ABSTRACT
BackgroundHouseholds are important for SARS-CoV-2 transmission due to high intensity exposure in enclosed living spaces over prolonged durations. Using contact tracing, the secondary attack rate in households is estimated at 18-20%, yet no studies have examined COVID-19 clustering within households to inform testing and prevention strategies. We sought to quantify and characterize household clustering of COVID-19 cases in Fulton County, Georgia and further explore age-specific patterns in household clusters. MethodsWe used state surveillance data to identify all PCR- or antigen-confirmed cases of COVID-19 in Fulton County, Georgia. Household clustered cases were defined as cases with matching residential address with positive sample collection dates within 28 days of one another. We described proportion of COVID-19 cases that were clustered, stratified by age and over time and explored trends in age of first diagnosed case within clusters and age patterns between first diagnosed case and subsequent household cases. ResultsBetween 6/1/20-10/31/21, there were 106,233 COVID-19 cases with available address reported in Fulton County. Of these, 31,449 (37%) were from 12,955 household clusters. Children were more likely to be in household clusters than any other age group and children increasingly accounted for the first diagnosed household case, rising from 11% in February 2021 to a high of 31% in August 2021. Bubble plot density of age of first diagnosed case and subsequent household cases mirror age-specific patterns in household social mixing. DiscussionOne-third of COVID-19 cases in Fulton County were part of a household cluster. High proportion of children in household clusters reflects higher probability of living in larger homes with caregivers or other children. Increasing probability of children as the first diagnosed case coincide with temporal trends in vaccine roll-out among the elderly in March 2021 and the return to in-person schooling for the Fall 2021 semester. While vaccination remains the most effective intervention at reducing household clustering, other household-level interventions should also be emphasized such as timely testing for household members to prevent ongoing transmission.
ABSTRACT
BackgroundWe present data on risk factors for severe outcomes among patients with coronavirus disease 2019 (COVID-19) in the southeast United States (U.S.). ObjectiveTo determine risk factors associated with hospitalization, intensive care unit (ICU) admission, and mortality among patients with confirmed COVID-19. DesignA retrospective cohort study. SettingFulton County in Atlanta Metropolitan Area, Georgia, U.S. PatientsCommunity-based individuals of all ages that tested positive for SARS-CoV-2. MeasurementsDemographic characteristics, comorbid conditions, hospitalization, ICU admission, death (all-cause mortality), and severe COVID-19 disease, defined as a composite measure of hospitalization and death. ResultsBetween March 2 and May 31, 2020, we included 4322 individuals with various COVID-19 outcomes. In a multivariable logistic regression random-effects model, patients in age groups [≥]45 years compared to those <25 years were associated with severe COVID-19. Males compared to females (adjusted odds ratio [aOR] 1.4, 95% confidence interval [CI]: 1.1-1.6), non-Hispanic blacks (aOR 1.9, 95%CI: 1.5-2.4) and Hispanics (aOR 1.7, 95%CI: 1.2-2.5) compared to non-Hispanic whites were associated with increased odds of severe COVID-19. Those with chronic renal disease (aOR 3.6, 95%CI: 2.2-5.8), neurologic disease (aOR 2.8, 95%CI: 1.8-4.3), diabetes (aOR 2.0, 95%CI: 1.5-2.7), chronic lung disease (aOR 1.7, 95%CI: 1.2-2.3), and "other chronic diseases" (aOR 1.8, 95%CI: 1.3-2.6) compared to those without these conditions were associated with increased odds of having severe COVID-19. ConclusionsMultiple risk factors for hospitalization, ICU admission, and death were observed in this cohort from an urban setting in the southeast U.S. Improved screening and early, intensive treatment for persons with identified risk factors is urgently needed to reduce COVID-19 related morbidity and mortality.
ABSTRACT
Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.
