ArticlePsychological medicine2023
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).
Article in Psychological medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Mitigating Suicide Risk During the Military-to-Civilian Transition: The VA Veteran Sponsorship Initiative.International journal of environmental research and public health · 2026Article
- SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions.Current psychiatry reports · 2026Review
- Joint models targeting U.S. Army soldiers at high-risk of post-separation unemployment, homelessness, and suicide-related behaviors.Npj mental health research · 2026Article
- Differentiating U.S. military women veterans with suicidal ideation, suicide attempts, and no suicidal ideation or attempts.Military psychology : the official journal of the Division of Military Psychology, American Psychological Association · 2026Article
- Predicting Firearm Suicide among US Army Veterans Transitioning from Active Service.Nature. Mental health · 2026Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 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
- External Validation and Updating of a Statistical Civilian-Based Suicide Risk Model in US Naval Primary Care.JAMA network open · 2023Article
Corrections and comments
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Authors and funding
18 authors at 10 institutions in 1 country.
Funding
Abstract
backgroundRisk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
methodsWe revised the original model in a sample of
resultsTwelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10-30% of respondents with the highest predicted risk included 44.9-92.5% of 12-month SAs.
conclusionsAn accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.