# Computational modeling to evaluate socio-structural interventions for HIV and substance use

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2024 · $745,519

## Abstract

ABSTRACT
Background: Black sexual and gender minorities (SGM) are disproportionately affected by HIV and existing
disparities could be exacerbated by increases in substance use disorders, as shifting trends in the opioid
epidemic have been accompanied by increases in methamphetamine and polysubstance use among Black
SGM. Evidence suggests that factors such as housing instability, incarceration, and unemployment may pose
significant barriers to engagement in HIV prevention and care for Black SGM, and these factors are also
associated with methamphetamine use. Because such interventions are resource intensive and logistically
challenging, particularly for vulnerable communities who are often highly mobile and less likely to engage in
research in traditional settings, guidance is needed at the intervention development stage to determine the
most impactful and efficient intervention strategies. Agent-based models (ABMs) can be used to virtually
evaluate candidate interventions to facilitate more efficient and timely intervention development. Because they
allow for the conduct of counterfactual experiments, ABMs can also facilitate identification of effects that would
be difficult to identify using traditional statistical approaches and can provide valuable insights to understand
causal mechanisms that give rise to complex systems. Objective: Building on an existing ABM platform, this
proposal will utilize multiple existing data sources to characterize relationships among socio-structural
stressors, substance use, mental health, and HIV prevention and care continuum outcomes among Black
SGM. We will combine methods from epidemiology, ABM, and robust decision making (RDM) to understand
the potential impact of structural interventions for reducing substance use, overdose, and HIV transmission.
Methods: We will apply statistical and computational methods to better understand how socio-structural
factors, substance use, and mental health impact engagement in HIV prevention and care continuums. We will
then conduct a series of experiments to evaluate how socio-structural factors impact the uptake of existing
biomedical interventions and compare outcomes under scenarios with different combinations of interventions
using RDM. Significance: A better understanding of where and how to focus intervention efforts offers
potential to improve substance use and HIV prevention and care outcomes for Black SGM. Once developed,
our methods and models can be adapted to other geographic areas to reflect local prevention priorities and
can serve as an example application of epidemiology, ABM, and RDM methods to advance HIV and substance
use prevention science.

## Key facts

- **NIH application ID:** 10932958
- **Project number:** 5R01DA057350-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Anna Hotton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $745,519
- **Award type:** 5
- **Project period:** 2023-09-30 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932958

## Citation

> US National Institutes of Health, RePORTER application 10932958, Computational modeling to evaluate socio-structural interventions for HIV and substance use (5R01DA057350-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10932958. Licensed CC0.

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