Invisible Wounds, Visible Savings? Using Microsimulation to Estimate theCosts and Savings Associated With Providing Evidence-Based Treatment

for PTSD and Depression to Veterans of Operation Enduring Freedom andOperation Iraqi Freedom

Beau Kilmer, Christine Eibner, Jeanne S. Ringel, and Rosalie Liccardo PaculaRAND

This research used microsimulation modeling to estimate the social costs of depression and posttraumaticstress disorder (PTSD) for the 261,827 troops deployed on June 30, 2008, for Operations EnduringFreedom and Iraqi Freedom. Given current standards of care, roughly half of these individuals will betreated for these conditions in the 2 years after they return, and 30% of those treated will receiveevidence-based treatment (EBT). Our results suggest that the 2-year social costs of depression and PTSDfor this cohort will be $923 million. Policy simulations evaluating the savings associated with universalaccess to EBT suggest that such access would generate cost savings of $138 million (15%).

Keywords: post traumatic stress disorder, depression, treatment, microstimulation, military

Since 2001, more than 1.64 million U.S. troops have deployedto Operation Iraqi Freedom (OIF) or Operation Enduring Freedom(OEF), a pace unmatched in the history of the all-volunteer force(Tanielian & Jaycox, 2008). The future health-related costs ofthese deployments will undoubtedly be high because of physicaland mental health injuries sustained during war. Calculation ofthese costs, which the government will be expected to pay, is adifficult task because of the inherent uncertainty underlying all thecomponents that are necessary to construct such an estimate,including the number of people harmed, the number seeking treat-ment, the types of treatment given, the cost associated with treat-ment, and the subsequent outcomes that may befall a servicemember because of impaired health.

In this article, we introduce an innovative method for estimatingthe future health costs for currently deployed service members thatis well equipped to handle and quantify the uncertainty embeddedin such a calculation: microsimulation modeling. Unlike standardaccounting methods, a microsimulation model takes a hypotheticalgroup of simulated individuals and predicts future cost-relatedevents, allowing the simulated population to experience mentalconditions, mental health treatment, and secondary outcomes suchas employment. An advantage of the microsimulation approach isthat it can treat mental disorders as chronic conditions, allowingfor both remission and relapse over time. In addition, the micro-simulation model can be useful for evaluating different policyscenarios. In our case, we were particularly interested in asking thefollowing policy question: If we increase the use of evidence-

based treatment, will we save money in the long-run? This type ofquestion would be difficult to evaluate in a standard accountingframework because standard accounting models are based on av-erage expenditures for a population and do not allow for differentindividuals to experience different treatments, subsequent out-comes, and costs. Data to parameterize the model, including in-formation about the probability of developing mental illness andthe probability of remission following treatment, were taken fromexisting literature.

We constructed a simplified microsimulation model that en-abled us to characterize the social cost over a 2-year period for twowell-publicized and extremely prevalent health conditions associ-ated with combat exposure: posttraumatic stress disorder (PTSD)and major depression (Hoge, Auchterlonie, & Milliken, 2006;Hoge et al., 2004; Schell & Marshall, 2008). We then demon-strated the true utility of these models by conducting policy sim-ulations that assessed the cost savings that might be achievedthrough a policy change, in particular, increasing the use ofevidence-based treatment (EBT) for these two conditions. Studiesof the civilian and military populations have found that it isrelatively common for individuals with a probable mental healthcondition to receive no treatment for these conditions. In a sampleof adults with likely major depression or anxiety disorder inter-viewed in 1997 and 1998, 17% received no treatment at all duringa 1-year period (Young, Klap, Sherbourne, & Wells, 2001). Amore recent study found that about 43% of individuals with PTSDor major depression received no treatment during the past year(Wang, Olfson, Pincus, Wells, & Kessler, 2005). Hoge et al.(2004) found that only 23% to 40% of veterans returning fromOEF and OIF who screened positive for a probable mental healthcondition including major depression and PTSD sought carewithin 3 to 4 months after returning from deployment. Schell andMarshall (2008) found that only about half of postdeployed servicemembers with mental health conditions received treatment. The

This article was published Online First May 16, 2011.Beau Kilmer, Jeanne S. Ringel, and Rosalie Liccardo Pacula, Santa

Monica, CA, RAND; Christine Eibner, RAND, Arlington, VA.Correspondence concerning this article should be addressed to Beau

Kilmer, RAND, 1776 Main Street, Santa Monica, CA 90407. E-mail:Beau_Kilmer@rand.org

Psychological Trauma: Theory, Research, Practice, and Policy © 2011 American Psychological Association2011, Vol. 3, No. 2, 201–211 1942-9681/11/$12.00 DOI: 10.1037/a0020592

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low probabilities of receiving treatment found in these studiesunderscore the need to consider policies to expand access to EBTs,including the cost and benefits of such policies.

In addition to the novel use of microsimulation methods, thisanalysis differs from prior research estimating the costs associatedwith deployment to OEF and OIF in two important ways. First,prior research has focused on the total medical costs associatedwith the conflicts (Bilmes, 2007; Projecting the Costs to Care forVeterans, 2007) or the total costs of the conflicts (Bilmes &Stiglitz, 2006; Stiglitz & Bilmes, 2008; Wallsten & Kosec, 2005),typically using aggregate data on total health care spending orspending per service member to project costs over time. Thesestudies have estimated that the lifetime cost associated with alldeaths and injuries sustained in Iraq alone could range from $300to $400 billion (Stiglitz & Bilmes, 2008; Wallsten & Kosec, 2005).In contrast, we took a more detailed look at two specific condi-tions: PTSD and depression. Rather than projecting per capitacosts over time, we used granular data on specific types of treat-ment, recovery and relapse rates, wages, and suicide probabilitiesto generate our estimates. This approach allowed us to determinehow costs would respond to specific policy changes, such as anincrease in the share of the population that receives EBT. Adrawback of our approach, however, is that it required data andassumptions that can legitimately change over time, such as infor-mation on relapse and recovery rates. As a result, we are onlycomfortable estimating costs for a 2-year window during which theprocesses these data represent are likely to be more stable.

Second, in contrast to some of the existing literature (Bilmes,2007; Projecting the Costs to Care for Veterans, 2007), we con-sidered societal costs rather than costs that accrue solely to thegovernment or the Veterans Administration (VA). The societalperspective accounts for costs that accrue to all segments ofsociety, including the Department of Defense, the VA, servicemembers, their families, employers, and others. We used thesocietal perspective because many stakeholders will be con-cerned with the total cost burden associated with the wars inIraq and Afghanistan, including costs born by veterans and theirfamilies, rather than simply the budgetary costs shouldered bythe government. Furthermore, Gold (1996) recommended thatall cost analyses consider the societal perspective because thisis the only approach that a cost to one member of society is abenefit or savings to another (as would be the case, e.g., if acharitable organization rather than the VA paid for mentalhealth treatment for some returning veterans). The specific costcategories that we included in our model were the costs oftreatment for PTSD and depression, the cost of lost productivitystemming from PTSD and depression, the medical costs ofsuicides resulting from PTSD and depression, and the value oflives lost due to suicide.

Results from a prior version of our simulation model appearedin a RAND report entitled Invisible Wounds of War: Psychologicaland Cognitive Injuries, Their Consequences, and Services to AssistRecovery (Tanielian & Jaycox, 2008). This analysis improved onthe prior estimates by incorporating new and more detailed data onthe characteristics of deployed personnel, by estimating confidenceintervals for all costs, and by refining assumptions regarding theprobability of seeking treatment. In addition, this analysis focusedon future costs for a cohort of currently deployed troops; the prioranalysis considered costs that had already been incurred.

Method

Microsimulation models pull together information from manydata sources to simulate life course outcomes of individuals withspecific baseline characteristics (e.g., age, gender) and exposure tospecific health risks or events (e.g., onset of a mental healthcondition). It has two main advantages over other approaches: Itexplicitly models the uncertainty of particular events and outcomesthrough the use of stochastic processes, and it captures the influ-ence of heterogeneity across individuals in the likelihood of sub-sequent outcomes occurring. The ability to model processes sto-chastically is an advantage because there is no way to know withcertainty which service members will develop mental illness andwho will recover in a given period of time. By incorporating thisinherent uncertainty into our analysis, we can estimate the range ofoutcomes that policymakers might expect, given the underlyingassumptions of our model.

Given that events in the microsimulation model are stochasticrather than deterministic, no two runs of the model will generatethe same exact outcomes or costs for any one individual. Thus,predictions from microsimulation models are obtained by runningthe model several times, tracking the outcomes and costs of eachseparate run, enabling us to see the full range of plausible valuesfor both. With this information, we are then able to constructconfidence intervals around our best estimates of these costs. Thissection briefly summarizes our methodology and highlights keyparameter estimates. The Appendix provides a “model map” withadditional details about the parameters and how the simulationworks.

Figure 1 illustrates the model dynamics, with arrows showingpossible transitions across states. Each state is defined by anindividual’s mental health status, treatment status, and employ-ment status. For ease of presentation, this figure focuses on asingle mental health condition, but our model incorporated threepossible mental health conditions (PTSD, major depression, andcomorbid PTSD and major depression). As a simplifying assump-tion, we constrained individuals from switching across conditions.This implies that, some individuals in our model had a singlemental health condition and some had a comorbid mental healthconditions, but no one with a single condition would ever developa comorbid condition, and no one with comorbid condition wouldever recover from one condition but not the other.

This microsimulation model was based on the 261,827 soldiersdeployed to OEF or OIF on June 30, 2008, and modeled theirdepression and PTSD trajectories over a period of 2 years (eightquarters) after they returned home, taking into account mentalhealth treatments received and events that may have occurred as aresult of a mental health condition.1 Information about the gender(89% male), branch (62% Army, 16% Navy, 12% Marine Corps,and 10% Air Force), and rank (86% � Enlisted 09) for thosedeployed to OEF and OIF was obtained from the Defense Man-power Data Center. Information about age, race/ethnicity, educa-tion, and time in service was imputed for each of these soldier-based distributions reported in published studies (Armed ServicesMedical Surveillance Monthly Report, 2007; Office of the Under-

1 For these individuals, information on branch, reserve status, and gen-der was provided by the Defense Manpower Data Center. Informationabout age, race/ethnicity, education, and time in service is imputed.

202 KILMER, EIBNER, RINGEL, AND PACULA

secretary of Defense for Personnel & Readiness, 2007; CBO,2004; DMDC, 2000). In conjunction with mental health status,these baseline characteristics influence wages, labor force status,and the probability of suicide.

An individual’s initial assignment into a mental health state wasbased on prevalence data reported in Hoge et al. (2004) andGrieger et al. (2006). We assumed that, immediately after return-ing from OEF or OIF, 5% of the sample had PTSD, and 7% of thesample had major depression alone. Delayed-onset PTSD coulddevelop at any time during our 2-year time horizon, such that anadditional 10% of the sample would develop PTSD during the2-year time period considered in the model. This rate of growth inPTSD over time corresponds to growth rates reported in Wolfe,Erickson, Sharkansky, King, and King (1999) in a 18- to 24-monthanalysis of PTSD among veterans of the first Gulf War. In addi-tion, the rate is roughly consistent with Milliken, Auchterlonie, andHoge (2007), who reported that 6% of Army soldiers screenedpositive for PTSD immediately after return from OIF, with anadditional 8% screening positive at a median of 6 months later.Our model assumptions also imply that 50% of individuals withPTSD develop the condition after 6 months, a figure that isconsistent with rates of delayed-onset PTSD among military pop-ulations reported by Andrews et al. (2007).

Based on figures reported in Grieger et al. (2006), we assumedthat 50% of individuals with PTSD also have comorbid majordepression. Because mental health outcomes in our model wereassigned stochastically, realized rates of mental health conditionsare variable. However, the model was designed so that, on average,7% of the sample would experience at least one bout of majordepression, 7.5% of the sample would experience at least one boutof PTSD, and 7.5% of the sample would experience at least onebout of comorbid PTSD and major depression during the 2-yearprojection interval.

Modeled individuals with a mental health condition had a prob-ability of receiving EBT or usual care, and these treatments influ-enced the course of illness. EBTs, described in Table 1, were basedon published guidance, recent randomized controlled trials show-

ing effectiveness, and the Institute of Medicine (2007) report onEBT for PTSD. Because treatment guidelines for major depressionvary depending on the severity of illness and the patient’s responseto therapies, we allowed for three potential treatment regimesamong those getting evidence-based care for major depression.Specifically, 37.5% of individuals receiving evidence-based carefor major depression got drugs only, 37.5% got psychotherapyonly, and 25% got combined therapy. We assumed that individualsreceiving combined therapy, as well as individuals receiving EBTfor comorbid PTSD and major depression, would take mainte-nance medication for a 1-year period should their symptoms remitfollowing an episode of treatment. Usual care for PTSD and majordepression, shown in Table 1, reflects the fact that it is common forindividuals with mental health conditions to receive suboptimallevels of medication and psychotherapy. In the absence of com-prehensive information on usual care for veterans with comorbidPTSD and major depression, we assumed that these individualswould get the same treatment as individuals with PTSD only.

Using figures reported in Hoge et al. (2004), Young, Klap,Sherbourne, & Wells, (2001), Schell and Marshall (2008), andWang et al. (2005), we modeled a baseline scenario where 52.4%of individuals in need get any care, and where 30% of care isevidence-based. This scenario assumed that there is a 30% chancethat an illness would be treated in the first quarter after returning,with the probability linearly increasing to 52.4% by the eighth(last) quarter. We assigned treatment success probabilities basedon remission rates reported in existing literature (Dimidjian et al.,2006; Keller, McCullough, Klein, & Arnow, 2000; Kessler, Son-nega, Bromet, Hughes, & Nelson, 1995; Kocsis et al., 1988;Ludman, Simon, Tutty, & Von Korff, 2007; Schnurr et al., 2007;Wells, Burnam, Rogers, Hays, & Camp, 1992). If the treatmentwas unsuccessful, we assumed that there was an 80% chance ofcontinuing treatment in each subsequent quarter with illness.

In addition to treatment, cost events addressed in the modelincluded lost productivity, suicide attempts, and suicide comple-tions. Based on the available evidence, the simulation assumed thatindividuals with active mental illness have a higher probability of

Figure 1. Model dynamics.

203COSTS AND SAVINGS OF PROVIDING MENTAL HEALTH TREATMENT

leaving Department of Defense service (Hoge et al., 2006). Thesimulation also accounted for the fact that individuals with activemental illness who are discharged have a lower probability ofworking in the civilian sector (Savoca & Rosenheck, 2000). Forthose with a mental health condition, we reduced the probability ofworking and wages conditional on working based on a study ofmental health illness and productivity in a group of Vietnamveterans (Savoca & Rosenheck, 2000); these estimates imply a15.75% wage reduction for PTSD and a 45.23% wage reductionfor major depression.2 Finally, only individuals with active mentalillness can attempt suicide (Gibbons et al., 2007). The probabilityof dying due to a suicide attempt is higher for active duty indi-viduals relative to discharged individuals, reflecting easier accessto weapons (Army Suicide Event Report, 2006; Goldsmith, Pell-mar, Kleinman, & Bunney, 2002).

Data for assigning medical care costs came from TRICARE andMedicare reimbursement rates3,4 and published prices for drugs aswell as VA negotiated rates (Department of Defense Pharmaco-economic Center, 2004; Dobscha, Winterbottom, & Snodgrass,2007; Fleming, 2006). Wages for active duty service memberscame from Department of Defense pay tables,5 and wages fordischarged personnel were derived from wage regressions forveterans in the 2006 Current Population Survey (Bureau of LaborStatistics, 2007). We estimated the medical costs of suicide usingfigures published by Corso, Mercy, Simon, Finkelstein, and Miller(2007); lives lost to suicide were valued at $7.9 million based on

figures reported by Viscusi and Aldy (2003) and updated to 2008dollars.

All of our parameter estimates were vetted by a group of expertsfrom RAND, the University of California–Los Angeles, and theUniformed Services University of Health Sciences. Given the lackof literature, we relied entirely on expert input to derive theassumptions discussed above related to the share of evidence-based care delivered as pharmacotherapy, psychotherapy, or com-bined therapy, and related to the share of individuals with ongoingmental illness who remained in treatment after 3 months.

In some cases, the data used to derive parameter and costestimates for the model were relatively thin. For example, we hadlimited information on how mental illness affects wage and careeroutcomes for active duty personnel, or on how comorbid PTSD

2 Because the reduction in wages associated with major depression inthis study is high relative to similar studies of the civilian population(Ettner, Frank, and Kessler, 1997), we use a more conservative figure inour low-cost scenario.

3 http://www.tricare.mil/allowablecharges/default.aspx, accessed Sep-tember 26, 2008.

4 http://www.cms.hhs.gov/pfslookup/02_PFSsearch.asp, accessed Sep-tember 26, 2008.

5 http://www.defenselink.mil/militarypay/pay/index.html, accessed Sep-tember 26, 2008.

Table 1Treatment for Posttraumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD)

ConditionMedication over

12 weeksPsychotherapy over

12 weeksMaintenance

medication required? Source(s)

Evidence-based treatment (EBT)

PTSD only Daily SSRI Ten 75- to 80- minsessions ofpsychotherapy

No Foa, Davidson, & Frances(1999); Institute ofMedicine (2007); Schnurret al. (2007)

MDD onlyDrugs only Daily SSRI None No De Maat, Dekker, Schoevers,

& De Jonghe (2006);Friedman & Detweiler-Bedell (2004); Keller et al.(2000); Ludman et al.(2007); Pampallona &Bollini (2004); Whooley& Simon (2000)

Therapy only None Ten 45- to 50-minsessions ofpsychotherapy

No

Drugs and therapy Daily SSRI Ten 45- to 50-minsessions ofpsychotherapy

Yes

Comorbid PTSD/MDD Daily SSRI Ten 75- to 80- sessionsof psychotherapy

Yes Foa et al. (1999)

Usual care

PTSD, comorbid PTSD/MDD 32% get daily SSRI, 48%get daily SSRI for1 month

3.2 50-min sessions No Rosenheck and Fontana(2007); Simon, Ludman,Tutty, Operskalski, & VonKorff (2004)

MDD Only 26% get an SSRI; ofthese, 60% get therecommended dose,40% discontinue after1 month

100% get one visit with aPCP, 15% get a visitwith a mental healthspecialist, 38% get two30-min sessions ofcounseling

No Young, Klap, Sherbourne, &Wells, et al. (2001);Simon et al. (2004)

Note. SSRI � selective serotonin reuptake inhibitor; PCP � Primary care provider.

204 KILMER, EIBNER, RINGEL, AND PACULA

and depression affect wages. Because we did not have access toDepartment of Defense or VA medical records for this study, wedid not know what fraction of service personnel received drugs atVA or Department of Defense negotiated prices and what fractiongot drugs through private insurance. There was also limited evi-dence on relapse rates for PTSD, and reported relapse rates fordepression vary considerably across studies (Vittengl, Clark,Dunn, & Jarrett, 2007). Finally, data on suicides for returningservice personnel were based on suicide attempts among depressedpersonnel that involved a contact with the health care system anddiagnoses code related to suicide (Gibbons et al., 2007). Thislimitation likely meant that we were underestimating actual suiciderates given that some attempts did not result in a hospitalizationand some completions were recorded with other diagnosis codes(e.g., single car crashes). At the same time, one recent study foundthat suicide rates for veterans with comorbid PTSD and depressionmay be lower than suicide rates for veterans with depression (Zivin etal., 2007). To address these issues, we modeled baseline, low-, andhigh-cost scenarios in which we varied assumptions about key pa-rameters for which consensus in the literature was limited. Table 2shows the changes made across the three modeled scenarios.

There are many other secondary costs that are likely to berelated to PTSD and major depression, such as costs stemmingfrom family stress, caregiver burden, homelessness, and substanceabuse comorbidity. We did not incorporate these effects into ourcost estimates for several reasons, including sparse literature, un-certainty about whether a mental health condition caused theproblem (as opposed to simply being correlated with the problem),and difficulty assigning a dollar figure to intangible outcomes such

as family well-being. Although a limitation of our study is that wecannot address all costs associated with mental health and cogni-tive conditions, we nevertheless think this analysis provides valu-able information in that it presents what can be thought of as alower bound estimate of societal costs.

Results

This section predicts the postdeployment social costs of PTSDand depression for those 261,827 individuals deployed on June 30,2008. As previously noted, we assumed that 22.2% of theseindividuals will suffer from PTSD, depression, or both in the 2years after retuning from OIF or OEF. For each scenario, we ranthe model 50 times and report mean values, standard errors, and95% confidence intervals.

Cost Analyses

The baseline scenario (Scenario 1) assumed there is a 30%chance that an illness will be treated in the first quarter afterreturning, with the probability linearly increasing to 52.4% by theeighth quarter (end of the second year). As previously discussed,those receiving treatment will have a 30% chance of receivingEBT. The solid line in Figure 2 presents the distribution of totalcost estimates of this baseline scenario based on 50 simulations.The mean and median for the 50 simulations are virtually identicalat $922.8 million and $922.9 million, respectively. The maindrivers of the total cost are lost productivity (64%) and the costs

Table 2Assumptions That Vary Across Model Scenarios

Assumption Baseline Low cost High cost

DoD earnings for those onactive duty

PTSD reduces wage by 7.88%,major depression or comorbidPTSD and major depressionreduces the wage by 22.6%

DoD wages are unrelated to amental health condition

PTSD reduces wage by15.75%, major depressionor comorbid PTSD andmajor depression reducesthe wage by 45.23%

Medication costs for dischargedpersonnel

35% of discharged personnelget prescriptions at the VAnegotiated price

All discharged personnel getprescriptions at the VAnegotiated price

All discharged personnel getprescriptions through privatehealth insurance

Wage adjustment for dischargedpersonnel with majordepression or comorbid majordepression and PTSD

PTSD reduces wage by 15.75%,major depression or comorbidPTSD and major depressionreduces the wage by 45.23%

15.75% lower than CPSestimate

45.23% lower than CPSestimate

Relapse rates for majordepression

54% relapse over 2 years 26% of those with EBTrelapse over 2 years; 36%of those with usual care orno care relapse over 2 years

54% of those with EBTrelapse over 2 years; 75%of those with usual care orno care relapse over 2 years

Relapse rates for PTSD 55% relapse over 2 years 25% relapse over 2 years 55% relapse over 2 yearsRate of attempted suicide Use age-specific rates reported

in Gibbons et al. (2007)Use age-specific rates reported

in Gibbons et al. (2007), butreduce by 25% forindividuals with PTSD orcomorbid PTSD and majordepression

Use age-specific rates reportedin Gibbons et al. (2007), butincrease by 25% to accountfor attempts andcompletions that weremissed or not recorded assuicide

Note. DoD � Department of Defense; PTSD � posttraumatic stress disorder; VA � Veterans Administration; CPS � Current Population Survey; EBT �evidence-based treatment. Sources used to inform these assumptions are as follows: Earnings: Ettner & Kessler, 1997; Savoca & Rosenheck, 2000. Relapse:Dimidjian et al., 2006; Keller et al., 2000; Kessler et al., 1995; Kocsis et al. 1988; Ludman et al., 2007; Schnurr et al., 2007; Wells et al., 1992; Wolfe etal., 1999. Attempted suicide: Gibbons et al., 2007.

205COSTS AND SAVINGS OF PROVIDING MENTAL HEALTH TREATMENT

related to suicide (32%). Only 4% of the costs are attributable totreatment.

Our baseline model predicts that for those 261,827 individualsdeployed on June 30, 2008, 37 will commit suicide in the 2 yearsafter returning home; this generates an annual rate of approxi-mately seven suicides per 100,000 veterans. Data from the Army(the branch that accounts for 62% of the troops in our simulation)suggest that the suicide rate was 18.8 per 100,000 soldiers in2007.6 Given that our simulation assumed that suicide can onlyoccur as a result of major depression or PTSD, our models under-estimate the true suicide rate because there are other reasons forsuicide.

Table 3 shows that there is a wide range between our low- andhigh-cost estimates. This range primarily reflects our uncertaintyabout how much a mental health condition affects the productivityof Department of Defense personnel. In our high-cost scenario(Scenario 2), where we assumed that a mental health condition hasthe same impact on productivity for active duty personnel as itdoes for civilian veterans in the baseline scenario, productivitylosses account for $902.5 million. In the low-cost scenario (Sce-nario 3), we assumed that a mental health condition has no effecton productivity for active duty personnel, and as a result produc-tivity losses account for $206 million.

Policy Simulations

We now consider the social costs of three alternative treatmentscenarios. In addition to changing the treatment mix and partici-pation, each scenario assumed that everyone eligible for treatmentwas treated in the first quarter of onset. Like before, if treatmentwas unsuccessful, we assumed that there was an 80% chance ofreentry in each subsequent quarter with illness.

Scenario 4 in Table 3 assumed that 50% of those with depres-sion or PTSD received treatment in the first quarter of onset, and

similar to the baseline scenario, only 30% of these individualsreceived EBT. The only major difference between this scenarioand baseline is that individuals in Scenario 4 were more likely toget treatment earlier. The savings associated with Scenario 4exceeded $30 million, and they were driven by reduced produc-tivity losses. The cost estimates for suicide are virtually identical,which is not entirely surprising because the coverage was similarand suicide was an unlikely event.

Scenario 5 maintained that 50% of those with PTSD or depres-sion received treatment, but now 100% received EBT. We wouldexpect the treatment costs to increase, and indeed they doubledfrom $45 million to $93 million. There was a significant decreasein lost productivity because EBT is more effective than usual care(compared with both Scenarios 1 and 4), but it was not enough tooffset the increase in treatment costs, t(98) � 3.15, p � .0021.Although the average suicide costs were lower for Scenario 5 thanthose for baseline, they were not statistically significant as therewas tremendous overlap with the 95% confidence intervals,t(98) � 0.56, p � .4233.

Finally, Scenario 6 allowed everyone with PTSD, depression, orboth to receive EBT during the first quarter of onset. Whereas theaverage treatment costs were more than 4 times as large as theywere in the baseline scenario ($186 million vs. $42 million), theaverage total costs for this scenario were 15% ($136 million) lessthan the baseline estimate. The 95% confidence intervals weredramatically different from each other, t(98) � 14.47, p � .000,and distributions of 50 estimates for each scenario had minimaloverlap (see Figure 2). In addition to savings in lost productivity,

6 Figure published in the New York Times based on data reported by Col.Elspeth Ritchie, psychiatric consultant to the Army Surgeon General, at apress conference in May 2008 (Suicide Rate for Soldiers Rose in ’07,2008).

0.0

02.0

04.0

06.0

08.0

1D

ensi

ty

650 750 850 950 1050Total Costs ($2008 millions)

Baseline 100% EBT

Figure 2. Projected distribution of total costs for baseline and 100% evidence-based treatment (EBT) scenar-ios. Note. Each distribution is based on the results of 50 simulations.

206 KILMER, EIBNER, RINGEL, AND PACULA

the major savings associated with this scenario were driven by areduction in suicide costs (from $291 million to $201 million).7

Discussion

Of the 261,827 troops deployed as part of OEF or OIF on June30, 2008, we predicted that over 58,000 of these individuals wouldsuffer from major depression or PTSD within 2 years after return-ing home. The methodology reported here estimated the socialcosts of these disorders and considered how the costs wouldchange under different treatment scenarios. Results from our mi-crosimulation suggest that the 2-year social costs of depression andPTSD for this cohort would be close to $925 million (low: $462million; high: $1,326 million), and that providing EBT to everyonewould generate savings of more than 15%.

Our findings suggest that—from a societal perspective—EBTfor PTSD and major depression would pay for itself within 2 years.Although EBT requires a high initial investment in drugs, psycho-therapy, or both, evidence-based care increases the probability ofrecovering from mental illness, which ultimately increases produc-tivity and reduces the risk of suicide. We did not evaluate the costsand benefits of increasing access to evidence-based care from a

purely Department of Defense or VA perspective. However,higher investments in EBT might make economic sense from theDepartment of Defense’s standpoint not only because of higherremission and recovery rates, but also because EBT would increaseproductivity of service members. Hoge et al. (2006) showed thatretention within the Department of Defense is higher among thosewithout a deployment-related mental health condition. But, regard-less of whether increased investment in mental health treatmentsaves money from the Department of Defense or VA perspective,our analysis argues that society as a whole would benefit if morereturning service members received evidence-based care.

Our cost estimates are likely to be conservative because theyfocus on only the 2 years after return and include only the costsassociated with treatment, lost productivity, and suicide. We didnot consider the potential costs stemming from the downstreamconsequences of these illnesses, including increased non–mental-health-related medical costs, caregiver burden, strain on family

7 The lost productivity costs are 33% lower in Scenario 6 than Scenario1. Holding everything else constant, this figure would have to be below9.5% in order to make the latter more attractive than the former.

Table 3Components of Total Cost Estimates, by Scenario ($2008 Millions)

Variable Mean SE 95% CI low 95% CI high

Scenario 1: BaselineTotal cost 922.77 7.73 907.24 938.30Lost productivity 591.27 3.66 583.92 598.62Mental health treatment 40.96 0.37 40.21 41.71Medical costs of suicide 1.71 0.01 1.69 1.73Cost of lives lost to suicide 288.83 6.65 275.47 302.18

Scenario 2: HighTotal cost 1325.63 9.59 1306.36 1344.89Lost productivity 902.54 3.98 894.53 910.54Mental health treatment 45.38 0.47 44.43 46.33Medical costs of suicide 2.14 0.01 2.12 2.17Cost of lives lost to suicide 375.56 8.45 358.57 392.55

Scenario 3: LowTotal cost 462.08 7.17 447.68 476.48Lost productivity 206.02 3.50 198.99 213.04Mental health treatment 31.53 0.29 30.94 32.12Medical costs of suicide 1.27 0.01 1.25 1.29Cost of lives lost to suicide 223.26 5.62 211.97 234.56

Scenario 4: 50% receive treatment, 30% receive EBTTotal cost 889.18 7.82 873.47 904.89Lost productivity 554.83 3.25 548.31 561.36Mental health treatment 45.04 0.38 44.28 45.80Medical costs of suicide 1.62 0.01 1.60 1.64Cost of lives lost to suicide 287.68 7.05 273.52 301.84

Scenario 5: 50% receive treatment, 100% receive EBTTotal cost 916.20 5.92 904.30 928.10Lost productivity 538.82 3.91 530.97 546.67Mental health treatment 93.08 0.62 91.84 94.32Medical costs of suicide 1.59 0.01 1.56 1.61Cost of lives lost to suicide 282.71 5.39 271.87 293.55

Scenario 6: 100% receive treatment, 100% receive EBTTotal cost 785.96 5.45 775.00 796.92Lost productivity 398.70 2.81 393.05 404.35Mental health treatment 185.93 0.36 185.21 186.66Medical costs of suicide 1.16 0.01 1.15 1.18Cost of lives lost to suicide 200.16 4.87 190.36 209.95

Note. CI � confidence interval; EBT � evidence-based treatment. Results for each scenario are based on 50 simulations.

207COSTS AND SAVINGS OF PROVIDING MENTAL HEALTH TREATMENT

relationships, domestic violence, substance abuse, crime, andhomelessness. Prior work has found associations between mentalhealth conditions and these outcomes, although results are typi-cally correlational rather than causal (Calhoun & Beckham, 2002;Dekel & Solomon, 2006; Kulka et al., 1990; Rosenheck & Fon-tana, 1994; Solomon et al., 1992).

It is also important to note that we did not incorporate the costsassociated with increasing the number of providers, training pro-viders in evidence-based practices, and providing outreach toservice members to encourage them to seek care. Currently, theDepartment of Defense reports lacking the funding and personnel“to adequately support the psychological health of service mem-bers and their families” (Department of Defense Task Force onMental Health, 2007); thus, our Scenario 6 (as well as some of theothers) would require additional expenditure. As for encouragingservice members to seek evidence-based care, we are not aware ofany studies that have examined this for military populations.However, prior studies in the civilian sector have found thatvigorous outreach aimed at moving depressed workers intoevidence-based care leads to cost savings from the employer’sperspective (Wang et al., 2007). Whether or not these efforts willproduce similar savings in military settings is an empirical ques-tion that deserves serious attention because they may simultane-ously improve health and reduce costs.

The ultimate goal of this research was to present an innovativeapproach to calculating the mental health costs for a cohort oftroops deployed for OEF or OIF. Despite the high costs of treatingthese disorders, we found that increasing spending on EBT may bemore than offset by the savings related to lost productivity andreduced suicide. This raises a larger question about whether cost–benefit ratios should drive decisions about the quality of care madeavailable to our troops, especially with respect to mental healthtreatment. The answer is beyond the scope of this article, but itshould be noted that accurate cost information is useful for pur-poses other than conducting cost–benefit analyses (e.g., budget-ing). Furthermore, being able to predict the onset and remission ofmental health conditions is useful for projecting events believed tobe associated with these conditions (e.g., domestic violence). Themicrosimulation modeling approach is flexible enough to considera range of related outcomes and particularly advantageous forassessing alternative scenarios in terms of their impact on currentand downstream events and costs.

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(Appendix follows)

209COSTS AND SAVINGS OF PROVIDING MENTAL HEALTH TREATMENT

Appendix

Microsimulation Map

Note. r� denotes a random number. Steps 1–12 constitute onerun of the simulation.

1. Generate Population

• Randomly select 65,000 of the 261,827 troops deployed aspart of OEF or OIF on June 30, 2008.

• Assign age, race/ethnicity, education, and civilian labor forcestatus (if they leave full-time active duty; based on 2007 CurrentPopulation Survey).

• Reservists immediately transition to nonmilitary civilian sta-tus on Day 1.

2. Assign Mental Illness

• Randomly select 58,126 (261,827 � 0.222) of these 65,000troops to develop depression, PTSD, or both during the 2 yearspostreturn. The remaining 6,874 troops (65,000–58,126) will serveas the control group for the productivity calculations.

• For those individuals assigned a mental illness:• Randomly assign PTSD to 68% of them; one third will have

PTSD on Day 1 and two thirds will have PTSD onset during thesimulation (quarter for delayed-onset PTSD is determined ran-domly).

• Fifty percent of those with PTSD will also have comorbiddepression (if it is delayed-onset PTSD/depression, the depressionwill also be delayed).

• The remaining troops are assigned depression only, beginningon Day 1.

3. Allow Instantaneous Attrition for Full-TimeActive Duty

• If r� � A�, the individual leaves military on Day 1.• A� is based on Hoge et al. (2004) and depends on mental

health status.• Individuals who leave military at this point do not enter the

reserves.The remaining sections of this model map describe what happensin every quarter. The simulation used for this report is based on 2years (eight quarters [Q]).

4. Did the Individual Enter Mental HealthTreatment in Qt?

8

• If the individual has a mental health condition and r1� � T�,the individual receives treatment. T� begins at 0.30 and increasesby 0.032 each quarter.

• If the individual receives treatment and r2� � 0.30, theindividual receives EBT instead of usual care.

• If treatment in Qt has an effect, assume that the effect will notbe seen until the beginning of Qt�1.

• If treatment in Qt is unsuccessful, the individual has an 80%chance of continuing the same course of treatment in Qt�1.

• Individuals cannot switch between EBT and usual care.

5. Did the Individual Attempt Suicide in Qt?

• If the individual has a mental health condition and r� � S�,they attempt suicide.

• S� is based on Gibbons et al. (2007) and varies by age.• Because attempt probabilities are annual and the model is

updated quarterly, randomly choose the quarter when a suicideattempt can occur.

6. Did the Individual Complete Suicide in Qt?

• If the individual attempts suicide and r� � F�, the suicideattempt is fatal.

• F� varies by military status (8.6% military, 4% civilians;various sources).

• Suicide is the only way someone can die in the model.

7. What Was the Individual’s Civilian Labor ForceStatus (CLFS) During Qt?

• Irrelevant if full-time military because no moonlighting isassumed.

• Initial CLFS (full, part, or unemployed) is based on distribu-tion of veterans in the 2007 Current Population Survey.

• CLFS can only change when an individual experiences achange in mental health status. These probabilities are based onSavoca and Rosenheck (2000).

8 Step 4 is specific to Scenarios 1–3. The procedures for Step 4 areslightly different for the policy scenarios (Scenarios 4–6).

(Appendix continues)

210 KILMER, EIBNER, RINGEL, AND PACULA

8. How Much Money Did the IndividualMake in Qt?

• Military pay for full-time and reservists is pulled from officialmilitary pay tables (depends on rank and years of service).

• Full-time military personnel also receive a subsistence allow-ance (depends on rank) and a housing allowance (depends ondependents; use a weighted average).

• Civilian wages come from predicted wage regressions basedon veterans in the 2006 Current Population Survey. Predictorsinclude age, gender, race/ethnicity, education, and marital status(this last variable is not tracked in the simulation; impute thesample average).

• Run separate regressions for full- and part-time workers.• Those who are unemployed are assigned a wage � 0.• For individuals with a mental health condition, wages are

decremented based on rates reported in Savoca and Rosenheck(2000).

• Wages in quarter of fatal suicide (and in subsequent quar-ters) � 0.

9. Did the Individual Leave Full-Time Active Duty atthe End of Qt?

• If r1� � L�, the individual leaves the Department of Defense.• L� is based on data reported by Hoge et al. (2004), and it

varies depending on mental health status and length of time sincereturning home.

• Attrition occurs only at the end of a quarter.• If the individual has a nonfatal suicide attempt and r2� � 0.80,

the individual leaves the military.

10. Did the Individual Leave Full-Time Active Dutyand Enter Reserves at the End of Qt?

• If the individual leaves the Department of Defense and r� �R�, the individual joins the reserves.

• R� varies depending on rank and branch.

• Individuals leaving because of suicide attempt do not enterreserves.

11. Was Individual Promoted to a Higher Rank at theBeginning of Qt�1?

• This is not a function of mental health.• Everyone in the military is eligible for a promotion.• If r� � X�, the individual is promoted.• X� is based on DMRR (2007) and varies depending on rank

and branch.• Because promotion probabilities are annual and model is

updated quarterly, the quarter of promotion consideration is ran-domly assigned across the year.

• Individuals can be promoted only once during the simulation.

12. What Was the Individual’s Mental Health Statusat the Beginning of Qt�1?

• Depends on whether treatment was received in Qt, the effec-tiveness of that treatment, and the natural course of the disorder(remission, delayed-onset, and relapse).

• If r1� � Y�, the individual remits.• Y� varies with treatment and type of mental health condition.• If r2� � Z�, then the individual relapses.• Z� varies with illness and, in some specifications, with treat-

ment.• Because relapse probabilities are biennial and model is up-

dated quarterly, the quarter of relapse consideration is randomlyassigned across a 2-year period after the illness remits. In somecases, the quarter of relapse consideration is greater than eight;thus, the individual does not relapse during the simulations.

• If quarter of delayed-onset PTSD or PTSD/depression � t,illness begins at the beginning of Qt�1.

Received May 27, 2009Revision received March 18, 2010

Accepted April 19, 2010 �

211COSTS AND SAVINGS OF PROVIDING MENTAL HEALTH TREATMENT