COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults. Issue 10 (12th October 2022)

• Record Type: Journal Article
• Authors: Knight, Spencer C; McCurdy, Shannon R; Rhead, Brooke; Coignet, Marie V; Park, Danny S; Roberts, Genevieve H L; Berkowitz, Nathan D; Zhang, Miao; Turissini, David; Delgado, Karen; Pavlovic, Milos; Haug Baltzell, Asher K; Guturu, Harendra; Rand, Kristin A; Girshick, Ahna R; Hong, Eurie L; Ball, Catherine A
• Other Names: author non-byline.; Banda Yambazi author non-byline.; Bi Ke author non-byline.; Burton Robert author non-byline.; Champine Marjan author non-byline.; Curtis Ross author non-byline.; Drokhlyansky Abby author non-byline.; Elrick Ashley author non-byline.; Foo Cat author non-byline.; Gaddis Michael author non-byline.; Gu Jialiang author non-byline.; Hateley Shannon author non-byline.; Harris Heather author non-byline.; King Shea author non-byline.; Maldonado Christine author non-byline.; McCartney-Melstad Evan author non-byline.; McFarland Alexandra author non-byline.; Miller Patty author non-byline.; Nguyen Luong author non-byline.; Noto Keith author non-byline.; Pei Jingwen author non-byline.; Petersen Jenna author non-byline.; Pew Scott author non-byline.; Sass Chodon author non-byline.; Schraiber Josh author non-byline.; Sedghifar Alisa author non-byline.; Smelter Andrey author non-byline.; South Sarah author non-byline.; Starr Barry author non-byline.; Vaughn Cecily author non-byline.; Wang Yong author non-byline.;
• Is Part Of: BMJ open. Volume 12:Issue 10(2022)
• Publication Date: 2022-10-12
• Subjects: COVID-19 -- Public health -- Epidemiology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2021-049657
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. Design: A cross-sectional study. Setting: AncestryDNA customers in the USA who consented to research. Participants: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. Results: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study. Conclusions: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.