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: 9817054
• 负责人: Magdalena Cerda
• 金额: $ 71.87万
• 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/9817054
• 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; learning strategy; 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，美国的过量死亡人数急剧上升。这一流行病引起了广泛的 联邦和州政府采取了行动，但死于吸毒过量的人数仍在继续增加。鉴于 加速和迅速演变的过量流行病，需要新的战略，以确定社区 我们呼吁各国政府采取措施，确保最易受危害的人的健康，并更有效地利用资源，遏制过量用药造成的死亡。为了解决这些公共卫生问题， 优先事项，我们将开发一种预测工具，在过量死亡发生之前预测它们，然后进行 随机，全州，社区一级的干预，以评估资源的目标，根据这些 预测。这项研究将在罗得岛进行，该州是美国吸毒过量死亡率第10高的州。 2016.研究分两个阶段。首先，我们将开发一个预测分析模型， 过量死亡率在社区一级，使用公开的信息和数据，从一个 多组分过量监测系统。这种工具，称为PROVIDENT（预防过量使用 环境信息和数据）将被用于预测未来的可能性 罗得岛每个社区都有吸毒过量死亡的案例接下来，我们将进行随机化的政策 实验，以评估是否有针对性的过量预防干预措施，以最高风险的社区 减少过量的发病率和死亡率。州卫生部将收到PROVIDENT模型 罗得岛39个城镇中有一半的预测。在这些城市/城镇，卫生部门将 与利益相关者合作，将过量预防干预措施的目标锁定在 未来过量死亡的可能性。干预措施包括努力：（1）防止高风险处方 （通过学术详述和其他教育工作）;（2）扩大阿片类激动剂治疗的可及性， 包括丁丙诺啡和美沙酮;（3）增加纳洛酮分布（通过社区和 （4）扩大街头同伴康复辅导和转介。控制 城市/城镇将继续接受这些干预措施，但不针对具体的社区。致命 对照城市/城镇的非致命性阿片类药物过量率将与接受 提供模型预测。为了实现这些目标，我们将利用一个独特的伙伴关系， 学术机构和州卫生部门，这使得前所未有的访问和共享 基于人群的过量用药监测数据。我们的研究结果将改善公共卫生决策， 向应作为循证预防、治疗 康复和过量抢救服务如果发现有效，PROVIDENT预测模型将 向其他国家传播，这些国家可以调整这一工具，以指导资源分配， 健康影响。总之，该项目高度响应了国家研究所的一项首要研究重点， 药物滥用，并直接解决国家最具挑战性的公共卫生危机之一。