Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a “system of systems.” This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.

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Abstract:A wide range of modeling methods have been used to inform health policies. In this chapter, we describe the use of 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, our 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 effect 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 can utilize a wide range of data sources and inform policy makers in developing programs for mitigating PTSD.

Systems modelling of health issues enriches analysis of health care policies by exploring interdependencies among system components. Considering a wide range of stakeholder behavior is a requisite for developing a useful model. Our paper shows how to systematically identify stakeholder behavior, formulate them in a model and test their impact on health outcomes. Stakeholder behavior are explained in the context of a simulation‐based analysis of infant mortality in Ohio. Using group model building techniques, we identify three levels of stakeholder behavior—individual, organizational and policy behaviors—and discuss how these behaviors are reflected in the model formulation and drive the key health outcomes. Specifically, we show how individual and policy behaviors are formulated to examine their influence on the impact of progesterone therapy—one of the interventions identified in the group model building session—on infant mortality. We conclude with recommendations for incorporating diverse stakeholder perspectives in systems modelling of health issues. © 2018 John Wiley & Sons, Ltd.

Objective To exploit state variations in infant mortality, identify diagnoses that contributed to reduction of the infant mortality rate (IMR), and examine factors associated with preterm-related mortality rate (PMR). Study Design Using linked birth-infant deaths files, we examined patterns in the leading causes of IMR. We compared these rates at both national and state levels to find reduction trends. Creating a cross-sectional time series of states' PMR and some explanatory variables, we implemented a fixed-effect regression model to examine factors associated with PMR at the state level. Results We found substantial state-level variations in changes of the IMR (range =  − 2.87–2.08) and PMR (−1.77–0.67). Twenty-one states in which the IMR declined more than the national average of 0.99 (6.89–5.90) were labeled as successful. In the successful states, we found reduction in the PMR accounted for the largest decline in the IMR—0.90 fewer deaths. Changes in the other subgroups of leading causes did not differ significantly in successful and unsuccessful states. Conclusion Trends in the causes of mortality are heterogeneous across states. Although its impact is not large, reducing the percentage of pregnant women with inadequate care is one of the mechanisms through which the PMR decrease.