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

项目摘要/摘要 这项申请寻求三年的学位论文资助，让一名计算型博士候选人面对 利用三个相互关联的跨学科目标推动公共卫生问题，并辅之以指导 以及培训，为他们未来的学术生涯做好准备，使用统计、计算和混合- 方法开发和分析应对阿片类药物危机的干预措施。超过950万人 2020年在美国报告使用阿片类药物，在此期间，每年约有257例与阿片类药物有关的过量死亡 天。从2013年到2020年，致命的过量用药率在全国范围内上升了274%，这在很大程度上是由于 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。街头获得的物质与 意想不到的成分，如含有芬太尼的可卡因或含有新型芬太尼和苯二氮卓的海洛因 衍生品，一直在导致大量过量使用。为了减轻这些大规模伤害事件的规模，超过 49个州的3000家机构使用过量检测地图应用程序[ODMAP]，该计划的特点是 一个基于“尖峰警报”的警报系统。当药物过量计数超过预设时，ODMAP发出峰值警报 在24小时内达到阈值，以帮助动员快速的公共卫生反应，以防止过量用药和拯救生命。 康涅狄格州是全国服药过量比率最高的州之一，每10万人中有39.1人服药过量 2020年，人们经历了致命的服药过量。为了应对这场危机，它实施了最多的 全国逐步建立基于证据的过量用药尖峰反应系统，每次ODMAP尖峰警报 正在接受公共卫生部的广泛人工审查，偶尔会出现 公共卫生警报。该系统的有效性取决于其准确和快速识别尖峰的能力 动员公共卫生响应来拯救生命，但尚不清楚该系统是否对过量服药率有任何影响 2)急救人员、减少危害组织和卫生系统如何利用该系统迅速 对过量用药尖峰做出反应3)如果可以修改系统以更准确地识别尖峰并激励 快速反应，拯救生命。因此，我建议1)估计康涅狄格州目前房价飙升的因果影响 2)评估当前系统的利用情况，障碍在于 预防服药过量和对替代现状的看法；以及3)开发和模拟 关于过量用药相关结果的替代尖峰警报策略。为了达到这些目标，我将使用一个组合 尖端因果推断、混合方法、时空回归和流行病学建模 技术，以及集成的数据源和来自关键利益攸关方的指导。这些发现将提供 改善康涅狄格州目前的高峰警报系统的可行建议，可以激励未来的政策工作来解决 并为寻求实施高峰警报的其他卫生部门提供了一个框架 对利益相关者的需求作出反应并比现状更有效地拯救生命的系统。

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.