Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case-control study.
Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case-control study.
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自杀预防研究的创新（INSPIRE）：基于人群的病例对照研究方案。
DOI：
10.1136/injuryprev-2022-044609
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Pence,BrianW
中科院分区：
文献类型：
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作者：
Ranapurwala,ShabbarI;Miller,VanessaE;Carey,TimothyS;Gaynes,BradleyN;Keil,AlexanderP;Fitch,CatherineVinita;Swilley-Martinez,MonicaE;Kavee,AndrewL;Cooper,Toska;Dorris,Samantha;Goldston,DavidB;Peiper,LewisJ;Pence,BrianW
背景过去 20 年来，美国的自杀死亡人数一直在增加，2020 年有 45 979 人死亡，自 1999 年以来增加了 29%。有潜力实施大型自杀预防举措的实体（健康保险公司、卫生机构和惩戒机构）之间缺乏数据链接，是开发自杀预防综合框架的障碍。目标死亡记录和几个大型管理数据集之间的数据链接，以 (1) 估计风险因素和自杀结果之间的关联， (2)开发预测算法和(3)建立长期数据链接工作流程，以确保持续的自杀监测。方法我们将从2006年起整合来自美国人口第十大州北卡罗来纳州的六个数据源，包括死亡证明记录、暴力死亡报告系统、大型私人健康保险索赔数据、医疗补助索赔数据、北卡罗来纳大学电子健康记录和涉及司法的出狱个人数据。我们将确定四个亚人群中自杀、自杀企图和意念导致的死亡发生率，以建立基准。我们将使用嵌套病例对照设计和发病率密度匹配的基于人群的控制，以（1）识别与自杀未遂和死亡相关的短期和长期风险因素，（2）开发基于机器学习的预测算法来识别有自杀死亡风险的个体。讨论我们将通过建立一个深入链接的自杀监测系统来弥补与先前研究的差距，该系统集成了多个大型综合数据库，允许建立基准、识别预测因素、评估预防工作和建立长期监测工作流程协议。
BackgroundSuicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention.ObjectivesData linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance.MethodsWe will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case–control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths.DiscussionWe will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols.