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

使用链接的民用电子健康记录数据预测退伍军人管理局卫生系统外退伍军人的枪支自杀

• 批准号: 10655968
• 负责人: Nathan A. Kimbrel
• 金额: $ 96.37万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655968
• 关键词: Accounting; Address; Administrator; Adult; Algorithms; Behavior; Bioethics Consultants; Caring; Cessation of life; Characteristics; Clinical; Clinical Protocols; Data; Data Element; Data Sources; Databases; Death Records; Department of Defense; Electronic Health Record; Elements; Environment; Environmental Risk Factor; Epidemic; Equity; Ethics; Firearms; Foundations; Future; Goals; Health; Health Services; Health Services Administration; Health system; Healthcare; Healthcare Systems; Incidence; Individual; Informatics; Infrastructure; Intervention; Interview; Knowledge; Link; Literature; Machine Learning; Medical; Medical Records; Mental Health; Methods; Military Personnel; Nature; North Carolina; Outcome; Patients; Pattern; Persons; Play; Population; Prevention; Preventive care; Primary Care; Private Sector; Protocols documentation; Provider; Recording of previous events; Records; Research; Research Personnel; Risk; Risk Factors; Role; Secure; Series; Services; Severities; Structure; Substance Use Disorder; Suicide; Suicide prevention; Survival Analysis; System; Training; Traumatic Brain Injury; United States Department of Veterans Affairs; Utah; Veterans; Veterans Health Administration; Work; combat; diagnostic signature; encryption; health care service; health care service utilization; health record; high risk; high risk population; improved; informant; longitudinal database; machine learning method; military patient; military veteran; mortality; multidisciplinary; novel; patient health information; patient population; prediction algorithm; privacy protection; prospective; psychologic; reducing suicide; research study; risk prediction model; service member; service utilization; social; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate; targeted delivery; trend

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.

摘要。美国前军事人员中持续的自杀流行病-每天17人死亡- 是美国20年来自杀率上升趋势的核心。退伍军人的自杀率约为 1.5由于退伍军人在医疗、心理和社会方面的独特负担， 环境风险因素加上容易获得致命手段。到目前为止，退伍军人自杀研究和 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身上 （VHA）。与此同时，大多数退伍军人不寻求VHA护理，但更喜欢私营部门的医疗服务。自2005年以来， VHA范围之外的退伍军人自杀率增加了一倍多， VHA用户（分别为57%和28%）。本研究的主要目的是开发有效的纵向 预测算法的自杀和枪支有关的自杀之间的退伍军人谁利用非 VHA医疗保健，通过分析有史以来最大的关联平民医疗记录数据库 与退伍军人自杀风险相关的数据。人们对在VHA之外接受护理的退伍军人知之甚少， 包括他们的健康状况的性质和严重程度，他们的医疗保健利用模式， 所有自杀和枪支相关自杀的独特风险因素。填补这些知识空白对于 目标是有意义地减少退伍军人群体的自杀率。为此，我们的多学科团队 全国杰出的研究人员将收集和分析一个前所未有的纵向数据库， 将退伍军人事务部和国防部的数据、健康记录、社会指标和退伍军人的死亡记录联系起来 在北卡罗来纳州和犹他州的5个大型民用卫生系统接受医疗保健。数据库会产生 估计有380万人年的观察，包括大约900起涉及枪支的自杀事件， 1,190人自杀身亡我们将分析这些数据来描述人口统计学和健康特征 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 结果和自杀死亡的发生率。我们将使用机器学习方法来开发 特定的风险算法，用于预测使用非枪支的退伍军人中所有自杀和与枪支有关的自杀， VHA医疗保健，以确定退伍军人在自杀风险升高。犹他州的合作者将使用链接的VHA 数据，以确定和描述风险模式的退伍军人谁结合联合收割机VHA和非VHA医疗保健。最后我们 将进行一系列关键的知情人访谈，以更好地了解整合这一点的障碍和促进因素 将一种算法应用于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： 文献通过利用大规模的，真实世界的数据来源，以产生自杀风险的新知识，而 为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 告知大型民用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。