Risk Factors, Methods, and Timing of Suicide Attempts Among US Army Soldiers

Importance  Suicide attempts in the US Army have risen in the past decade. Understanding the association between suicide attempts and deployment, as well as method and timing of suicide attempts, can assist in developing interventions.

Objective  To examine suicide attempt risk factors, methods, and timing among soldiers currently deployed, previously deployed, and never deployed at the time this study was conducted.

Design, Setting, and Participants  This longitudinal, retrospective cohort study of Regular Army–enlisted soldiers on active duty from 2004 through 2009 used individual-level person-month records to examine risk factors (sociodemographic, service related, and mental health), method, and time of suicide attempt by deployment status (never, currently, and previously deployed). Administrative data for the month before each of 9650 incident suicide attempts and an equal-probability sample of 153 528 control person-months for other soldiers were analyzed using a discrete-time survival framework.

Main Outcomes and Measures  Suicide attempts and career, mental health, and demographic predictors were obtained from administrative and medical records.

Results  Of the 9650 enlisted soldiers who attempted suicide, 86.3% were male, 68.4% were younger than 30 years, 59.8% were non-Hispanic white, 76.5% were high school educated, and 54.7% were currently married. The 40.4% of enlisted soldiers who had never been deployed (n = 12 421 294 person-months) accounted for 61.1% of enlisted soldiers who attempted suicide (n = 5894 cases). Risk among those never deployed was highest in the second month of service (103 per 100 000 person-months). Risk among soldiers on their first deployment was highest in the sixth month of deployment (25 per 100 000 person-months). For those previously deployed, risk was highest at 5 months after return (40 per 100 000 person-months). Currently and previously deployed soldiers were more likely to attempt suicide with a firearm than those never deployed (currently deployed: OR, 4.0; 95% CI, 2.9-5.6; previously deployed: OR, 2.7; 95% CI, 1.8-3.9). Across deployment status, suicide attempts were more likely among soldiers who were women (currently deployed: OR, 3.4; 95% CI, 3.0-4.0; previously deployed: OR, 1.5; 95% CI, 1.4-1.7; and never deployed: OR, 2.4; 95% CI, 2.3-2.6), in their first 2 years of service (currently deployed: OR, 1.9; 95% CI, 1.5-2.3; previously deployed: OR, 2.2; 95% CI, 1.9-2.7; and never deployed: OR, 3.1; 95% CI, 2.7-3.6), and had a recently received a mental health diagnosis in the previous month (currently deployed: OR, 29.8; 95% CI, 25.0-35.5; previously deployed: OR, 22.2; 95% CI, 20.1-24.4; and never deployed: OR, 15.0; 95% CI, 14.2-16.0). Among soldiers with 1 previous deployment, odds of a suicide attempt were higher for those who screened positive for depression or posttraumatic stress disorder after return from deployment and particularly at follow-up screening, about 4 to 6 months after deployment (depression: OR, 1.4; 95% CI, 1.1–1.9; posttraumatic stress disorder: OR, 2.4; 95% CI, 2.1–2.8).

Conclusions and Relevance  Identifying the timing and risk factors for suicide attempt in soldiers requires consideration of environmental context, individual characteristics, and mental health. These factors can inform prevention efforts.

Suicide attempts (SAs), similar to suicides, have increased in the US Army over the past decade.^1,2 Soldiers who deployed in support of Operation Enduring Freedom/Operation Iraqi Freedom have increased risk of psychiatric morbidity^3,4 and have often,^2,5,6 but not always,^7,8 been found to have increased risk of suicide compared with those who never deployed (ND). Despite their importance as a gateway to suicide and their associated morbidity and costs, SAs have been less studied in general and in the Army in particular.

Worldwide, about 0.4% of adults report that they have made an SA within the past year.⁹ The World Health Organization and the US government have called for increased attention to suicide and SAs.^10,11 Women are at about 50% increased risk of SAs compared with men. Those younger than 50 years of age are at about twice the risk of those who are older.⁹ The presence of a mental disorder and the number of disorders substantially increase risk.¹² In the Army, findings based on data from 2004 to 2009 indicate that women and soldiers who are early in their career, younger, and those with a recent mental health diagnosis are at increased risk. Those findings also highlighted the significant role of deployment status (DS), with ND soldiers at highest risk.¹³ However, little is known about how risk factors for SA may vary by DS, which can inform the development of interventions.^14,15

Using a life/career history perspective, we examined risk of Army SAs as a function of DS, individual characteristics, and mental health. We examined method of SA by DS because access to firearms is increased in theater and may contribute to increased suicide deaths during deployment.^5,16

Box Section Ref ID

This component of the Army Study to Assess Risk and Resilience in Servicemembers)¹⁷ was approved by the institutional review boards of the Uniformed Services University, University of Michigan Institute for Social Research, University of California–San Diego, and Harvard Medical School, which determined that the study did not constitute human participant research because it relied on deidentified secondary data; therefore, informed consent was not obtained. Using a longitudinal, retrospective cohort study design and individual-level person-month records for all soldiers on active duty between January 1, 2004, and December 31, 2009 (N = 1.66 million),¹⁷ we selected our analytic sample from 975 057 Regular Army soldiers (excluding activated Army National Guard/Reserve), 9791 of whom had a documented SA. This study focused on Regular Army–enlisted soldiers, accounting for nearly 99% of SAs from 2004 through 2009.¹³ The final analytic sample included all 9650 enlisted soldiers who attempted suicide and 153 528 control person-months. Data were analyzed using a discrete-time survival framework with person-month as the unit of analysis¹⁸ and each month in the soldier’s career treated as a separate observational record. We reduced computational intensity by selecting from the population an equal-probability 1:200 sample of control person-months stratified by sex, rank, time in service, DS at the time of SA (never, currently, or previously deployed), and historical time.¹⁹

We identified SA using the Department of Defense Suicide Event Report and International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes E950-E958 (self-inflicted poisoning or injury with suicidal intent) from health care encounter data systems for military and civilian treatment facilities, combat operations, and aeromedical evacuations (eTable 1 in the Supplement). The E959 code (late effects of a self-inflicted injury) was excluded because it confounds the temporal associations between the predictors and SA.²⁰ For soldiers with multiple SAs, we selected the first attempt using a hierarchical classification.¹ The Department of Defense Suicide Event Report and International Classification of Diseases, Ninth Revision, Clinical Modification E950-E958 diagnostic codes for method of SA were organized into 2 categories: firearms (excluding explosives, n = 1) and other methods (eg, overdose, hanging, and electrocution) (eTable 2 in the Supplement).

Using International Classification of Diseases, Ninth Revision, Clinical Modification codes from the Military Health System Data Repository, Theater Medical Data Store, and Transportation Command Regulating and Command and Control Evacuating System records, we identified previous diagnoses of depression, posttraumatic stress disorder (PTSD), and substance use disorder (SUD), and created an indicator for any previous mental health diagnosis that included most International Classification of Diseases, Ninth Revision, Clinical Modification codes for mental disorders (eTable 3 in the Supplement). To examine self-reported depression and PTSD following deployment, we used heath screening data from the Post-Deployment Health Assessment (PDHA; administered just prior to or immediately after return) and Post-Deployment Health Reassessment (PDHRA; administered 3-6 months after return) (eAppendix in the Supplement).^21,22 We included soldiers completing both assessments, using a time frame of 1 month before return to 3 months after return (PDHA) and 4 to 6 months after return (PDHRA), capturing most screening data.

Statistical analyses were conducted using SAS version 9.3 (SAS Institute Inc).²³ To examine the association of DS with method of SA, we used an overall data array with separate data files for method of attempt (firearm and other) stacked and distinguished by dummy variables. The stacked sample was modeled with a 2-way interaction between DS and sample that controlled for sociodemographic and service-related variables.

To examine multivariate associations of sociodemographic and service-related variables with SA, separate logistic regression analyses were conducted within groups of soldiers stratified by DS. In separate models, we examined the incremental predictive effects of depression, PTSD, and SUD diagnoses; the number of those diagnoses in previous months and since entering service; and any previous mental health diagnosis. Using the subset of soldiers with 1 previous deployment who completed both the PDHA and PDHRA prior to their person-month record, we examined the effects of PTSD or depression diagnosis either early (PDHA) or late (PDHRA). Logistic regression coefficients were exponentiated to obtain odds ratios (ORs) and 95% CIs. Final model coefficients were used to generate standardized risk estimates (SRE)²⁴ (number of SAs per 100 000 person-years) for each category of each predictor under the model, assuming other predictors were at their samplewide means. Logistic regression models included a dummy predictor for calendar month and year to control for increasing rates of SA from 2004 to 2009.¹ Coefficients of other predictors can be interpreted as averaged within-month associations based on the assumption that effects of other predictors do not vary over time.

To examine the risk of SA as a function of time and DS, we used discrete-time hazard functions and linear spline models. Analyses estimated risk by (1) months since entering service among ND soldiers, (2) months since deploying among those currently on their first deployment (CD), and (3) months since returning from first deployment among those previously deployed (PD). Splines (piecewise linear functions) were examined using χ² tests, deviance, and the Akaike Information Criterion to test whether knots and additional linear segments improved model fit to assess nonlinearities in changes in risk by time in service.

Of the 163 178 enlisted soldiers included in this study, 9650 enlisted soldiers attempted suicide. The 40.4% of ND soldiers (n = 12 421 294 person-months) accounted for 61.1% of enlisted soldiers who attempted suicide (n = 5894 cases) and had the highest rate of attempt (569 per 100 000 person-years), followed by the 36.2% (n = 11 120 816 person-months) who were PD (29.2% of soldiers who attempted suicide [n = 2816 cases]; 304 per 100 000 person-years) and the 23.4% (n = 7 173 140 person-months) of CD soldiers (9.7% of soldiers who attempted suicide [n = 940 cases]; 157 per 100 000 person-years) (Figure 1). Firearms were used by 56 (1.0%), 127 (13.6%), and 108 (3.9%) ND, CD, and PD soldiers, respectively (eTable 4 in the Supplement). Overdose was the most common method (52.2%-56.9%) among soldiers who used other methods. Deployment status was associated with SAs by firearm (χ²₂ = 65.4; P < .001) and by other methods (χ²₂ = 895.6; P < .001). The interaction of DS by method of SA was significant (χ²₂ = 222.5; P < .001). Odds of a SA by firearm were higher among CD soldiers (OR, 4.0; 95% CI, 2.9-5.6) and PD soldiers (OR, 2.7; 95% CI, 1.8-3.9) than ND soldiers. Previously deployed soldiers had lower odds of an SA by firearm than CD soldiers (OR, 0.7; 95% CI, 0.5-0.9) (eFigure 1 in the Supplement).

Standardized risk estimates were highest among ND soldiers and lowest among CD soldiers for all sociodemographic and service-related risk factors. Female soldiers had higher odds of SA than male soldiers (CD: OR, 3.4; 95% CI, 3.0-4.0; PD: OR, 1.5; 95% CI, 1.4-1.7; and ND: OR, 2.4; 95% CI, 2.3-2.6). The association of sex differed by DS (χ²₁ = 80.1; P < .001). Risk of SA decreased with longer time in service in each DS, and associations varied across groups (χ²₆ = 25.6; P < .001). Soldiers with 1 to 2 years in service had higher odds than those with 5 to 10 years (CD: OR, 1.9; 95% CI, 1.5-2.3; PD: OR, 2.2; 95% CI, 1.9-2.7; and ND: OR, 3.1; 95% CI, 2.7-3.6). Among soldiers with comparable time in service, the SRE was higher among ND soldiers (106-744 per 100 000 person-years) and PD soldiers (114-556 per 100 000 person-years) than among CD soldiers (63-250 per 100 000 person-years) (Table, eFigure 2, and eTable 5 in the Supplement).

History of any mental health diagnosis was associated with SA in all deployment groups, controlling for sociodemographic and service-related variables. Soldiers with a diagnosis in the previous month had the highest odds vs those with no diagnosis (CD: OR, 29.8; 95% CI, 25.0-35.5; PD: OR, 22.2; 95% CI, 20.1-24.4; and ND: OR, 15.0; 95% CI, 14.2-16.0). Odds decreased monotonically as time increased since the most recent diagnosis. Never-deployed soldiers with a diagnosis in the previous month had the highest SRE (4813 per 100 000 person-years), twice that of CD or PD soldiers with a comparably recent diagnosis (2383 and 2340 per 100 000 person-years, respectively) (Figure 2; eTable 6 and eTable 7 in the Supplement).

Odds were highest for more recently recorded diagnoses of depression, PTSD, and SUD. Soldiers with depression in the previous month had the highest odds of SA (CD: OR, 14.1; 95% CI, 10.4-19.1; PD: OR, 10.6; 95% CI, 9.3-12.0; and ND: OR, 10.2; 95% CI, 9.3-11.2). The odds associated with past-month PTSD ranged from 2.4 for PD to 8.1 for CD, and the odds associated with past-month SUD ranged from 4.3 for ND to 5.7 for CD soldiers. Associations of depression (χ²₈ =21.2;  P = .007), PTSD (χ²₈ =37.8; P < .001), and SUD (χ²₈ =47.1; P < .001) varied by DS. Never-deployed soldiers with depression, PTSD, or SUD in the previous month were at greatest risk (SREs of 4465, 1566, and 2256 per 100 000 person-years, respectively) (eTable 6 and eTable 7 and eFigure 3 in the Supplement).

The odds of SA increased as number of diagnoses (depression, PTSD, and SUD) in the previous month increased from 1 (CD: OR, 13.2; 95% CI, 10.1-17.2; PD: OR, 9.4; 95% CI, 8.5-10.4; and ND: OR, 8.8; 95% CI, 8.1-9.5) to 2 or more (CD: OR, 37.1; 95% CI, 20.2-68.4; PD: OR, 17.0; 95% CI, 13.7-21.1; and ND: OR, 17.1; 95% CI, 13.5-21.6). This association varied by DS (P = .002), with the highest odds associated with CD soldiers. The pattern was similar for the total number of mental health diagnoses since entering the Army for each DS. Odds increased from no diagnoses to 1 diagnosis (CD: OR, 3.6; 95% CI, 3.1-4.1; PD: OR, 4.7; 95% CI, 4.3-5.2; and ND: OR, 5.4; 95% CI, 5.0-5.7) and 2 or more (CD: OR, 7.0; 95% CI, 5.5-8.6; PD: OR, 12.3; 95% CI, 11.2-13.6; ND: OR, 11.2; 95% CI, 10.0-12.4) (eTable 6 and eTable 7 and eFigure 3 in the Supplement).

Among soldiers with 1 deployment, those who screened positive for depression or PTSD early (PDHA) had greater risk of SA (OR, 1.4; 95% CI, 1.1–1.8) but odds were highest among those who screened positive later (PDHRA) (OR, 2.7; 95% CI, 2.3-3.1). Standard risk estimates for soldiers with a negative screen, early positive screen only, and late positive screen for depression or PTSD were 259, 370, and 689 per 100 000 person-years, respectively. Examined simultaneously in the same model, depression (P < .001) and PTSD (P < .001) were significant and continued to independently be associated with SA during the later screen. Soldiers with a late positive depression screen were more likely to attempt suicide (OR, 2.4; 95% CI, 2.0-2.9), whereas the odds among those with an early positive depression screen were not elevated (OR, 1.2; 95% CI, 0.9-1.7). Odds were higher among soldiers with a late positive PTSD screen relative to those with a negative screen (OR, 1.7; 95% CI, 1.5-2.1) but not higher among those with only an early positive PTSD screen (OR, 1.3; 95% CI, 0.9-1.7) (eTable 8 in the Supplement).

Never-deployed soldiers were at greatest risk in the second month of service (103 per 100 000 person-months). Risk in this group increased early in service, followed by a decline, then increased at a gradual and constant rate through the end of the first year of service. The median deployment length was 12 months, with 75% having a deployment 11 to 15 months long. Among soldiers on their first deployment, risk of SA was highest in the sixth month of deployment (25 per 100 000 person-months), followed by a gradual decline in risk over the remaining months. Among soldiers who had returned from their first deployment, risk of SA increased and peaked around the fifth month after return (40 per 100 000 person-months), followed by a decrease over time (Figure 3).

Hazard functions indicated that monthly risk of SA was highest for those with a late positive screen for depression or PTSD (62-107 per 100 000 person-months). Monthly risk was lower for those with only an early positive screen after deployment (21-66 per 100 000 person-months), and lowest for those who screened negative for both disorders (19-29 per 100 000 person-months). Spline analyses indicated that the risk decreased over the 24 months after deployment but remained elevated for those who screened positive on the PDHRA (Figure 4).

To our knowledge, SA has rarely been studied with data as comprehensive as available for these analyses. The findings highlight the importance of life and career history in understanding SAs in soldiers and the complexity of risk and protective factors across contexts. We found the highest rates of SA were among ND soldiers and those in their first years of service. Future research should examine the degree to which this risk is accounted for by those who recently entered service. Prior studies found that female soldiers are more likely than male soldiers to make a medically documented^13,25 or self-reported SA.²⁶ We found this effect across all deployment contexts, with CD women having the highest odds of SA relative to men. Although men are more likely to die by suicide in any deployment context, this pattern of increased risk among CD women has also been observed for suicide death.²⁷ Further research is required to understand factors that may contribute to these sex differences: occupational differences among those deployed, variation in social support by DS, the role of sexual assault or harassment,²⁸ or sex differences in response to deployment-related stressors.

The association of mental health diagnosis with suicidal behaviors is well established.²⁹ We found that the magnitude of this association across deployment contexts increased with recent diagnosis. This pattern was observed for a history of any mental health diagnosis and specifically for depression, PTSD, and SUD. This finding is consistent with evidence that 75% of service members had contact with the health care system in the month prior to a self-inflicted injury.³⁰ Although the importance of recent diagnosis may be expected because it is a potential indicator of acuity, it also highlights the challenges of prevention even among soldiers identified as having psychiatric risk. Posttraumatic stress disorder, SUD, and depression each increased risk even after adjusting for the other 2 disorders.

The higher risk among ND soldiers in their second month of service, a stressful time during basic training and Army acculturation, reinforces the importance of developing and evaluating effective risk detection and intervention strategies early in a soldier’s career. Whether this risk pattern was associated with expanded Army recruitment during war or anticipated deployments or is a persistent pattern of risk among soldiers in training remains to be determined.

Although SA by firearm made up a small percentage of attempts, the risk was increased during deployment and after deployment, suggesting that the deployment environment, with high access to firearms, may contribute to the lethality of suicidal behavior. Indirect support for this comes from previous Army Study to Assess Risk and Resilience in Servicemembers research indicating that CD enlisted soldiers had a higher rate of suicide death than ND soldiers,⁵ and nearly all suicides in theater were by firearm.³¹ The rate of SA during deployment peaked in the sixth month, the approximate mid-deployment period (often a time of return home and changing anticipation of deployment length). A similar curvilinear relationship for suicide and suicide ideation, peaking at mid-deployment, was observed in early Army reports and was associated with the worst levels of self-reported morale and mental health problems.³² Information on temporal risk patterns can inform health care professionals so that intervention strategies are available and accessible during high-risk periods. The SA rate during this mid-deployment period remains lower than in ND soldiers during their first year of service. This may be the result of screening prior to deployment, fewer mental health diagnoses among those deployed, or other individual or contextual factors.

Among PD soldiers, risk increased over the first 5 months after deployment. Difficulties with postdeployment readjustment are well documented.³³ The peak risk at 5 months after deployment is consistent with previous research suggesting that self-reported mental health concerns are significantly greater 3 to 6 months postdeployment than immediately on return.³⁴ This is further supported by our finding that soldiers who screened positive for depression or PTSD during the later assessment (PDHRA) had higher risk than those with a negative screen and those with an early (PDHA) positive screen only. Risk among those with a late positive depression screen decreased over time but remained elevated up to 2 years after deployment. These data suggest that self-report screening, in addition to clinician diagnosis, may be a valuable part of a larger risk detection program. Programs to help soldiers in the months following redeployment may be particularly important²²; however, the effectiveness of these programs can be hampered by underreporting of difficulties.³⁵

Research by Nock et al²⁶ found increased odds of a past SA among PD soldiers. However, there are important methodological differences.¹³ Whereas this study includes ND soldiers who are still in training (a period of greatly increased risk), Nock et al²⁶ surveyed soldiers in later stages of their careers who were presumably healthy and resilient enough to complete training. In addition, Nock et al²⁶ studied self-reported first lifetime SA, whereas our study examines first medically documented SA during Army service. Future studies using a combination of Army Study to Assess Risk and Resilience in Servicemembers survey data with linked administrative data can further detail the reasons for these differences.

Several limitations in this study are noteworthy. First, ascertainment of SAs was limited to events captured by the health care system that vary in degree of lethality, particularly attempts by firearm. They are subject to coding errors and changes in policy and procedures (eg, electronic vs paper medical records). Second, findings represent 2004 to 2009. Third, this study examined a limited set of predictors. Fourth, postdeployment health findings may not represent all PD soldiers because they were limited to soldiers who deployed only once and completed both assessments. Finally, differences across DS are not evidence of within-person changes over time because the composition of these groups is affected by the nonrandom nature of deployment and Army attrition.^36-38

Deployment context is important in identifying SA risk among Army-enlisted soldiers. A life/career history perspective can assist in identifying high-risk segments of a population based on factors such as timing, environmental context, and individual characteristics. Our findings, while most relevant to active-duty US Army soldiers, highlight considerations that may inform the study of suicide risk in other contexts such as during the transition from military to civilian life.³⁹

Corresponding Author: Robert J. Ursano, MD, Department of Psychiatry, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814 (robert.ursano@usuhs.edu).

Submitted for Publication: November 6, 2015; final revision received February 29, 2016; accepted February 29, 2016.

Published Online: May 25, 2016. doi:10.1001/jamapsychiatry.2016.0600.

Author Contributions: Dr Ursano had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Ursano, Kessler, Stein, Naifeh, Wryter, Colpe, Schoenbaum, Heeringa.

Acquisition, analysis, or interpretation of data: Ursano, Kessler, Naifeh, Aliaga, Fullerton, Wynn, Vegella, Ng, Zhang, Sampson, Kao, Schoenbaum, McCarroll, Cox, Heeringa.

Drafting of the manuscript: Ursano, Naifeh, Alaiga, Vegella, Ng, Zhang, McCarroll.

Critical revision of the manuscript for important intellectual content: Ursano, Kessler, Stein, Naifeh, Fullerton, Wynn, Ng, Wryter, Sampson, Kao, Colpe, Schoenbaum, Cox, Heeringa.

Statistical analysis: Ursano, Alaiga, Wynn, Vegella, Ng, Zhang, Sampson, Kao.

Obtained funding: Ursano, Kessler, Heeringa.

Administrative, technical, or material support: Ursano, Kessler, Fullerton, Wryter, Colpe, Schoenbaum, Heeringa.

Study supervision: Ursano, Sampson.

Conflict of Interest Disclosures: Dr Kessler has been a consultant over the past 3 years for J and J Wellness and Prevention Inc, Lake Nona Institute, Ortho-McNeil Janssen Scientific Affairs, Sanofi-Aventis Groupe, Shire US Inc, and Transcept Pharmaceuticals Inc. He has had research support for his epidemiological studies from EPI-Q, Sanofi-Aventis Groupe, and Walgreens Co and owns 25% share in DataStat Inc. Dr Stein has been a consultant for Healthcare Management Technologies, Janssen Pharmaceuticals, Pfizer, and Tonix Pharmaceuticals. No other disclosures were reported.

Funding/Support: Army STARRS was sponsored by the Department of the Army and funded under cooperative agreement U01MH087981 with the US Department of Health and Human Services, National Institutes of Health, and the National Institute of Mental Health.

Role of the Funder/Sponsor: As a cooperative agreement, scientists employed by the National Institute of Mental Health (Drs Colpe and Schoenbaum) and Army liaisons/consultants (COL Steven Cersovsky, MD, MPH USAPHC and Kenneth Cox, MD, MPH USAPHC) had a role in the design and conduct of the study; and collection, management, analysis, and interpretation of the data. Although a draft of this manuscript was submitted to the Army and National Institute of Mental Health for review and comment prior to submission, this was with the understanding that comments would be no more than advisory and they had no direct role in the preparation, review, or approval of the manuscript, or decision to submit the manuscript for publication.

Group Information: The Army STARRS Team consists of coprincipal investigators Robert J. Ursano, MD (Uniformed Services University of the Health Sciences) and Murray B. Stein, MD, MPH (University of California–San Diego and VA San Diego Healthcare System); site principal investigators Steven Heeringa, PhD (University of Michigan), and Ronald C. Kessler, PhD (Harvard Medical School); National Institute of Mental Health collaborating scientists Lisa J. Colpe, PhD, MPH, and Michael Schoenbaum, PhD; Army liaisons/consultants COL Steven Cersovsky, MD, MPH (Uniformed Services University of the Health Sciences), and Kenneth Cox, MD, MPH (Uniformed Services University of the Health Sciences).

Other team members include Pablo A. Aliaga, MS (Uniformed Services University of the Health Sciences); COL David M. Benedek, MD (Uniformed Services University of the Health Sciences); K. Nikki Benevides, MA (Uniformed Services University of the Health Sciences); Paul D. Bliese, PhD (University of South Carolina); Susan Borja, PhD (National Institute of Mental Health); Evelyn J. Bromet, PhD (Stony Brook University School of Medicine); Gregory G. Brown, PhD (University of California–San Diego); Christina L. Wryter, BA (Uniformed Services University of the Health Sciences); Laura Campbell-Sills, PhD (University of California–San Diego); Catherine L. Dempsey, PhD, MPH (Uniformed Services University of the Health Sciences); Carol S. Fullerton, PhD (Uniformed Services University of the Health Sciences); Nancy Gebler, MA (University of Michigan); Robert K. Gifford, PhD (Uniformed Services University of the Health Sciences); Stephen E. Gilman, ScD (Harvard School of Public Health); Marjan G. Holloway, PhD (Uniformed Services University of the Health Sciences); Paul E. Hurwitz, MPH (Uniformed Services University of the Health Sciences); Sonia Jain, PhD (University of California–San Diego); Tzu-Cheg Kao, PhD (Uniformed Services University of the Health Sciences); Karestan C. Koenen, PhD (Columbia University); Lisa Lewandowski-Romps, PhD (University of Michigan); Holly Herberman Mash, PhD (Uniformed Services University of the Health Sciences); James E. McCarroll, PhD, MPH (Uniformed Services University of the Health Sciences); James A. Naifeh, PhD (Uniformed Services University of the Health Sciences); Tsz Hin Hinz Ng, MPH (Uniformed Services University of the Health Sciences); Matthew K. Nock, PhD (Harvard University); Rema Raman, PhD (University of California–San Diego); Holly J. Ramsawh, PhD (Uniformed Services University of the Health Sciences); Anthony Joseph Rosellini, PhD (Harvard Medical School); Nancy A. Sampson, BA (Harvard Medical School); LCDR Patcho Santiago, MD, MPH (Uniformed Services University of the Health Sciences); Michaelle Scanlon, MBA (National Institute of Mental Health); Jordan W. Smoller, MD, ScD (Harvard Medical School); Amy Street, PhD (Boston University School of Medicine); Michael L. Thomas, PhD (University of California–San Diego); Patti L. Vegella, MS, MA (Uniformed Services University of the Health Sciences); Leming Wang, MS (Uniformed Services University of the Health Sciences); Christina L. Wassel, PhD (University of Pittsburgh); Simon Wessely, FMedSci (King’s College London); Hongyan Wu, MPH (Uniformed Services University of the Health Sciences); LTC Gary H. Wynn, MD (Uniformed Services University of the Health Sciences); Alan M. Zaslavsky, PhD (Harvard Medical School); and Bailey G. Zhang, MS (Uniformed Services University of the Health Sciences).

Disclaimer: The contents are solely the responsibility of the authors and do not necessarily represent the views of the Department of Health and Human Services, the National Institute of Mental Health, the Department of the Army, or the Department of Defense.

Additional Contributors: John Mann, MD, Maria Oquedo, MD, Barbara Stanley, PhD, Kelly Posner, PhD, and John Keilp, PhD (Department of Psychiatry Columbia University, College of Physicians and Surgeons, and New York State Psychiatric Institute), contributed to the early stages of the US Army STARRS development.