Alternate Emergency Over Dose Response in Chicago

芝加哥的替代紧急剂量反应

• 批准号: 10425012
• 负责人: HAROLD Alexander POLLACK
• 金额: $ 20.5万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10425012
• 关键词: 911 call; Address; Algorithms; Ambulances; Area; Assertiveness; Authorization documentation; Buprenorphine; Case Management; Catchment Area; Characteristics; Chicago; Cities; Clinical Data; Collaborations; Communication; Communities; Consolidated Framework for Implementation Research; County; Data; Data Set; Dose; Effectiveness; Emergency Situation; Emergency medical service; Emergency response; Fire - disasters; Funding; Future; Grant; Health; Health Professional; Health Service Area; Individual; Injecting drug user; Intervention; Interview; Journals; Machine Learning; Medical Examiners; Medical emergency; Modeling; Neighborhoods; Opioid; Outcome; Overdose; Paramedical Personnel; Participant; Performance; Persons; Pharmaceutical Preparations; Police; Policies; Population; Predictive Analytics; Prevalence; Probability; Program Effectiveness; Public Health; Publications; Randomized; Recovery; Research; Research Design; Resources; Risk; Risk Factors; Safety; Services; Side; Signal Transduction; Source; Specialist; System; Technology Transfer; Text; University resources; adverse outcome; austin; base; behavioral health; brief intervention; care coordination; community based care; computer code; cooking; design; experience; follow-up; high risk; hybrid type 1 study; implementation barriers; implementation facilitators; improved; innovation; insight; longitudinal analysis; member; mortality; open source; operation; opioid overdose; opioid use disorder; outreach; outreach services; overdose risk; patient engagement; peer; peer support; post intervention; prescription opioid; process evaluation; programs; random forest; response; service delivery; service engagement; service intervention; sociodemographics; tool; treatment arm; unstructured data

PROJECT ABSTRACT Chicago is a national epicenter of opioid overdose (OD) and related harms. Opioid-related Emergency Service Calls (ORESCs) are critical opportunities for service engagement and intervention. Nationally and in Chicago, high mortality rates subsequent to non-fatal OD underscore that such opportunities are often missed. Chicago will pilot two innovative responses to address these challenges. Alternate Immediate Response (AIR) will provide assertive outreach and engagement, including connecting people with medication opioid use disorder treatment and other related services. Alternate Immediate Response plus Follow-up (AIR-F) includes AIR, along with 8 weeks of follow-up services. AIR response teams consisting of a community paramedic and a peer support specialist/recovery coach will be deployed to provide assertive outreach services, including developing treatment, safety, and follow-up plans, delivering brief interventions, and providing transport to pertinent other services. AIR-F engagement will include linkages to follow-up services that promote treatment retention, including case management, care coordination, and connections to community-based care and treatment. We propose to use machine learning (ML) to develop a tool to identify individuals at highest risk of OD and OD-related mortality in order to prioritize service delivery and follow-up services. In particular, we will develop a random forest (RF) classifier that will combine data from the Chicago Department of Public Health, the Office of Emergency Management and Communications, Chicago Police and Fire departments, Chicago Office of Public Safety Administration, and Cook County Medical Examiner’s Office to create an integrated dataset to trace emergency calls from origination to final disposition. We will also extract data from unstructured text included in CFD ambulance data. By incorporating multiple large administrative datasets, the tool will capitalize on diverse sources of “signal,” maximizing prediction accuracy. We will then use our integrated data to predict individuals at highest risk of subsequent ORESCs, OD, arrest, and other adverse outcomes. AIR/AIR-F staff will use these indicators along with other clinical data to allocate scarce follow-up resources. We will use difference-in-differences estimation to compare post-intervention outcomes within the service area on Chicago’s west side (Humboldt Park, West Garfield Park, and East Garfield Park) to that observed in contiguous communities (Austin, North Lawndale, South Lawndale, Lower West Side, and West Town) to gauge the population impact of AIR/AIR-F. Finally, we will conduct qualitative interviews with various stakeholders to provide additional insight into the pilot and explore how Chicago can better serve at-risk individuals. To help reduce the prevalence of ORESCs’ and associated mortality, all computer code developed for this grant will be made available open-source. We will disseminate project findings widely throughout the policy and scientific communities, including publication in leading scientific journals and policymaker and media outreach.

项目摘要 芝加哥是阿片类药物过量(OD)及相关危害的国家中心。与阿片类药物相关的急救服务 呼叫(ORESC)是服务参与和干预的关键机会。在全国和芝加哥， 非致命性服药过量后的高死亡率突出表明，这种机会往往被错过。芝加哥 将试行两项创新应对措施，以应对这些挑战。备用即时响应(AIR)将 提供自信的外展和参与，包括将阿片类药物使用障碍患者联系起来 治疗及其他相关服务。备用即时反应加后续(AIR-F)包括空气， 以及为期8周的后续服务。空中反应小组由一名社区护理人员和一名 将部署同伴支持专家/康复教练，以提供果断的外展服务，包括 制定治疗、安全和后续计划，提供简短的干预措施，并为 相关的其他服务。空军的参与将包括与促进治疗的后续服务的联系 保留，包括病例管理、护理协调以及与基于社区的护理和 治疗。我们建议使用机器学习(ML)来开发一种工具来识别处于最高风险的个人 为确定服务提供和后续服务的优先顺序，将药物过量和与药物有关的死亡率列为优先事项。特别是，我们将 开发一种随机森林(RF)分类器，它将结合芝加哥公共卫生部的数据， 芝加哥警察和消防局应急管理和通信办公室，芝加哥 公共安全管理办公室和库克县法医办公室共同打造综合 跟踪紧急呼叫从始发到最终处置的数据集。我们还将从以下位置提取数据 CFD救护车数据中包含的非结构化文本。通过整合多个大型管理数据集， 该工具将利用不同的“信号”来源，最大限度地提高预测精度。然后，我们将使用我们的 集成数据以预测后续ORESCs、吸毒、逮捕和其他不良反应风险最高的个人 结果。AIR/AIR-F工作人员将使用这些指标以及其他临床数据来分配稀缺的随访 资源。我们将使用差异差估计来比较干预后的结果 芝加哥西侧的服务区(洪堡公园、西加菲尔德公园和东加菲尔德公园) 在邻近的社区(奥斯汀、北劳恩代尔、南劳恩代尔、下西区和西部)观察 城镇)，以衡量空气/空气-F对人口的影响。最后，我们将对不同的人进行定性的采访 利益相关者提供对试点的更多见解，并探索芝加哥如何更好地为有风险的人提供服务 个人。为了帮助降低ORESCs的患病率和相关死亡率，所有的计算机代码都开发出来了 对于这笔赠款，将以开源方式提供。我们将把项目研究结果广泛传播到 政策和科学界，包括在主要科学期刊和政策制定者和媒体上发表文章 外展。