Association of Cancer with Risk and Mortality of COVID-19: Results from the UK Biobank

  1. Zhuqing Shi
  2. W. Kyle Resurreccion
  3. Chi-Hsiung Wang
  4. Jun Wei
  5. Rong Na
  6. S. Lilly Zheng
  7. Liana K. Billings
  8. Brian T. Helfand
  9. Janardan Khandekar
  10. Jianfeng Xu

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Abstract

Although cancer has been associated with COVID-19 risk and mortality in hospital-based studies, few population-based studies have been reported. Utilizing data from the UK Biobank (UKB), a population-based prospective cohort, we formally tested the association of over 44 different types of cancer with COVID-19 infection and mortality among 7,661 subjects who were tested by June 17, 2020. Compared to non-cancer subjects, cancer subjects (N=1,521) had significantly lower overall risk for COVID-19 infection [odds ratio (OR) and 95% confidence interval (CI): 0.79 (0.68-0.92), P =2.60E-03]. However, a trend of higher risk for COVID-19 mortality was found among 256 COVID-19 positive cancer patients, especially for hematologic cancers such as non-Hodgkin lymphoma [3.82 (1.17-12.01), P=0.02]. In cancer patients, while few demographic, lifestyle, genetic and comorbidity factors predicted risk for COVID-19 infection, older age, male sex, heart disease and hypertension significantly predicted COVID-19 mortality. The lower risk for COVID-19 infection is likely due to extra caution in COVID-19 prevention and more testing among cancer patients, an encouraging finding that demonstrates the feasibility of intervention. These results, if confirmed in future releases of UKB data and other independent populations, may provide guidance for COVID-19 prevention and treatment among cancer patients.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.10.20151076: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Several limitations of this study are noted. One is the relatively small sample size of tested subjects for COVID-19, which limited statistical power. A related limitation is that none of the reported P-values were corrected for multiple testing. Therefore, both positive and negative results of the study should be interpreted with caution. Additional released COVID-19 data from the UKB will be helpful to address both limitations. Another major limitation is the small number of minority subjects in the UKB. Finally, the lack of detailed clinical information on COVID-19 symptoms/treatment and cancer treatment limited our ability to perform a more comprehensive analysis. In conclusion, in this first association study of cancer with COVID-19 risk and mortality from a population-based cohort, we showed that cancer patients in general have lower observed risk for COVID-19 infection but higher risk for COVID-19 death after infection, especially for hematologic cancers. The lower risk for COVID-19 infection, most likely due to extra caution in COVID-19 prevention and more testing among cancer patients, is encouraging and demonstrates the feasibility of intervention. We also showed that a subset of cancer patients is more likely to die of COVID-19 after the infection. These results, if confirmed, may provide guidance for COVID-19 prevention and treatment among cancer patients. Stronger personal protection should be made for cancer patients, and more intensive surveillance and/or treat...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.10.20151076v1 on medRxiv