» Articles » PMID: 22491443

From Model to Action: Using a System Dynamics Model of Chronic Disease Risks to Align Community Action

Overview
Publisher Sage Publications
Date 2012 Apr 12
PMID 22491443
Citations 33
Authors
Affiliations
Soon will be listed here.
Abstract

Health planners in Austin, Texas, are using a System Dynamics Model of Cardiovascular Disease Risks (SD model) to align prevention efforts and maximize the effect of limited resources. The SD model was developed using available evidence of disease prevalence, risk factors, local contextual factors, resulting health conditions, and their impact on population health. Given an interest in understanding opportunities for upstream health protection, the SD model focused on the portion of the population that has never had a cardiovascular event. Leaders in Austin used this interactive simulation model as a catalyst for convening diverse stakeholders in thinking about their strategic directions and policy priorities. Health officials shared insights from the model with a range of organizations in an effort to align actions and leverage assets in the community to promote healthier conditions for all. This article summarizes the results from several simulated intervention scenarios focusing specifically on conditions in East Travis County, an area marked by higher prevalence of adverse living conditions and related chronic diseases. The article also describes the formation of a new Chronic Disease Prevention Coalition in Austin, along with shifts in its members' perceived priorities for intervention both before and after interactions with the SD model.

Citing Articles

Participatory Modeling for High Complexity, Multi-System Issues: Challenges and Recommendations for Balancing Qualitative Understanding and Quantitative Questions.

Deutsch A, Frerichs L, Perry M, Jalali M Syst Dyn Rev. 2025; 40(4.

PMID: 39831133 PMC: 11741230. DOI: 10.1002/sdr.1765.


Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care.

Ye J, Orji I, Birkett M, Hirschhorn L, Walunas T, Smith J JAMA Netw Open. 2024; 7(8):e2430213.

PMID: 39190307 PMC: 11350485. DOI: 10.1001/jamanetworkopen.2024.30213.


Systems Thinking and Complexity Science Methods and the Policy Process in Non-communicable Disease Prevention: A Systematic Scoping Review.

Clifford Astbury C, Lee K, McGill E, Clarke J, Egan M, Halloran A Int J Health Policy Manag. 2023; 12:6772.

PMID: 37579437 PMC: 10125079. DOI: 10.34172/ijhpm.2023.6772.


Success of community-based system dynamics in prevention interventions: A systematic review of the literature.

Felmingham T, Backholer K, Hoban E, Brown A, Nagorcka-Smith P, Allender S Front Public Health. 2023; 11:1103834.

PMID: 37033017 PMC: 10080052. DOI: 10.3389/fpubh.2023.1103834.


Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia.

Zhu H, Liu S, Zheng W, Belay H, Zhang W, Qian Y PLoS One. 2022; 17(7):e0271231.

PMID: 35881650 PMC: 9321453. DOI: 10.1371/journal.pone.0271231.