Predicting fatal and non-fatal overdose in Los Angeles County with Rapid Overdose Surveillance Dashboard to target street-based addiction treatment and harm reduction services

利用快速过量用药监测仪表板预测洛杉矶县的致命和非致命用药过量，以针对街头成瘾治疗和减少伤害服务

• 批准号: 10589518
• 负责人: David Goodman
• 金额: $ 162.41万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 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/10589518
• 关键词: Accident and Emergency department; Acquired Immunodeficiency Syndrome; Acute; Address; Area; Awareness; California; Censuses; Centers for Disease Control and Prevention (U.S.); Cessation of life; Cities; Clinical; Code; Communities; Complex; Coroner; Coroners and Medical Examiners; County; Data; Data Sources; Dose; Electronic Health Record; Emergency medical service; Epidemiology; Fire - disasters; Geographic Locations; Geography; Goals; Government Agencies; Harm Reduction; Health; Health Personnel; Health Policy; Health Services; Helping to End Addiction Long-term; Homelessness; Injecting drug user; International Classification of Disease Codes; Intervention; Local Government; Los Angeles; Machine Learning; Measures; Medical Examiners; Medical emergency; Medicine; Methods; Modeling; Monitor; National Institute of Drug Abuse; Natural Language Processing; Needle-Exchange Programs; Opioid; Outcome; Overdose; Persons; Pharmaceutical Preparations; Play; Policies; Population; Prevalence; Process; Public Health; Reaction Time; Receiver Operating Characteristics; Records; Research; Resources; Services; Source; Speed; Stimulant; Strategic Planning; Substance Use Disorder; Surveys; Syringes; Text; Time; United States; Work; addiction; authority; base; biomedical informatics; computerized data processing; dashboard; data acquisition; design; diverse data; evidence base; experience; improved; injection drug use; innovation; interest; mortality; mortality statistics; opioid epidemic; opioid use; opioid use disorder; outreach; overdose prevention; predictive modeling; response; stem; surveillance data; tool; treatment services

PROJECT SUMMARY (ABSTRACT) Street medicine teams play a key role in the local overdose response, yet existing data sources lag and are no more granular than the county- or zip code level. Geolocated data exist from various sources that could be used to identify hotspots and inform street-based overdose prevention and addiction treatment, but the data sources are not harmonized or available to the public. One geographic area where these concerns are particularly acute is Los Angeles County (LAC), California, with a population of over 10 million, and which had the largest number of fatal overdoses of any U.S. county in 2020. A rising share of fatal overdoses in LAC occur among unhoused people and involve both stimulants and opioids. The objective of the project is to develop tools and processes to improve timely data acquisition from, rapid processing and integration of diverse data sources, geospatial anal- ysis of overdose hotspots (fatal and non-fatal), and nowcasting of overdose, opioid use disorder (OUD), and injection drug use in LAC. Building on our research team's work in mobile overdose prevention and treatment of OUD in LAC, we will collaborate with five local government agencies with interest and experience in improving local overdose response. Our partners span public health, medicine, emergency medical services (EMS), med- ical examiner and coroner, syringe services programs, and street-based harm reduction. Together, we propose to develop a publicly available Rapid Overdose Surveillance Los Angeles online dashboard that can provide local, granular data and more timely estimates of countywide metrics. To establish the dashboard, we pursue two specific aims. In Aim 1, we will establish data flows to collate geolocated fatal overdose data from the coroner and non-fatal overdose from EMS, adapting natural language processing (NLP) methods to classify free-text data that characterize the specific drugs involved. We then employ geostatistical methods to identify hotspots at the census tract level, providing localization to inform placement of mobile and street-based services. Finally, we will develop nowcasting models to “predict the present” of fatal and non-fatal overdose at the county-level based on incomplete surveillance data. In Aim 2, we will develop further NLP strategies to identify upstream outcomes of overdose (i.e., OUD and injection drug use) in electronic health record data. We will then incorporate metrics from substance use disorder treatment, syringe services, and street medicine to improve our estimates of OUD, injection drug use, and overdose at the county-level. We will visualize these data and nowcasting results through Rapid Overdose Surveillance Los Angeles online dashboard. Findings from these efforts will serve as a model for other jurisdictions to leverage and combine data from diverse stakeholders to improve local situational awareness of overdose. Ultimately, the goal is to produce tools and processes that can speed up the time from data to action to better provide overdose prevention and addiction treatment services.

项目摘要（摘要） 街头医学团队在当地的过量响应中起着关键作用，但现有的数据源滞后，没有 比县或邮政编码水平更颗粒状。地理分配数据来自可以使用的各种来源 识别热点并告知基于街道的预防和成瘾治疗，但数据来源 不统一或向公众使用。这些问题特别敏锐的地理区域 是加利福尼亚州洛杉矶县（LAC），人口超过1000万，数量最多 2020年美国任何美国县的致命过量服药。 人并涉及兴奋剂和阿片类药物。该项目的目的是开发工具和流程 改善从潜水员数据源的快速处理和集成中的及时数据获取，地理空间肛门 过量的热点（致命和非致命）的YSI，以及过量，阿片类药物使用障碍（OUD）和 lac的注射药物使用。在我们的研究团队在移动过量预防和治疗方面的工作基础 Oud在LAC，我们将与五个具有兴趣和经验的地方政府机构合作 局部过量反应。我们的合作伙伴涵盖了公共卫生，医学，紧急医疗服务（EMS），医学 ICAR检查员和验尸官，注射器服务计划以及减少街道伤害。我们一起提出 为了开发公开可用的快速过量监视洛杉矶在线仪表板，可以提供 当地的，颗粒状的数据和全县指标的及时估计。要建立仪表板，我们购买 两个具体的目标。在AIM 1中，我们将建立数据流以整理验尸官的地理分配致命过量数据 EMS的非致命药物过量，适应自然语言处理（NLP）方法来对自由文本进行分类 表征涉及的特定药物的数据。然后，我们员工的地统计学方法以识别 人口普查水平，提供本地化，以告知移动和街头服务的安置。最后， 我们将开发现实的模型，以“预测县级的致命和非致命过量用药” 基于不完整的监视数据。在AIM 2中，我们将制定进一步的NLP策略来识别上游 电子健康记录数据中过量服用的结果（即OUD和注射药物使用）。然后我们将合并 来自药物使用障碍治疗，注射器服务和街头医学的指标以改善我们的估计 县级的OUD，注射毒品使用和过量。我们将可视化这些数据并将结果显示 通过快速的过量监视洛杉矶在线仪表板。这些努力的发现将成为 其他司法管辖区的模型，以利用和结合潜水员利益相关者的数据以改善本地情况 意识过量。最终，目标是生产可以加快时间的工具和流程 采取行动的数据以更好地提供预防和成瘾治疗服务。