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Predicting firearm suicide among US Army veterans transitioning from active service

Nature Mental Health volume 4, pages 125–135 (2026)Cite this article

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Abstract

US veterans are significantly more likely than civilians to die by suicide. Machine-learning models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most veteran suicides, and firearm-specific preventions exist. We used data from US Army veterans from 2010 to 2019 (N = 800,579) to develop and compare firearm-specific machine-learning models with a method-agnostic model to predict firearm suicides among transitioning Army veterans up to 10 years after discharge. The models performed comparably overall (area under the receiver operating characteristic curve = 0.710–0.708; integrated calibration index = 0.0003–0.0005% for firearm-specific and method-agnostic models, respectively), with the best model depending on the intervention threshold. Results from this study show the method-agnostic model was better at predicting firearm suicides at the highest intervention threshold, whereas the firearm-specific model was better at lower thresholds. When considering fairness with respect to sex and race/ethnicity, the firearm-specific model was best across all thresholds. Thus, model choice depends on weighing numerous factors, and optimal thresholds might differ for coordinated firearm-specific and method-agnostic interventions.

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Fig. 1: Decision curves for net benefit across models.
Fig. 2: Predictor importance based on SHAP values of the ensemble firearm-specific model.

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Data availability

STARRS-LS Wave 1, Wave 2 and Wave 3 data, as well as data from the earlier Army STARRS New Soldier Study (NSS), All Army Study (AAS) and Pre and Post Deployment studies (PPDS), are available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan (https://www.icpsr.umich.edu/web/ICPSR/studies/35197). STARRS-LS and Army STARRS data are restricted from general dissemination, meaning that a confidential data use agreement must be established before access. Researchers interested in gaining access to the data can submit their applications via ICPSR’s online Restricted Contracting System. Guidelines for applying for access to this data can be found under the data and documentation tab at the above URL. The STARRS Historical Administrative Data Study (HADS) data are not available for public release.

Code availability

Code used to produce the statistical output can be located via https://osf.io/csm59/. Because the data used in this study are not publicly available and require DoD clearance to access, we cannot post the source code used to read in the Army administrative data (which requires DoD clearance) and create the 1,700+ variables used in this analysis. However, we have provided detailed appendices about each of the variables used as covariates in the analyses and have provided here the statistical code for generating the tables and figures.

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Acknowledgements

The Army STARRS Team consists of co-principal investigators R. J. Ursano (Uniformed Services University) and M. B. Stein (University of California San Diego and VA San Diego Healthcare System); site principal investigators J. Wagner (University of Michigan) and R. C. Kessler (Harvard Medical School); Army scientific consultant/liaison K. Cox (Office of the Assistant Secretary of the Army (Manpower and Reserve Affairs)). Other team members are P. A. Aliaga (Uniformed Services University); D. M. Benedek (Uniformed Services University); L. Campbell-Sills (University of California San Diego); C. L. Dempsey (Uniformed Services University); C. S. Fullerton (Uniformed Services University); N. Gebler (University of Michigan); M. House (University of Michigan); P. E. Hurwitz (Uniformed Services University); S. Jain (University of California San Diego); T.-C. Kao (Uniformed Services University); C. J. Kennedy (Massachusetts General Hospital); L. Lewandowski-RompsD (University of Michigan); A. Luedtke (University of Washington and Fred Hutchinson Cancer Research Center); H. H. Mash (Uniformed Services University); J. A. Naifeh (Uniformed Services University); M. K. Nock (Harvard University); N. A. Sampson (Harvard Medical School); R. Shor (Uniformed Services University); and A. M. Zaslavsky (Harvard Medical School). C.H. and E.R.E. were supported in part by the United States Department of Veterans Affairs, Clinical Sciences Research and Development Service (CSR&D) VA-STARRS Researcher-in-Residence Program (project SPR-002-24F). C.J.K. was supported by the National Institute of Mental Health (K01MH135131). Army STARRS was sponsored by the Department of the Army and funded under cooperative agreement number U01MH087981 (2009–2015) with the National Institute of Mental Health (NIMH). Subsequently, STARRS-LS was sponsored and funded by the Department of Defense (USUHS grant number HU0001-15-2-0004). The contents are solely the responsibility of the authors and do not necessarily represent the views of the NIMH, the Department of the Army, the Department of Defense or the Department of Veteran Affairs.

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Authors and Affiliations

  1. Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA

    Claire Houtsma

  2. South Central Mental Illness Research, Education and Clinical Center, New Orleans, LA, USA

    Claire Houtsma

  3. Department of Psychiatry and Behavioral Sciences, Tulane University, New Orleans, LA, USA

    Claire Houtsma

  4. Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Chris J. Kennedy & Matthew K. Nock

  5. Department of Health Care Policy, Harvard Medical School, Boston, MA, USA

    Howard Liu, Nancy A. Sampson & Ronald C. Kessler

  6. VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA

    Emily R. Edwards

  7. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Emily R. Edwards

  8. VISN 2 Mental Illness Research, Education and Clinical Center, US Department of Veterans Affairs, Washington, DC, USA

    Joe C. Geraci

  9. Resilience Center for Veterans and Families, Teachers College, Columbia University, New York, NY, USA

    Joe C. Geraci

  10. Behavioral Science Division, National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA

    Brian P. Marx

  11. Department of Psychology, Harvard University, Cambridge, MA, USA

    Matthew K. Nock

  12. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

    James Wagner

  13. Departments of Psychiatry and School of Public Health, University of California San Diego, La Jolla, CA, USA

    Murray B. Stein

  14. Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD, USA

    Robert J. Ursano

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Contributions

C.H. and R.C.K. were responsible for the study concept and design. C.J.K., H.L., N.A.S., J.C.G., B.P.M. and R.C.K. acquired, analyzed and interpreted the data. C.H., C.J.K., H.L. and R.C.K. were involved in drafting of the manuscript. C.J.K. and H.L. conducted the primary statistical analyses. B.P.M., J.W., M.B.S., R.J.U. and R.C.K. obtained funding. E.R.E., N.A.S., J.C.G., B.P.M., M.K.N., J.W., M.B.S., R.J.U. and R.C.K. provided technical, administrative and material support. C.J.K., N.A.S., J.C.G., B.P.M., M.K.N., J.W., M.B.S., R.J.U. and R.C.K. provided study supervision. All authors were involved in critical revision of the manuscript for important intellectual content and approved the final version for submission.

Corresponding author

Correspondence to Claire Houtsma.

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Competing interests

In the past 3 years, R.C.K. was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Child Mind Institute, Holmusk, Massachusetts General Hospital, Partners Healthcare, Inc., RallyPoint Networks, Inc., Sage Therapeutics and University of North Carolina. He has stock options in Cerebral Inc., Mirah, PYM (Prepare Your Mind), Roga Sciences and Verisense Health. In the past 3 years, M.B.S. received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, atai Life Sciences, Boehringer Ingelheim, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, Engrail Therapeutics, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals and Roche/Genentech. He has stock options in Oxeia Biopharmaceuticals and EpiVario. He is paid for his editorial work on Biological Psychiatry (deputy editor) and UpToDate (co-editor-in-chief for psychiatry). No other disclosures reported.

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Supplementary Fig. 1, Tables 1–6, Measures and References.

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Houtsma, C., Kennedy, C.J., Liu, H. et al. Predicting firearm suicide among US Army veterans transitioning from active service. Nat. Mental Health 4, 125–135 (2026). https://doi.org/10.1038/s44220-025-00559-4

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