CE-22-008 Using Data Linkage to Understand Suicide Attempts, Self-Harm and Unintentional Drowning Deaths

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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.