Over the course of the COVID-19 pandemic, there has been a growing focus on its disproportionate impacts on people of color, particularly as availability of data to understand racial disparities has increased. This brief summarizes key findings from data and analyses examining COVID-19 related cases, deaths, hospitalizations, and testing by race and ethnicity as of early August 2020 to provide increased insight into these disparities. Key findings include the following: Multiple analyses of available federal, state, and local data show that people of color are experiencing a disproportionate burden of COVID-19 cases and deaths. They show particularly large disparities in cases and deaths for Black and American Indian and Alaska Native (AIAN) people and widespread disparities in cases among Hispanic people compared to their White counterparts. For example, KFF analysis of state reported data showed that, as of August 3, 2020, Black individuals accounted for more cases and deaths relative to their share of the population in 30 of 49 states reporting cases and 34 of 44 states reporting deaths. Other analysis of state-reported data finds that, as of August 4, the COVID-19 related death rate among Black people was over twice as high as the rate for White people, while the mortality rate for AIAN people was nearly two times that of White people. Data also reveal disparities for Asian and Native Hawaiian and Pacific Islander (NHOPI) individuals in certain areas and show a sharp, recent rise in mortality rates for NHOPI and Hispanic people. Analyses further find that disparities in COVID-19 related deaths persist across age groups and that people of color experience more deaths among younger people relative to White individuals. There is limited data and research to understand of impacts for subgroups, such as immigrants, who may be at increased risk. Data show that Black, Hispanic, and AIAN people are at increased risk of hospitalization due to COVID-19. For example, data from Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET) show that, from March through July 18, 2020, age-adjusted hospitalization rates due to COVID-19 for Black, Hispanic, and AIAN people were roughly five times higher than that of White people. Several studies using health system data also point to a higher risk of hospitalization for Black and Hispanic patients. Reflecting these higher hospitalization rates, analyses show that people of color make up a disproportionate share of COVID-19 hospitalizations relative to their share of the population or total hospital visits. Studies find racial/ethnic disparities in COVID-19 among Medicare beneficiaries, nursing home facilities, pregnant women, and children. Preliminary Medicare COVID-19 data show that Black, Hispanic, and AIAN Medicare beneficiaries had higher rates of infection and hospitalization compared to White beneficiaries. Analysis finds that nursing homes where a higher share of residents are people of color are more likely to report a COVID-19 case. Studies also find disproportionate shares of infection among Hispanic and Black pregnant women and a higher risk of hospitalization among Black and Hispanic children. Data to understand variation in testing by race/ethnicity remains very limited but suggest people of color may face increased barriers to testing. Very few states report testing data by race/ethnicity. Data on testing within community health centers analyzed by KFF show that people of color represented more than half of all people tested (57%) and confirmed cases (56%) at health centers, and that Hispanic patients made up a higher share of positive tests compared to their share of total tested patients. Analyses suggest that testing sites in and near predominantly Black and Hispanic neighborhoods are likely to face greater demand than those near predominantly White areas, which could contribute to longer wait times, and the share of people of color in an area is associated with an increase in travel time to a testing site. One study also found that, in New York City, more tests were performed in neighborhoods with a higher share of White residents, while the highest shares of positive tests were in neighborhoods with more people of color and lower socioeconomic measures. Reporting on testing site locations in Texas suggests that testing sites are disproportionately located in areas with larger shares of White residents. Together, these data show that people of color are bearing a disproportionate burden of COVID19 cases, deaths, and hospitalizations and that they may face increased barriers to access testing. Other analyses also suggest that the COVID-19 pandemic is taking a larger economic toll on people of color. These disparities in COVID-19 reflect and compound longstanding underlying social, economic, and health inequities that stem from structural and systemic barriers across sectors, including racism and discrimination. For example, prior to the pandemic, people of color had higher rates of health conditions, were more likely to be uninsured and face barriers to accessing health care, and were more likely to have lower incomes and face financial challenges. These underlying disparities put people of color at increased risk for exposure to the virus, experiencing serious illness if they are infected, and facing barriers to accessing testing and treatment. The health and economic impacts of COVID-19 could further widen racial disparities at a time when there is a growing focus on and call for racial justice and health equity. Overall, the findings highlight the importance of considering how COVID-19 relief and response efforts will address inequities, including in decisions related to distribution of treatments and vaccines once they become available. Prioritizing equity will be key for addressing the current gaps in COVID-19 and health care more broadly and preventing widening of disparities in the future.

How each potential explanation does or does not apply, consider different age groups, SES, and take a stand on these hypotheses:

• Direct income hypothesis
• The allostatic load hypothesis of health disparities
• Productive time hypothesis: SES differences are caused by disparities in health.