Harmonizing Multiple Data Sources And Psychological Autopsy To Characterize Suicides Among Opioid-Related Deaths

项目总结/摘要 这个指导以患者为导向的研究职业发展奖旨在为申请人提供 与先进的培训，必要的建立一个独立的研究计划，在流行病学， 过量死亡和自杀提出了一个综合培训计划，结合正式的课程， 指导和实践培训经验，旨在发展数据链接/协调方面的专门知识， 潜在类建模（LCA）、定性心理解剖（PA）和混合方法研究。 随着过量死亡的增加，它们继续被视为阿片类药物使用变化引起的事故。 然而，流行病学研究表明，这些死亡中的许多人可能是自杀，这一点很重要。 对适当定向干预的影响。我们将衡量这种错误分类的程度， 死亡方式（MOD;故意性），并确定可能指导体检医师更多的因素 准确地对自杀死者进行分类我们假设，大约三分之一的阿片类药物相关的死亡， 不确定的方式是真正的自杀，LCA可以区分死者的亚组， 自杀意图的可能性。我们将在先前未确定的意图死者的子集中使用PA进行测试 药物衍生类的预测能力，并表征药物过量的不同途径。 我们建议使用人口统计学方法分析2006-2019年马里兰州所有阿片类药物过量死亡（n= 13，861）， 社会和临床数据，我们将从马里兰州自杀数据仓库链接到死亡率数据， 首席体检医师办公室（OCME）首先，取样本的三分之一， 使用三因素分析法，从意外（n=756）和未确定意图（n= 3，748）中分类为自杀（n=115） 多项逻辑回归接下来，被发现是最突出的比较器的变量将被用于 其余病例，不知道OCME MOD分类（n= 9，286）。将经验派生类与 OCME指定，我们将评估指定的自杀和事故在每个班级的比例。最后， 从每一个潜在类别中，我们将选择40名被OCME指定为“未确定方式”的死者 由多个附带面谈PA进一步检查，以证实这些潜在类别。通过推广 根据LCA和PA的研究结果，可以做出更准确的自杀率估计。调查结果将影响未来 MOD指定以及潜在的预防干预措施如何针对意外过量和自杀。 培训和指导计划将使候选人能够成为一名独立的医生- 流行病学家，能够利用定性和定量方法验证大型关联数据 描述了相互关联的自杀和吸毒过量的危机。他的长期职业目标包括阐明 自伤死亡率的机制，使用混合方法的产生和调查的新的 关于自杀途径的假设，以及将这些发现转化为自杀预防的能力。

Project Summary/ Abstract This Mentored Patient-Oriented Research Career Development Award is designed to provide the applicant with the advanced training necessary to establish an independent program of research in the epidemiology of overdose mortality and suicide. A comprehensive training program is proposed, combining formal coursework, mentoring, and hands-on training experiences designed to develop expertise in data linkage/ harmonization, latent class modeling (LCA), qualitative psychological autopsy (PA) and mixed methods research. As overdose deaths increase, they continue to be treated as accidents resulting from changes in opioid use. However, epidemiologic research suggests that many of these deaths are likely suicides, This has important implications for appropriately targeting interventions. We will measure the magnitude of this misclassification of manner of death (MOD; intentionality) and identify factors that may guide medical examiners to more accurately classify suicide decedents. We hypothesize that approximately one third of opioid related deaths of undetermined manner are truly suicides, and that LCA can distinguish subgroups of decedents with greater likelihood of suicidal intent. We will use PA in a subset of previously undetermined intent decedents to test predictive ability of empirically-derived classes and characterize the diverse paths to overdose. We propose to analyze all opioid overdose deaths in Maryland from 2006-2019 (n=13,861) using demographic, social, and clinical data which we will link from the Maryland Suicide Data Warehouse to mortality data from the Office of the Chief Medical Examiner (OCME). First, taking one third of this sample, we will compare cases classified as suicidal (n=115) from accidental (n=756) and undetermined intent (n=3,748) using a three-way multinomial logistic regression. Next, variables found to be most salient comparators will be used in LCA of the remaining cases, agnostic of OCME MOD class (n=9,286). Comparing the empirically derived classes with the OCME designations, we will assess the proportion of designated suicides and accidents in each class. Finally, from each of these latent classes, we will select 40 decedents designated by OCME as ‘undetermined manner’ to be further examined by multiple collateral interview PA, to corroborate these latent classes. By generalizing findings from LCA and PA, more accurate suicide rate estimates can be made. Findings would impact future MOD designation and potentially, how prevention interventions target accidental overdoses and suicides. Training and mentorship plans will leave the candidate well positioned to become an independent physician- epidemiologist, able to utilize both qualitative and quantitative methods for the validation of large linked data sets describing the interrelated suicide and overdose crises. His long term career goals include the elucidation of mechanisms of self injury mortality, the use of mixed methods for the generation and investigation of novel hypotheses regarding pathways to suicide, and the ability to translate these findings into suicide prevention.