Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial

使用预测分析降低药物相关死亡率：一项随机、全州范围的社区干预试验

• 批准号: 10026087
• 负责人: Magdalena Cerda
• 金额: $ 65.41万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10026087
• 关键词: Academic Detailing; Address; Admission activity; American; Area; Buprenorphine; Censuses; Cities; Classification; Communities; Community Pharmacy; Data; Decision Making; Disease Outbreaks; Dose; Emergency medical service; Environment; Epidemic; Future; Goals; Health; Health Priorities; Hospitals; Institution; Intervention; Intervention Community Trial; Intervention Trial; Light; Machine Learning; Methadone; Modeling; Morbidity - disease rate; Naloxone; National Institute of Drug Abuse; Neighborhoods; Overdose; Pharmaceutical Preparations; Phase; Policies; Population; Predictive Analytics; Prevention; Prevention program; Preventive Intervention; Probability; Public Health; Publishing; Randomized; Recording of previous events; Records; Recovery; Research Priority; Resource Allocation; Resources; Rhode Island; Risk; Services; Source; Strategic Planning; Sum; System; Testing; United States; Visit; Work; addiction; base; evidence base; experience; experimental study; high risk; improved; indexing; machine learning method; mortality; novel; opioid agonist therapy; opioid misuse; opioid mortality; opioid overdose; opioid policy; opioid use disorder; overdose death; overdose prevention; overdose risk; peer; population based; prediction algorithm; predictive modeling; prescription opioid; prevent; primary outcome; programs; public health priorities; referral services; resource guides; response; spatiotemporal; surveillance data; tool; treatment arm

PROJECT SUMMARY Overdose deaths have skyrocketed in the United States since 1999. The epidemic has prompted widespread federal and state actions, yet the number of people who die of an overdose continues to increase. In light of the accelerating and rapidly evolving overdose epidemic, new strategies are needed to identify communities most at risk, and to utilize resources more effectively to curb overdose deaths. To address these public health priorities, we will develop a forecasting tool to predict overdose deaths before they occur, and then conduct a randomized, statewide, community-level intervention to evaluate resource targeting based on these predictions. The study will take place in Rhode Island, a state with the 10th highest rate of overdose fatality in 2016. The study has two phases. First, we will develop a predictive analytics model that forecasts future overdose mortality at the neighborhood-level, using publicly available information and data from a multicomponent overdose surveillance system. This tool, called PROVIDENT (Preventing Overdose using Information and Data from the Environment) will be used to predict the likelihood of magnitude of future overdose deaths in every neighborhood across Rhode Island. Next, we will conduct a randomized policy experiment to evaluate whether targeting overdose prevention interventions to neighborhoods at highest risk reduces overdose morbidity and mortality. The state's department of health will receive PROVIDENT model predictions for half of the 39 cities/towns in Rhode Island. Within these cities/town, the health department will work with stakeholders to target overdose prevention interventions to neighborhoods with the highest probability of future overdose deaths. Interventions include efforts to: (1) prevent high-risk prescribing (through academic detailing and other educational efforts); (2) expand access to opioid agonist therapy, including buprenorphine and methadone; (3) increase naloxone distribution (through community and pharmacy-based efforts); and (4) expand street-based peer recovery coaching and referrals. Control cities/town will continue to receive these interventions, but without targeting to specific neighborhoods. Fatal and non-fatal opioid overdose rates in the control cities/towns will be compared to those that received the PROVIDENT model predictions. To achieve these aims, we will leverage a unique partnership between an academic institution and a state's health department, which allows for unprecedented access to and sharing of population-based overdose surveillance data. Our results will improve public health decision-making and inform resource allocation to communities that should be prioritized for evidence-based prevention, treatment, recovery, and overdose rescue services. If found to be effective, the PROVIDENT forecasting model will be disseminated to other states, which could adapt the tool to guide resource allocation and maximize public health impact. In sum, this project is highly responsive to a top research priority of the National Institute on Drug Abuse, and directly addresses one of the nation's most challenging public health crises.

项目摘要 自1999年以来，美国的过量死亡在美国飙升。流行病已引发了广泛的促进 联邦和州的行动，但死亡过量的人数仍在增加。鉴于 需要新的策略来确定社区的加速和迅速发展的过量流行病 大多数人处于危险之中，并更有效地利用资源来遏制过量死亡。解决这些公共卫生 优先级，我们将开发一种预测工具来预测过量死亡发生，然后进行一次 随机，全州，社区级的干预措施以根据这些评估资源定位 预测。这项研究将在罗德岛州进行，这是一个州的死亡率最高第10的状态 2016年。该研究有两个阶段。首先，我们将开发一个预测分析模型，以预测未来 使用公共可用的信息和数据，在社区层面的过量死亡率 多组分过量监视系统。该工具称为Provident（防止过量使用 来自环境的信息和数据将用于预测未来数量的可能性 罗德岛各地的每个社区的过量死亡。接下来，我们将执行随机政策 实验以评估是否针对最高风险的社区预防过量干预措施 降低了过量的发病率和死亡率。该州卫生部将获得公众模式 预测罗德岛州39个城镇的一半。在这些城市/城镇中，卫生部门将 与利益相关者合作，将预防过量干预措施靶向最高的社区 未来过量死亡的概率。干预措施包括：（1）防止高风险处方 （通过学术细节和其他教育工作）； （2）扩大接受阿片类激动剂疗法的机会， 包括丁丙诺啡和美沙酮； （3）增加纳洛酮分布（通过社区和 基于药房的工作）； （4）扩大基于街道的同行恢复教练和推荐。控制 城市/城镇将继续接受这些干预措施，而无需针对特定社区。致命的 将控制城市/城镇中的非致命阿片类药物过量率与接受 公益模型预测。为了实现这些目标，我们将利用 学术机构和州卫生部门，允许前所未有的访问和共享 基于人群的过量监视数据。我们的结果将改善公共卫生决策和 将资源分配给社区的资源分配，应优先考虑循证预防，治疗， 恢复和过量救援服务。如果发现有效，则公益人的预测模型将是 传播到其他州，可以调整该工具以指导资源分配并最大化公众 健康影响。总而言之，该项目对国家研究所的重点敏感 滥用毒品，直接解决该国最具挑战性的公共卫生危机之一。