Justin Cirone1, Peter Bendix1, Gary An2. 1. Department of Surgery, The University of Chicago, Chicago, Illinois. 2. Department of Surgery, The University of Vermont, Burlington, Vermont. Electronic address: docgca@gmail.com.
Abstract
BACKGROUND: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Erin McVey; Juan C Duchesne; Siavash Sarlati; Michael O'Neal; Kelly Johnson; Jennifer Avegno Journal: J Trauma Acute Care Surg Date: 2014-07 Impact factor: 3.313