Impact of the COVID-19 Pandemic on Patients with Alzheimer's Disease and Alzheimer's Disease Related Dementias

摘要 一年多来，新冠肺炎大流行给卫生系统、照顾者和患者带来了压力。数以百计 数千人死亡，数百万人受到感染威胁和缓解努力的影响 疾病蔓延。为了应对这些挑战，医疗保险计划推出了紧急政策， 允许在提供门诊和住院护理方面有更大的灵活性，包括放宽 远程提供门诊护理和增加医院容量。不幸的是，有有限的 关于这些临时政策使提供护理服务发生变化的信息如何影响 在病毒传播和缓解政策时好时坏的时期，患者的结局。在这个项目中， 我们将研究门诊和住院护理的变化对临床事件发生率和死亡率的影响。 阿尔茨海默病和阿尔茨海默病相关痴呆(AD/ADRD)老年患者中 在社区里。这些患者特别容易受到社会孤立或护理中断的影响，以及 可能无法清楚地表达他们的需求。还有其他缺点的，如虚弱或更低 收入似乎更容易受到新冠肺炎感染不良影响的影响。一些关心 然而，分娩方式的改变可能会增加临床医生与患者互动的数量或质量， 例如，由于旅行需要减少，远程医疗访问更加频繁。因此，要检查 根据紧急医疗保险政策，我们将解决三个目标：1)评估和 根据大流行期间收集的数据，完善研究变量定义；2)检查 门诊服务对临床事件发生率的变化，例如急诊科和住院率；以及 3)研究住院服务的变化对死亡率的影响。值得注意的是，探视和转介方面的变化 大流行期间的模式可能会影响信息收集；因此，基于诊断的AD/ADRD定义 在使用大流行时代时，在大流行前时代形成的状态、虚弱或精神错乱可能不那么准确 数据。我们将检查这些护理模式，评估基于索赔的措施的准确性，并制定 使用机器学习方法的预测模型和链接、重叠的数据集；我们还将说明 记录在案的新冠肺炎感染和所有目标县一级的就业情况的时间差异。 鉴于这些复杂的数据和分析问题，我们将与NIH的社会、行为和经济 研究新冠肺炎联合体，完善这类工作的数据来源和方法。这个 来自这个大型自然实验的信息对于准备应对未来的疫情或其他对人类健康的冲击至关重要 卫生系统，以确定哪些紧急政策，如果有，应延长，并通知 关于医疗保险中地方灵活性和国家标准之间的平衡的辩论。

Abstract The COVID-19 pandemic has stressed health systems, caregivers, and patients for over a year. Hundreds of thousands have died, and millions more have been impacted by the threat of infection and efforts to mitigate disease spread. In response to these challenges, the Medicare program introduced emergency policies that permitted greater flexibility in the provision of outpatient and inpatient care, including relaxing rules governing the remote delivery of outpatient care and increasing hospital capacity. Unfortunately, there is limited information on how the changes in care delivery made possible by these temporary policies have impacted patient outcomes during times when viral spread and mitigation policies both waxed and waned. In this project, we will examine the impact of changes in outpatient and inpatient care on clinical event rates and deaths among older patients with Alzheimer’s Disease and Alzheimer’s Disease Related Dementias (AD/ADRD) living in the community. These patients are particularly vulnerable to social isolation or disruptions in their care, and might not be able to articulate their needs. Those with yet additional disadvantages such as frailty or lower incomes appear to have been even more susceptible to adverse effects of COVID-19 infections. Some care delivery changes, however, might have increased the number or quality of clinician interactions with patients, e.g., more frequent tele-health visits because of less need to travel. Thus, to examine the impact of the changes in care delivery under the emergency Medicare policies, we will address three aims: 1) To assess and refine study variable definitions given data collected during the pandemic; 2) To examine the impact of changes in outpatient care on clinical event rates, e.g., emergency department and hospitalization rates; and 3) To examine the impact of changes in inpatient care on mortality. Notably, changes in visit and referral patterns during the pandemic could impact information capture; thus, diagnosis-based definitions of AD/ADRD status, frailty, or delirium developed in the pre-pandemic era could be less accurate when using pandemic era data. We will examine these care patterns, assess the accuracy of claims-based measures, and develop prediction models using machine learning methods and linked, overlapping datasets; we also will account for temporal variation in documented COVID-19 infections and employment at the county level across all aims. Given these complex data and analytic issues, we will work with NIH’s Social, Behavioral, and Economic Research on COVID-19 Consortium to improve the data resources and methods for this type of work. The information from this large natural experiment is critical for preparing for future outbreaks or other shocks to the health system, to determine which of the emergency policies, if any, should be extended, and to inform debates concerning the balance between local flexibility and national standards within Medicare.