Characteristics and Risk Factors for Mortality by Coronavirus Disease 2019 Pandemic Waves in Fulton County, Georgia: A Cohort Study March 2020-February 2021.

Background: We examined differences in mortality among coronavirus disease 2019 (COVID-19) cases in the first, second, and third waves of the COVID-19 pandemic. Methods: A retrospective cohort study of COVID-19 cases in Fulton County, Georgia, USA, reported to a public health surveillance from March 2020 through February 2021. We estimated case-fatality rates (CFR) by wave and used Cox proportional hazards random-effects models in each wave, with random effects at individual and long-term-care-facility level, to determine risk factors associated with rates of mortality. Results: Of 75 289 confirmed cases, 4490 (6%) were diagnosed in wave 1 (CFR 31 deaths/100 000 person days [pd]), 24 293 (32%) in wave 2 (CFR 7 deaths/100 000 pd), and 46 506 (62%) in wave 3 (CFR 9 deaths/100 000 pd). Compared with females, males were more likely to die in each wave: wave 1 (adjusted hazard ratio [aHR], 1.5; 95% confidence interval [CI], 1.2-1.8), wave 2 (aHR 1.5, 95% CI, 1.2-1.8), and wave 3 (aHR 1.7, 95% CI, 1.5-2.0). Compared with non-Hispanic whites, non-Hispanic blacks were more likely to die in each wave: wave 1 (aHR, 1.4; 95% CI, 1.1-1.8), wave 2 (aHR, 1.5; 95% CI, 1.2-1.9), and wave 3 (aHR, 1.7; 95% CI, 1.4-2.0). Cases with any disability, chronic renal disease, and cardiovascular disease were more likely to die in each wave compared with those without these comorbidities. Conclusions: Our study found gender and racial/ethnic disparities in COVID-19 mortality and certain comorbidities associated with COVID-19 mortality. These factors have persisted throughout the COVID-19 pandemic waves, despite improvements in diagnosis and treatment.

Characteristics and Risk Factors for Mortality by COVID-19 Pandemic Waves in Fulton County, Georgia: A Cohort Study March 2020–February 2021

Background We examined differences in mortality among COVID-19 cases in the first, second and third waves of the COVID-19 pandemic. Methods A retrospective cohort study of COVID-19 cases in Fulton County, Georgia, USA, reported to a public health surveillance from March 2020 through February 2021. We estimated case fatality rates (CFR) by wave and used Cox proportional hazards random effects models in each wave, with random effects at individual and long-term-care-facility level, to determine risk factors associated with rates of mortality. Results Of 75,289 confirmed cases, 4,490 (6%) were diagnosed in wave one (CFR 31 deaths/100,000 person days [pd]), 24,293 (32%) in wave two (CFR 7 deaths/100,000 pd), and 46,506 (62%) in wave three (CFR 9 deaths/100,000 pd). Compared to females, males were more likely to die in each wave: Wave one (adjusted hazard ratio [aHR] 1.5, 95% confidence interval [CI] 1.2–1.8), wave two (aHR 1.5, 95% CI 1.2–1.8), and wave three (aHR 1.7, 95% CI 1.5–2.0). Compared to non-Hispanic Whites, non-Hispanic Blacks were more likely to die in each wave: Wave one (aHR 1.4, 95% CI 1.1–1.8), wave two (aHR 1.5, 95% CI 1.2–1.9), and wave three (aHR 1.7, 95% CI 1.4–2.0). Cases with any disability, chronic renal disease, and cardiovascular disease were more likely to die in each wave compared to those without these comorbidities. Conclusions Our study found gender and racial/ethnic disparities in COVID-19 mortality, and certain comorbidities associated with COVID-19 mortality. These factors have persisted throughout the COVID-19 pandemic waves, despite improvements in diagnosis and treatment.

Mass Screening for SARS-CoV-2 Infection among Residents and Staff in Twenty-eight Long-term Care Facilities in Fulton County, Georgia

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 prevalence in preventive facilities was 1.6% (p <0.001).

Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.

BACKGROUND: 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. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data. METHODS: We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification. RESULTS: The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons. CONCLUSIONS: These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.

Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data.

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.

Mass Screening for SARS-CoV-2 Infection among Residents and Staff in Twenty-eight Long-term Care Facilities in Fulton County, Georgia.

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 prevalence in preventive facilities was 1.6% (p <0.001).