The U.S. Army experienced a sharp increase in soldier suicides beginning in 2004. Administrative data reveal that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder.
The objective of the study is to develop an actuarial risk algorithm predicting suicide in the 12 months after U.S. Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care.
There were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted from a wide range of data systems (sociodemographic, U.S. Army career, criminal justice, and medical or pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations.