ABSTRACT
SARS-CoV-2 testing data in North Carolina during the first three months of the state's COVID-19 pandemic were analyzed to determine if there were disparities among intersecting axes of identity including race, Latinx ethnicity, age, urban-rural residence, and residence in a medically underserved area. Demographic and residential data were used to reconstruct patterns of testing metrics (including tests per capita, positive tests per capita, and test positivity rate which is an indicator of sufficient testing) across race-ethnicity groups and urban-rural populations separately. Across the entire sample, 13.1% (38,750 of 295,642) of tests were positive. Within racial-ethnic groups, 11.5% of all tests were positive among non-Latinx (NL) Whites, 22.0% for NL Blacks, and 66.5% for people of Latinx ethnicity. The test positivity rate was higher among people living in rural areas across all racial-ethnic groups. These results suggest that in the first three months of the COVID-19 pandemic, access to COVID-19 testing in North Carolina was not evenly distributed across racial-ethnic groups, especially in Latinx, NL Black and other historically marginalized populations, and further disparities existed within these groups by gender, age, urban-rural status, and residence in a medically underserved area.
Subject(s)
Black or African American/statistics & numerical data , COVID-19 Testing/statistics & numerical data , COVID-19/diagnosis , Healthcare Disparities/statistics & numerical data , Hispanic or Latino/statistics & numerical data , White People/statistics & numerical data , Adolescent , Adult , Age Distribution , Aged , Child , Child, Preschool , Female , Health Services Accessibility/statistics & numerical data , Humans , Infant , Male , Middle Aged , North Carolina , Rural Population , SARS-CoV-2/isolation & purification , Urban Population , Young AdultABSTRACT
Abstract After an initial delay, Sub-Saharan Africa (SSA) is being hit by the pandemic. Demand for exports is falling and caseloads are rising. Governments have approached this crisis with a range of policy options. Optimal policy balances reduced infection rates with lost economic output. This paper discusses how an economic-epidemiological model used to analyze policy in high-income countries could be adapted to a context where poverty considerations are paramount. Differences in country characteristics across the continent affect benefits and costs of alternative policy designs. We conclude by highlighting data needs and model calibration challenges for COVID-19 policy research in SSA.