Aging in Community Health Clinics (ACHES) Changes in Multimorbidity and Disability Among Race/Ethnic Older Adults COVID-19 Related Administrative Supplement

项目摘要 这项工作将促进我们对特定的预先存在的共生慢性疾病的贡献作用的了解 疾病(多发病)模式和社会人口学因素与感染新冠肺炎和 在中年和老年弱势成年人中经历与健康相关的不良后果。我们会 解决知识上的主要差距：(1)是否有特定的多病模式会增加新冠肺炎的风险 感染，以及它们是否因年龄和种族/民族而异？(2)特定的 新冠肺炎患者的多发病模式和疾病积累情况是否因年龄不同而不同 或种族/民族？(3)多发病模式与经历更严重的疾病之间有什么关系 新冠肺炎的病程是否因年龄和种族/民族而异？以及，(4)是否存在差异 新冠肺炎患者中多发病模式的死亡风险，以及年龄或种族/民族 缓和这段关系？ 我们建议的研究利用来自大型社区卫生中心网络的临床电子健康记录(EHR)数据 快速评估新冠肺炎对多发病患者的影响。我们期待着重要的见解 从将为临床流程(工作流程、临床护理决策)和服务策略提供信息的研究结果 通过识别多发病模式预测快速新冠肺炎相关健康的安全网人群 经济低迷。我们提出两个目标： SA1.识别与新冠肺炎诊断相关的多病模式。检查特定疾病 与诊断相关的模式(例如，心血管、代谢性疾病)和社会人口学相关 (例如，年龄、种族/民族)，这会增加新冠肺炎的易感性。 SA2：确定新冠肺炎患者健康相关不良结局的轨迹。了解 柯萨奇病毒19对慢性疾病进展(即多病堆积)的影响 不同的多发病模式，并确定先前存在的不同多发病模式是否 与感染COVID 19造成的更大的不良健康后果有关，例如更严重的疾病 病程(如入院、ICU入院、住院天数、死亡率)。我们还将评估是否 社会人口学因素(例如，年龄、种族/民族)与更多的多发病有关 新冠肺炎病程越重，病死率越高。

Project Summary This work will advance our knowledge of the contributing role of specific pre-existing co-occurring chronic disease (multimorbidity) patterns and sociodemographic factors in the likelihood of contracting COVID-19 and experiencing adverse health-related outcomes among middle-aged and older vulnerable adults. We will address key gaps in knowledge: (1) Are there specific multimorbidity patterns that increase risk of COVID-19 infections, and do they vary by age and race/ethnicity? (2) What is the relationship between specific multimorbidity patterns and disease accumulation among patients with COVID-19, and does this differ by age or race/ethnicity? (3) What is the relationship between multimorbidity patterns and experiencing more severe COVID-19 disease course, and does that differ by age and race/ethnicity? And, (4) Are there differential mortality risks by multimorbidity pattern among patients with COVID-19, and does age or race/ethnicity moderate this relationship? Our proposed research leverages clinical electronic health record (EHR) data from large networks of CHCs for rapid evaluation of the impact of COVID-19 among people with multimorbidity. We anticipate important insights from findings that will inform clinical processes (workflow, clinical care decision-making) and policies that serve safety-net populations by identifying multimorbidity patterns predictive of rapid COVID-19 related health downturns. We propose two aims to: SA1. Identify multimorbidity patterns associated with COVID-19 diagnoses. Examine specific disease patterns associated with diagnoses (e.g., cardiovascular, metabolic disease) and sociodemographic correlates (e.g., age, race/ethnicity) that confer greater susceptibility to COVID-19. SA2: Identify trajectories of adverse health-related outcomes for patients with COVID-19. Understand the impact of COVID 19 on progression of chronic disease (i.e., multimorbidity accumulation) in patients with various multimorbidity patterns and determine whether different patterns of pre-existing multimorbidity are associated with greater adverse health outcomes from COVID 19 infection, such as more severe disease course (e.g., hospital admissions, ICU admissions, inpatient days, mortality). We will also evaluate whether sociodemographic correlates (e.g., age, race/ethnicity) are associated with greater multimorbidity accumulation, more severe course of COVID-19, and mortality.