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.

项目总结/摘要 该申请寻求三年的论文资助，让计算博士候选人面对一个 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，同时提供指导 和培训，这将使他们准备在未来的学术生涯中使用统计，计算和混合- 方法技术，以制定和分析干预措施，以解决阿片类药物危机。超过950万人 2020年在美国报告使用阿片类药物，在此期间，每100人中约有257人因阿片类药物过量死亡。 天从2013年到2020年，全国致命的过量服用率增长了274%，这主要是由于 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。街头获得的物质， 意想不到的组合物，如含有芬太尼的可卡因或含有新芬太尼和苯二氮杂卓的海洛因 衍生物，一直导致过量的药物。为了减轻这些大规模伤害事件的规模， 49个州的3000个机构使用过量检测映射应用程序[ODMAP]，该程序的特点是 一个基于“尖峰警报”的警告系统当过量计数超过预设值时，ODMAP发出尖峰警报 在24小时内达到阈值，以帮助动员迅速的公共卫生反应，防止过量用药和拯救生命。 康涅狄格州是全国吸毒过量率最高的州之一，每10万人中有39.1人吸毒过量。 在2020年经历致命过量的人。为了应对这场危机，它实施了一项最重要的 在全国逐步建立以证据为基础的过量尖峰响应系统，每个ODMAP尖峰警报 正在接受公共卫生部的广泛人工审查，偶尔会以 公共卫生警报。该系统的有效性取决于其准确识别尖峰的能力， 动员公共卫生响应以拯救生命，但目前尚不清楚1）该系统是否对过量服用率有任何影响 2)第一反应者、减少危害组织和卫生系统如何利用该系统迅速 3）如果系统可以修改，以更准确地识别尖峰和激励 快速反应，拯救生命。因此，我建议：1）估计康涅狄格州当前飙升的因果影响 随后过量相关结果的警报系统; 2）评估当前系统的利用情况， 过量预防和对现状的替代方案的意见;和3）开发和模拟的影响， 过量相关结局的替代峰值警报策略。为了实现这些目标，我将结合 最前沿的因果推理、混合方法、时空回归和流行病学建模 技术，沿着综合数据源和关键利益攸关方的指导。这些发现将提供 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 并为其他希望实施尖峰警报的卫生部门提供了一个框架 能够响应利益攸关方需求，并能比现状更有效地拯救生命的系统。