Using a Novel Comprehensive Linked Dataset to Determine Early Predictors of Opioid Overdose

项目摘要/摘要 为了解决2000年至2015年期间阿片类药物过量导致的死亡率翻两番以上的问题，联邦和 国家机构已经促进了临床医生的教育和指导方针，以减少有风险的处方。上一首 研究已经确定了与阿片类药物过量风险增加有关的处方模式，然而 患者与环境因素、处方使用/滥用轨迹和过量用药之间的关系 可能性在很大程度上仍不得而知。这项提案是根据PAR-16-234(加速 使用现有数据的药物滥用研究的速度)，将开发评估阿片类药物的综合模型 过量风险，填补了了解处方阿片类药物使用/滥用如何随时间变化的关键空白， 这些变化如何影响过量用药风险，哪些患者最容易受到危险模式的影响，以及所起的作用 家庭和社区层面的处方风险在过量用药中发挥作用。具体目标是： 1.患者人口学和临床特征、患者处方模式的模型效应及其影响 阿片类药物过量(致命或非致命)的相互作用。 2.确定家庭层面处方可获得性对阿片类药物过量的影响。 3.确定社区处方可获得性对阿片类药物过量的影响。 我们研究的一个关键优势是我们的新关联数据集：俄勒冈州阿片类药物综合风险登记 (Corr)，它将支付者的处方和临床历史与不同的过量数据来源联系起来， 包括来自俄勒冈州处方药监测计划、医疗补助索赔、生命记录和 医院出院登记，以及所有付款人/所有索赔和紧急医疗服务数据。我们的研究 将根据患者人口统计学的相互作用来确定与阿片类药物相关的过量用药的几率， 诊断/合并症、最初的阿片类药物处方、家庭处方风险水平和社区 处方药风险水平。这项研究将使用模型来检查风险如何随着时间的推移而积累，并确定处方 在早期阶段预示风险增加的模式。创新：我们的研究创建了一个新的关联数据集，并 应用复杂的分析方法从根本上扩大对患者个体风险的理解 环境。意义：这项研究将通过产生新的知识为临床实践提供帮助 确定风险最高的患者，并修改有关他们的阿片类药物处方决定。影响：层次化 将个人的处方轨迹和临床病史与家庭层面和 社区层面的风险因素可以扩展到其他出现不良后果的复杂疾病 作为不同层次作用的结果。

PROJECT SUMMARY/ABSTRACT To address more than a quadrupling of death rates from opioid overdose between 2000 and 2015, federal and state agencies have promoted clinician education and guidelines to reduce risky prescribing. Previous research has identified prescription patterns associated with elevated risk of opioid overdose, yet the relationship between patient and environmental factors, prescription use/misuse trajectories, and overdose likelihood remains largely unknown. This proposal, submitted in response to PAR-16-234 (Accelerating the Pace of Drug Abuse Research Using Existing Data), will develop comprehensive models for assessing opioid overdose risk, filling critical gaps in understanding of how prescription opioid use/misuse changes over time, how such changes affect overdose risk, which patients are most vulnerable to risky patterns, and what role household- and community-level prescription risk plays in overdose. The specific aims are: 1. Model effects of patient demographic and clinical characteristics and patient prescription patterns and their interactions on opioid-involved overdose (fatal or nonfatal). 2. Determine the effect of household-level prescription availability on opioid overdose. 3. Determine the effect of community-level prescription availability on opioid overdose. A key strength of our study is our novel linked dataset: the Oregon Comprehensive Opioid Risk Registry (CORR), which links prescription and clinical history across payers with diverse sources of overdose data, including data from the Oregon Prescription Drug Monitoring Program, Medicaid Claims, Vital Records, and Hospital Discharge registry, as well as All Payer/All Claims and Emergency Medical Services data. Our study will determine the odds of opioid-related overdose based on interactions of patient demographics, diagnoses/comorbidities, initial opioid prescriptions, household prescription risk levels, and community prescription risk levels. The study will use models to examine how risk builds over time and identify prescription patterns that portend increased risk at an early stage. Innovation: Our study creates a novel linked dataset and applies a complex analytic approach to radically expand understanding of patients' individual risk environments. Significance: This study will inform clinical practice by generating new knowledge that can help identify the most at-risk patients and modify opioid prescribing decisions regarding them. Impact: Hierarchical models which combine individuals' prescription trajectories and clinical histories with household-level and community-level risk factors can be extended to other complex diseases in which the adverse outcomes occur as a result of effects acting at different levels.