# Patterns and predictors of viral suppression: A Big Data approach

> **NIH NIH R01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $93,131

## Abstract

Abstract/Summary
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 (EtHE): A Plan for America” federal campaign launched in
2019. Underrepresented populations, such as racial or ethnic minority populations, sexual and gender minority
groups, and socioeconomically disadvantaged populations are usually disproportionately affected by HIV and
subsequently experience a more striking virological failure. The COVID-19 pandemic is affecting People living
with HIV (PLWH) in unique ways. It reveals the more apparent systemic inequities of HIV care due to the
exacerbated preexisting structural disparities among underrepresented populations and consequently puts the
already vulnerable populations at increased risk of worse HIV outcomes, including viral suppression. The
parent grant (R01 AI164947) funded in 2021 aims to examine the longitudinal dynamic pattern of viral
suppression, develop optimal predictive models of various viral suppression indicators, and translate the
models to service-ready tools for clinical use using the South Carolina (SC) statewide HIV electronic health
record (EHR) data. However, the SC statewide HIV database, a real-world data, cannot capture an adequate
sample of underrepresented populations due to their historically limited access to specialty care and academic
medical centers that serve as the primary sources for EHR data. The All of Us Research Program, a national
historic effort supported by the NIH, aims to recruit a broad diverse group of the US population with more
than 50% of the participants from racial and ethnic minority groups and more than 80% from populations
historically underrepresented in biomedical research. The All of Us Research Program is harmonizing data
from multiple sources on an ongoing basis and currently it has recruited ~4800 PLWH with a series of self-
reported survey data (e.g., Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant
longitudinal EHR data (laboratory and medication). Given the limitations of the parent grant (R01 AI164947),
this administrative supplement expands the parent grant to target a broadly defined underrepresented HIV
population and develop a personalized viral suppression prediction model using machine learning techniques
by incorporating multilevel factors (e.g., COVID-19 interruption, psychological wellbeing, healthcare
utilization, and social environmental factors) using All of Us big data resources. The availability of
comprehensive phenotypic data and the Researcher Workbench in All of Us platform fully assures the
transparency and reproducibility of the proposed project and thus increases the generalizability of research
findings. The proposed personalized viral suppression prediction can provide data driven evidence on tailored
HIV treatment strategies to different underrepresented populations particularly in the face of the unexpected
in...

## Key facts

- **NIH application ID:** 10658458
- **Project number:** 3R01AI164947-02S1
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Bankole Olatosi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $93,131
- **Award type:** 3
- **Project period:** 2021-06-09 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10658458

## Citation

> US National Institutes of Health, RePORTER application 10658458, Patterns and predictors of viral suppression: A Big Data approach (3R01AI164947-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10658458. Licensed CC0.

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