학술논문
COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults
Document Type
article
Author
Yong Wang; Robert Burton; Cecily Vaughn; Miao Zhang; Brooke Rhead; Heather Harris; Spencer C Knight; Shannon R McCurdy; Marie V Coignet; Danny S Park; Genevieve H L Roberts; Nathan D Berkowitz; David Turissini; Karen Delgado; Milos Pavlovic; Asher K Haug Baltzell; Harendra Guturu; Kristin A Rand; Ahna R Girshick; Eurie L Hong; Catherine A Ball; Yambazi Banda; Ke Bi; Marjan Champine; Ross Curtis; Abby Drokhlyansky; Ashley Elrick; Cat Foo; Michael Gaddis; Jialiang Gu; Shannon Hateley; Shea King; Christine Maldonado; Evan McCartney-Melstad; Alexandra McFarland; Patty Miller; Luong Nguyen; Keith Noto; Jingwen Pei; Jenna Petersen; Scott Pew; Chodon Sass; Josh Schraiber; Alisa Sedghifar; Andrey Smelter; Sarah South; Barry Starr
Source
BMJ Open, Vol 12, Iss 10 (2022)
Subject
Language
English
ISSN
2044-6055
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.