Innovations in Modeling Existing and Emerging Policies to Improve Warning Systems for Opioid Overdoses

现有和新兴政策建模创新，以改进阿片类药物过量预警系统

• 批准号: 10752283
• 负责人: Archana Ram
• 金额: $ 4.77万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-26 至 2026-09-25
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752283
• 关键词: Accounting; Address; Affect; Benzodiazepines; Cessation of life; Cocaine; Communities; Connecticut; Country; Data; Data Sources; Detection; Doctor of Philosophy; Effectiveness; Evaluation; Event; Face; Feedback; Fentanyl; Funding; Future; Goals; Harm Reduction; Health; Health Personnel; Health system; Healthcare Systems; Heroin; Hour; Individual; Injury; Intervention; Manuals; Maps; Mentorship; Methods; Modeling; Modification; Outcome; Overdose; Overdose reduction; Persons; Policies; Provider; Public Health; Reporting; Rest; Series; Source; Street Drugs; Surveys; System; Techniques; Testing; Time; Training; Work; career; data integration; epidemiological model; evidence base; experience; first responder; improved; innovation; novel; opioid epidemic; opioid overdose; opioid use; overdose death; overdose prevention; prevent; programs; response; statistics; synthetic opioid; theories; trend

PROJECT SUMMARY/ABSTRACT This application seeks three years of dissertation funding to have a computational PhD candidate confront a pressing public health issue using three interrelated, interdisciplinary aims supported by concomitant mentorship and training that will prepare them for a future academic career in using statistical, computational and mixed- methods techniques to develop and analyze interventions to address the opioid crisis. Over 9.5 million people reported using opioids in the US in 2020, during which time there were ~257 opioid-related overdose deaths per day. Fatal overdose rates have grown nationally by 274% from 2013 to 2020, largely due to the growing presence of synthetic opioids and other additives, primarily found in street drug supplies. Street-obtained substances with unexpected composition, such as cocaine containing fentanyl or heroin with novel fentanyl and benzodiazepine derivatives, have been causing overdoses en masse. To mitigate the scale of these mass injury events, over 3000 agencies in 49 states use the Overdose Detection Mapping Application Program [ODMAP], which features a “spike alert”-based warning system. ODMAP issues a spike alert when overdose counts exceed preset thresholds within 24 hours to help mobilize rapid public health responses to prevent overdoses and save lives. The state of Connecticut has one of the highest overdose rates in the country, with 39.1 out of every 100,000 people experiencing a fatal overdose in 2020. To address this crisis, it has implemented one of the most progressive evidence-based overdose spike response systems in the nation, with each ODMAP spike alert undergoing an extensive manual review by the Department of Public Health that occasionally culminates in a public health alert. The effectiveness of this system rests on its ability to accurately identify spikes and rapidly mobilize a public health response to save lives, but it is unclear 1) if the system has any effect on overdose rates 2) how first responders, harm reduction organizations and health systems make use of the system to rapidly respond to overdose spikes 3) if the system can be modified to more accurately identify spikes and motivate rapid responses to save lives. I therefore propose to 1) estimate the causal effect of Connecticut’s current spike alert system on subsequent overdose-related outcomes; 2) assess utilization of the current system, barriers to overdose prevention and opinions on alternatives to the status quo; and 3) develop and simulate the impact of alternative spike alert strategies on overdose-related outcomes. To address these Aims, I will use a combination of cutting-edge causal inference, mixed-methods, space-time regression and epidemiological modeling techniques, along with integrated data sources and guidance from key stakeholders. These findings will provide actionable advice to improve Connecticut’s current spike alert system, can motivate future policy work to address the overdose crisis and provide a framework for other health departments looking to implement spike alert systems that are responsive to stakeholder needs and can more effectively save lives than the status quo.

项目概要/摘要 该申请寻求三年的论文资助，以使计算博士候选人面临 利用三个相互关联的跨学科目标解决紧迫的公共卫生问题，并得到相应的指导 以及培训，帮助他们为未来的学术生涯做好使用统计、计算和混合的培训 方法开发和分析干预措施以解决超过 950 万人的阿片类药物危机。 据报道，2020 年美国使用阿片类药物，在此期间，每人约有 257 人因阿片类药物过量死亡。 从 2013 年到 2020 年，全国服药过量致死率增加了 274%，这主要是由于服药过量的现象不断增加。 合成阿片类药物和其他添加剂，主要存在于街头毒品供应中。 意想不到的成分，例如含有芬太尼的可卡因或含有新型芬太尼和苯二氮卓类的海洛因 为了减轻这些大规模伤害事件的规模，已经导致了大规模的药物过量。 49 个州的 3000 个机构使用过量检测绘图应用程序 [ODMAP]，其特点 当过量计数超过预设值时，基于“峰值警报”的警告系统会发出峰值警报。 24 小时内确定阈值，以帮助动员快速的公共卫生反应，防止用药过量并拯救生命。 康涅狄格州是全国服药过量率最高的州之一，每 10 万人中有 39.1 人服药过量 2020 年，人们经历了服药过量而致命的情况。为了解决这场危机，它实施了一项最 全国逐步建立基于证据的药物过量峰值响应系统，每个 ODMAP 峰值警报 接受公共卫生部的广泛人工审查，有时最终会产生 该系统的有效性取决于其准确识别峰值和快速的能力。 动员公共卫生应对措施来拯救生命，但尚不清楚 1) 该系统是否对用药过量率有任何影响 2) 急救人员、减害组织和卫生系统如何利用该系统快速 响应过量剂量峰值 3) 是否可以修改系统以更准确地识别峰值并激励 因此，我建议 1）估计康涅狄格州当前峰值的因果影响。 关于随后的过量用药相关结果的警报系统；2) 评估当前系统的利用情况、障碍 过量预防和对现状替代方案的意见；3) 制定并模拟其影响； 针对过量相关结果的替代峰值警报策略 为了实现这些目标，我将使用组合。 尖端因果推理、混合方法、时空回归和流行病学建模 这些研究结果将提供综合数据源和主要利益相关者的指导。 改善康涅狄格州当前峰值警报系统的可行建议，可以激励未来的政策工作来解决 药物过量危机，并为其他希望实施峰值警报的卫生部门提供框架 能够响应利益相关者需求并且能够比现状更有效地拯救生命的系统。