The H1N1 Crisis Predicted Covid-19’s Toll on Black Americans

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The H1N1 Crisis Predicted Covid-19’s Toll on Black Americans
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During the “swine flu” outbreak, nonwhite patients got sicker faster, recovered more slowly, and died at higher rates. Scientists hoped their research would help the US prepare for a future pandemic. It didn't. (From 2020)

,” interconnected environmental factors that impact someone’s risk for conditions like diabetes, asthma, or hypertension. For example, obesity rates are higher among African-Americans and Latinos. That, in turn, increases the risk for hypertension, diabetes, heart disease, and even chronic kidney disease, all of which are risk factors for Covid complications. “These are driven by structural factors and by the environment around the person,” Fiscella says.

While independent researchers emphasized the importance of connecting structural factors to susceptibility to pandemics, the federal response was less specific. A 2012from the Department of Health and Human Services noted that minorities were hospitalized due to H1N1 complications at higher rates but said “the reasons for these disparities are unknown.” It said access to care and underlying health conditions “may play a role.

In March, President Trump announced partnerships for Covid-19 tests with CVS, Walgreens, and Walmart. Butby Vox on the Chicago sites found these test centers were largely inaccessible for black residents. Social determinants, like whether testing or vaccinations are even available in your area, are crucial in understanding the potential dangers of underlying health conditions, the role of individual choice, and resisting victim blaming. But, this social data is rarely recorded by the very institutions now scrambling to understand these disparities.

“As you think about our health care data systems, they're optimized for incentives around reimbursement,” says Hilary Placzek, a senior public health researcher at Clarify Health Solutions who alsoShe says hospitals typically record quantitative data on a patient’s procedures, diagnoses, or length of stay, but little social data.

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