Applications of System Dynamics Models in Chronic Disease Prevention: A Systematic Review

• Published in: Preventing chronic disease 2021-12, Vol.18, p.E103-E103, Article 210175
• Main Authors: Wang, Ying, Hu, Bo, Zhao, Yuxue, Kuang, Guofang, Zhao, Yaling, Liu, Qingwei, Zhu, Xiuli
• Format: Article
• Language: English
• Subjects: Births; Chronic Disease; Chronic illnesses; Citation management software; Computer simulation; Disease prevention; Drug resistance; Exercise; Feedback; Humans; Incidence; Mortality; Population; Qualitative research; Quality of Life; Research Design; Systematic Review; Variables
• ISSN: 1545-1151; 1545-1151
• DOI: 10.5888/PCD18.210175

Chronic disease is a serious health problem worldwide. Given that health care resources are limited, a comprehensive, effective, and affordable way is needed to provide insights to prevent chronic diseases. System dynamics models provide a comprehensive and systematic method that can predict results over time. These models can simulate and predict appropriate prevention measures for chronic diseases to determine the best practice. Two researchers (Y.W., B.H.) independently searched databases (PubMed, Web of Science, Scopus, and Embase) for full-text articles published from January 2000 through February 2021. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020-compliant search was carried out to review system dynamics models of chronic disease prevention. A total of 34 articles were included in our study. We divided the prevention measures of system dynamics models into 2 main categories: upstream prevention and downstream prevention. Upstream prevention measures include lifestyle (eg, tobacco control, balanced diet, mental health, moderate exercise), obesity prevention, and social factors. Downstream prevention measures include clinical treatment of chronic diseases. Results showed that effective upstream prevention measures could reduce the prevalence of chronic diseases, and downstream prevention measures could reduce the incidence of complications, improve quality of life, prolong life, save medical costs, and reduce mortality. To our knowledge, our systematic review is the first to evaluate the application of system dynamics models in preventing chronic diseases. Such models can provide effective simulations. Hence, we can use system dynamics models to design and implement effective prevention measures for people with chronic diseases.