Department of Surgery, The University of Chicago, Chicago, Illinois.
Department of Surgery, The University of Vermont, Burlington, Vermont.
J Surg Res. 2020 Mar;247:258-263. doi: 10.1016/j.jss.2019.10.015. Epub 2019 Nov 6.
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.
暴力干预项目(VIP)可以减少人际暴力(IPV);然而,鉴于 IPV 的复杂动态,优化 VIP 的实施具有挑战性。系统动力学模型(SDM)为可视化此类复杂系统中的动态和因果关系提供了一种手段。我们使用 IPVSDM 来描述和检查 IPV、VIP 和健康的社会决定因素(SDH)之间的关系。
该仿真模型是从一个图创建的,该图将 VIP、SDH 和 IPV 事件之间假定的因果关系联系起来。然后,使用仿真规则根据 SDH 计算暴力风险参数,该参数驱动从低风险到高风险人群的转变,并反过来影响 IPV 事件率。定性关系方法用于评估 VIP 对 IPV 事件的长期影响。
该模型在 IPV 事件、人口转移和相对 VIP 总体影响方面产生了定性上合理的行为。模拟运行收敛到稳定的稳定状态,VIP 对减少 IPV 的影响呈指数级增长,在 1-2 年后效果最佳。VIP 以可识别的方式发挥作用,减缓了从低风险到高风险人群的转变。
该 IPVSDM 的初始实施产生了可识别的基线行为,同时纳入了 VIP 的可能影响。该模型允许进行因果关系和反事实测试,这在体内是不切实际的。社区层面的 VIP 工作应该在几年后显示出效益。未来的工作将强调为 IPVSDM 添加复杂性,并确定现实世界的指标来帮助模型的测试、验证和预测。