Race and Ethnicity

The goal of this request was to describe the baseline and clinical characteristics of patients with various stages of COVID-19 and explore whether racial and/or ethnic disparities exist. COVID-19 stages included: tested, tested positive, case, hospitalized, and critically hospitalized.

The study period included data from April 1, 2020 to March 31, 2021. Data from 22 PCORnet Data Marts are included in this report. We distributed this request on June 14, 2022.

Systemic factors predispose United States (US) racial minority groups to a greater risk of severe COVID-19 outcomes. This poster determines the association between race and COVID-19 outcomes adjusted for baseline demographic, clinical, and socioeconomic characteristics. It was presented at the 39th International Conference on Pharmacoepidemiology and Therapeutic Risk Management.
 
 

The U.S. Food and Drug Administration’s Sentinel System is a national medical product safety surveillance system consisting of a large multi-site distributed database of administrative claims supplemented by electronic healthcare record (EHR) data. The program seeks to improve data capture of race and ethnicity for pharmacoepidemiology studies.

We conducted a narrative literature review of published research on data augmentation and imputation methods to improve race and ethnicity capture in U.S. health care systems databases. We focused on methods with limited (5-digit ZIP codes only) or full patient identifiers available to link to external sources of self-reported data. We organized the literature by themes: 1) variation in data capture of self-reported data, 2) data augmentation from external sources of self-reported data, and 3) imputation methods, including Bayesian analysis and multiple regression.

In this presentation, authors shared their findings from a narrative literature review that explored methods to improve the capture of race and ethnicity data for the FDA Sentinel Distributed Database. This work supported Aim 1 of the "Improving Capture of Race and Ethnicity Data" project, which sought to better understand the availability and validity of race and ethnicity data elements of interest used in pharmacoepidemiology studies.

This presentation was shared at Health Care Systems Research Network (HCSRN) 2023 Annual Conference.

​​​​​This poster examines racial differences in COVID-19 testing, positivity, hospitalization, and mortality during the first year of the pandemic. It was presented as a Spotlight Poster at the 38th International Conference on Pharmacoepidemiology and Therapeutic Risk Management. 

In this report we described racial and ethnic differences in COVID-19 testing, positivity, hospitalization, and mortality among individuals in the rapidly refreshed version of the Sentinel Distributed Database (rSDD) designed for COVID-19 analyses, during the first year of the pandemic.

The study period includes data from April 1, 2020 to March 31, 2021. We distributed this request to six Sentinel Data Partners on January 27, 2022.

Race and ethnicity are commonly used in medical, public health, and epidemiologic research. Utilizing these variables can help to characterize utilization and to identify groups at high or low risk of pharmacoepidemiologic health outcomes of interest. For health plans, these data have great utility for care delivery, understanding health disparities impacting health plan members and identifying points of intervention, and designing targeted quality improvement activities.

Health plans’ data on race and ethnicity of their members are historically undercaptured, particularly in healthcare claims data1 . Data that are captured often lack adequate and accurate detail, particularly to describe multiracial populations. Data collection is not consistent across entities covered by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) – e.g., hospitals, health plans – and much depends on private sector vs. public sector regulations and incentives2 .

This project seeks to better understand the availability and validity of race and ethnicity data elements of interest, as well as consider improvements to the use of these data in the Sentinel System. These efforts will ultimately improve capacity to study racial and ethnic disparities in the pharmacoepidemiologic arena related to either medical product exposures or health outcomes of interest. This project includes two aims:

• Aim 1: A literature review synthesizing findings and trends in race and ethnicity in pharmacoepidemiological studies.

• 1Ng JH, Ye F, Ward LM, Haffer SC, Scholle SH. Data on race, ethnicity, and language largely incomplete for managed care plan members. Health Aff (Millwood). 2017;36(3):548–52. https://www.healthaffairs.org/doi/10.1377/hlthaff.2016.1044
• 2National Research Council (US) Panel on DHHS Collection of Race and Ethnic Data; Ver Ploeg M, Perrin E, editors. Eliminating Health Disparities: Measurement and Data Needs. Washington (DC): National Academies Press (US); 2004. 6, Private-Sector Collection of Data on Race, Ethnicity, Socioeconomic Position, and Acculturation and Language Use. Available from: https://www.ncbi.nlm.nih.gov/books/NBK215758/

Epidemiologic data^1-4 indicate that communities of color continue to be disproportionally impacted by the ongoing COVID-19 pandemic. We sought to determine the association between race and COVID-19 outcomes among persons diagnosed or hospitalized with COVID-19, after adjusting for relevant clinical and demographic risk factors.

Our specific aims were:

• To evaluate the association between race and hospitalization with COVID-19 among patients diagnosed with COVID-19 after adjusting for demographic and clinical risk factors.

• To evaluate the association between race and critical COVID-19 disease among patients hospitalized with COVID-19 after adjusting for demographic and clinical risk factors.

• To evaluate the association between race and mortality among patients hospitalized with COVID-19 after adjusting for demographic and clinical risk factors.

To meet these aims, we identified cohorts of individuals who had evidence of a COVID-19 infection and separately evaluated the occurrence of any hospitalization with COVID-19 and hospitalization with COVID-19 without elements of critical COVID-19 within 30 days of cohort entry. We also identified cohorts of individuals who were hospitalized with COVID-19, and separately evaluated the occurrence of hospitalization with critical COVID-19, inpatient all-cause mortality, or any all-cause mortality within 30 days of cohort entry. We described all study cohorts at baseline by race, urbanicity, and outcome status. We evaluated crude and adjusted associations between race and COVID-19 outcomes using univariate and multivariable logistic regression models run at each Data Partner site.

Effect estimates from all participating sites were individually reviewed for any warnings related to model convergence. Only results for those sites where the multivariable outcome regression models converged without any warnings are included in the final study report published on our website. Site-specific estimates were pooled at Sentinel Operations Center in a random-effects meta-analysis using local plus programming. A publicly available macro was used to conduct the meta-analysis.

The analytic package associated with this analysis can be found externally in Sentinel's Git Repository located here. The Git Repository serves as Sentinel's version control tracking system for analytic packages and technical documentation

Sources:

1. Laboratory Confirmed COVID-19-Associated Hospitalizations. https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html

2. The Color of Coronavirus: COVID-19 Deaths by Race and Ethnicity in the U.S. https://www.apmresearchlab.org/covid/deaths-by-race

3. United States Census Bureau. Quick Facts. https://www.census.gov/quickfacts/fact/table/US/PST045219

4. CDC COVID Data Tracker. https://covid.cdc.gov/covid-data-tracker/#demographics

This project was funded by U.S. Food and Drug Administration (FDA) Office of Minority Health and Health Equity (OMHHE).