Piloting a naloxone intervention in an emergency response community to reduce opioid overdoses in Philadelphia

项目总结/摘要 不断升级的阿片类药物过量流行病是美国面临的最严重的公共卫生问题之一。 美国2014年，美国有创纪录的47,055人死于药物过量，超过了死亡人数 车祸死亡率大约是1.5倍。海洛因与滥用处方 药物过量导致的死亡是一个重要的， 费城的死亡率和发病率不断上升。2015年，近700例药物过量死亡 阿片 是2014年大部分药物过量的原因之一。 是费城凶杀案死亡人数的两倍作为回应，卫生政策、立法、 研究资金越来越多地集中在支持阿片类药物纳洛酮的分配上 拮抗剂，和社区为基础的阿片类药物过量预防计划（阿片类药物）。成功的成功的 取决于旁观者对过量事件做出反应的意愿和有效性， 注射纳洛酮应急响应社区（ERC）是一个专门的基于智能手机的社交网络。 成员是特定药物的批准携带者和/或潜在使用者的网络，例如 纳洛酮ERC方法非常适合支持、促进和鼓励纳洛酮给药 在阿片类药物过量的紧急情况下，但迄今为止尚未测试。该模型结合了GPS和IP定位跟踪 通过智能手机应用程序或应用程序识别潜在的阿片类药物过量。为此，我们 建议在开发和试点测试中使用ERC 基于智能手机应用程序的奈索龙 在费城的ERC-NAX。 需要进行研究，以了解 阿片类药物过量的高风险社区，这将有助于有效的设计，适应和实施 一个ERC-NAX应用程序 追求三个具体 根据研究人员的初步数据，拟议的试点项目将 目标：1）识别接受和使用智能手机的障碍和促进因素- 基于ERC纳洛酮干预; 2）在由55名 非医疗类阿片使用者和55名社区成员; 3）综合在目标1和2中获得的知识 用于随后的R 01研究，以进行大规模、多中心实施试验。这3年 研究将提供关于基于应用程序的纳洛酮的可行性和可接受性的可操作证据 按照ERC模式进行干预。它将与两个社区合作开发和测试 在预防过量用药方面有丰富经验的费城合作伙伴。这项拟议的干预措施是 意义重大，因为它利用了可持续发展的日益成功，并将赋予社区权力 高风险且 在发生药物过量时提供快速、安全和有效的应急响应。这 新的干预措施，旨在加强紧急医疗服务， 阿片类药物过量 纳洛酮过量的受害者，可能是有效的，在降低阿片类药物过量死亡的流行， 费城和其他地方。

PROJECT SUMMARY/ABSTRACT The escalating opioid overdose epidemic is one of the most serious public health problems confronting the U.S. In 2014, a record 47,055 people died from a drug overdose in the U.S., which outnumbered deaths from motor vehicle crashes by approximately one and a half times. Heroin and misuse of prescription Death due to drug overdose is a significant and rising cause of mortality and morbidity in Philadelphia. In 2015, almost 700 drug overdose deaths were opioids contributed to a majority of drug overdoses in 2014. reported in Philadelphia – twice as many as deaths from homicide. In response, health policy, legislation, and research funding are increasingly converging in support of the distribution of naloxone, an opioid antagonist, and community-based opioid overdose prevention programs (OPP). The success of OPP is contingent upon the willingness and effectiveness of bystanders to respond to an overdose event and administer naloxone. Emergency Response Communities (ERC) are specialized smartphone-based social networks in which members are approved carriers and/or potential users of a specific medication, such as naloxone. The ERC approach is ideally suited to support, facilitate, and encourage naloxone administration in opioid overdose emergencies, but is untested to date. The model combines GPS and IP-location tracking to identify potential opioid overdose through a smartphone application or app. Towards this end, we are proposing to utilize an ERC in the development and pilot testing of a smartphone app-based naxolone intervention (ERC-NAX) in Philadelphia. Research is needed to understand the needs and barriers in communities at high-risk for opioid overdose that will enable effective design, adaption, and implementation of an ERC-NAX app. pursue three specific Guided by preliminary data from study investigators, the proposed pilot project will aims: 1) Identify barriers and facilitators of acceptance and use of a smartphone- based ERC naloxone intervention; 2) Pilot test the implementation of ERC-NAX in an ERC consisting of 55 non-medical opioid users and 55 community members; 3) Synthesize knowledge acquired in Aims 1 and 2 to be used in a subsequent R01 study to conduct a large-scale, multi-site implementation trial. This 3-year study will provide actionable evidence regarding the viability and acceptance of an app-based naloxone intervention following the ERC model. It will be developed and tested in partnership with two community partners in Philadelphia with extensive experience in overdose prevention. This proposed intervention is significant since it leverages the growing success of OPP and will empower communities at high-risk for to provide rapid, secure, and effective emergency response in the event of overdose. This novel intervention, which is designed to augment emergency medical services in the rapid delivery of opioid overdose naloxone to overdose victims, may be effective at deescalating the epidemic of opioid overdose fatalities in Philadelphia and communities elsewhere.