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.

摘要 医疗保健安全和监管系统受到用于指导决策的数据质量的限制; 将通过以下措施加快努力，全面实现跨民族、种族、年龄、残疾和地理的健康公平： 数据，最好的措施和捕捉所有美国人的健康和他们接受他们的背景下， 在乎与公平临床试验招募、少数群体、老年人群、农村 人口和残疾人在现实世界的数据中代表性不足， 越来越多地用于药物和器械安全性和有效性的研究。为了应对这些挑战， 我们的应用程序和应对U 01 RFA-FD-23-009中提到的健康公平障碍，我们的整体 研究目标有两个方面。首先，我们将评估针对COVID-19的指南一致性护理的交付情况 使用的数据来自美国家庭医学委员会的PRIME注册中心。其中包括纵向 自2016年以来，来自美国小型独立诊所的近800万初级保健患者的数据。 患者的地理位置和社会背景多种多样，包括居住在美国所有50个州和一半以上的州的患者 我们的方法将建立在与斯坦福大学 和美国家庭医学委员会，以管理PRIME注册数据，供监管使用， 将其转化为一个研究数据集，即美国家庭队列（AFC）。尽管代表了大部分的 医疗，来自初级保健的数据在当前的大部分现实世界的数据景观中明显缺失， 用于联邦政府资助的研究的数据来自学术医疗中心， 三级和四级护理。其次，我们建议通过开发和 应用方法从电子病历中更好地获取与健康公平性分析相关的数据 例如种族、民族、地理和社会环境。我们将应用这些数据来了解健康 初级保健环境中COVID-19和长期COVID的发病率和治疗不平等。 目的1：比较COVID-19、长期COVID和COVID-19指南一致治疗的发病率 按种族、族裔和地区划分的初级保健环境中的社会剥夺。 目标2：评估地理和区域社会剥夺对COVID-19发病率的影响， 长期COVID和初级保健环境中COVID-19的指南一致治疗。 目标3：评估COVID-19和长期COVID诊断的汇总统计数据的差异， 使用不同数据类型生成的估计值。 虽然我们的具体目标是展示这些数据对COVID特定健康公平研究的效用， 19和长期COVID，这项提案将使这些基础数据的开发能够更普遍地使用 检查各种健康状况下药物、程序和器械安全的公平性。