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
Case studies have revealed neurological problems in severely affected COVID-19 patients. However, there is little information regarding the nature and broader prevalence of cognitive problems post-infection or across the full spread of severity. We analysed cognitive test data from 84,285 Great British Intelligence Test participants who completed a questionnaire regarding suspected and biologically confirmed COVID-19 infection. People who had recovered, including those no longer reporting symptoms, exhibited significant cognitive deficits when controlling for age, gender, education level, income, racial-ethnic group and pre-existing medical disorders. They were of substantial effect size for people who had been hospitalised, but also for mild but biologically confirmed cases who reported no breathing difficulty. Finer grained analyses of performance support the hypothesis that COVID-19 has a multi-system impact on human cognition. Significance statementThere is evidence that COVID-19 may cause long term health changes past acute symptoms, termed long COVID. Our analyses of detailed cognitive assessment and questionnaire data from tens thousands of datasets, collected in collaboration with BBC2 Horizon, align with the view that there are chronic cognitive consequences of having COVID-19. Individuals who recovered from suspected or confirmed COVID-19 perform worse on cognitive tests in multiple domains than would be expected given their detailed age and demographic profiles. This deficit scales with symptom severity and is evident amongst those without hospital treatment. These results should act as a clarion call for more detailed research investigating the basis of cognitive deficits in people who have survived SARS-COV-2 infection.
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.
ABSTRACT
Mass screening for SARS-CoV-2 infection in long-term care facilities revealed significantly higher prevalence of infection in facilities that screened in response to a known infection compared to those that screened as a prevention measure. "Response" facilities had a SARS-CoV-2 prevalence of 28.9% while "preventive" facilities prevalence was 1.6% (p <0.001).
