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

健康的新机会和自杀的复原力措施(无伤害) 预防 项目摘要/摘要 自杀是一个紧迫的公共卫生问题，在全国和华盛顿州金县，要求 采取全面、跨部门的方法进行有效预防。跨越死亡的孤岛数据， 急救和危机服务，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制度 妨碍了对风险和保护性因素以及干预点的人口层面的了解。 滞后的数据和非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。推进主动的自杀监测、研究和 当地公共卫生行动，西雅图金县公共卫生调查人员提出了这部小说和 创新的健康机会和自杀复原力措施(无伤害) 预防项目，与疾病控制中心(CDC)合作，地方主题 事务专家、计划管理员和跨部门数据所有者。我们将链接七个新的 从金县综合数据中心(IDH)到跨部门人级数据的数据集， 构建数据元素，然后分析这些数据以检查风险和保护因素，并 系统触点，与全面理解自杀和故意自我相关 伤害。目标1开发了一个数据链接和质量保证流程，以开发一种“活的”数据 由12个跨部门和7个地区一级的数据来源组成的资源转换为个别记录。 这一目标将建立在IDH关于概率和确定性数据链接的既定协议的基础上。 目标2定义和构造与个人、关系和社区相关的数据元素- 与以下内容相关的级别风险和保护因素、系统接触点、个人背景信息 自杀和故意自残，利用综合管理数据和共识- 基于流程的。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、跨部门的数据链接将产生丰富的数据源。《目标3》使用了这部小说 通过开发用于监测、评估和研究的无害化预防数据资源 可视化描述性发现并进行推理分析的交互式仪表板 与自杀和故意自残相关的风险和保护因素。我们会发现风险是如何 保护因素通过潜在的类别在我们的人群中聚集成独特的风险概况 分析。使用跨越近十年的数据，我们还将应用病例对照设计来 严格检查风险和保护因素与故意自残之间的关系。 作为此类数据链接的第一次，无伤害预防将促进科学 和预防自杀的做法，将成为其他司法管辖区可复制的模式。

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.