Appalachian Node

项目摘要/摘要 由SARS-CoV-2引发的新冠肺炎大流行导致了社会疏远措施，总共 其后果，特别是与药物使用和药物过量死亡有关的后果尚不清楚。 新闻报道和早期研究表明，阿片类药物过量死亡人数有所增加。此外，大流行 也改变了医生的行医方式，面对面的就诊减少了，与之相称的 远程医疗的增长速度较慢。鉴于阿片类药物过量死亡在美国呈上升趋势， 在大流行之前，治疗阿片使用障碍的药物(MOUD)已经受到限制，确保获得 成瘾治疗现在比以往任何时候都更加紧迫。IQVIA纵向处方(IQVIA LRx)数据库 包含全国范围内所有处方的92%，包括丁丙诺啡配方。我们寻求 利用这个数据库了解新冠肺炎相关社交疏远措施的影响，穆德 整个美国围绕远程医疗的监管变化，以及获得丁丙诺啡的失业问题。 我们的第一个目标是了解丁丙诺啡的使用是如何受到几个政策变化的影响的 发生在特定时间点，包括社会疏远措施的实施和联邦政府的变化 Moud开出了与远程医疗相关的指南。我们将通过引导中断时间来实现这一点 用分段回归分析了解丁丙诺啡之间的时变关系 在特定时间点的访问和事件。我们的第二个目标是解决保险覆盖面的影响 关于获得治疗的问题。从2020年3月到5月，美国至少有4000万人申请失业。 考虑到大多数有健康保险的人都是通过他们的雇主获得的，我们预计会有很大的 减少获得以雇主为基础的医疗保险的机会。因此，我们假设患者 商业健康保险将对他们访问Moud造成更多干扰，特别是 丁丙诺啡，与公共保险(医疗补助/医疗保险)患者相比。这些发现将是 第一个刻画了新冠肺炎背景下穆德处方的变化。在随后的研究中， 我们将寻求利用其他数据集来确定Moud Access和 阿片类药物相关发病率和死亡结果，包括致命性和非致命性过量。了解这些 关系将成为Moud处方的最佳实践，以减少在 大流行。

Project Summary/Abstract The COVID-19 pandemic caused by SARS-CoV-2 has resulted in social distancing measures, the total consequences of which, particularly as they relate to substance use and drug overdose deaths, are unknown. News reports and early studies have suggested increases in opioid overdose deaths. In addition, the pandemic has also changed how medicine is practiced, with decreased face-to-face visits and a commensurate, albeit slower, rise in telemedicine. Given that opioid overdose deaths are on the rise in the United States and access to medications for opioid use disorder (MOUD) was already limited before the pandemic, ensuring access to addiction treatment is more urgent now than ever. The IQVIA Longitudinal Prescription (IQVIA LRx) database contains 92% of all prescriptions dispensed nationwide, including buprenorphine formulations. We seek to utilize this database to understand the impact of COVID-19-related social distancing measures, MOUD regulatory changes around telemedicine, and unemployment on access to buprenorphine across the entire US. Our first aim seeks to understand how access to buprenorphine was affected by several policy changes occurring at specific timepoints, including implementation of social distancing measures and changes in federal MOUD prescribing guidelines related to telemedicine. We will achieve this by conducting an interrupted time series with segmented regression analysis to understand the time-varying relationship between buprenorphine access and events at specific time points. Our second aim seeks to address the impact of insurance coverage on access to treatment. From March to May 2020, at least 40 million people filed for unemployment in the US. Given that most people with health insurance receive it through their employer, we anticipate significant reductions in access to employer-based health insurance coverage. As a result, we hypothesize that patients with commercial health insurance will have more disruptions in their access to MOUD, specifically buprenorphine, compared to patients with public insurance (Medicaid/Medicare). These findings will be among the first to characterize the changes to MOUD prescribing in the setting of COVID-19. In subsequent research, we will seek to utilize additional datasets to determine the relationship between changes in MOUD access and opioid-related morbidity and mortality outcomes, including fatal and nonfatal overdoses. Understanding these relationships will inform best practices for MOUD prescribing to reduce patient harm in the setting of a pandemic.