Predicting firearm suicide in military veterans outside the VA health system using linked civilian electronic health record data

抽象的。前美国军人中自杀的流行--每天有17人死亡-- 是美国20年来自杀率上升趋势的核心。退伍军人的自杀率大约是 是平民人口的1.5倍，由于退伍军人独特的医疗、心理和社会负担- 环境风险因素加上容易获得致命手段。到目前为止，资深自杀研究和 预防工作几乎完全集中在退伍军人健康管理局服务的人群上 (VHA)。与此同时，大多数退伍军人不寻求VHA护理，而是更喜欢私营部门的医疗服务。自2005年以来， 在VHA覆盖范围之外的退伍军人中，自杀率增加了一倍多 VHA用户(57%对28%)。这项研究的主要目标是开发有效的纵向 自杀和枪支相关自杀的预测算法在退伍军人中使用非 VHA Healthcare，通过分析有史以来最大的链接平民医疗记录数据库 与退伍军人自杀风险相关的数据。人们对退伍军人在VHA之外接受护理的情况知之甚少， 包括他们健康状况的性质和严重程度，他们的医疗保健利用模式，以及他们的 所有自杀和与枪支有关的自杀的独特危险因素。填补这些知识空白对于 有意义地减少退伍军人总体自杀率的目标。为此，我们的多学科团队 将汇集和分析一个史无前例的纵向数据库 将退伍军人的退伍军人数据、健康记录、社会指标和死亡记录联系起来 从北卡罗来纳州和犹他州的5个大型民用医疗系统获得医疗服务。数据库将产生 估计每年观察380万人，包括大约900起与枪支有关的自杀和 共有1190人自杀身亡。我们将分析这些数据以描述人口统计和健康特征 使用非VHA医疗服务的退伍军人，他们的医疗利用模式，他们的死亡率 结果，以及自杀死亡的发生率。我们将使用机器学习方法来开发 预测退伍军人中所有自杀和与枪支相关自杀的特定风险算法 VHA Healthcare，以识别自杀风险较高的退伍军人。总部位于犹他州的协作者将使用链接的VHA 用于识别和描述VHA和非VHA医疗保健相结合的退伍军人的风险模式的数据。最后，我们 将进行一系列关键的信息提供人访谈，以更好地了解整合此服务的障碍和促进者 将算法类型纳入民用卫生系统工作流程。总之，拟议的工作将填补以下方面的关键空白 这些文献通过利用大规模的、真实的数据来源来产生关于自杀风险的新知识，同时 向旨在减少退伍军人自杀的预防工作提供信息。我们还将收集实施信息 告知多大的民间卫生系统将能够使用这些信息来识别和干预 在他们的病人群体中自杀风险最大的退伍军人。

ABSTRACT. The ongoing epidemic of suicide among former U.S. military personnel—17 deaths every day— lies at the core of a 20-year trend of increasing suicide rates in the U.S. The rate of suicide in veterans is about 1.5 times that of the civilian population, due to veterans' unique burden of medical, psychological, and social- environmental risk factors compounded by easy access to lethal means. To date, veteran suicide research and prevention efforts have focused almost entirely on the population served by the Veterans Health Administration (VHA). Meanwhile, most veterans do not seek VHA care but prefer private-sector health services. Since 2005, suicides among veterans outside the reach of VHA have increased at more than double the rate seen among VHA users (57% vs. 28%, respectively). This study's primary objective is to develop efficient longitudinal predictive algorithms for suicide and firearm-related suicide among military veterans who utilized non- VHA health care, by analyzing the largest database ever assembled of linked civilian medical record data pertinent to veteran suicide risk. Too little is known about veterans receiving care outside the VHA, including the nature and severity of their health conditions, their patterns of healthcare utilization, and their unique risk factors for all suicide and firearm-related suicide. Filling these gaps in knowledge is crucial to the goal of meaningfully reducing suicide in the veteran population overall. To that end, our multi-disciplinary team of nationally distinguished researchers will assemble and analyze an unprecedented longitudinal database of linked VA and Department of Defense data, health records, social indicators, and death records of veterans receiving health care from 5 large civilian health systems in North Carolina and Utah. The database will yield an estimated 3.8 million person-year observations, including approximately 900 firearm-involved suicides and 1,190 total suicide deaths. We will analyze these data to describe the demographic and health characteristics of veterans who utilize non-VHA healthcare services, their patterns of healthcare utilization, their mortality outcomes, and their incidence of suicide deaths, by method. We will use machine learning methods to develop specific risk algorithms for predicting all suicides and firearm-related suicides among veterans who utilize non- VHA healthcare, to identify veterans at elevated risk of suicide. Utah-based collaborators will use linked VHA data to identify and describe risk patterns for veterans who combine VHA and non-VHA healthcare. Finally, we will conduct a series of key informant interviews to better understand barriers and facilitators to integrating this type of algorithm into civilian health system workflows. In summary, the proposed work will fill critical gaps in the literature by leveraging large-scale, real-world data sources to yield novel knowledge of suicide risks, while informing prevention efforts aimed at reducing veteran suicide. We will also gather implementation information to inform how large civilian health systems will be able to use this information to identify and intervene with the veterans who are at greatest risk of suicide within their patient populations.