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

COVID-19 大流行对阿尔茨海默病和阿尔茨海默病相关痴呆症患者的影响

• 批准号: 10423845
• 负责人: JOHN HSU
• 金额: $ 85.8万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10423845
• 关键词: Accident and Emergency department; Accounting; Address; Admission activity; Advance Directives; Adverse effects; Affect; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease related dementia; Ambulances; Ambulatory Care; Ambulatory Care Facilities; American; Antipsychotic Agents; Area; Attention; COVID-19; COVID-19 pandemic; Caregivers; Caring; Censuses; Cessation of life; Clinical; Code; Cognitive; Communities; County; Data; Data Analytics; Data Collection; Data Set; Death Certificates; Delirium; Diagnosis; Disadvantaged; Disease; Disease Outbreaks; Drug usage; Elderly; Electronic Health Record; Emergency Situation; Employment; Equilibrium; Ethnic Origin; Event; Future; Goals; Health; Health system; Healthcare Systems; Hospitalization; Hospitals; Incidence; Individual; Infection; Inpatients; Intervention; Link; Low income; Machine Learning; Measures; Medical; Medicare; Medicare claim; Methodology; Methods; Modeling; Natural experiment; Nature; Nursing Homes; Outcome; Outpatients; Patient-Focused Outcomes; Patients; Pattern; Patterns of Care; Policies; Population; Race; Reference Standards; Regulation; Reporting; Research; Resources; Retirement; Risk Factors; SARS-CoV-2 infection; Shock; Social Distance; Social isolation; Social support; Stress; Surveys; Symptoms; System; Time; Travel; Uncertainty; United States National Institutes of Health; Vaccination; Validation; Variant; Viral; Visit; Vulnerable Populations; Waxes; Work; adverse outcome; aged; base; behavioral economics; beneficiary; care delivery; cognitive function; complex data; coronavirus disease; data resource; disease transmission; ethnic minority population; flexibility; frailty; hospital readmission; hospitalization rates; improved; inpatient service; machine learning method; mortality; mortality risk; older patient; pandemic disease; patient home care; payment; predictive modeling; programs; public health emergency; racial and ethnic; remote delivery; remote health care; response; social; social factors; socioeconomic disadvantage; telehealth; time use

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.

摘要 COVID-19大流行给卫生系统、护理人员和患者带来了一年多的压力。数百名 数千人死亡，另有数百万人受到感染威胁和减轻 疾病传播。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治疗方面有更大的灵活性，包括放宽有关 远程提供门诊治疗和提高医院能力。不幸的是， 关于这些临时政策所带来的医疗服务变化如何影响 在病毒传播和缓解政策的盛衰时期，患者的结局。在本项目中， 我们将研究门诊和住院治疗的变化对临床事件率和死亡的影响 在患有阿尔茨海默病和阿尔茨海默病相关痴呆（AD/ADRD）的老年患者中， 在社区这些病人特别容易受到社会孤立或护理中断， 可能无法表达他们的需求。那些还有其他缺点，如虚弱或较低 收入似乎更容易受到COVID-19感染的不利影响。一些护理 然而，递送变化可能增加了临床医生与患者交互的数量或质量， 例如，在一个实施例中，由于旅行需求减少，远程保健访问更加频繁。因此，为了检查 根据紧急医疗保险政策，我们将致力于三个目标：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）研究 门诊治疗对临床事件发生率的影响，例如，急诊和住院率; 3)研究住院治疗变化对死亡率的影响。值得注意的是，访问和转诊的变化 大流行期间的模式可能会影响信息采集;因此，基于诊断的AD/ADRD定义 使用大流行时期时，大流行前时期出现的状态、虚弱或谵妄可能不太准确 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 预测模型使用机器学习方法和链接，重叠的数据集;我们还将考虑 在所有目标中，县级记录的COVID-19感染和就业的时间变化。 考虑到这些复杂的数据和分析问题，我们将与NIH的社会、行为和经济研究中心合作， 研究COVID-19联盟，以改善此类工作的数据资源和方法。的 从这一大型自然实验中获得的信息对于为未来的疫情或其他冲击做好准备至关重要。 卫生系统，以确定哪些紧急政策，如果有的话，应该延长，并告知 关于医疗保险中地方灵活性和国家标准之间平衡的辩论。