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

协调多个数据源和心理尸检来描述阿片类药物相关死亡中的自杀特征

基本信息

批准号：
10623253
负责人：
Paul Sasha Nestadt
金额：
$ 19.74万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10623253
关键词：
Accidents Autopsy Categories Cause of Death Cessation of life Characteristics Classification Clinical Clinical Data Collaborations Coroner Country Data Data Collection Data Linkages Data Set Detection Determination of Death Development Drug usage Electronic Health Record Epidemiologist Epidemiology Face Family Fellowship Funding Future Generations Geography Goals Grant Individual Intervention Interview Investigation Link Logistic Regressions Manuscripts Maryland Measures Medical Examiners Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methods Modeling National Institute of Drug Abuse Opioid Overdose Pathway interactions Physicians Policies Positioning Attribute Precipitating Factors Predisposing Factor Prevention Probability Protocols documentation Psychiatric Diagnosis Psychiatric epidemiology Psychiatrist Psychological Interview Public Health Publications Recording of previous events Research Research Methodology Research Personnel Resources Risk Risk Factors Sampling Self-Injurious Behavior Societies Strategic Planning Subgroup Suicide Suicide prevention System Testing Time Toxicology Training Training Programs Translating Validation Work Writing addiction career case-based data warehouse demographics design diverse data endophenotype epidemiologic data epidemiology study experience improved informant innovation large datasets mental state mortality multiple data sources novel opioid epidemic opioid mortality opioid overdose opioid use opioid user overdose death overdose risk peer preventive intervention programs psychologic scaffold skill acquisition skills social statistics suicidal suicidal morbidity suicide rate theories therapy development

项目摘要

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.

项目摘要/摘要这个以患者为导向的导师研究职业发展奖旨在为申请者提供有必要的高级培训，以建立一个独立的流行病学研究方案服药过量死亡和自杀。提出了一个全面的培训计划，结合正规课程，指导和实践培训经验，旨在发展数据联系/协调方面的专门知识，潜伏班模型(LCA)、定性心理尸检(PA)和混合方法研究。随着过量死亡人数的增加，他们继续被视为阿片类药物使用变化造成的事故。然而，流行病学研究表明，这些死亡中的许多人很可能是自杀，这一点很重要适当针对干预措施的影响。我们将衡量这种错误分类的严重程度死亡方式(MOD；意向性)，并确定可能引导法医进行更多对自杀死者进行准确分类。我们假设大约三分之一与阿片类药物有关的死亡不确定的方式是真正的自杀，LCA可以区分出死者的亚群具有更大的有自杀意图的可能性。我们将在之前未确定的意图遗赠者的子集中使用PA进行测试经验派生类的预测能力，并表征过量用药的不同途径。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，我们将从马里兰自杀数据仓库链接到其中的死亡数据首席验尸官办公室(OCME)。首先，取这个样本的三分之一，我们将比较案例从意外(n=756)和未确定的意图(n=3,748)三方面分类为自杀(n=115) 多项Logistic回归。接下来，被发现是最显著的比较器的变量将被用于其余病例为OCME MOD分类不可知(n=9286)。将经验派生的类与在指定OCME时，我们将评估每一类指定自杀和事故的比例。最后，从每个潜在类别中，我们将选出40名被OCME指定为“待定方式”的遗属将由多个附属面谈PA进一步检查，以证实这些潜在的类别。通过泛化根据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国防部的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处于有利地位，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对大型关联数据进行验证描述相互关联的自杀和吸毒过量危机的集合。他的长期职业目标包括阐明自伤死亡的机制，使用混合方法产生和研究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。