & AIDSPUBLIC HEALTHSTATISTICS & RESEARCH METHODS

Identifiers

PMID37188476
PMCPMC10186088
OpenAlexW4376642009

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.