Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder


Ghaffarzadegan N, Larson RC, Fingerhut H, Jalali MS, Ebrahimvandi A, Quaadgras A, et al. Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder. In: Policy Analytics, Modelling, and Informatics. Springer; 2018. p. 387-406.
Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder


A wide range of modeling methods have been used to inform health policies. In this chapter, we describe three models for understanding the complexities of post-traumatic stress disorder (PTSD), a major mental disorder. The models are: (1) a qualitative model describing the social and psychological complexities of PTSD treatment; (2) a system dynamics model of a population of PTSD patients in the military and the Department of Veterans Affairs (VA); and (3) a Monte Carlo simulation model of PTSD prevalence and clinical demand over time among the OEF/OIF population. These models have two characteristics in common. First, they take systems approaches. In all models, we set a large boundary and look at the whole system, incorporating both military personnel and veterans. Second, the models are informed by a wide range of qualitative and quantitative data. Model I is rooted in qualitative data, and models II and III are calibrated to several data sources. These models are used to analyze the effects of different policy alternatives, such as more screening, more resiliency, and better recruitment procedures, on PTSD prevalence. They also provide analysis of healthcare costs in the military and the VA for each policy. Overall, the developed models offer examples of modeling techniques that incorporate a wide range of data sources and inform policy makers in developing programs for mitigating PTSD, a major premise of policy informatics.

Last updated on 02/11/2019