African Americans are overrepresented among reported coronavirus disease 2019 (COVID-19) cases and deaths. There are a multitude of factors that may explain the African American disparity in COVID-19 outcomes, including higher rates of comorbidities. While individual-level factors predictably contribute to disparate COVID-19 outcomes, systematic and structural factors have not yet been reported. It stands to reason that implicit biases may fuel the racial disparity in COVID-19 outcomes. To address this racial disparity, we must apply a health equity lens and disaggregate data explicitly for African Americans, as well as other populations at risk for biased treatment in the health-care system.
Maximum carotid artery wall thickness was utilized in a primary prevention population and compared with baseline risk factors. Carotid wall thickness was measured between the blood–intima and media–adventitia interfaces by B-mode ultrasonography using software calipers at points of protrusion. Long-axis measures were confirmed by short-axis assessment. The maximum carotid wall thickness for each subject was divided by age in years to yield an annual accretion rate (called carotid intima–media thickness accretion rate [CIMTAR]). The entire study population was then divided by median CIMTAR to investigate the association with baseline variables used in standard risk assessments with the bifurcated groups. Traditional risk factors such as age, diabetes, smoking, hyperlipidemia, and obesity were not associated with greater than median CIMTAR. Only male gender (P = 0.02) and systolic blood pressure (P = 0.002) in baseline variables were associated with an elevated CIMTAR for the entire population. Among those not taking lipid-lowering therapy at baseline, only systolic blood pressure remained significant (P = 0.0002). Correlations between low-density lipoprotein (LDL) cholesterol level and maximum carotid wall thickness/CIMTAR were weak for the entire population (r = −0.17/r = −0.12, respectively). Measure of maximum carotid wall thickness may select patients earlier for treatment than traditional risk factors. The addition of CIMTAR to risk algorithms may permit a single-point assignation of subsequent vascular risk that is more efficacious than traditional risk factors.
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