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Predicting Suicides Among US Army Soldiers After Leaving Active Service.

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Military suicide risk can be predicted using administrative data before service members leave active duty. This model identifies high-risk individuals for targeted suicide prevention interventions.

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Area of Science:

  • Military health
  • Public health
  • Data science

Background:

  • Military suicide rates significantly increase post-service.
  • Early identification of high-risk service members is crucial for intervention.

Purpose of the Study:

  • Develop a predictive model for suicide risk in US Army soldiers transitioning to civilian life.
  • Utilize administrative data for predicting suicides 1-120 months post-service.

Main Methods:

  • Prognostic study using a consolidated administrative database of US Army soldiers (2010-2019).
  • Machine learning models (Lasso, Super Learner) trained to predict suicide.
  • Validation performed on a 30% hold-out sample.

Main Results:

  • A Lasso model accurately predicted suicides up to 10 years post-service.
  • Key predictors included sociodemographics, career characteristics, and psychopathology.
  • The top 10% highest-risk soldiers accounted for 30-46% of all suicides.

Conclusions:

  • Administrative data can effectively predict long-term suicide risk in transitioning soldiers.
  • Model-informed prevention strategies show positive net benefit.
  • Further research needed on intervention cost-effectiveness.