Advertisement

The First Year After Military Service: Predictors of U.S. Veterans’ Suicidal Ideation

      Introduction

      Little is known about predictors of military veterans’ suicidal ideation as they transition from service to civilian life, a potentially high-risk period that represents a critical time for intervention. This study examined factors associated with veterans’ suicidal ideation in the first year after military separation.

      Methods

      A national sample of U.S. veterans (N=7,383) from The Veterans Metrics Initiative Study reported on their mental health, psychosocial well-being, and demographic/military characteristics in an online survey at 3 and 9 months after separation. Cross-validated random forest models and mean decrease in accuracy values were used to identify key predictors of suicidal ideation. Bivariate ORs were calculated to examine the magnitude and direction of main effects associations between predictors and suicidal ideation. Data were collected in 2016/2017 and analyzed in 2021.

      Results

      In the first year after separation, 15.1% of veterans reported suicidal ideation. Endorsing depression symptoms and, to a lesser extent, identifying oneself as experiencing depression, were most predictive of suicidal ideation. Other psychopathology predictors included higher anxiety and posttraumatic stress disorder symptoms. Psychosocial well-being predictors included higher health satisfaction and functioning, community satisfaction and functioning, and psychological resilience. Logistic models performed similarly to random forest models, suggesting that relationships between predictors and suicidal ideation were better represented as main effects than interactions.

      Conclusions

      Results highlight the potential value of bolstering key aspects of military veterans’ mental health and psychosocial well-being to reduce their risk for suicidal ideation in the first year after separation. Findings can inform interventions aimed at helping veterans acclimate to civilian life.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      REFERENCES

        • Jobes DA
        • Joiner TE
        Reflections on suicidal ideation (editorial).
        Crisis. 2019; 40: 227-230https://doi.org/10.1027/0227-5910/a000615
        • Kleiman EM.
        Suicidal thinking as a valuable clinical endpoint.
        EClinicalMedicine. 2020; 23100399https://doi.org/10.1016/j.eclinm.2020.100399
        • Nock MK
        • Borges G
        • Bromet EJ
        • et al.
        Cross-national prevalence and risk factors for suicidal ideation, plans and attempts.
        Br J Psychiatry. 2008; 192: 98-105https://doi.org/10.1192/bjp.bp.107.040113
        • Hoffmire CA
        • Monteith LL
        • Denneson LM
        • et al.
        A sex-stratified analysis of suicidal ideation correlates among deployed post-9/11 veterans: results from the Survey of Experiences of Returning Veterans.
        J Affect Disord. 2021; 294: 824-830https://doi.org/10.1016/j.jad.2021.07.015
        • Blosnich JR
        • Montgomery AE
        • Dichter ME
        • et al.
        Social determinants and military veterans’ suicide ideation and attempt: a cross-sectional analysis of electronic health record data.
        J Gen Intern Med. 2020; 35: 1759-1767https://doi.org/10.1007/s11606-019-05447-z
        • Elbogen EB
        • Molloy K
        • Wagner HR
        • et al.
        Psychosocial protective factors and suicidal ideation: results from a national longitudinal study of veterans.
        J Affect Disord. 2020; 260: 703-709https://doi.org/10.1016/j.jad.2019.09.062
        • Kline A
        • Ciccone DS
        • Falca-Dodson M
        • Black CM
        • Losonczy M.
        Suicidal ideation among National Guard troops deployed to Iraq: the association with postdeployment readjustment problems.
        J Nerv Ment Dis. 2011; 199: 914-920https://doi.org/10.1097/NMD.0b013e3182392917
        • Brenner LA
        • Barnes SM.
        Facilitating treatment engagement during high-risk transition periods: a potential suicide prevention strategy.
        Am J Public Health. 2012; 102: S12-S14https://doi.org/10.2105/AJPH.2011.300581
        • Brenner LA
        • Gutierrez PM
        • Cornette MM
        • Betthauser LM
        • Bahraini N
        • Staves P.
        A qualitative study of potential suicide risk factors in returning combat veterans.
        J Ment Health Couns. 2008; 30: 211-225https://doi.org/10.17744/mehc.30.3.n6418tm72231j606
        • Pease JL
        • Billera M
        • Gerard G.
        Military culture and the transition to civilian life: suicide risk and other considerations.
        Soc Work. 2016; 61: 83-86https://doi.org/10.1093/sw/swv050
        • Thompson JM
        • Dursun S
        • VanTil L
        • et al.
        Group identity, difficult adjustment to civilian life, and suicidal ideation in Canadian Armed Forces Veterans: Life After Service Studies 2016.
        J Mil Veteran Fam Health. 2019; 5: 100-114https://doi.org/10.3138/jmvfh.2018-0038
      1. Solid Start Act of 2020, Bill Text, S. 4724 (Introduced in the Senate), 116ᵗʰ Congress (2020). https://www.congress.gov/bill/116th-congress/senate-bill/4724/text. Accessed 29 April, 2022

        • Gradus JL
        • King MW
        • Galatzer-Levy I
        • Street AE.
        Gender differences in machine learning models of trauma and suicidal ideation in veterans of the Iraq and Afghanistan wars.
        J Trauma Stress. 2017; 30: 362-371https://doi.org/10.1002/jts.22210
        • Burke TA
        • Ammerman BA
        • Jacobucci R.
        The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: a systematic review.
        J Affect Disord. 2019; 245: 869-884https://doi.org/10.1016/j.jad.2018.11.073
        • Franklin JC
        • Ribeiro JD
        • Fox KR
        • et al.
        Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research.
        Psychol Bull. 2017; 143: 187-232https://doi.org/10.1037/bul0000084
        • Colic S
        • He JC
        • Richardson JD
        • St. Cyr K
        • Reilly JP
        • Hasey GM
        A machine learning approach to identification of self-harm and suicidal ideation among military and police Veterans.
        J Mil Veteran Fam Health. 2022; 8: 56-67https://doi.org/10.3138/jmvfh-2021-0035
        • Gradus JL
        • Rosellini AJ
        • Horváth-Puhó E
        • et al.
        Prediction of sex-specific suicide risk using machine learning and single-payer health care registry data from Denmark.
        JAMA Psychiatry. 2020; 77: 25-34https://doi.org/10.1001/jamapsychiatry.2019.2905
        • Verheij RA
        • Curcin V
        • Delaney BC
        • McGilchrist MM.
        Possible sources of bias in primary care electronic health record data use and reuse.
        J Med Internet Res. 2018; 20: e185https://doi.org/10.2196/jmir.9134
        • Vogt D
        • Perkins DF
        • Copeland LA
        • et al.
        The Veterans Metrics Initiative study of US veterans’ experiences during their transition from military service.
        BMJ Open. 2018; 8e020734https://doi.org/10.1136/bmjopen-2017-020734
        • Office of Suicide and Mental Health Prevention
        • U.S. Department of Veterans Affairs
        2020 National Veterans suicide prevention annual report.
        Office of Suicide and Mental Health Prevention, U.S. Department of Veterans Affairs, Washington, DCNovember 2020 (Published)
        • Dillman DA
        • Smyth JD
        • Christian LM.
        Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method.
        3rd ed. John Wiley & Sons, New York, NY2011 (Incorporated)
        • Kroenke K
        • Spitzer RL
        • Williams JBW.
        The PHQ-9: validity of a brief depression severity measure.
        J Gen Intern Med. 2001; 16: 606-613https://doi.org/10.1046/j.1525-1497.2001.016009606.x
      2. VA research on suicide prevention. U.S. Department of Veterans Affairs. https://www.research.va.gov/topics/suicide.cfm. Updated January 15, 2021. Accessed April 29, 2022.

        • Wisco BE
        • Marx BP
        • Wolf EJ
        • Miller MW
        • Southwick SM
        • Pietrzak RH.
        Posttraumatic stress disorder in the U.S. veteran population: results from the National Health and Resilience in Veterans Study.
        J Clin Psychiatry. 2014; 75: 1338-1346https://doi.org/10.4088/JCP.14m09328
        • Na PJ
        • Yaramala SR
        • Kim JA
        • et al.
        The PHQ-9 item 9 based screening for suicide risk: a validation study of the Patient Health Questionnaire (PHQ)−9 item 9 with the Columbia Suicide Severity Rating Scale (C-SSRS).
        J Affect Disord. 2018; 232: 34-40https://doi.org/10.1016/j.jad.2018.02.045
        • Corson K
        • Denneson LM
        • Bair MJ
        • Helmer DA
        • Goulet JL
        • Dobscha SK.
        Prevalence and correlates of suicidal ideation among Operation Enduring Freedom and Operation Iraqi Freedom veterans.
        J Affect Disord. 2013; 149: 291-298https://doi.org/10.1016/j.jad.2013.01.043
        • Vogt D
        • Taverna EC
        • Nillni YI
        • et al.
        Development and validation of a tool to assess military veterans’ status, functioning, and satisfaction with key aspects of their lives.
        Appl Psychol Health Well Being. 2019; 11: 328-349https://doi.org/10.1111/aphw.12161
        • Stekhoven DJ
        • Bühlmann P.
        MissForest–non-parametric missing value imputation for mixed-type data.
        Bioinformatics. 2012; 28: 112-118https://doi.org/10.1093/bioinformatics/btr597
        • Yadav S
        • Shukla S.
        Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification.
        in: Paper presented at: 2016 IEEE 6th International Conference on Advanced Computing (IACC). 2016https://doi.org/10.1109/IACC.2016.25 (February 27-28)
        • Hothorn T
        • Hornik K
        • Zeileis A.
        Unbiased recursive partitioning: a conditional inference framework.
        J Comput Graph Stat. 2006; 15: 651-674https://doi.org/10.1198/106186006X133933
        • Kuhn M.
        Caret: classification and regression training. Version R Package 6.0-86.
        R Foundation for Statistical Computing, Vienna, Austria2020
        • Strobl C
        • Boulesteix AL
        • Zeileis A
        • Hothorn T.
        Bias in random forest variable importance measures: illustrations, sources and a solution.
        BMC Bioinformatics. 2007; 8: 25https://doi.org/10.1186/1471-2105-8-25
        • R Foundation for Statistical Computing
        R: a language and environment for statistical computing.
        R Foundation for Statistical Computing, Vienna, Austria2020
        https://www.r-project.org/
        Date accessed: April 29, 2022
        • Strobl C
        • Boulesteix AL
        • Kneib T
        • Augustin T
        • Zeileis A
        Conditional variable importance for random forests.
        BMC Bioinformatics. 2008; 9: 307https://doi.org/10.1186/1471-2105-9-307
        • James G
        • Witten D
        • Hastie T
        • Tibshirani R.
        An Introduction to Statistical Learning.
        Springer, Cham, Switzerland2013https://doi.org/10.1007/978-1-4614-7138-7
        • Strobl C
        • Malley J
        • Tutz G.
        An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
        Psychol Methods. 2009; 14: 323-348https://doi.org/10.1037/a0016973
      3. Hosmer Jr, DW Lemeshow S Sturdivant RX Applied logistic regression. 3rd edition. Wiley, Hoboken, NJ2013https://doi.org/10.1002/9781118548387
        • Graziano RC
        • Aunon FM
        • LoSavio ST
        • et al.
        A network analysis of risk factors for suicide in Iraq/Afghanistan-era veterans.
        J Psychiatr Res. 2021; 138: 264-271https://doi.org/10.1016/j.jpsychires.2021.03.065
        • Department of Defense Office of Inspector General
        Evaluation of the Department of Defense's implementation of suicide prevention resources for transitioning uniformed service members.
        Department of Defense Office of Inspector General, Alexandria, VANovember 9, 2021 (Published)
      4. U.S. Preventive Services Task Force. Screening for depression: recommendations and rationale.
        Ann Intern Med. 2002; 136: 760-764https://doi.org/10.7326/0003-4819-136-10-200205210-00012
        • U.S. Department of Veterans Affairs
        Analysis of VA health care utilization among Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND) veterans.
        2017 (Published)
        • Nock MK
        • Ramirez F
        • Rankin O.
        Advancing our understanding of the who, when, and why of suicide risk.
        JAMA Psychiatry. 2019; 76: 11-12https://doi.org/10.1001/jamapsychiatry.2018.3164
        • Geraci JC
        • Mobbs M
        • Edwards ER
        • et al.
        Expanded roles and recommendations for stakeholders to successfully reintegrate modern warriors and mitigate suicide risk.
        Front Psychol. 2020; 11: 1907https://doi.org/10.3389/fpsyg.2020.01907
        • Tenhula WN
        • Nezu AM
        • Nezu CM
        • et al.
        Moving forward: a problem-solving training program to foster veteran resilience.
        Prof Psychol Res Pract. 2014; 45: 416-424https://doi.org/10.1037/a0037150
        • U.S. Department of Veterans Affairs
        VA REACH VET Initiative helps save veterans lives: program signals when more help is needed for at-risk veterans.
        U.S. Department of Veterans Affairs, Washington, DCApril 3, 2017 (Published)
      5. Measurement-Based Transition Assistance (MBTA): Evaluating the Promise of a Web-Based Approach to Promoting Veterans’ Support Seeking. VA Health Services Research and Development Service. https://www.hsrd.research.va.gov/research/center_studies.cfm?center=2&center_site_descr=Bedford%20and%20Boston,%20MA. Updated April 15, 2022. Accessed April 29, 2022.

        • Kessler RC
        • Warner CH
        • Ivany C
        • et al.
        Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).
        JAMA Psychiatry. 2015; 72: 49-57https://doi.org/10.1001/jamapsychiatry.2014.1754