# Localizing Health Disparities and Predicting Morbidity and Mortality for HIV-Related Opportunistic Infections

> **NIH NIH K08** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2024 · $191,484

## Abstract

PROJECT SUMMARY
People who present with Acquire Immunodeficiency Syndrome (AIDS) are at high risk of developing an
opportunistic infection and death. Opportunistic infections (OIs) are any infection that is more frequent or more
severe because of HIV-mediated immunosuppression. Different states in the United States (US) have varying
rates of deaths among people with HIV (PWH) and AIDS due to variable health insurance coverage rates,
demographic health disparities, and access to health care. Data science public health tools to predict disease
and poor outcomes are rapidly advancing, but have not yet been sufficiently applied to improve outcomes
among PWH. In this K08 Mentored Career Development Award, Dr. Catherine Bielick, a fellow physician in
Infectious Diseases at the University of Virginia and rising data scientist, proposes to use artificial intelligence
(AI) to 1) predict the change in OI hospitalization rates by simulating Medicaid expansion (ME) in the South
and identify associated health inequities, 2) predict the change in OI-related mortality rates by simulating ME
and identify associated health inequities, and 3) create a time-series machine learning model to predict poor
clinical outcomes for individual PWH. The first two aims will be accomplished using the State Inpatient
Database (SID), which is hospitalization-level data for over 97% of hospitals in the US. The South was chosen
based on preliminary data finding an association with OI hospitalizations, mortality, and being uninsured.
Demographics, diagnosis codes with presence on admission indicators, and hospital information will all be
used to simulate the effect of Medicaid expansion in each state and predict the change in OI hospitalization
and mortality rates for PWH. The measured impact of this simulated intervention will provide important
groundwork to inform progress on state-level social determinants of health (SDOH), the need for health
insurance for all PWH, and future cost-effectiveness analyses. The last aim will use multisite longitudinal
electronic medical record (EMR) data called the ADVANCE network consisting entirely of underrepresented
patient populations from whom this subset of PWH will be the focus. A time-series deep learning model will be
used to create a risk score for individual PWH which predicts loss of viral suppression, development of an OI,
or all-cause mortality in the following 6 months. Accomplishing this goal will produce a foundational tool on
which future machine learning models can optimize model generalizability, safety, privacy, and responsible
implementation in an EMR for real-time predictions made at an intervenable time. This proposal benefits from a
strong advisory team which includes leading experts in HIV health and policy, data analytics, machine learning,
biostatistics, PWH data use, and AI health care. Drawing from the mentorship, collaboration, and support from
the Division of Infectious Disease, Public Health Sciences, the McManus ...

## Key facts

- **NIH application ID:** 11009209
- **Project number:** 1K08AI181606-01A1
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** Catherine Bielick
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $191,484
- **Award type:** 1
- **Project period:** 2024-09-05 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11009209

## Citation

> US National Institutes of Health, RePORTER application 11009209, Localizing Health Disparities and Predicting Morbidity and Mortality for HIV-Related Opportunistic Infections (1K08AI181606-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11009209. Licensed CC0.

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