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

社区健康诊所的老龄化 (ACHES) 种族/族裔老年人中多种疾病和残疾的变化 COVID-19 相关行政补充

• 批准号: 10214273
• 负责人: Ana Roman Quinones
• 金额: $ 26.4万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214273
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Administrative Supplement; Admission activity; Adult; Adult Respiratory Distress Syndrome; Adverse event; Aging; Award; COVID-19; COVID-19 diagnosis; COVID-19 mortality; COVID-19 patient; COVID-19 susceptibility; Cardiovascular system; Chronic Disease; Clinic; Clinical; Communities; Community Health; Community Health Networks; Contracts; Data; Decision Making; Diagnosis; Differential Mortality; Disease; Disease Progression; Elderly; Electronic Health Record; Ethnic Origin; Evaluation; Goals; Health; Hispanics; Hospitalization; Impact evaluation; Incidence; Individual; Inpatients; Knowledge; Longevity; Metabolic Diseases; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; Neighborhood Health Center; Not Hispanic or Latino; Outcome; Parents; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Play; Policies; Population; Predisposition; Prevalence; Prevention; Process; Race; Research; Risk; Role; SARS-CoV-2 exposure; SARS-CoV-2 infection; SARS-CoV-2 positive; Specific qualifier value; Subgroup; Testing; Time; Work; adverse outcome; care seeking; clinical care; data modeling; disability; experience; inclusion criteria; insight; middle age; mortality; mortality risk; multiple chronic conditions; outreach; parent grant; patient oriented; safety net; severe COVID-19; social health determinants; sociodemographic factors; sociodemographics; young adult

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.

项目摘要 这项工作将推进我们的知识的贡献作用，具体预先存在的共同发生的慢性 感染COVID-19的可能性中的疾病（多发病）模式和社会人口因素， 在中年和老年弱势成年人中经历与健康相关的不良后果。我们将 解决知识方面的关键差距：（1）是否存在增加COVID-19风险的特定多发病模式 感染，它们是否因年龄和种族/民族而异？(2)具体是什么关系 COVID-19患者的多发病模式和疾病积累，这是否因年龄而异 或种族/民族？(3)多发病模式和经历更严重的疾病之间的关系是什么 COVID-19病程，是否因年龄和种族/民族而异？（4）是否存在差异 根据COVID-19患者的多发病模式，以及年龄或种族/民族 缓和这种关系？ 我们提出的研究利用来自大型CHC网络的临床电子健康记录（EHR）数据， 快速评估COVID-19对多发性硬化症患者的影响。我们期待重要的见解 从将告知临床流程（工作流程，临床护理决策）和政策的发现中， 通过识别预测快速COVID-19相关健康的多发病模式， 经济衰退我们提出两个目标： SA 1.识别与COVID-19诊断相关的多发病模式。检查特定疾病 与诊断相关的模式（例如，心血管、代谢疾病）和社会人口统计学相关因素 (e.g.,年龄、种族/民族），这些因素会增加对COVID-19的易感性。 SA 2：确定COVID-19患者的不良健康相关结局轨迹。理解 COVID 19对慢性疾病进展的影响（即，多药耐药蓄积） 各种多模态模式，并确定是否预先存在的多模态的不同模式， 与COVID 19感染导致的更严重的不良健康后果相关，例如更严重的疾病 课程（例如，入院、ICU入院、住院天数、死亡率）。我们还将评估 社会人口统计学相关性（例如，年龄、种族/民族）与更高的多发病率相关 积累，更严重的COVID-19过程和死亡率。