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用于指导决策的数据质量的限制； 为了充分实现跨民族、种族、年龄、残疾和地域的健康公平努力，将加速 最能衡量和捕捉所有美国人的健康状况以及他们接受健康的环境的数据 类似于公平临床试验招募、少数群体、老年人口、农村地区的挑战。 现实世界数据中人口和残疾人的代表性不足 越来越多地用于药物和设备安全性和功效的研究，以应对这些挑战，如中所述。 我们的申请以及应对 U01 RFA-FD-23-009 中解决的健康公平障碍，我们的总体 研究目标有两个：首先，我们将评估针对 COVID-19 的指南一致护理的实施情况。 使用从美国家庭医学委员会 PRIME 登记处获得的数据，其中包括纵向数据。 自 2016 年以来关于美国各地小型独立诊所近 800 万初级保健患者的数据 这些患者的地理位置和社会背景各异，包括居住在所有 50 个州和超过一半的患者 美国的邮政编码 我们的方法将建立在斯坦福大学之间为期三年的合作基础上 和美国家庭医学委员会整理 PRIME 注册数据以供监管使用， 将它们转化为研究数据集，美国家庭队列 (AFC)，尽管代表了大部分。 护理方面，来自初级护理的数据在当前现实世界的数据格局中明显缺失，正如许多 用于联邦资助研究的数据来自专注于住院患者的学术医疗中心， 其次，我们建议通过开发和改进 AFC 数据。 应用方法更好地从电子病历中捕获与健康公平分析相关的数据 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状况。 初级保健机构中 COVID-19 和长期 COVID 的发病率和治疗不平等。 目标 1：比较 COVID-19、长期 COVID 的发病率以及针对 COVID-19 的指南一致治疗 按种族、族裔和地区划分的初级保健机构中的社会剥夺状况。 目标 2：估计地理和地区社会剥夺对 COVID-19 发病率的影响， 初级保健机构中针对 COVID-19 的长期 COVID 治疗和指南一致治疗。 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证明这些数据对于针对新冠病毒的健康公平研究的效用， 19 和长期的新冠肺炎，该提案将使这些基础数据的开发能够用于更通用的用途 检查适合各种健康状况的公平药物、程序和设备安全性。