# R34-3: Using Systems Science to Inform the Implementation and Scale-up of Evidence-based Tobacco Cessation Treatment in Community Mental Health Programs

> **NIH NIH P50** · JOHNS HOPKINS UNIVERSITY · 2024 · $265,054

## Abstract

Tobacco smoking represents the single largest contributor to premature mortality in people with serious mental
illness (SMI). Although effective, evidence-based smoking cessation treatment exists, fewer than 1 in 10
smokers with SMI receive it. One of the keys to scaling cessation treatment is to identify implementation
strategies that can effectively address implementation determinants that facilitate or hinder implementation
efforts. However, the process of identifying strategies appropriately matched to determinants can be
challenging, with opportunities to test possible combinations and variations of strategies largely limited to costly
and time-intensive randomized implementation trials. In this study, we propose a novel application of systems
science methods to identify implementation strategies to facilitate the delivery of smoking cessation treatment
in community mental health settings. Systems science is an interdisciplinary field of science focused on
modeling complex systems which can be used to simulate the potential effects of implementation strategies on
outcomes prior to implementation. The proposed study seeks to build on our Center’s existing systems
modeling work to develop a system model that will serve as a “virtual laboratory” to explore the potential effects
of various implementation strategies on the delivery of cessation treatment for people with SMI. To do so, we
will leverage our team’s complementary sets of expertise, theory-driven constructs of an implementation
science framework, and an extensive array of data sources. In Aim 1, we will iteratively engage with
community partners to develop and refine a conceptual model that identifies candidate implementation
strategies that can increase delivery of smoking cessation treatment in community mental health settings.
Using the conceptual model as a foundation, in Aim 2, we will build an integrated agent-based and system
dynamics model to assess the impacts of different implementation strategies on delivery of cessation
treatment. The integrated system model will be calibrated using robust empirical data from our team’s prior
studies on cessation treatment for people with SMI, new data that will be collected as part of the proposed
research, and a literature review of anticipated effect sizes of candidate implementation strategies. In Aim 3,
we will follow a User-Centered Design approach to construct an interactive, online dashboard that displays the
results of the system model. The purpose of the dashboard will be to assist community partners with decision-
making on how to implement and scale-up cessation treatment by allowing users to “virtually test” how
adjustments in the selection, dosing, and combination of implementation strategies would potentially affect
intervention delivery. This innovative study will result in a system model that will advance our efforts to support
scaling of smoking cessation treatment in community mental health settings, including the design...

## Key facts

- **NIH application ID:** 10843615
- **Project number:** 2P50MH115842-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Christina T. Yuan
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $265,054
- **Award type:** 2
- **Project period:** 2018-08-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843615

## Citation

> US National Institutes of Health, RePORTER application 10843615, R34-3: Using Systems Science to Inform the Implementation and Scale-up of Evidence-based Tobacco Cessation Treatment in Community Mental Health Programs (2P50MH115842-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10843615. Licensed CC0.

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