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Our policy analysis results are divided in two sections: First, we discuss the effects of future engagements in wars; second, we present the effects of improvements in diagnosis, treatment, and prevention in the military, and study their consequences not only on the military but also on the VA and the total system. To distinguish these policies in the following sections, we call the future engagements in wars “scenarios” and the improvement interventions “policies.”
Scenarios based on future engagements in wars
We consider three scenarios for future engagements in wars:
- Scenario 1: Minimum deployment to intense/combat zones (1% of military personnel); this was the status quo in 2014 (see Figure A in S1 File for more information);
- Scenario 2: Twice the current policy, i.e., 2% deployment to intense/combat zones.
- Scenario 3: Significantly larger than the current policy, i.e., 5% deployment to intense/combat zones.
As a baseline for comparison, from 2001 to 2014, on average, 6.6% of US military personnel were deployed annually to combat zones, a rate that reached a maximum of 10.8% in 2008. Fig 2 presents the base run simulations of the model for diagnosis rate in the military through the historical period, starting in 2000, and then in the future through 2025. In addition to simulation outputs, historical data (2000–2014) is also presented in Fig 2; it can be seen how closely the simulated results are a fit with the historical data.
Fig 2. Historical data vs. simulation results, and scenarios based on future engagements in wars.
(A) Diagnosis rate in military [new cases per year]. (B) Diagnosed cases in military. (C) Diagnosed cases in veterans.
Fig 2-A depicts the diagnosis rate in the military, which is annual new cases. As the figure shows, the number of new cases of PTSD has been declining since 2013, which is mainly due to the decreasing number of troops in Iraq and Afghanistan in recent years. The future trend, however, is very sensitive to US involvement in future wars, represented by the three scenarios. As depicted in Fig 2-B, the population of people with PTSD in the military significantly declines over time, reaching 28,000 in scenario 1 and 36,000 in scenario 2in 2025. In scenario 3, PTSD prevalence in the military increases greatly; diagnosed cases are estimated to be 58,000. Fig 2-C presents the PTSD population among veterans. Overall, the population of patients among veterans declines very slowly in comparison to the military, since people remain in the post-military stage for a long time (basically until death). Despite decreasing deployments, new cases will be diagnosed every year among veterans, because there is a delay between symptom onset and presenting at a clinic for mental health services, revealing the long-lasting effects of wars. Under scenario 3, which assumes more US troop involvement in future wars, the population of patients among veterans stays relatively constant at around 600,000 over the next decade.
We also conducted two additional analyses with the model: annual costs of PTSD and the delay in mitigating the psychological effects of a war.
Fig 3-A presents our estimation of direct annual costs of PTSD for the military and VA healthcare systems (based on the dollar value in 2012). Cost estimation was performed based on the assumption of constant costs per PTSD patient in the military and VA over the next decade. Average cost per patient was extracted from a 2014 report by the Institute of Medicine: $4,500 and $6,244 per PTSD patient in the military and VA, respectively . Accordingly, in scenario 1, estimated healthcare costs for the military and VA were $125 million and $2.95 billion, respectively. In scenario 2, these estimates rose to $164 million for the military and $3.15 billion for the VA. In scenario 3, the estimates reached $264 million for the military and $3.63 billion for the VA.
Fig 3. Cost projection and inertia analysis in the military and post-military systems.
(A) PTSD health costs in 2025, based on the dollar value in 2012. (B) A counterfactual analysis to measure the effects of a short-term war (between years zero and five) on PTSD prevalence in the military and among veterans.
Delay in mitigating the psychological effects of a war.
The simulation runs discussed above captured various factors that occurred during the period 2000 to 2014. Here, we ran a counter-factual simulation from a steady-state condition, presented in Fig 3-B. The purpose of this simulation was to analyze the inertia in the system and measure how long it takes to mitigate the effects of a hypothetical 5-year war with 10% troop deployment (around the maximum deployment in Iraq). We used the estimated parameters from the model, but isolated all exogenous variables (i.e., variables assumed to change from outside our model). The results, presented in Fig 3-B, show the long delay in mitigating the psychological effects of a war. Controlling for treatment, screening, and training policies, Fig 3-B shows that it takes about 40 to 45 years for the veteran population to become PTSD-free. This represents the long-lasting effects of a war. Furthermore, Fig 3-B shows that the peak for the PTSD population among veterans emerges about six years after the war ends, and the PTSD population is much higher among veterans than in the military.
Interventions based on diagnosis, treatment, and prevention
Similar to most other health interventions, our policies focus on improving diagnosis, treatment effectiveness, and prevention. We also had a base run scenario—without any interventions—considered as control group. The overall goal in this section is to study how policies implemented in the military will affect not only military personnel but also veterans and the total system.
In Policy 1, improving diagnosis, the focus was on screening. We formulated this policy by doubling the screening sensitivity in the military (i.e., the annual rate at which undiagnosed PTSD is diagnosed). Given that there is no data on how many military personnel have PTSD and are undiagnosed, we estimated the rate at which undiagnosed military personnel are diagnosed through model calibration. In a nutshell, the rate of diagnosis during service (people/year) is a product of the fractional rate of revealing symptoms (a constant coefficient, estimated to be 0.043/year) and the population of ill-undiagnosed military members (a dynamic variable, unit: person); see S1 File for more information about the formulation, estimation, and sensitivity analysis on the estimation. In this policy, the fractional rate of revealing symptoms is doubled, representing higher speed of diagnosis, and the effect is analyzed.
In Policy 2, the focus was on improving treatment, and we tested the effects of doubling the successful treatment rate while PTSD patients are in the military. Despite the increased attention in the literature, lack of comprehensive outcome data on treating combat-related PTSD  and high heterogeneity among different findings  are still significant challenges. We therefore estimated the successful treatment rate through model calibration. The rate of successful treatment (people/year) is a product of fractional treatment rate (a constant coefficient, estimated to be 0.125/year) and ill-diagnosed military members (a dynamic variable, unit: person); see S1 File for more information. In this policy test, we doubled the fractional treatment rate, and the effect was examined.
In Policy 3, we tested the effects of improvement in PTSD prevention. This policy represented effective training programs that might improve the resilience of military personnel to PTSD (i.e., the ability of individuals to maintain a stable equilibrium, which can be achieved through psychological pathways such as hardiness, self-enhancement, repressive coping, and positive emotion—see  for more discussion on each pathway). It should also be noted that non-psychological factors, such as genetic and neurobiological dysfunctions; are also dimensions which affect resilience . Overall, we formulated Policy 3 by decreasing the average likelihood of getting PTSD after trauma. We formulated a condition where resiliency to trauma was doubled, meaning that the chance of developing PTSD after experiencing trauma was halved (the base likelihood of 17%  is considered in our model).
In Policies 1–3, a single model input was changed and model outputs were analyzed accordingly; however, we tested all combinations of these three policies, which created four additional policies (i.e., Policies 4–7, see Fig 4). It should be noted that our model is fully reported (see Supporting Information), and if future researchers find different base line values (e.g., a different base line value for the average likelihood of developing PTSD after trauma), or prefer to test different changes in the base line (e.g., instead of doubling the resilience rate to trauma, increase it by 50% or so), they can simply implement the changes in the model, run the simulation, and observe the results. In order to do this, the model can be run offline using the Vensim software program (by Ventana Systems, Inc.) or online without any software requirements at http://jalali.mit.edu/ptsd-simulation—the offline version includes more features (see S2 File); the online version is developed in a more interactive environment.
Fig 4. Simulation results for PTSD prevalence in 2025 for the military, the VA, and the total military-VA system under the three scenarios and interventions (Policy 1–7).
Note: See the instructions under Fig 5 on how to read the figure.
For each sector (military, veterans, and total system), we analyzed Policies 1–7 along with the base line vs. the three scenarios of future involvement in wars; therefore, 24 combination model outputs are presented. We used two major outputs as policy measures: PTSD prevalence and PTSD healthcare costs. Figs 4 and 5 presents these policy measures for 2025. Figs 4 and 5 present PTSD prevalence (%) and healthcare costs (in billions), respectively. Values in both figures are color coded to emphasize the magnitude of the numbers and provide comparisons across all conditions (darker colors represent larger numbers). Fig 6 also provides a more visual presentation of the results in Figs 4 and 5.
Fig 5. Simulation results for PTSD healthcare costs in 2025 for the military, the VA, and the total military-VA system under the three scenarios and interventions (Policy 1–7).
For each sector (military, veterans, and total system), 24 combinations of the three scenarios and seven policy interventions along with the base run are presented.—Darker colors represent larger numbers.—One way to read the figures is to compare the effects of policies in a scenario (e.g., scenario 1). In Fig 4, e.g., in the military under scenario 1 (little involvement in future wars), PTSD prevalence was estimated to be 6.7% in the base run. If policies 1 or 2 were implemented, PTSD prevalence decreased slightly to 6.4%. However, with Policy 3, the prevalence decreased to 4.7%. In Figure 5, e.g., in military, under scenario 1, PTSD healthcare costs are estimated to be $0.12B in the base run. Going down in the same column, e.g., for policies 1 through 3, the PTSD healthcare costs in the military are estimated to be $0.17B, $0.09B, and $0.10B, respectively.—Another way to read the figures is to compare different scenarios over a policy.—Based on Fig 4, policy 7 results in lower prevalence and based on Figure 5, policy 6 results in the most cost saving.
Based on Figs 4 and 6, it is notable that Policy 1 (sole focus on screening) and Policy 2 (sole focus on treatment) had minimal effect compared to the corresponding values in the base run. The main reason for achieving this result for Policy 1 is that with the current effectiveness of treatments (argued to be low; see  for more discussion), a sole focus on screening may only lead to finding more PTSD-positive individuals who are not treated successfully. In Policy 2, a focus on treatment alone also does not help much either, because many PTSD patients are not diagnosed or are late-diagnosed. Consequently, the combination of Policies 1 and 2 (i.e., Policy 4) slightly decreased the prevalence rate compared to the base run. However, the prevalence rates of Policy 3 (sole focus on prevention) and any other combination of policies which include the focus on prevention (i.e., policies 5–7) were considerably lower—e.g., under Scenario 1 in Fig 6, compare the red dots of policies 3 and 5–7 to those of the base line and policies 1–2 and 4. The same pattern—where policies with a focus on prevention decreased the prevalence noticeably—is also achieved for the military, the VA, and the total system across all three scenarios (see Fig 6).
Based on the results in Fig 4, it is important to note that under scenario 1, the effects of all policies were still limited to the small population of military personnel with PTSD, and the VA will be still facing a large number of PTSD patients (between 9.6% and 9.8% across all policies).
PTSD healthcare costs.
Policy 1 (sole focus on screening) not only costs the military and VA more than the base run, but it is also more expensive than all other policies across the three scenarios (see Fig 6). As discussed in the previous section, under Policy 1, since more people are screened, more PTSD positives are diagnosed. However, without advancements in treatment, screening improvement only increases the demand for care. In other words, because the successful treatment rate is already small, this policy mainly results in higher demand for treatment and a higher rate of discharge from the military. Consequently, not only do costs for the military increase, but also, unexpectedly, costs (and demand for care) for the VA rise due to more military discharges, an example of shifting the burden in a complex system of systems. Thus, Policy 1 alone can make us worse off in cost-related measures and also has minimal effect on PTSD prevalence (see Figs 4 and 6).
Policy 2 (sole focus on treatment) decreases the costs in comparison to the baseline. This policy mainly helps treat already diagnosed patients at a more successful pace; however, it also has a limited effect on PTSD prevalence, as many patients remained undiagnosed. Overall, Policy 3, which focuses on prevention by increasing the resilience to PTSD, results in costs similar to Policy 2; however, with more involvement in future wars, Policy 2 becomes more costly—compare the costs of policies 2 and 3 across the three scenarios in Fig 6 (a similar pattern can also be seen for policies 4 and 5).
Among the policies with a focus on a combination of screening, treatment, and prevention (i.e., policies 4–6), Policy 6 (focus on treatment and prevention) has the lowest costs. In fact, Policy 6 has the lowest costs in comparison to all other policies as well as the base line; however, its PTSD prevalence rate is relatively higher than that of policies 5 and 7 (see Fig 6). Policy 7, which is a combination of screening, treatment, and prevention, is similar to policies 2 and 3 in terms of costs, but it has the lowest PTSD prevalence rate compared to all other policies, which makes it a potential choice for mitigating PTSD. One might argue that Policy 6 is cheaper and its PTSD prevalence rate is not much different from the prevalence rate of Policy 7. This might seem reasonable, but in fact, it is missing improvements in screening that are critical to intervention with diagnosed PTSD patients, and without which many individuals may remain undiagnosed in the system.