Researching Effective Strategies to Prevent Opioid Death (RESPOND)

项目摘要 在2021年4月至2022年3月期间，美国经历了超过10万例药物过量死亡。作为阿片类药物 流行病扩大，过量的流行病学正在发生变化。在全国范围内，黑人和 拉丁裔人口的增长速度是白色人的五倍多。同样，在1999- 2015年，女性的处方阿片类药物过量率增加了男性的两倍多。 虽然阿片类药物供应的差异可以解释一些种族和性别差异，但我们的方式， 实施阿片类药物治疗也可能驱使他们。因此，我们必须研究如何 为解决类阿片危机而实施的政策和干预措施影响到卫生公平。 研究预防阿片类药物死亡的有效策略（RESPOND）是仿真模型OUD和OUD 在一个国家进行治疗。RESPOND利用对扩大的公共卫生数据基础设施的投资， 为模型参数提供了信息，并促进了对来自NIDA资助的实施研究的数据的新用途。我们 使用RESPOND调查政策选择和健康的健康效益、成本和成本效益 护理模式，以防止阿片类药物过量。在上一个资助期，我们开发了RESPOND， 已发表的研究报告预测了扩大卫生保健服务供应对公共卫生和卫生经济的影响， 阿片类药物使用障碍（MOUD）在各种设置。我们目前正在扩大响应， 模拟肯塔基州、俄亥俄州和纽约结果。 我们现在建议加强RESPOND，以调查特别感兴趣的人群的结果， 阿片类药物反应：1）有色人种社区和2）女性。然后，我们将使用该模型来研究健康 规范OUD和创新药物处方的政策对经济和健康公平性的影响 为OUD患者提供护理模式。我们将进行分销成本效益分析， 这是一种新兴的创新方法，用于调查最大限度地提高人口效益和 确保公平分配成本和收益。我们的具体目标是： 目标1：加强RESPOND模型，并根据种族和性别分层目标对其进行校准， 关注阿片类药物危机中的利益人群：有色人种和妇女 目标2：使用RESPOND对不断变化的政策进行分配成本效益分析 规范MOUD处方 目的3：使用RESPOND调查临床、种族公平影响和经济价值， 在OUD护理级联的每个步骤中支持吸毒者的干预措施。 我们使用RESPOND来生成证据，以指导政策和干预措施的大规模实施， 防止过量服用。未来5年，我们将继续成为OUD仿真建模领域的领导者 并将使用RESPOND作为阿片类药物危机中正义的重要工具。

PROJECT SUMMARY Between April 2021 and March 2022, the U.S. endured over 100,000 drug overdose deaths. As the opioid epidemic expands, the epidemiology of overdose is changing. Nationally, the rate of overdose in Black and Latinx people is growing more than five times faster than it is among white people. Similarly, between 1999- 2015, the prescription opioid overdose rate among women increased at more than twice the rate for men. While differences in opioid supply could explain some racial and gender disparities, the ways that we implement opioid treatment could also drive them. It is therefore essential that we investigate how implementation of policy and interventions to address the opioid crisis impacts health equity. The Researching Effective Strategies to Prevent Opioid Death (RESPOND) is simulation model OUD and OUD treatment delivery in a state. RESPOND leverages investments in expanded public health data infrastructure to inform model parameters, and it catalyzes new uses of data from NIDA-funded implementation studies. We use RESPOND to investigate the health benefits, costs, and cost-effectiveness of policy choices and health care delivery models to prevent opioid overdose. In the previous funding period, we developed RESPOND and published studies projecting public health and health economic impacts of expanding the availability of medications for opioid use disorder (MOUD) in a variety of settings. We are currently expanding RESPOND to simulate outcomes in Kentucky, Ohio, and New York. We now propose to enhance RESPOND to investigate outcomes among populations of special interest to opioid response: 1) communities of color and 2) women. We will then use the model to investigate health economic and health equity impacts of policies regulating prescribing for medications for OUD and innovative care delivery models for people with OUD. We will perform distributional cost-effectiveness analyses, which is an emerging and innovative method to investigate trade-offs between maximizing population-level benefits and ensuring equitable distribution of costs and benefits. Our specific aims are: Aim 1: To enhance the RESPOND model and calibrate it to race and gender stratified targets in order to focus on populations of interest in the opioid crisis: people of color and women Aim 2: To use RESPOND to perform distributional cost-effectiveness analyses of changing policies regulating MOUD prescribing Aim 3: To employ RESPOND to investigate the clinical, racial equity impact, and economic value of interventions that support people who use drugs along each step of the OUD care cascade. We use RESPOND to generate evidence to guide large-scale implementation of policies and interventions to prevent overdose. In the coming 5-years we will continue to be leaders in the field of OUD simulation modeling and will employ RESPOND as an important tool for justice in the opioid crisis.