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

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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.