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

  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

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.

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  1. Version published to 10.1136/bmjopen-2021-049657
  2. ScreenIT

    SciScore for 10.1101/2020.10.08.20209593: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Association analysis: Analyses were performed either with the statsmodels package in Python3 or in base R with the glm function.
    Python3
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: We note that there are some inherent limitations of self-reported data for studying COVID-19 risk factors. The most severe cases, especially those resulting in mortality, were not sampled. As a result, many of the risk factor effect estimates may be underestimated. Additionally, the AncestryDNA cohort is self-selected, slightly older, more European, and more female than the broader U.S. population. Another potential issue is that those who reported a negative test may have underestimated their exposures and symptoms relative to those who tested positive, leading to upwardly biased exposure effect estimates. Finally, misclassification of COVID-19 positive status is likely given the uneven availability of tests over the time period surveyed, potentially leading to susceptibility effect estimates that are biased toward the null. However, the fact that most of the associations observed in this study were similar to those previously reported in the literature and the fact that risk model performance remained high when data were stratified by age, sex, and genetic ancestry lends confidence to our findings in spite of limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.10.08.20209593v3 on medRxiv
  4. Version published to 10.1101/2020.10.08.20209593v2 on medRxiv
  5. Version published to 10.1101/2020.10.08.20209593v1 on medRxiv