Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-Emptive Public Health Responses

预测预防：阿片类药物过量的动态时空分析以指导预防性公共卫生应对

• 批准号: 10618998
• 负责人: Cici Bauer
• 金额: $ 68.65万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10618998
• 关键词: Address; Adopted; Affect; Area; Authorization documentation; Autopsy; Biometry; COVID-19 pandemic; Clinical; Code; Collaborations; Communities; Country; Data; Data Science; Data Set; Death Records; Decision Making; Detection; Dimensions; Early Diagnosis; Effectiveness of Interventions; Ensure; Epidemic; Epidemiology; Etiology; Evaluation; Flare; Fostering; Future; Geography; Goals; Government; Hospitals; Individual; Intervention; Knowledge; Link; Location; Massachusetts; Measures; Medicine; Modeling; National Institute of Drug Abuse; Outcome; Output; Overdose; Pattern; Policies; Policy Maker; Population; Population Surveillance; Positioning Attribute; Prevention; Public Health; Public Health Informatics; Public Health Practice; Research; Resource Allocation; Risk; Role; Societal Factors; Source; Spottings; Strategic Planning; Technical Expertise; Time; Toxicology; Update; Validation; Visualization; Visualization software; Vulnerable Populations; Work; addiction; authority; community intervention; community partnership; community-level factor; dashboard; data warehouse; drug abuse prevention; effectiveness evaluation; evidence base; experience; healing; implementation science; improved; innovation; insight; instrument; intervention effect; member; models and simulation; novel; opioid epidemic; opioid mortality; opioid overdose; overdose death; overdose prevention; population health; predictive modeling; prescription monitoring program; prevent; public health intervention; response; simulation; social; social health determinants; socioeconomics; spatiotemporal; success; tool

Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-Emptive Public Health Responses PROJECT SUMMARY/ABSTRACT Opioid overdose (OD) fatalities have reached crisis levels in all socioeconomic and geographic communities in the US. By utilizing a first-of-its-kind statewide Public Health Data Warehouse (PHD) with multiple linked administrative datasets and state-of-the-art Bayesian spatiotemporal models, we are in a unique position to fill in the fundamental gaps in the field’s ability to rapidly identify current OD patterns, predict future OD epidemics, and evaluate the effectiveness of public health and clinical interventions. In Massachusetts (MA), the State Legislature enacted policy in 2015 that provided authorization to the MA Department of Public Health (MDPH) to develop a massively linked administrative dataset to allow public health officials and policymakers to better understand the extent of and contributors to the opioid OD epidemic. The PHD Warehouse, representing 98% of the MA population, currently links data from 25+ distinct sources (e.g., death records, all-payer claims, post-mortem toxicology, hospital discharges, and the prescription monitoring program). Supported by strong preliminary studies demonstrating the power of the PHD and our strong partnership with MDPH, we aim to develop a new population health analytic framework to support opioid OD control in MA that can be generalizable to other parts of the country. Our Specific Aims are to: 1) Develop a Bayesian multilevel spatiotemporal model to identify individual, interpersonal, community, and societal factors that contribute to opioid OD; 2) develop an efficient Bayesian spatiotemporal model to identify time- space OD clusters, and extend the model to construct a dynamic predictive model; and, 3) evaluate and predict policy and intervention effects through model-based simulation studies to provide practical guidance and decision-making support to public health officials. Aims 1, 2 and 3 can be easily adopted and reproduced by users in other public health jurisdictions and sectors to foster cross-sector, cross-agency opioid OD control. Our approach is innovative due to the use of PHD and sophisticated Bayesian spatiotemporal modeling approaches. The proposed study is highly significant, because it is conceptualized to improve current and future public health practice, facilitating data-driven and evidence-based implementation science interventions in the locations at greatest risk and at the time when they are most needed. Our results can immediately and significantly influence opioid OD prevention policies and practices, guiding pre-emptive public health and clinical responses. We will develop our visualization tools, analytical approaches, and related code, in collaboration with MDPH and our Community Advisory Board (CAB), to enhance PHD capabilities and improve dissemination of findings. Our tools, approaches, and code will also be made available for national dissemination, providing paradigm shifting approaches to address the opioid crisis. Our research directly addresses NIDA’s goal to “Develop new and improved strategies to prevent drug use and its consequences.”

预测预防：阿片类药物过量的动态时空分析 预防性公共卫生对策 项目摘要/摘要 阿片类药物过量（OD）死亡率在所有社会经济和地理社区都达到了危机水平 在美国.通过利用首个全州公共卫生数据仓库（PHD）， 链接的管理数据集和最先进的贝叶斯时空模型，我们处于一个独特的 填补了该领域快速确定当前OD模式的能力方面的根本空白， 预测未来OD流行，评估公共卫生和临床干预措施的有效性。 在马萨诸塞州，州议会于2015年颁布了一项政策，授权马萨诸塞州 公共卫生部（MDPH）开发一个大规模链接的行政数据集， 卫生官员和政策制定者更好地了解阿片类药物滥用流行的程度和贡献者。 PHD Warehouse代表98%的MA人口，目前链接来自25个以上不同来源的数据 (e.g.,死亡记录，所有付款人索赔，死后毒理学，出院，和处方 监控程序）。通过强有力的初步研究，证明了PHD和我们的力量的支持， 与MDPH建立强有力的伙伴关系，我们的目标是制定一个新的人口健康分析框架，以支持阿片类药物 MA的OD控制可以推广到全国其他地区。我们的具体目标是：1）开发 贝叶斯多级时空模型，以确定个人，人际，社区和社会 影响阿片类药物OD的因素; 2）开发有效的贝叶斯时空模型，以确定时间- 空间OD聚类，并扩展模型以构建动态预测模型;以及，3）评估和 通过基于模型的模拟研究预测政策和干预效果，提供实际指导 以及对公共卫生官员的决策支持。目标1、2和3易于采用和复制 由其他公共卫生管辖区和部门的使用者提供，以促进跨部门、跨机构的阿片类药物滥用控制。 我们的方法是创新的，由于使用PHD和复杂的贝叶斯时空建模 接近。这项拟议的研究非常重要，因为它的概念是为了改善当前的和 未来的公共卫生实践，促进数据驱动和循证实施科学干预措施 在最危险的地方和最需要的时候。我们的结果可以立即和 显著影响阿片类药物滥用预防政策和做法，指导先发制人的公共卫生， 临床反应。我们将开发可视化工具、分析方法和相关代码， 与MDPH和我们的社区咨询委员会（CAB）合作，以提高PHD的能力， 传播调查结果。我们的工具，方法和代码也将提供给国家 传播，为解决类阿片危机提供范式转变方法。我们的研究直接 NIDA的目标是“制定新的和改进的战略，以防止药物使用及其后果。