BMJ Open (Oct 2022)

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

  • 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

DOI
https://doi.org/10.1136/bmjopen-2021-049657
Journal volume & issue
Vol. 12, no. 10

Abstract

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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.