BACKGROUND Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment. METHODS Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011-2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods. RESULTS Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75-.77. 21.3-40.4% of SAs occurred among the 5-10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4-5.7%. CONCLUSIONS SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration.

中文翻译：

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部署后和部署后自杀未遂的事前预测因素：陆军评估服役人员风险和应变能力的研究结果。

背景技术与部署相关的经验可能是士兵自杀的危险因素，在这种情况下，在部署之前识别易受伤害的士兵可以为预防工作提供信息。我们使用部署前的调查和行政数据对美军士兵进行了调查，以研究这种可能性的可能性，从而为部署期间和部署后不久的自杀未遂（SA）建立风险模型。方法数据来自陆军研究，用于评估服务员部署后调查（PPDS）中的风险和应变能力。士兵在2011-2012年部署到阿富汗之前不久完成了基线调查。在部署后的30个月内，使用调查措施来预测SA（使用管理和后续调查数据定义）的SA。使用惩罚回归和集成机器学习方法构建模型。结果部署前的重要危险因素为颅脑外伤史，部署前12个月内进行了9次以上的心理健康就诊，年轻，女性，已婚和关系质量低下。最佳惩罚模型和集成模型的交叉验证AUC为0.75-0.77。在这些高风险士兵中，具有最高预测风险和正预测值（PPV）的士兵中，有5-10％的士兵中有21.3-40.4％的SA发生率为4.4-5.7％。结论从部署前的数据可以显着预测SA，但是干预计划需要考虑PPV。最佳惩罚模型和集成模型的交叉验证AUC为0.75-0.77。在这些高风险士兵中，具有最高预测风险和正预测值（PPV）的士兵中，有5-10％的士兵中有21.3-40.4％的SA发生率为4.4-5.7％。结论从部署前的数据可以显着预测SA，但是干预计划需要考虑PPV。最佳惩罚模型和集成模型的交叉验证AUC为0.75-0.77。在这些高风险士兵中，具有最高预测风险和正预测值（PPV）的士兵中，有5-10％的士兵中有21.3-40.4％的SA发生率为4.4-5.7％。结论从部署前的数据可以显着预测SA，但是干预计划需要考虑PPV。