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）和相关危害的国家震中。阿片类药物相关的紧急服务 呼叫（ORESC）是服务参与和干预的关键机会。在全国和芝加哥， 非致命的OD之后的高死亡率强调，通常会错过这种机会。芝加哥 将试用两个创新的回应来应对这些挑战。替代立即响应（空气）将 提供自信的宣传和参与，包括与药物治疗障碍联系人 治疗和其他相关服务。替代立即响应加上随访（AIR-F）包括空气， 由社区护理人员和一个组成的空气响应团队和 同伴支持专家/恢复教练将被部署，以提供自信的外展服务，包括 制定治疗，安全和后续计划，提供简短的干预措施，并提供运输 其他服务。 AIR-F参与度将包括与促进治疗的后续服务联系 保留，包括案件管理，护理协调以及与社区护理的联系 治疗。我们建议使用机器学习（ML）开发一种工具来识别具有最高风险的人 OD和与OD相关的死亡率是为了确定服务交付和随访服务的优先级。特别是，我们会 开发一个随机森林（RF）分类器，该分类器将结合芝加哥公共卫生部的数据， 芝加哥警察和消防部门紧急管理与通讯办公室，芝加哥 公共安全管理办公室和库克县医学检查员办公室，以创建一个综合的 数据集以追踪从起源到最终处置的紧急呼叫。我们还将从中提取数据 CFD救护车数据中包含的非结构化文本。通过合并多个大型管理数据集， 工具将利用“信号”的潜水员来源，最大化预测准确性。然后我们将使用我们的 集成数据以预测随后的ORESC，OD，逮捕和其他广告的最高风险的个人 结果。 Air/Air-F员工将使用这些指标以及其他临床数据来分配稀缺后续行动 资源。我们将使用差异性估计的差异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尔菲尔德公园） 在连续的社区中观察到（奥斯汀，北拉恩代尔，南拉恩代尔，下西区和西部 镇）以衡量航空/空气f的人口影响。最后，我们将与各种定性采访 利益相关者提供对飞行员的更多见解，并探索芝加哥如何更好地为处于危险中服务 个人。为了帮助降低ORESC和相关死亡率的流行，所有计算机代码都开发了 对于这笔赠款，将提供开源。我们将在整个过程中广泛传播项目的发现 政策和科学社区，包括在领先的科学期刊，决策者和媒体上出版 外展。