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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要自1999年以来，美国吸毒过量死亡人数直线上升。这一流行病引发了广泛的联邦和州采取了行动，但死于服药过量的人数仍在继续增加。鉴于随着药物过量流行病的加速和迅速发展，需要新的策略来识别社区风险最大的国家，并更有效地利用资源来遏制服药过量死亡。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，在用药过量死亡发生之前进行预测，然后进行随机的、全州范围的、社区层面的干预，以评估基于这些的资源目标预测。该研究将在罗德岛州进行，该州的药物过量死亡率排名第十。 2016年。该研究分为两个阶段。首先，我们将开发一个预测分析模型来预测未来使用公开信息和来自某个社区的数据计算社区一级的服药过量死亡率多成分过量监测系统。该工具称为 PROVIDENT（使用预防过量来自环境的信息和数据）将用于预测未来严重程度的可能性罗德岛州每个社区都有服药过量死亡的情况。接下来我们将进行随机策略评估是否针对风险最高的社区进行过量预防干预的实验降低用药过量的发病率和死亡率。该州卫生部将收到 PROVIDENT 模型对罗德岛州 39 个城市/城镇中一半的预测。在这些城市/城镇内，卫生部门将与利益相关者合作，针对用药过量预防干预措施最高的社区未来因服药过量死亡的可能性。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努力）； (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) 增加纳洛酮的分布（通过社区和基于药学的努力）； (4) 扩大基于街道的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，但不针对特定社区。致命的对照城市/城镇的非致命阿片类药物过量使用率将与接受治疗的城市/城镇进行比较 PROVIDENT 模型预测。为了实现这些目标，我们将利用与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药物过量监测数据。我们的结果将改善公共卫生决策和告知应优先用于循证预防、治疗、康复和服药过量救援服务。如果发现有效，PROVIDENT 预测模型将传播到其他国家，这些国家可以调整该工具来指导资源分配并最大限度地提高公共利益健康影响。总之，该项目高度响应国家研究所的首要研究重点药物滥用，并直接解决美国最具挑战性的公共卫生危机之一。