The Covid-19 pandemic has had a disproportionate, negative impact on racial and ethnic minorities in the United States as measured by rates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, Covid-19 hospitalization and mortality. Race and ethnicity per se are not underlying health risk factors. Rather, race and ethnicity are markers of common characteristics – comorbid health conditions, socioeconomic characteristics, trust of and interaction with the medical system, living and working conditions, and so on – that put some groups at higher risk for adverse outcomes. Many of these “common characteristics” result from long-standing patterns of structural racism.
In our study, we endeavored to understand whether racial and ethnic disparities in SARS-CoV-2 testing and Covid-19 outcomes existed even within a relatively narrowly defined economic group: adult members of a Medicaid managed care plan. Said differently, we wanted to understand whether racial and ethnic disparities observed in other work could be partly explained by differences in economic status across groups. To do this, we analyzed data from the more than 84,000 adults enrolled in the Medicaid managed care plan in Contra Costa County, California, an ethnically and economically diverse suburban Bay Area county and assigned to receive primary care at at Contra Costa Regional Medical Center (CCRMC), the county public hospital and its affiliated health centers. To be included in our study patients had to have been CCRMC patients as of March 1, 2020.
Within this cohort of patients, we compared the share by race who had SARS-CoV-2 tests performed by the county between February 28, 2020 and March 4, 2021. We also compared racial differences over the same period in Covid-19 hospitalizations in the county health system and deaths based on data from the California Reportable Disease Information Exchange system. Patients were classified as Asian, Black, Latino, white, or of other/unknown race based on self-identification. Our comparisons adjusted for demographic and clinical characteristics that, based on prior reports, were likely to affect Covid-19 outcomes. We also adjusted for neighborhood-level characteristics.
We found that Latinos were more likely to be tested for SARS-CoV-2 and receive a positive test result than whites.
We found that Latinos were more likely to be tested for SARS-CoV-2 and receive a positive test result than whites. Higher testing rates among Latinos may have been partly attributable to concerted county efforts to target this group in response to their observed higher disease burden. Latinos also had higher rates of hospitalization and death relative to whites even after accounting for demographic, clinical, and neighborhood characteristics. Moreover, the disparities for Latinos were large – with three to five times higher odds of testing positive, hospitalization or death than whites. These patterns are particularly noteworthy given that Latino patients were disproportionately younger and overall, Latinos in the U.S. are healthier than whites – a phenomenon known as the “Hispanic paradox.” Even among a population of Medicaid patients, who are similarly economically disadvantaged, Latinos have shouldered an unfair burden of the Covid-19 pandemic in California.
An important lesson from our work is that disparities in the burden of Covid-19 are not uniform across the country. Rather, racial and ethnic disparities depend on local context. For example, while the burden in Contra Costa was concentrated among Latinos, both Latinos and Blacks fared worse than whites in a Wisconsin health system. By contrast, adjusted odds of hospitalization were similar for Black and Latino patients relative to white patients in a New York City health system.
Finally, the findings from our work remain somewhat puzzling. What is it about Latinos in Contra Costa that puts them at higher risk for poor Covid-19 outcomes? Latinos in California are more likely than whites and many other groups to be essential workers and to live in multi-generational households. Both factors, which we couldn’t measure in the study, increase the risk of exposure to SARS-CoV-2. We suspect these factors played an important role in Contra Costa, although more work remains to be done to fully understand these patterns.
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