Opioid and SUD Data Enclave (O-SUDDEn): Bringing real-time data to the opioid crisis

阿片类药物和 SUD 数据飞地 (O-SUDDEn)：为阿片类药物危机提供实时数据

• 批准号: 10590246
• 负责人: Soledad A Fernandez
• 金额: $ 205.36万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590246
• 关键词: Address; Adopted; Algorithms; Area; Artificial Intelligence; Censuses; Cessation of life; Characteristics; Code; Communities; Country; County; Data; Data Analyses; Data Linkages; Data Reporting; Data Set; Data Sources; Decision Making; Dose; Dose-Rate; Environment; Environmental Risk Factor; Event; Evidence based practice; Family; Fentanyl; Gender; Geography; Harm Reduction; Health; Health Insurance Portability and Accountability Act; Human; Individual; Informatics; Information Systems; Intervention; Justice; Link; Locales; Machine Learning; Medical; Modeling; Neighborhoods; Ohio; Opioid; Outcome; Patients; Pattern; Performance; Pharmaceutical Preparations; Policy Maker; Predictive Analytics; Prevention; Prevention strategy; Race; Recovery; Reproducibility; Research; Research Personnel; Resource Allocation; Respondent; Rural Appalachia; Secure; Substance Use Disorder; System; Time; United States; Update; Urine; Vulnerable Populations; base; black men; cocaine use; community center; dashboard; data enclave; data harmonization; data infrastructure; data integration; data lake; data management; data mining; data resource; data tools; demographics; design; drug testing; experience; high risk population; indexing; informatics tool; insight; machine learning method; novel; novel strategies; opioid epidemic; opioid misuse; opioid use; opioid use disorder; overdose death; predictive modeling; response; satisfaction; secondary analysis; social health determinants; sociodemographics; stimulant misuse; stimulant use; substance use; synthetic opioid; tool; treatment strategy; user-friendly; working group

ABSTRACT The widespread availability of synthetic opioids (fentanyl) has fueled the rapidly rising rates of unintentional over dose (OD) fatalities. Policy makers, state and local agencies, and investigators have focused on the Ohio experience as a bellwether for the experiences of other states because of the representativeness of Ohio’s demographics. The lack of timely geospatially-linked longitudinal data sources has impeded the ability of communities and state agencies to pivot allocation of resources to regions where they are needed most. Limited integration of environmental risk factors such as sociodemographic characteristics fail to support identification of new targets for intervention or new approaches to emerging threats from changes in local drug supply. We believe that agile data systems and informatics tools that can be used to demonstrate the utility of predictive analytics and machine intelligence approaches on how to enable data-driven decision making will ultimately prove translatable across the country and diverse localities. Our proposed Opioid and Substance Use Disorder Data Enclave (O-SUDDEn) will provide a novel and transformative approach to support rigorous and reproducible research on the opioid and substance use crises. O-SUDDEn will fill several existing gaps in data infrastructure and prediction models that include machine learning, geospatial analyses, and community context. The specific aims for O-SUDDEn are as follows: Aim 1: Data linkage with establishment of O-SUDDEn. We will develop a geospatially-sensitive, individual-level secure data lake that integrates multiple disparate data sources that meets the requirement of a coded-limited set under HIPAA. New data sources include real-time individual-level longitudinal data from urine drug testing (UDT) and community contextual data based on the Ohio Opportunity Index, an area-level social determinants of health developed and used by our team. We will develop query and use tools for data harmonization and integration, prepare and release data sets for dissemination and secondary data analyses; and facilitate secondary use of related administrative data to generate evidence that informs targeted opioid interventions. Aim 2: Develop Predictive Models and Surveillance algorithms. Geospatial and machine learning will be used to model the contribution of opioid, cocaine, and stimulant use on OD, OD death and opioid use disorder/substance use disorder (OUD/SUD); temporal relationship of real time data including UDT and demographic and contextual variables to identify high- risk populations and subpopulations or geographic locales. We will validate model performances and predictive power and then disseminate surveillance and forecasting algorithms through the O-SUDDEn portal for end-user access. Aim 3: Deploy a human-centered platform and actional informatics. Applying co-design principles with a human-centered work group consisting of end-user stakeholders (community, researchers, and state policy makers/agencies), we will develop and deploy an end-user tailored portal and effective user-friendly tools to provide actionable insights to inform opioid treatment and targeted harm reduction strategies.

摘要 合成阿片类药物（芬太尼）的广泛可用性助长了无意过度使用率的迅速上升。 剂量（OD）死亡。政策制定者、州和地方机构以及调查人员都把重点放在了俄亥俄州 由于俄亥俄州的代表性， 人口统计学由于缺乏及时的地理空间联系纵向数据源， 社区和国家机构将资源分配重点放在最需要的地区。有限 环境风险因素的整合，如社会人口特征，不能支持识别 制定新的干预目标或采取新的办法应对当地毒品供应变化带来的新威胁。我们 我认为，敏捷的数据系统和信息学工具，可用于证明预测的效用， 关于如何实现数据驱动决策的分析和机器智能方法最终将 可以在全国各地和不同的地方翻译。我们提出的阿片类药物和物质使用障碍 数据飞地（O-SUDDEn）将提供一种新颖的变革性方法， 关于阿片类药物和物质使用危机的可重复研究。O-SUDDEn将填补现有的几个数据空白 基础设施和预测模型，包括机器学习、地理空间分析和社区背景。 目标1：与建立可持续农业发展网络建立数据联系。我们 将开发一个地理空间敏感的、个人级别的安全数据湖，该数据湖集成了多个不同的数据 符合《健康保险与责任法案》（HIPAA）规定的代码限制集要求的来源。新数据源包括实时 来自尿液药物检测（UDT）的个人水平纵向数据和社区背景数据， 俄亥俄州机会指数，由我们的团队开发和使用的地区级健康社会决定因素。我们将 开发查询和使用数据统一和整合工具，编制和发布数据集， 传播和辅助数据分析;并促进相关行政数据的辅助使用， 提供证据，为有针对性的阿片类药物干预提供信息。目标2：开发预测模型， 监控算法地理空间和机器学习将用于模拟阿片类药物的作用， 可卡因和兴奋剂使用对OD、OD死亡和阿片类药物使用障碍/物质使用障碍（OUD/SUD）的影响; 真实的时间数据的时间关系，包括UDT和人口统计和背景变量，以确定高 风险人群和亚人群或地理区域。我们将验证模型性能和预测 通过O-SUDDEn门户网站为最终用户提供监控和预测算法，然后进行传播 access.目标3：部署以人为中心的平台和信息化。应用协同设计原则 由最终用户利益相关者（社区、研究人员和国家）组成的以人为本的工作组 政策制定者/机构），我们将开发和部署一个为最终用户量身定制的门户网站和有效的用户友好工具 提供可操作的见解，为阿片类药物治疗和有针对性的减少伤害策略提供信息。