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