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.

抽象的 合成阿片类药物（芬太尼）的宽度可用性已加剧 剂量（OD）死亡。政策制定者，州和地方机构以及调查人员的重点是俄亥俄州 由于俄亥俄州的代表性，作为其他国家经历的领头羊的经验 人口统计。缺乏及时的地理空间链接纵向数据源阻碍了 社区和州机构将资源分配到最需要的地区。有限的 社会人口统计学特征等环境风险因素的整合无法支持识别 干预的新目标或新的方法来造成当地药物供应变化的新威胁。我们 相信可以用来证明预测性实用性的敏捷数据系统和信息工具 分析和机器智能方法有关如何启用数据驱动决策的方法最终将 在全国各地都可以翻译。我们提出的阿片类药物和药物使用障碍 数据飞地（O-Sudden）将提供一种新颖而变革的方法来支持严格的和 关于阿片类药物和药物使用犯罪的可再现研究。 O-Sudden将填补数据中的几个现有空白 基础架构和预测模型，包括机器学习，地理空间分析和社区环境。 O-Sudden的具体目的如下：AIM 1：与O-Sudden建立的数据链接。我们 将开发一个对地理空间敏感的个人级安全数据湖，该湖集成了多个不同的数据 符合HIPAA下的编码限制设置的要求的来源。新数据源包括实时 尿液药物测试（UDT）和社区上下文数据的个人级纵向数据基于 俄亥俄州机会指数是我们团队开发和使用的健康卫生社会决定者。我们将 开发查询并使用工具进行数据协调和集成，准备和发布数据集 传播和次要数据分析；并促进辅助使用相关的管理数据 产生证据表明有针对性的阿片类药物干预措施。目标2：开发预测模型和 监视算法。地理空间和机器学习将用于建模阿片类药物的贡献， 可卡因以及OD，OD死亡和阿片类药物使用障碍/药物使用障碍（OUD/SUD）的刺激性使用； 实时数据的临时关系，包括UDT和人口统计和上下文变量，以识别高 风险人群和亚群或地理区域。我们将验证模型性能和预测性 权力，然后通过O-Sudden门户网站传播监视和预测算法 使用权。目标3：部署以人为本的平台和活动信息。应用共同设计原则 由以人为本的工作组组成，由最终用户利益相关者（社区，研究人员和州）组成 政策制定者/代理商），我们将开发和部署最终用户量身定制的门户网站和有效的用户友好工具 提供可行的见解，以告知阿片类药物治疗和靶向损害策略。