Using Data Linkage to Understand Suicide?Attempts, Self-Harm and Unintentional Drowning Deaths (U01) - 2022

使用数据链接了解自杀未遂、自残和意外溺水死亡 (U01) - 2022

• 批准号: 10587307
• 负责人: Amy Anderson Laurent
• 金额: $ 35万
• 依托单位: SEATTLE-KING COUNTY PUBLIC HEALTH DEPT
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587307
• 关键词: Using; Data; Linkage; Understand; Suicide

New Opportunities for Health and Resilience Measures for Suicide (NO HARMS) Prevention Project Summary / Abstract Suicide is an urgent public health problem, nationally and in King County, Washington, requiring comprehensive, cross-sector approaches for effective prevention. Siloed data across death, emergency and crisis services, sociodemographic characteristics, intentional self-harm, inpatient/outpatient services, behavioral and physical health care, and the criminal legal system hinder a population-level understanding of risk and protective factors and points of intervention. Lagged data and non-standard definitions limit the ability to systematically link, process, and analyze data rapidly and accurately. To advance proactive suicide monitoring, research, and local public health action, Public Health Seattle King County investigators propose the novel and innovative New Opportunities for Health and Resilience Measures for Suicide (NO HARMS) Prevention project, in partnership with the Centers for Disease Control (CDC), local subject matter experts, program administrators, and cross-sector data owners. We will link seven new data sets to cross-sector person-level data from King County’s Integrated Data Hub (IDH), construct data elements, then analyze these data to examine risk and protective factors and system touchpoints, relevant to a comprehensive understanding of suicide and intentional self- harm. Aim 1 develops a data linkage and quality assurance process to develop a ‘living’ data resource comprised of 12 cross-sector and seven areal-level data sources to individual records. This aim will build on IDH’s established protocols for probabilistic and deterministic data linkage. Aim 2 defines and constructs data elements related to individual, relational, and community- level risk and protective factors, system touchpoints, individual contextual information related to suicide and intentional self-harm, utilizing the integrated administrative data and consensus- based processes. Natural Language Processing methods applied to underexamined text fields and multi-level cross-sector data linkages will result in a rich data source. Aim 3 uses the novel NO HARMS Prevention data resource for monitoring, evaluation, and research by developing an interactive dashboard visualizing descriptive findings and conducting inferential analyses of risk and protective factors related to suicide and intentional self-harm. We will discover how risk and protective factors cluster in our population into unique risk profiles through latent class analysis. Using data spanning nearly a decade, we will also apply a case-control design to rigorously examine associations between risk and protective factors and intentional self-harm. As the first-ever data linkage effort of its kind, NO HARMS Prevention will advance the science and practice of suicide prevention and will be a replicable model for other jurisdictions.

自杀的健康和复原力措施的新机会（无危害） 预防 项目总结/摘要 自杀是全国和华盛顿金县的一个紧迫的公共卫生问题，需要 采取全面、跨部门的办法进行有效预防。在死亡之间存储数据， 紧急和危机服务，社会人口特征，故意自残， 住院/门诊服务、行为和身体保健以及刑事法律的制度 阻碍了民众对风险和保护因素以及干预点的了解。 滞后的数据和不标准的定义限制了系统地链接、处理和 快速准确地分析数据。为了推进积极主动的自杀监测，研究， 当地的公共卫生行动，公共卫生西雅图金郡调查人员提出的小说， 创新的自杀健康和复原力措施的新机会（无伤害） 预防项目，与疾病控制中心合作，地方主题 问题专家、程序管理员和跨部门数据所有者。我们将连接七个新的 数据集到来自金郡综合数据中心（IDH）的跨部门个人数据， 构建数据元素，然后分析这些数据以检查风险和保护因素， 系统接触点，与自杀和故意自我的全面理解有关， 伤害目标1开发数据连接和质量保证过程，以开发“活”数据 该资源由12个跨部门数据源和7个地区一级数据源组成，用于单独记录。 这一目标将建立在IDH的概率和确定性数据链接的既定协议。 目标2定义和构造与个人、关系和社区相关的数据元素- 级别风险和保护因素、系统接触点、与以下方面相关的个人背景信息 自杀和故意自残，利用综合行政数据和共识- 基于过程。应用于未充分检查的文本字段的自然语言处理方法 多层次、跨部门的数据连接将形成丰富的数据源。Aim 3使用小说 通过开发用于监测、评估和研究的无危害预防数据资源 一个交互式仪表板，可视化描述性结果并进行推理分析， 与自杀和故意自残有关的风险和保护因素。我们会发现风险 和保护因素聚集在我们的人群中，通过潜在的类别形成独特的风险特征， 分析.使用跨越近十年的数据，我们还将采用病例对照设计， 严格检查风险和保护因素与故意自残之间的联系。 作为有史以来第一次数据链接的努力，无危害预防将推动科学 及防止自杀的措施，并会成为其他司法管辖区可仿效的模式。