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

基本信息

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
项目类别：
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

项目摘要

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以指导公共卫生先发制人的应对措施项目概要/摘要所有社会经济和地理社区的阿片类药物过量 (OD) 死亡人数已达到危机水平在美国，利用首个全州范围的公共卫生数据仓库 (PHD)，其中包含多个数据链接的管理数据集和最先进的贝叶斯时空模型，我们处于独特的地位填补该领域快速识别当前 OD 模式能力的基本空白，预测未来的 OD 流行病，并评估公共卫生和临床干预措施的有效性。在马萨诸塞州 (MA)，州立法机构于 2015 年颁布了政策，向 MA 提供授权公共卫生部 (MDPH) 将开发一个大规模链接的行政数据集，以便公众能够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原因。 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（例如，死亡记录、所有付款人索赔、尸检毒理学、出院情况和处方以强有力的初步研究为支持，证明了博士学位的力量和我们的与 MDPH 建立强有力的合作伙伴关系，我们的目标是开发一个新的人口健康分析框架来支持阿片类药物马萨诸塞州的 OD 控制可以推广到全国其他地区，我们的具体目标是： 1) 发展。用于识别个人、人际、社区和社会的贝叶斯多级时空模型影响阿片类药物 OD 的因素；2) 开发有效的贝叶斯时空模型来识别时间- 空间 OD 聚类，并扩展模型以构建动态预测模型；3）评估和通过基于模型的模拟研究预测政策和干预效果，提供实践指导目标 1、2 和 3 可以很容易地被采纳和复制。其他公共卫生管辖区和部门的用户，以促进跨部门、跨机构的阿片类药物消耗控制。我们的方法是创新的，因为使用了 PHD 和复杂的贝叶斯时空建模拟议的研究非常重要，因为它的概念是为了改进当前和未来的公共卫生实践，促进数据驱动和基于证据的实施科学干预措施在风险最大的地点和最需要的时候，我们可以立即提供结果。显着影响阿片类药物滥用预防政策和实践，指导预防性公共卫生和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，以增强 PHD 能力并改进我们的工具、方法和代码也将提供给国家。传播，范式提供了直接解决阿片类药物危机的转变方法。解决了 NIDA 的目标，即“制定新的和改进的策略以防止吸毒及其后果”。