A System Dynamics Model of Violent Trauma and the Role of Violence Intervention Programs

• Published in: The Journal of surgical research Vol. 247; pp. 258 - 263
• Main Authors: Cirone, Justin, Bendix, Peter, An, Gary
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2020
• Subjects: Computer Simulation; Humans; Interpersonal violence; Models, Statistical; Risk Assessment; Risk Factors; System dynamics model; Systems Analysis; United States - epidemiology; Violence - prevention & control; Violence - statistics & numerical data; Violence - trends; Violence intervention program; Wounds and Injuries - epidemiology; Wounds and Injuries - etiology; Wounds and Injuries - prevention & control; United States; Interpersonal violence; System dynamics model; Violence intervention program
• ISSN: 0022-4804; 1095-8673
• DOI: 10.1016/j.jss.2019.10.015

Violence intervention programs (VIPs) can reduce interpersonal violence (IPV); however, optimizing the implementation of VIPs is challenging, given the complex dynamics of IPV. System dynamics models (SDMs) provide a means of visualizing dynamic and causal relationships in such complex systems. We use the IPVSDM to characterize and examine the relationship between IPV, VIPs, and the social determinants of health (SDH). The simulation model was created from a diagram that links putative causal relationships between VIPs, SDH, and IPV events. Simulation rules are then used to calculate a risk of violence parameter based on the SDH, which drives the transition from low-risk to high-risk populations and in turn influences IPV event rates. A qualitative relational approach was used to evaluate long-term effects of VIP on IPV events. The model produced qualitatively plausible behavior with respect to IPV events, population transitions, and relative overall VIP effect. Simulation runs converged to stable steady states with an exponential benefit of VIP on reducing IPV that is best appreciated after 1-2 y. The VIP functioned in a recognizable fashion by slowing the shift from low-risk to high-risk populations. This initial implementation of the IPVSDM produced recognizable baseline behavior while incorporating the possible effects of a VIP. The model allows causality and counterfactual testing, which is impractical in vivo. Community-level VIP efforts should show benefit particularly after a couple years. Future work will emphasize adding complexity to the IPVSDM and identifying real-world metrics to aid in testing, validation, and prediction of the model.