# A community-based systems science approach to assess risk and protective factors and improve the efficacy and equity of intervention strategies for stimulant use, use disorder, and overdose

> **NIH ALLCDC R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $361,467

## Abstract

PROJECT SUMMARY/ABSTRACT
Stimulant-involved fatal overdoses have increased rapidly since 2014; deaths involving psychostimulants,
primarily methamphetamine, have quadrupled, while fatal overdoses involving cocaine have tripled. Black,
Latino, and Native American people are increasingly using stimulants and dying from overdoses, though low-
income White men remain the largest group of people who use stimulants (PWUS) and die of overdose. The
proposed research targets RFA-CE-21-002 Objective 2: “[to] assess risk and protective factors for illicit
stimulant use, use disorder, and overdose that can contribute to the development or adaptation of intervention
strategies.” Our goal is to identify intervention strategies that could be developed or adapted to reduce
stimulant use (SU) and improve SU health outcomes (SU disorder, recovery, and overdoses). We will conduct
our research in two states with distinct SU crises: Massachusetts (MA), where cocaine and fentanyl overdoses
are rising, including among Black and Latino individuals, and South Dakota (SD), where rapid increases in
methamphetamine use and overdose have disproportionately affected Native Americans.
Our team of addiction and systems science experts will assess how multi-level risk and protective factors for
SU and SU health outcomes interact dynamically, with a focus on disparities related to social determinants of
health, in MA and SD (Aim 1). We will conduct a living systematic review of multi-level factors influencing SU
and SU health outcomes and of multi-level intervention strategies that could synergistically reduce SU and
improve SU health outcomes (Aim 1.1). We will also engage community partners across MA and SD at
multiple levels of the system underlying stimulant use (PWUS, providers, and policymakers) in semi-structured
qualitative interviews to develop a unified causal loop diagram (CLD) that depicts how risk and protective
factors interact dynamically (Aim 1.2). Next, we will identify intervention strategies to reduce SU and improve
SU health outcomes, including generalizable strategies for all populations and adapted strategies to reduce
health disparities in underrepresented populations (Aim 2). We will develop a system dynamics simulation
model, using the CLD and the living systematic review, to explain SU and SU health outcome trends in MA and
SD (Aim 2.1). We will then utilize the model to explore and identify intervention strategies that have the
greatest potential to reduce SU and improve SU health outcomes in MA and SD, while also generating insight
that is applicable nationally (Aim 2.2).
The proposed research will address the public health problems of SU, SU disorder, and overdose through the
identification of efficacious and equitable intervention strategies. Our engagement with community partners
using a systems modeling approach will allow us to provide immediately translatable insight to policymakers in
MA and SD. In the long term, our findings will inform interve...

## Key facts

- **NIH application ID:** 10492381
- **Project number:** 5R01CE003358-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Arielle R. Deutsch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $361,467
- **Award type:** 5
- **Project period:** 2021-09-30 → 2024-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10492381

## Citation

> US National Institutes of Health, RePORTER application 10492381, A community-based systems science approach to assess risk and protective factors and improve the efficacy and equity of intervention strategies for stimulant use, use disorder, and overdose (5R01CE003358-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10492381. Licensed CC0.

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