ArticleMolecular psychiatry2022
Predicting suicide attempts among U.S. Army soldiers after leaving active duty using information available before leaving active duty: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).
Article in Molecular psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Supporting servicemembers and veterans during their transition to civilian life using certified sponsors: A three-arm randomized controlled trial.Psychological services · 2023Trial
- Mitigating Suicide Risk During the Military-to-Civilian Transition: The VA Veteran Sponsorship Initiative.International journal of environmental research and public health · 2026Article
- Joint models targeting U.S. Army soldiers at high-risk of post-separation unemployment, homelessness, and suicide-related behaviors.Npj mental health research · 2026Article
- Predicting suicide death among veterans after psychiatric hospitalization using transformer based models with social determinants and NLP.Scientific reports · 2025Article
- Effects of exposure to pandemic-related stressors on anxiety and mood difficulty during versus before the COVID-19 pandemic in United States Army soldiers and veterans.Nature. Mental health · 2025Article
- Direct and indirect associations of childhood adversities with functional impairment and life stress among military personnel.Psychological medicine · 2025Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Prospective associations of alcohol and drug misuse with suicidal behaviors among US Army soldiers who have left active service.Psychological medicine · 2025Article
- Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.Translational psychiatry · 2025Article
- Importance of variables from different time frames for predicting self-harm using health system data.Journal of biomedical informatics · 2024Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Interactive effects of genetic liability and combat exposure on risk of alcohol use disorder among US service members.Drug and alcohol dependence · 2024Article
- Importance of variables from different time frames for predicting self-harm using health system data.medRxiv : the preprint server for health sciences · 2024Article
- Military experiences, connection to military identity, and time since military discharge as predictors of United States veteran suicide risk.Military psychology : the official journal of the Division of Military Psychology, American Psychological Association · 2024Article
- Predicting Homelessness Among Transitioning U.S. Army Soldiers.American journal of preventive medicine · 2024Article
- A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).Psychological medicine · 2023Article
- Building an Integrated Data Infrastructure to Examine the Spectrum of Suicide Risk Factors in Philadelphia Medicaid.Administration and policy in mental health · 2023Article
- Associations of active-duty mental health trajectories with post-military adjustment: Results from the STARRS Longitudinal Study.Journal of affective disorders · 2023Article
- Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 1 country.
Funding
Abstract
Suicide risk is elevated among military service members who recently transitioned to civilian life. Identifying high-risk service members before this transition could facilitate provision of targeted preventive interventions. We investigated the feasibility of doing this by attempting to develop a prediction model for self-reported suicide attempts (SAs) after leaving or being released from active duty in the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS). This study included two self-report panel surveys (LS1: 2016-2018, LS2: 2018-2019) administered to respondents who previously participated while on active duty in one of three Army STARRS 2011-2014 baseline self-report surveys. We focus on respondents who left active duty >12 months before their LS survey (n = 8899). An ensemble machine learning model using predictors available prior to leaving active duty was developed in a 70% training sample and validated in a 30% test sample. The 12-month self-reported SA prevalence (SE) was 1.0% (0.1). Test sample AUC (SE) was 0.74 (0.06). The 15% of respondents with highest predicted risk included nearly two-thirds of 12-month SAs and over 80% of medically serious 12-month SAs. These results show that it is possible to identify soldiers at high post-transition self-report SA risk before the transition. Future model development is needed to examine prediction of SAs assessed by administrative data and using surveys administered closer to the time of leaving active duty.
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