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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：
10444263
负责人：
Cici Bauer
金额：
$ 76万
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/10444263
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 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 Population Population Surveillance Positioning Attribute Prevention Public Health Public Health Informatics Public Health Practice Research Resource Allocation Risk Role Societal Factors Source Strategic Planning Technical Expertise Time Toxicology Update Validation Visualization Visualization software Vulnerable Populations Work addiction base community intervention community partnership community-level factor dashboard data warehouse drug abuse prevention effectiveness evaluation evidence base experience healing implementation science improved innovation insight 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指导