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

项目总结 阿片类药物过量导致的死亡是一个紧迫的公共卫生危机，每年的死亡人数为 越来越多。黑人服药过量致命率的上升速度比其他任何种族/民族都要快， 这在很大程度上是由于合成阿片类药物日益突出。黑人拥有最高比例的 可归因于合成阿片类药物的过量死亡，并经历了相关 死亡率。迫切需要能够有效降低服药过量死亡率的干预措施。 对黑人来说是公平的。预防服药过量政策(OPP)(即好心的撒玛利亚法律和纳洛酮 获取法)，一类旨在减少过量用药死亡率的州级政策干预措施，或许能够 解决致命过量中日益加剧的种族不平等问题。然而，文献中存在着关于 业务方案的有效性，包括缺乏可更好地减少过量用药的规定的事先研究 黑人的死亡，以及缺乏关于当地背景因素在多大程度上改变 国家级OPP条款的影响。此外，制定OPP条款的常见做法是 包给标准的因果推理方法带来了巨大的方法论挑战。 评估个别OPP条款的有效性。这项研究计划的科学目标是 评估州级OPP规定公平降低过量用药死亡率的有效性，并确定 可能产生条款种族主义效力的地方因素。这个项目使用了新的因果关系 机器学习方法与来自国家生命周期的受限死亡率数据的组合 统计系统和多种公开可用的数据来源，以应对方法挑战和填补 上文概述的关键差距。这一创新的数据驱动方法将得到以下分类的补充 阿片类药物政策专家小组使用Delphi得出的假想OPP拨备有效性 方法。通过这样做，这个项目将经验地评估哪些OPP条款集在以下方面最有效 总体上减少服药过量死亡率，特别是在黑人中，并估计当地 生产中的背景因素(例如，获得减少危害服务或当地执法做法) OPP条款的各种影响。这项研究计划与职业发展计划相辅相成 以申请者在流行病学和生物统计学方面的背景为基础，并包括新的(1)培训 制定和实施国家级药品政策；(2)政策的衡量和评价 干预有效性；以及(3)机器学习方法，以识别显著的因果测量与高度相关的度量。 维度数据。综合研究和培训计划将使申请者为成功过渡做好准备 致力于独立的研究事业，旨在使用新的统计和计算方法识别和 评估减少物质使用相关危害中的种族不平等的干预措施。

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.