Journal Article

COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults

McCurdy, Shannon R, Rand, Kristin A, Sass, Chodon, King, Shea, Pew, Scott, Haug Baltzell, Asher K, Roberts, Genevieve H L, Elrick, Ashley, Maldonado, Christine, Champine, Marjan, Schraiber, Josh, Starr, Barry, Miller, Patty, Sedghifar, Alisa, Smelter, Andrey, Wang, Yong, Knight, Spencer C, Petersen, Jenna, Hong, Eurie L, Rhead, Brooke, Park, Danny S, Girshick, Ahna R, Gaddis, Michael, Burton, Robert, South, Sarah, Foo, Cat, Drokhlyansky, Abby, Berkowitz, Nathan D, Pei, Jingwen, Turissini, David, Delgado, Karen, Zhang, Miao, Harris, Heather, Banda, Yambazi, Vaughn, Cecily, Hateley, Shannon, Pavlovic, Milos, Bi, Ke, Curtis, Ross, McCartney-Melstad, Evan, Nguyen, Luong, Coignet, Marie V, Gu, Jialiang, McFarland, Alexandra, Noto, Keith, Guturu, Harendra, Ball, Catherine A

2022

Overview

ObjectivesThe 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.DesignA cross-sectional study.SettingAncestryDNA customers in the USA who consented to research.ParticipantsThe 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.ResultsWe 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.ConclusionsThe results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

• Publisher: British Medical Journal Publishing Group,BMJ Publishing Group LTD,BMJ Publishing Group
• Subject: Coronaviruses / COVID-19 / Cross-sectional studies / Epidemiology / Pandemics / Public health / Risk factors / Self report