학술논문
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).
Document Type
Article
Author
Kearns, Jaclyn C.; Edwards, Emily R.; Finley, Erin P.; Geraci, Joseph C.; Gildea, Sarah M.; Goodman, Marianne; Hwang, Irving; Kennedy, Chris J.; King, Andrew J.; Luedtke, Alex; Marx, Brian P.; Petukhova, Maria V.; Sampson, Nancy A.; Seim, Richard W.; Stanley, Ian H.; Stein, Murray B.; Ursano, Robert J.; Kessler, Ronald C.
Source
Subject
*SUICIDE risk factors
*SELF-evaluation
*MACHINE learning
*RISK assessment
*SUICIDAL behavior
*SEVERITY of illness index
*QUESTIONNAIRES
*DESCRIPTIVE statistics
*RESEARCH funding
*PSYCHOLOGY of military personnel
*PREDICTION models
*RECEIVER operating characteristic curves
*SENSITIVITY & specificity (Statistics)
*DATA analysis software
*PSYCHOLOGICAL resilience
*LONGITUDINAL method
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Language
ISSN
0033-2917
Abstract
Background: Risk 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. Methods: We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample. Results: Twelve-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. Conclusions: An 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. [ABSTRACT FROM AUTHOR]