Alternate Emergency Over Dose Response in Chicago

项目摘要 芝加哥是阿片类药物过量（OD）和相关危害的国家中心。与阿片类药物有关的紧急服务 呼叫（ORESC）是服务参与和干预的关键机会。无论是全国还是芝加哥， 非致命性OD后的高死亡率强调了这些机会经常被错过。芝加哥 将试行两项创新对策来应对这些挑战。备用即时响应（AIR） 提供自信的外展和参与，包括将患有阿片类药物使用障碍的人联系起来 治疗及其他相关服务。替代立即反应加随访（AIR-F）包括AIR， 沿着8周的随访服务。空中反应小组由一名社区护理人员和一名 将部署同侪支助专家/康复教练，提供积极的外展服务，包括 制定治疗、安全和随访计划，提供简短的干预措施，并提供交通工具， 其他相关服务。AIR-F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、护理协调和与社区护理的联系， 治疗我们建议使用机器学习（ML）来开发一种工具，以识别最高风险的个体。 OD和OD相关死亡率，以便优先提供服务和后续服务。特别是要 开发随机森林（RF）分类器，其将联合收割机组合来自芝加哥公共卫生部的数据， 应急管理和通信办公室，芝加哥警察局和消防局，芝加哥 公共安全管理办公室和库克县医学检查办公室，以创建一个综合的 数据集，以跟踪紧急呼叫从起源到最终处置。我们还将从 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， 该工具将利用不同的“信号”来源，最大限度地提高预测精度。我们将使用我们的 综合数据，以预测个体在随后的ORESC，OD，逮捕和其他不利的风险最高， 成果。AIR/AIR-F工作人员将使用这些指标沿着其他临床数据来分配稀缺的随访 资源我们将使用差异中的差异估计来比较干预后的结果， 芝加哥西侧的服务区（洪堡公园，西加菲猫公园和东加菲猫公园） 在邻近的社区（奥斯汀、北劳恩代尔、南劳恩代尔、下西区和西 镇），以衡量空气/空气-F的人口影响。最后，我们将进行定性访谈， 利益相关者提供额外的洞察试点，并探讨如何芝加哥可以更好地服务于风险 个体为了帮助降低ORESC的患病率和相关死亡率，所有开发的计算机代码都是 将以开源的方式提供。我们会在全港各区广泛宣传计划的研究结果， 政策和科学界，包括在主要科学期刊上发表文章，以及决策者和媒体 外展

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.