Racial Inequities in Opioid Overdose Prevention: The role of local context in the effectiveness of state-level overdose prevention policies

项目摘要 阿片类药物过量导致的死亡是一个紧迫的公共卫生危机，每年的死亡人数是 增加。黑人的致命药物过量率上升速度比任何其他种族/族裔群体都快， 主要是由于合成阿片类药物的日益突出。黑人的比例最高， 合成阿片类药物导致的过量死亡， 死亡率。迫切需要能够有效降低过量死亡率的干预措施， 对黑人公平。过量预防政策（OPPs）（即，好撒玛利亚人法和纳洛酮 准入法），一类旨在减少过量死亡率的国家级政策干预措施，可能能够 解决日益严重的种族不平等问题然而，在有关的文献中， 口服避孕药的有效性，包括缺乏事先研究，哪些规定可以更好地减少过量用药 黑人的死亡，以及缺乏对当地背景因素在多大程度上改变了黑人的死亡的事先研究， 国家一级反垄断规定的影响。此外，在《公约》中颁布反洗钱规定的通常做法是， 包创建了一个重大的方法论挑战标准的因果推理方法， 评估个别《公约》条款的有效性。这项研究计划的科学目标是 评估州一级预防性药物滥用规定的有效性，以公平地降低过量死亡率，并确定 地方一级的因素，可能产生种族化的规定的效力。这个项目使用了新的因果关系 机器学习方法结合国家生命统计局的有限死亡率数据， 统计系统和多种公开数据来源，以应对方法上的挑战， 上文概述的关键差距。这种创新的数据驱动方法将得到以下分类的补充： 一个阿片类药物政策专家小组使用德尔菲法得出的假设的药物供应有效性 法通过这样做，本项目将根据经验评估哪套可持续发展规定在以下方面最有效： 减少过量死亡率的整体，特别是在黑人，并估计当地的作用， 背景因素（例如，获得减少危害服务或当地执法实践） 不同的影响力的规定。该研究计划由职业发展计划补充， 基于申请人在流行病学和生物统计学方面的背景，并包括以下方面的新培训（1） 制定和实施国家一级的药物政策;（2）政策的衡量和评价 干预有效性;以及（3）机器学习方法，以识别来自高风险的显著因果措施。 维度数据结合研究和培训计划将准备申请人成功过渡 一个独立的研究生涯，旨在使用新的统计和计算方法，以确定和 评估干预措施，以减少物质使用相关危害中的种族不平等。

PROJECT SUMMARY Deaths due to opioid overdose are a pressing public health crisis and the number of deaths per year is increasing. Rates of fatal overdose are rising faster for Black people than for any other racial/ethnic group, largely driven by the increased prominence of synthetic opioids. Black people have the highest percentage of overdose mortality attributable to synthetic opioids and have experienced the greatest increase in related mortality rates. Interventions are critically needed that can effectively reduce overdose mortality and do so equitably for Black people. Overdose prevention policies (OPPs) (i.e., Good Samaritan laws and naloxone access laws), a class of state-level policy interventions intended to reduce overdose mortality, may be able to address rising racial inequities in fatal overdose. However, there are critical gaps in the literature regarding the effectiveness of OPPs, including a lack of prior research into which provisions may better reduce overdose deaths for Black people, and a lack of prior research into the extent to which local contextual factors modify the effects of state-level OPP provisions. Additionally, the common practice of enacting OPP provisions in packages creates a significant methodological challenge for standard causal inference approaches of assessing the effectiveness of individual OPP provisions. The scientific objective of this research plan is to assess the effectiveness of state-level OPP provisions to equitably reduce overdose mortality and identify local-level factors that may produce racialized effectiveness of provisions. This project uses novel causal machine learning methods in conjunction with a combination of restricted mortality data from the National Vital Statistics System and multiple publicly available data sources to address the methodological challenge and fill the critical gaps outlined above. This innovative data-driven approach will be complemented by taxonomies of hypothesized OPP provision effectiveness produced by a panel of opioid policy experts using the Delphi method. By doing so, this project will empirically evaluate which sets of OPP provisions are most effective at reducing overdose mortality overall, and specifically among Black people, and estimate the role of local contextual factors (e.g., access to harm reduction services or local law enforcement practices) in producing varied effects of OPP provisions. This research plan is complemented by a career development plan that builds upon the applicant’s background in epidemiology and biostatistics and includes new training in (1) development and implementation of state-level drug policies; (2) measurement and evaluation of policy intervention effectiveness; and (3) machine learning methods to identify salient causal measures from high- dimensional data. The combined research and training plan will prepare the applicant to successfully transition to an independent research career aimed at using novel statistical and computational methods to identify and evaluate interventions to reduce racial inequities in substance use related harms.