Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study

  1. Michela Antonelli
  2. Rose S Penfold
  3. Jordi Merino
  4. Carole H Sudre
  5. Erika Molteni
  6. Sarah Berry
  7. Liane S Canas
  8. Mark S Graham
  9. Kerstin Klaser
  10. Marc Modat
  11. Benjamin Murray
  12. Eric Kerfoot
  13. Liyuan Chen
  14. Jie Deng
  15. Marc F Österdahl
  16. Nathan J Cheetham
  17. David A Drew
  18. Long H Nguyen
  19. Joan Capdevila Pujol
  20. Christina Hu
  21. Somesh Selvachandran
  22. Lorenzo Polidori
  23. Anna May
  24. Jonathan Wolf
  25. Andrew T Chan
  26. Alexander Hammers
  27. Emma L Duncan
  28. Tim D Spector
  29. Sebastien Ourselin
  30. Claire J Steves

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Abstract

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  1. Version published to 10.1016/s1473-3099(21)00460-6
  2. ScreenIT

    SciScore for 10.1101/2021.05.24.21257738: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Ethical approval: All app users provided informed consent for data usage for COVID-19-related research.
    IRB: In the UK, the app and study were approved by King’s College London (KCL) ethics committee (REMAS no. 18210, reference LRS-19/20–18210).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was run using Python 3.7 and the following packages: numpy v1.19.2, pandas v1.1.3, scipy 1.5.2, and statsmodels v0.12.1.
    Python
    suggested: (IPython, RRID:SCR_001658)
    numpy
    suggested: (NumPy, RRID:SCR_008633)
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: This study used data from a large population of individuals reporting on a mobile application. This population, while large, was disproportionately female and under-represented individuals of lower socio-economic status as indicated by the skew toward people living in less deprived areas (Table 1). Information was self-reported and therefore recording of comorbidities and test results may not be completely accurate. However, previous data from this study have concurred well with population-based COVID-19 studies (47), including the influence of socio-demographic factors (42). A strength of the mobile data collection method is the ability to collect daily information prospectively, on a comprehensive set of symptoms, allowing analysis of both individual symptoms and overall illness duration. We acknowledge also that by virtue of data censoring dates, symptom duration may be underestimated in both cases and controls, as some individuals only had two weeks of logging after their positive test result. The design of our study, including matching cases and controls for health-care worker status and time of infection, reduces potential for bias, although small differences between the groups remained on matched variables. Risk of reporting a positive SARS-CoV-2 test is higher amongst frontline healthcare workers vs. the general population (14), reflecting exposure; and appropriately, healthcare workers were prioritised for vaccination in the UK (48). Our da...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.05.24.21257738v2 on medRxiv
  4. Version published to 10.1101/2021.05.24.21257738v1 on medRxiv