Assessing the racial, ethnic, and geographic equitability of COVID-19 treatments in a primary care population

评估初级保健人群中 COVID-19 治疗的种族、民族和地理公平性

• 批准号: 10764768
• 负责人: David H Rehkopf
• 金额: $ 50万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-10 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10764768
• 关键词: Assessing; racial; ethnic; geographic; equitability

ABSTRACT Healthcare safety and regulatory systems are limited by the quality of the data used to guide decision making; efforts to fully realize health equity across ethnicity, race, age, disability, and geography, will be accelerated by data that best measures and captures the health of all Americans and the contexts in which they receive their care. Similar to challenges in equitable clinical trial recruitment, minoritized populations, older populations, rural populations and persons with disabilities have been underrepresented in the real-world data that are increasingly used for studies of drug and device safety and efficacy. To address these challenges, as noted in our application and in responding to the barriers to health equity addressed in U01 RFA-FD-23-009, our overall research objectives are twofold. First, we will evaluate the delivery of guideline concordant care for COVID-19 using data obtained from the American Board of Family Medicine’s PRIME Registry. These include longitudinal data since 2016 on nearly eight million primary care patients from small independent clinics across the U.S. The patients are diverse geographically and socially, including patients living in all 50 states and over half of the ZIP codes in the U.S. Our approach will build on a three-year collaboration between Stanford University and the American Board of Family Medicine to curate the PRIME Registry data for regulatory use and transform them into a research dataset, the American Family Cohort (AFC). Despite representing the bulk of care, data from primary care is notably absent in much of the current real-world data landscape as much of the data used for federally funded research comes from academic medical centers which focus on inpatient, tertiary and quaternary care. Second, we propose to enhance the AFC data through the development and application of methods to better capture data relevant to health equity analyses from electronic medical records such as race, ethnicity, geography and social circumstances. We will apply these data to understanding health inequalities in incidence of and treatment for COVID-19 and long-COVID in the primary care setting. Aim 1: Compare the incidence of COVID-19, long-COVID, and guideline concordant treatment for COVID-19 by race, ethnicity and area based social deprivation in the primary care setting. Aim 2: Estimate the effect of geography and area based social deprivation on the incidence of COVID-19 and long-COVID and guideline concordant treatment for COVID-19 in the primary care setting. Aim 3: Evaluate differences in summary statistics for COVID-19 and long-COVID diagnoses contrasting estimates generated using different data types. While our Specific Aims are to demonstrate the utility of this data for health equity research specific to COVID- 19 and long-COVID, this proposal will enable the development of these underlying data for more general use examining equity for drugs, procedures and device safety for a variety of health conditions.

抽象的 医疗保健安全和监管系统受到指导决策的数据质量的限制； 努力在种族，种族，年龄，残疾和地理上充分实现健康公平，将加速 最能衡量并捕捉所有美国人的健康的数据以及他们收到的环境 关心。类似于公平临床试验招募，少数人口，较老的人群，农村的挑战 人群和残疾人在现实世界中的代表性不足 越来越多地用于药物和设备安全和效率的研究。解决这些挑战，如 我们的申请以及回应U01 RFA-FD-23-009中解决卫生公平障碍的过程，我们的总体 研究目标是双重的。首先，我们将评估COVID-19 使用从美国家庭医学委员会的主要注册表获得的数据。这些包括纵向 自2016年以来，美国近800万名初级保健患者的数据来自美国的小型独立诊所 患者在地理和社会上都是多样的，包括生活在所有50个州和一半以上的患者 美国的邮政编码我们的方法将基于斯坦福大学之间的三年合作 以及美国家庭医学委员会策划主要注册表数据以供监管使用和 将它们转换为研究数据集，即美国家庭队列（AFC）。尽管代表了大部分 护理，在当前的许多现实数据景观中，初级保健的数据显然不存在 用于联邦资助的研究的数据来自专注于住院的学术医疗中心， 第三和第四纪护理。其次，我们建议通过开发增强AFC数据和 从电子病历中进行与健康公平分析相关的数据的应用程序应用 例如种族，种族，地理和社会环境。我们将把这些数据应用于了解健康 Covid-19的发病率和治疗不平等，在初级保健环境中长期兴奋。 AIM 1：比较Covid-19的事件，长期循环和指南一致性治疗19 根据种族，种族和基于地区的社会剥夺在初级保健环境中。 目标2：估计地理和基于地区的社会剥夺对Covid-19和Covid事件的影响 在初级保健环境中，长期循环和指南一致的治疗。 AIM 3：评估COVID-19和长期诊断的摘要统计差异的差异 使用不同数据类型生成的估计值。 尽管我们的具体目的是证明该数据对特定于covid-的健康公平研究的实用性 19和长期浮动的，该提案将使这些基本数据的开发供更多普遍使用 检查用于各种健康状况的药物，程序和设备安全的股权。