Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T07:08:02.380Z Has data issue: false hasContentIssue false
  • English
  • Français

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army

Published online by Cambridge University Press:  04 April 2017

A. J. Rosellini ,
M. B. Stein ,
D. M. Benedek ,
P. D. Bliese ,
W. T. Chiu ,
I. Hwang ,
J. Monahan ,
M. K. Nock ,
M. V. Petukhova  and
N. A. Sampson
...Show all authors
A. J. Rosellini
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
M. B. Stein
Affiliation:
Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, La Jolla, California, USA VA San Diego Healthcare System, San Diego, CA, USA
D. M. Benedek
Affiliation:
Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine, Bethesda, MD, USA
P. D. Bliese
Affiliation:
Darla Moore School of Business, University of South Carolina, Columbia, South Carolina, USA
W. T. Chiu
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
I. Hwang
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
J. Monahan
Affiliation:
School of Law, University of Virginia, Charlottesville, VA, USA
M. K. Nock
Affiliation:
Department of Psychology, Harvard University, Cambridge, Massachusetts, USA
M. V. Petukhova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
N. A. Sampson
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
A. E. Street
Affiliation:
National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts, USA Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA
A. M. Zaslavsky
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
R. J. Ursano
Affiliation:
Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine, Bethesda, MD, USA
R.C. Kessler*
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
*
*Address for correspondence: R. C. Kessler, Ph.D., Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA, USA. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

The U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service.

Methods

21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011–2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition.

Results

The best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk.

Conclusions

Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Keywords

Armydisciplinary problemsmental healthmilitarypredictive modelingrisk assessmentviolence
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 13 , October 2017 , pp. 2275 - 2287
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Afifi, TO, Taillieu, T, Zamorski, MA, Turner, S, Cheung, K, Sareen, J (2016). Association of child abuse exposure with suicidal ideation, suicide plans, and suicide attempts in military personnel and the general population in Canada. JAMA Psychiatry 73, 229–238.CrossRefGoogle ScholarPubMed
Back, MD, Schmukle, SC, Egloff, B (2009). Predicting actual behavior from the explicit and implicit self-concept of personality. Journal of Personality and Social Psychology 97, 533–548.Google Scholar
Booth-Kewley, S, Highfill-McRoy, RM, Larson, GE, Garland, CF (2010). Psychosocial predictors of military misconduct. Journal of Nervous and Mental Disease 198, 91–98.CrossRefGoogle ScholarPubMed
Bulzacchelli, MT, Sulsky, SI, Rodriguez-Monguio, R, Karlsson, LH, Hill, MO (2014). Injury during U.S. Army basic combat training: a systematic review of risk factor studies. American Journal of Preventive Medicine 47, 813–822.Google Scholar
Canham-Chervak, M, Hooper, TI, Brennan, FH Jr., Craig, SC, Girasek, DC, Schaefer, RA, Barbour, G, Yew, KS, Jones, BH (2010). A systematic process to prioritize prevention activities sustaining progress toward the reduction of military injuries. American Journal of Preventive Medicine 38 (1 Suppl.), S11–S18.Google Scholar
Cassidy, JD, Carroll, LJ, Peloso, PM, Borg, J, von Holst, H, Holm, L, Kraus, J, Coronado, VG (2004). Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. Journal of Rehabilitation Medicine 36 (43 Suppl.), 28–60.CrossRefGoogle Scholar
Dahlberg, LL (1998). Youth violence in the United States: major trends, risk factors, and prevention approaches. American Journal of Preventive Medicine 14, 259–272.CrossRefGoogle ScholarPubMed
Department of Defense (2014 a). Department of Defense 2014-2016 Sexual Assault Prevention Strategy. Department of Defense: Arlington, Virginia (http://sapr.mil/public/docs/prevention/DoD_SAPR_Prevention_Strategy_2014-2016.pdf). Accessed 1 February 2016.Google Scholar
Department of Defense (2014 b). DoD Workplace Violence Prevention and Response Policy. Department of Defense: Arlington, Virginia (http://www.dtic.mil/whs/directives/corres/pdf/143806p.pdf). Accessed 1 February 2016.Google Scholar
Department of the Army (2015 a). AR-600-24 Health Promotion, Risk Reduction, and Suicide Prevention. Department of the Army: Washington, DC (http://www.lewis-mcchord.army.mil/dhr/asap/Doc/14%20APR%2015%20p600_24.pdf). Accessed 1 February 2016.Google Scholar
Department of the Army (2015 b). AR-600-63 Army Health Promotion. Department of the Army: Washington, DC (https://www.army.mil/e2/downloads/rv7/r2/policydocs/r600_63.pdf). Accessed 1 February 2016.Google Scholar
Department of the US Army (2010). Army Health Promotion, Risk Reduction, and Suicide Prevention: Report 2010. US Army: Washington, DC.Google Scholar
Department of the US Army (2012). Army 2020: Generating Health & Discipline in the Force ahead of the Strategic Reset. US Army: Washington, DC.Google Scholar
Drasgow, F, Stark, S, Chernyshenko, OS, Nye, CD, Hulin, CL, White, LA (2012). Technical Report 1311 – Development of the Tailored Adaptive Personality Assessment System (TAPAS) to Support Army Selection and Classification Decisions. U.S. Army Research Institute for the Behavioral and Social Sciences: Fort Belvoir, Virginia (http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA564422). Accessed 1 February 2016.Google Scholar
Elbogen, EB, Fuller, S, Johnson, SC, Brooks, S, Kinneer, P, Calhoun, PS, Beckham, JC (2010). Improving risk assessment of violence among military veterans: an evidence-based approach for clinical decision-making. Clinical Psychology Review 30, 595–607.CrossRefGoogle ScholarPubMed
Elmasry, H, Boivin, MR, Feng, X, Packnett, ER, Cowan, DN (2017). Preenlistment and early service risk factors for traumatic brain injury in the Army and Marine Corps: FY 2002–2010. Journal of Head Trauma Rehabilitation 32, E1–E7.Google Scholar
Foster, EM, Jones, D (2006). Can a costly intervention be cost-effective?: an analysis of violence prevention. Archives of General Psychiatry 63, 1284–1291.Google Scholar
Friedman, J, Hastie, T, Tibshirani, R (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22.Google Scholar
Fuentes, D, Tavares, H, Artes, R, Gorenstein, C (2006). Self-reported and neuropsychological measures of impulsivity in pathological gambling. Journal of the International Neuropsychological Society: JINS 12, 907–912.CrossRefGoogle ScholarPubMed
Golubnitschaja, O, Costigliola, V (2012). General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA Journal 3, 14.Google Scholar
Halli, SS, Rao, KV (1992). Advanced Techniques of Population Analysis. Plenum Press: New York, NY.Google Scholar
Herman-Stahl, MA, Krebs, CP, Kroutil, LA, Heller, DC (2007). Risk and protective factors for methamphetamine use and nonmedical use of prescription stimulants among young adults aged 18 to 25. Addictive Behaviors 32, 1003–1015.CrossRefGoogle ScholarPubMed
Huntjens, RJ, Rijkeboer, MM, Krakau, A, de Jong, PJ (2014). Implicit versus explicit measures of self-concept of self-control and their differential predictive power for spontaneous trait-relevant behaviors. Journal of Behavior Therapy and Experimental Psychiatry 45, 1–7.Google Scholar
Institute of Medicine (2010). Returning Home fom Iraq and Afghanistan: Preliminary Assessment of Readjustment Needs of Veterans, Service Members, and their Families. The National Academies Press: Washington, DC.Google Scholar
Iribarren, C, Sidney, S, Jacobs, DR Jr., Weisner, C (2000). Hospitalization for suicide attempt and completed suicide: epidemiological features in a managed care population. Social Psychiatry and Psychiatric Epidemiology 35, 288–296.Google Scholar
Jeffords, CR (1984). The impact of sex-role and religious attitudes upon forced marital intercourse norms. Sex Roles 11, 543–552.Google Scholar
Kapp, L (2013). Recruiting and Retention: An Overview of FY2011 and FY2012 Results for Active and Reserve Component Enlisted Personnel. Congressional Research Service: Washington, DC (https://www.fas.org/sgp/crs/natsec/RL32965.pdf). Accessed 1 February 2016.Google Scholar
Kaufman, KR, Brodine, S, Shaffer, R (2000). Military training-related injuries: surveillance, research, and prevention. American Journal of Preventive Medicine 18, 54–63.Google Scholar
Kessler, RC, Warner, CH, Ivany, C, Petukhova, MV, Rose, S, Bromet, EJ, Brown, M III, Cai, T, Colpe, LJ, Cox, KL, Fullerton, CS, Gilman, SE, Gruber, MJ, Heeringa, SG, Lewandowski-Romps, L, Li, J, Millikan-Bell, AM, Naifeh, JA, Nock, MK, Rosellini, AJ, Sampson, NA, Schoenbaum, M, Stein, MB, Wessely, S, Zaslavsky, AM, Ursano, RJ (2015). Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). JAMA Psychiatry 72, 49–57.Google Scholar
Kilpatrick, DG, Acierno, R, Saunders, B, Resnick, HS, Best, CL, Schnurr, PP (2000). Risk factors for adolescent substance abuse and dependence: data from a national sample. Journal of Consulting and Clinical Psychology 68, 19–30.Google Scholar
Kirst, M, Mecredy, G, Borland, T, Chaiton, M (2014). Predictors of substance use among young adults transitioning away from high school: a narrative review. Substance Use and Misuse 49, 1795–1807.CrossRefGoogle ScholarPubMed
Knapik, JJ, Jones, BH, Hauret, K, Darakjy, S, Piskator, E (2004). A Review of the Literature on Attrition from the Military Services: Risk Factors for Attrition and Strategies to Reduce Attrition: USACHPPM REPORT NO. 12-HF-01Q3A-04. U.S. Army Center for Health Promotion and Preventative Medicine: Aberdeen Proving Ground, MD (http://www.dtic.mil/dtic/tr/fulltext/u2/a427744.pdf). Accessed 1 February 2016.Google Scholar
Kubisiak, UC, Lentz, E, Horgen, KE, Bryant, RH, Connell, PW, Tuttle, MD, Borman, WC, Young, MC, Morath, R (2009). ARI Research Note 2009–13. Review of Interventions for Reducing Enlisted Attrition in the U.S. Military: an Update. United Sates Army Research Institute for the Behavioral and Social Sciences: Arlington, VA (http://www.dtic.mil/dtic/tr/fulltext/u2/a508188.pdf). Accessed 1 February 2016.Google Scholar
MacManus, D, Dean, K, Al Bakir, M, Iversen, AC, Hull, L, Fahy, T, Wessely, S, Fear, NT (2012 a). Violent behaviour in U.K. military personnel returning home after deployment. Psychological Medicine 42, 1663–1673.Google Scholar
MacManus, D, Dean, K, Iversen, AC, Hull, L, Jones, N, Fahy, T, Wessely, S, Fear, NT (2012 b). Impact of pre-enlistment antisocial behaviour on behavioural outcomes among UK military personnel. Social Psychiatry and Psychiatric Epidemiology 47, 1353–1358.Google Scholar
Miller, L, Davies, M, Greenwald, S (2000). Religiosity and substance use and abuse among adolescents in the National Comorbidity Survey. Journal of the American Academy of Child and Adolescent Psychiatry 39, 1190–1197.Google Scholar
Moore, TM, Gur, RC, Thomas, ML, Brown, GG, Nock, MK, Savitt, AP, Keilp, JG, Heeringa, S, Ursano, RJ, Stein, MB (2017). Development, administration, and structural validity of a brief, computerized neurocognitive battery. Assessment. Published online 30 January 2017. doi: http://dx.doi.org/10.1177%2F1073191116689820.Google Scholar
Niebuhr, DW, Gubata, ME, Oetting, AA, Weber, NS, Feng, X, Cowan, DN (2013). Personality assessment questionnaire as a pre-accession screen for risk of mental disorders and early attrition in U.S. Army recruits. Psychological Services 10, 378–385.Google Scholar
Nock, MK, Deming, CA, Fullerton, CS, Gilman, SE, Goldenberg, M, Kessler, RC, McCarroll, JE, McLaughlin, KA, Peterson, C, Schoenbaum, M, Stanley, B, Ursano, RJ (2013). Suicide among soldiers: a review of psychosocial risk and protective factors. Psychiatry 76, 97–125.Google Scholar
Nonnemaker, J, McNeely, C, Blum, R (2003). Public and private domains of religiosity and adolescent health risk behaviors: evidence from the National Longitudinal Study of Adolescent Health. Social Science and Medicine 57, 2049–2054.CrossRefGoogle ScholarPubMed
Parkkari, J, Taanila, H, Suni, J, Mattila, VM, Ohrankammen, O, Vuorinen, P, Kannus, P, Pihlajamaki, H (2011). Neuromuscular training with injury prevention counselling to decrease the risk of acute musculoskeletal injury in young men during military service: a population-based, randomised study. BMC Medicine 9, 35.Google Scholar
Ritchie, MD (2005). Bioinformatics approaches for detecting gene-gene and gene-environment interactions in studies of human disease. Neurosurgical Focus 19, 1–4.Google Scholar
Rosellini, AJ, Heeringa, SG, Stein, MB, Ursano, RJ, Chiu, WT, Colpe, LJ, Fullerton, CS, Gilman, SE, Hwang, I, Naifeh, JA, Nock, MK, Petukhova, M, Sampson, NA, Schoenbaum, M, Zaslavsky, AM, Kessler, RC (2015). Lifetime prevalence of DSM-IV mental disorders among new soldiers in the U.S. Army: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Depression and Anxiety 32, 13–24.Google Scholar
Rosellini, AJ, Monahan, J, Street, AE, Heeringa, SG, Hill, ED, Petukhova, M, Reis, BY, Sampson, NA, Bliese, P, Schoenbaum, M, Stein, MB, Ursano, RJ, Kessler, RC (2016). Predicting non-familial major physical violent crime perpetration in the US Army from administrative data. Psychological Medicine 46, 303–316.Google Scholar
Rudd, MD, Bryan, CJ, Wertenberger, EG, Peterson, AL, Young-McCaughan, S, Mintz, J, Williams, SR, Arne, KA, Breitbach, J, Delano, K, Wilkinson, E, Bruce, TO (2015). Brief cognitive-behavioral therapy effects on post-treatment suicide attempts in a military sample: results of a randomized clinical trial with 2-year follow-up. American Journal of Psychiatry 172, 441–449.Google Scholar
Rytila-Manninen, M, Lindberg, N, Haravuori, H, Kettunen, K, Marttunen, M, Joukamaa, M, Frojd, S (2014). Adverse childhood experiences as risk factors for serious mental disorders and inpatient hospitalization among adolescents. Child Abuse and Neglect 38, 2021–2032.CrossRefGoogle ScholarPubMed
Salas-Wright, CP, Vaughn, MG, Hodge, DR, Perron, BE (2012). Religiosity profiles of American youth in relation to substance use, violence, and delinquency. Journal of Youth and Adolescence 41, 1560–1575.Google Scholar
SAS Institute Inc. (2010). SAS/STATR Software. SAS Institute Inc.: Cary, NC.Google Scholar
Schensul, JJ, Burkholder, GJ (2005). Vulnerability, social networks, sites, and selling as predictors of drug use among urban African American and Puerto Rican emerging adults. Journal of Drug Issues 35, 379–408.Google Scholar
Senn, CY, Eliasziw, M, Barata, PC, Thurston, WE, Newby-Clark, IR, Radtke, HL, Hobden, KL (2015). Efficacy of a sexual assault resistance program for university women. New England Journal of Medicine 372, 2326–2335.Google Scholar
Shea, MT, Lambert, J, Reddy, MK (2013). A randomized pilot study of anger treatment for Iraq and Afghanistan veterans. Behaviour Research and Therapy 51, 607–613.Google Scholar
Street, AE, Rosellini, AJ, Ursano, RJ, Heeringa, SG, Hill, ED, Monahan, J, Naifeh, JA, Petukhova, MV, Reis, BY, Sampson, NA, Bliese, PD, Stein, MB, Zaslavsky, AM, Kessler, RC (2016). Developing a risk model to target high-risk preventive interventions for sexual assault victimization among female US Army soldiers. Clinical Psychological Science 4, 939–956.Google Scholar
Suris, A, Lind, L (2008). Military sexual trauma: a review of prevalence and associated health consequences in veterans. Trauma, Violence and Abuse 9, 250–269.Google Scholar
Theodoroff, SM, Lewis, MS, Folmer, RL, Henry, JA, Carlson, KF (2015). Hearing impairment and tinnitus: prevalence, risk factors, and outcomes in US service members and veterans deployed to the Iraq and Afghanistan wars. Epidemiologic Reviews 37, 71–85.Google Scholar
Turchik, JA, Wilson, SM (2010). Sexual assault in the U.S. military: A review of the literature and recommendations for the future. Aggression and Violent Behavior 15, 267–277.Google Scholar
Upstill-Goddard, R, Eccles, D, Fliege, J, Collins, A (2013). Machine learning approaches for the discovery of gene-gene interactions in disease data. Briefings in Bioinformatics 14, 251–260.Google Scholar
Ursano, RJ, Colpe, LJ, Heeringa, SG, Kessler, RC, Schoenbaum, M, Stein, MB (2014). The army study to assess risk and resilience in servicemembers (Army STARRS). Psychiatry 77, 107–119.CrossRefGoogle ScholarPubMed
U.S. Department of Justice (2011). National Corrections Reporting Program, 2009 (ICPSR 30799). National Achive of Criminal Justice Data: Ann Arbor, MI (http://www.icpsr.umich.edu/icpsrweb/NACJD/studies/30799?archive=NACJD&permit%5B0%5D=AVAILABLE&q=30799&x=0&y=0). Accessed 1 February 2016.Google Scholar
Vungkhanching, M, Heinemann, AW, Langley, MJ, Ridgely, M, Kramer, KM (2007). Feasibility of a skills-based substance abuse prevention program following traumatic brain injury. Journal of Head Trauma Rehabilitation 22, 167–176.CrossRefGoogle ScholarPubMed
Willett, JB, Singer, JD (1993). Investigating onset, cessation, relapse, and recovery: why you should, and how you can, use discrete-time survival analysis to examine event occurrence. Journal of Consulting Clinical Psychology 61, 952–965.Google Scholar
Zou, H, Hastie, T (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B 67, 301–320.Google Scholar
Supplementary material: File

Rosellini supplementary material

Appendix Table 1

Download Rosellini supplementary material(File)
File 21.6 KB