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

阿片类药物过量预防中的种族不平等：当地环境在州级过量预防政策有效性中的作用

• 批准号: 10641173
• 负责人: John Richard Pamplin
• 金额: $ 17.86万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641173
• 关键词: Address; Behavior; Biometry; Black Populations; Black race; COVID-19 pandemic; Cessation of life; Complement; Computing Methodologies; County; Data; Data Set; Data Sources; Development; Development Plans; Disparity; Drug user; Effectiveness; Effectiveness of Interventions; Epidemiology; Equity; Ethnic Population; Fatality rate; Foundations; Harm Reduction; Individual; Intervention; Knowledge; Law Enforcement; Laws; Literature; Machine Learning; Measurement; Measures; Mentorship; Methodology; Methods; Naloxone; Neighborhoods; Opioid; Overdose; Overdose reduction; Overdose reversal; Pattern; Persons; Pharmaceutical Preparations; Policies; Policy Analysis; Psychiatric epidemiology; Public Health; Questionnaires; Race; Recovery; Reporting; Research; Research Personnel; Resources; Role; Services; Site; Statistical Methods; System; Taxonomy; Training; United States; United States National Center for Health Statistics; Vital Statistics; attributable mortality; career; career development; contextual factors; data framework; effective intervention; effectiveness evaluation; experience; help-seeking behavior; innovation; machine learning method; mortality; multidimensional data; multidisciplinary; novel; opioid epidemic; opioid mortality; opioid overdose; opioid policy; opioid user; overdose death; overdose prevention; prescription drug abuse; racial disparity; racial diversity; racial population; recruit; response; skills; substance use; synthetic opioid; treatment effect

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.

项目概要 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 黑人吸毒过量致死率的上升速度比任何其他种族/族裔群体都要快。 很大程度上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率增加幅度最大 迫切需要采取干预措施来有效降低用药过量死亡率。 公平对待黑人过量预防政策（OPP）（即好撒玛利亚人法和纳洛酮）。 获取法），一类旨在降低用药过量死亡率的国家级政策干预措施，可能能够 解决致命过量用药中日益严重的种族不平等问题。然而，有关这一问题的文献存在重大空白。 OPP 的有效性，包括缺乏事先研究哪些规定可以更好地减少用药过量 黑人的死亡人数，以及缺乏对当地背景因素在多大程度上改变黑人死亡的先前研究 此外，制定 OPP 条款的常见做法。 包对标准因果推理方法提出了重大的方法论挑战 评估个别 OPP 条款的有效性 本研究计划的科学目标是 评估州级 OPP 规定的有效性，以公平地降低用药过量死亡率并确定 可能产生种族化条款效力的地方因素 该项目使用新颖的因果关系。 机器学习方法结合国家重要死亡率数据 统计系统和多个公开可用的数据源，以解决方法论挑战并填补 上述关键差距将得到分类法的补充。 剥夺了阿片类药物政策专家小组使用 Delphi 得出的 OPP 规定有效性 通过这样做，该项目将根据经验评估哪些 OPP 条款最有效。 总体上降低用药过量死亡率，特别是黑人，并估计当地的作用 生产过程中的环境因素（例如，获得减少伤害服务或当地执法实践） OPP 规定的不同影响 该研究计划由职业发展计划补充。 以申请人的流行病学和生物统计学背景为基础，包括 (1) 方面的新培训 (2)政策的衡量与评估 干预有效性；（3）机器学习方法，从高水平中识别显着的因果措施 联合研究和培训计划将为申请人的成功过渡做好准备。 从事独立研究事业，旨在使用新颖的统计和计算方法来识别和 评估减少药物使用相关危害中种族不平等的干预措施。