Identifying the longitudinal outcomes of suicide loss in a population-based cohort

确定基于人群的队列中自杀损失的纵向结果

• 批准号: 10716673
• 负责人: Jaimie L. Gradus
• 金额: $ 73.83万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10716673
• 关键词: Accidents; Address; Adverse event; Big Data; Caring; Cessation of life; Child; Computerized Medical Record; Data; Data Sources; Denmark; Diagnostic; Disease; Distress; Epidemiology; Event; Exposure to; Family; First Degree Relative; Foundations; Friends; Funding; Future; General Population; Goals; Government; Health; Healthcare; Healthcare Systems; Individual; Insurance; International Classification of Disease Codes; Intervention; Knowledge; Link; Literature; Medical; Mental Health; Methods; National Institute of Mental Health; Outcome; Pattern; Persons; Policy Maker; Population; Prevention; Psychiatric epidemiology; Public Health; Publishing; Records; Registries; Reporting; Research; Resources; Sample Size; Sampling; Selection Bias; Source; Spouses; Subgroup; Suicide; Suicide prevention; Survivors; Time; Trauma; United States; United States Department of Veterans Affairs; Work; adverse outcome; cohort; comorbidity; comparison group; cost efficient; data registry; design; eHealth; emotional distress; epidemiology study; experience; follow-up; high risk; improved; longitudinal design; novel; physical conditioning; population based; prevent; secondary analysis; sex; social; sociodemographics; suicidal individual; suicidal morbidity; therapy design; traumatic stress; unsupervised learning

PROJECT ABSTRACT The impact of suicide reaches well-beyond individual suicide decedents. For each suicide death, an estimated 135 people are exposed to the potential trauma of suicide loss. Research indicates that exposure to suicide loss can result in mental and physical health distress, with those experiencing adverse outcomes called “suicide loss survivors.” At the same time, the literature is severely limited by a lack of appropriate comparison groups (e.g., accident death loss), examining a limited number of outcomes without considering comorbidity, and focusing on one type of familial relation (e.g., spouses) while ignoring non-familial loss survivors (e.g., cohabitants). Consequently, the lack of high-quality population-level longitudinal epidemiologic studies of suicide loss survivors hinders our ability to understand the full health effects of suicide and the full extent of the suicide public health crisis. The overall goal of this project is to use Danish national registry data to document the mental and physical health outcomes and comorbidities among the population of individuals exposed to suicide loss over a 30-year period. Denmark has a universal healthcare system, with government supported nationwide electronic health and social registries, and the ability to link records across registries and individuals using unique personal/family identifiers and address information. Our project will leverage the registries to directly address gaps in the suicide loss literature. We will develop a cohort of all first-degree relatives and cohabitants exposed to suicide loss between 1994 and 2024, as well as two comparison cohorts (1) exposed to accident loss, and (2) from the general population (Aim 1). The cohorts will include all available socio-demographic and electronic medical record data over the 30-year follow-up period. These data will be used to conduct an epidemiologic outcome-wide analysis of suicide loss (Aim 2). We will identify all mental and physical health ICD-coded diagnostic outcomes that are specific to suicide loss (compared to accident loss and the general population), and examine how outcomes vary by time since loss, relationship type, and sex. This approach will inform novel and more precise targets for prevention and intervention within the field of suicide postvention. The cohort also will be used to identify the most salient patterns of diagnostic comorbidity that follow suicide loss (Aim 3). Unsupervised machine learning will identify latent subgroups of suicide loss survivors characterized by common patterns of mental and physical health comorbidity, with the goal of informing transdiagnostic prevention/treatment and generating mechanistic hypotheses. This study is an efficient way to lay a foundation for the epidemiology of suicide loss. Our results will provide clinicians and policymakers with the information needed to design and study both disorder-specific and transdiagnostic interventions to prevent and treat currently unexplored and underexplored effects of suicide on loss survivors. The cohort also can serve as an enduring resource for future research on suicide loss. Going forward, results can be replicated across other populations (e.g., smaller US samples) to further contextualize our findings.

项目摘要