COVID-19 susceptibility and severity risks in a survey of over 500,000 individuals

  1. Spencer C. Knight
  2. Shannon R. McCurdy
  3. Brooke Rhead
  4. Marie V. Coignet
  5. Danny S. Park
  6. Genevieve H.L. Roberts
  7. Nathan D. Berkowitz
  8. Miao Zhang
  9. David Turissini
  10. Karen Delgado
  11. Milos Pavlovic
  12. AncestryDNA Science Team
  13. Asher K. Haug Baltzell
  14. Harendra Guturu
  15. Kristin A. Rand
  16. Ahna R. Girshick
  17. Eurie L. Hong
  18. Catherine A. Ball
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Abstract

Background

The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analyzing population-scale datasets in real time to monitor and better understand the evolving pandemic.

Methods

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 U.S. in just under four months, including over 4,700 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 males even after adjusting for known exposures and age (adjusted odds ratio [aOR]=1.36, 95% confidence interval [CI] = (1.19, 1.55)). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization 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.

Conclusion

The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

THUMBNAIL

What is already known on this subject

  • The COVID-19 pandemic has exacted a historic toll on human lives, healthcare systems and global economies, with over 83 million cases and over 1.8 million deaths worldwide as of January 2021.

  • COVID-19 risk factors for susceptibility and severity have been extensively investigated by clinical and public health researchers.

  • Several groups have developed risk models to predict COVID-19 illness outcomes based on known risk factors.

What this study adds

  • We performed association analyses for COVID-19 susceptibility and severity in a large, at-home survey and replicated much of the previous clinical literature.

  • Associations were further adjusted for known COVID-19 exposures, and we observed elevated positive test odds for males even after adjustment for these known exposures.

  • We developed risk models and evaluated them across different age, sex, and genetic ancestry cohorts, and showed robust performance across all cohorts in a holdout dataset.

  • Our results establish large-scale, self-reported surveys as a potential framework for investigating and monitoring rapidly evolving pandemics.

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