Identifying the longitudinal outcomes of suicide loss in a population-based cohort
Identifying the longitudinal outcomes of suicide loss in a population-based cohort
批准号:
10716673
负责人:
Jaimie L. Gradus
金额:
$73.83万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-06 至 2027-07-31
关键词:
AccidentsAddressAdverse eventBig DataCaringCessation of lifeChildComputerized Medical RecordDataData SourcesDenmarkDiagnosticDiseaseDistressEpidemiologyEventExposure toFamilyFirst Degree RelativeFoundationsFriendsFundingFutureGeneral PopulationGoalsGovernmentHealthHealthcareHealthcare SystemsIndividualInsuranceInternational Classification of Disease CodesInterventionKnowledgeLinkLiteratureMedicalMental HealthMethodsNational Institute of Mental HealthOutcomePatternPersonsPolicy MakerPopulationPreventionPsychiatric epidemiologyPublic HealthPublishingRecordsRegistriesReportingResearchResourcesSample SizeSamplingSelection BiasSourceSpousesSubgroupSuicideSuicide preventionSurvivorsTimeTraumaUnited StatesUnited States Department of Veterans AffairsWorkadverse outcomecohortcomorbiditycomparison groupcost efficientdata registrydesigneHealthemotional distressepidemiology studyexperiencefollow-uphigh riskimprovedlongitudinal designnovelphysical conditioningpopulation basedpreventsecondary analysissexsocialsociodemographicssuicidal individualsuicidal morbiditytherapy designtraumatic stressunsupervised learning
中文摘要
项目摘要
自杀的影响远远超出了自杀死者的个人。对于每一个自杀死亡,
135人面临自杀损失的潜在创伤。研究表明,接触自杀
损失可能导致精神和身体健康困扰,那些经历不良后果的人被称为
“自杀损失幸存者”与此同时,由于缺乏适当的比较,文献受到严重限制
组(例如,事故死亡损失),检查有限数量的结果而不考虑合并症,
并且关注一种类型的家庭关系(例如,配偶)而忽略非家庭损失幸存者(例如,
同居者)。因此,缺乏高质量的人群水平的纵向流行病学研究,
自杀损失幸存者阻碍了我们理解自杀对健康的全面影响以及自杀对健康的全面影响的能力。
自杀公共卫生危机该项目的总体目标是利用丹麦国家登记册数据记录
暴露于以下物质的人群的精神和身体健康结果以及合并症:
30年来的自杀损失。丹麦有一个全民医疗保健系统,政府支持
全国性的电子健康和社会登记册,以及将登记册之间的记录联系起来的能力,
使用唯一的个人/家庭标识符和地址信息的个人。我们的项目将利用
登记处直接解决自杀损失文献中的空白。我们将培养一批所有的第一学位
1994年至2024年期间暴露于自杀损失的亲属和同居者,以及两个比较队列
(1)暴露于事故损失,和(2)从一般人群(目标1)。队列将包括所有可用的
30年随访期间的社会人口统计学和电子病历数据。这些数据将
用于进行自杀损失的流行病学结果分析(目的2)。我们将识别所有精神和
身体健康ICD编码的诊断结果,特定于自杀损失(与事故损失相比,
一般人群),并研究结果如何随时间变化,因为损失,关系类型,和性别。这
该方法将为自杀领域的预防和干预提供新的和更精确的目标
预防后该队列还将用于确定诊断合并症的最显著模式,
自杀损失(目标3)。无监督机器学习将识别自杀损失的潜在亚组
幸存者的特点是共同的模式,精神和身体健康的comormacy,目标是
为跨诊断预防/治疗提供信息,并产生机制假设。本研究是一项
为自杀损失的流行病学研究奠定基础。我们的研究结果将为临床医生和
政策制定者掌握设计和研究疾病特异性和转诊断所需的信息
干预措施,以预防和治疗目前尚未探索和探索不足的自杀对损失幸存者的影响。
该队列还可以作为未来自杀损失研究的持久资源。展望未来,成果
可以在其他群体中复制(例如,更小的美国样本),以进一步将我们的发现置于背景中。
英文摘要
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.
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海外基金