Abstract
Prior literature has established a positive association between sickle cell disease and risk of contracting SARS-CoV-2. Data from a cross-sectional study evaluating COVID-19 testing devices (n = 10,567) was used to examine the association between underlying health conditions and SARS-CoV-2 infection in an urban metropolis in the southern United States. Firth's logistic regression was used to fit the model predicting SARS-CoV-2 positivity using vaccine status and different medical conditions commonly associated with COVID-19. Another model using the same method was built using SARS-CoV-2 positivity as the outcome and hemoglobinopathy presence, age (<16 Years vs. ≥16 Years), race/ethnicity and comorbidities, including hemoglobinopathy, as the factors. Our first model showed a significant association between hemoglobinopathy and SARS-CoV-2 infection (OR: 2.28, 95 % CI: (1.17,4.35), P = 0.016). However, in the second model, this association was not maintained (OR: 1.35, 95 % CI: (0.72,2.50), P = 0.344). We conclude that the association between SARS-CoV-2 positivity and presence of hemoglobinopathies like sickle cell disease is confounded by race, age, and comorbidity status. Our results illuminate previous findings by identifying underlying clinical/demographic factors that confound the reported association between hemoglobinopathies and SARS-CoV-2. These findings demonstrate how social determinants of health may influence disease manifestations more than genetics alone.