# Big Data analytics of HIV treatment gaps in South Carolina: Identification and prediction

> **NIH NIH R01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2021 · $589,629

## Abstract

Project Summary/Abstract. Early HIV diagnosis as well as linkage into and retention in HIV medical care for
HIV+ individuals is important for patient survival and treatment. Missed opportunities for early HIV diagnosis
continues even with recommended routine HIV testing. National and South Carolina (SC) estimates of
retention in HIV medical care are slightly above fifty percent, indicating a gap in HIV treatment. With significant
proportions of HIV+ individuals not receiving HIV medical care, improved outcomes of care and HIV prevention
as part of national HIV/AIDs strategies are difficult to achieve. The purpose of this study is to use novel
machine learning algorithms to further explore, identify, characterize, and explain predictors of missed
opportunities for HIV medical care utilization among all living HIV+ individuals in SC. Profiles of HIV+
individuals based on their patterns of HIV medical care seeking behavior will be developed with concomitant
identification of both gaps in HIV care and missed opportunities for reengagement into HIV care. Health
utilization behavior for HIV+ individuals' pre-HIV diagnosis also will be studied to identify where missed
opportunities for HIV testing occurs. Findings will be integrated with the ongoing effort of the SC Department of
Health and Environmental Control (DHEC)'s Data-to-Care (DTC) program as well as the Ryan White Care
Program. The public health value that HIV treatment brings includes improved survival outcomes of care
among HIV+ individuals as well as reduced HIV transmission. These important components form part of the
overall strategy for fighting and controlling the HIV epidemic in the United States and aligns closely with the
strategic goals of reducing new HIV infections. Using state-level CD4 and Viral Load (VL) testing data available
for all SC HIV+ individuals since 2004, the study will link inpatient and outpatient claims data sources, the state
electronic HIV/AIDS reporting system, Area Health Resource Files, and data from the state corrections
database to create a unique population based dataset spanning 10 years (2004-2013). Advanced Big Data
analytical algorithms will be used to create person-level profile patterns of pre- and post- HIV diagnosis health
utilization behaviors and for identifying best predictors of linkage and retention in HIV medical care. These
algorithms will be useful in unearthing hidden features/predictors of HIV medical care utilization. A predictive
model useful for predicting where HIV+ individuals who are not in care will access routine medical care
(missed opportunities) also will be developed. Findings will provide fresh guidance for public health
interventions targeting early HIV testing and linkage to and retention in HIV medical care for SC HIV-infected
individuals.

## Key facts

- **NIH application ID:** 10160773
- **Project number:** 5R01AI127203-05
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Xiaoming Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $589,629
- **Award type:** 5
- **Project period:** 2017-06-20 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10160773

## Citation

> US National Institutes of Health, RePORTER application 10160773, Big Data analytics of HIV treatment gaps in South Carolina: Identification and prediction (5R01AI127203-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10160773. Licensed CC0.

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