Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study

  1. Lauren Lapointe-Shaw
  2. Benjamin Rader
  3. Christina M. Astley
  4. Jared B. Hawkins
  5. Deepit Bhatia
  6. William J. Schatten
  7. Todd C. Lee
  8. Jessica J. Liu
  9. Noah M. Ivers
  10. Nathan M. Stall
  11. Effie Gournis
  12. Ashleigh R. Tuite
  13. David N. Fisman
  14. Isaac I. Bogoch
  15. John S. Brownstein

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1371/journal.pone.0239886
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.20107391: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Ethics Review Board of University Health Network.
    RandomizationThe Angus Reid Institute COVID-19 symptom poll was administered online from April 1-6, 2020 to a randomly selected sample of Angus Reid Forum panel members (over 50, 000 Canadian residents).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were analyzed using SAS software, version 9.4 (SAS Institute Inc.,
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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:
    Our study also has several limitations. The variable time frames used in the three data sources complicate cross-study comparison, and longer time periods of self-report (e.g. “in the past month”) may lead to higher levels of recall bias than shorter time periods. Similarly, household-level reporting does not easily compare to individual report, and combining symptoms experienced within a household may erroneously attribute all those symptoms to the same individual-these are important caveats. Furthermore, survey questions varied in terms of symptoms covered and inclusion of questions relating to healthcare encounters or testing results. Sample sizes were also quite small within subgroups, particularly when looking at those that reported testing or testing positive. Although the Angus Reid and Forum Research polls had a random sampling strategy, respondents on COVID Near You were self-selected, and so it was important to compare their characteristics, symptom reports, and testing rates to those obtained in the other two studies. Finally, despite their overall higher risk for COVID-19, those residing in long-term care and other institutional settings are likely not represented in these data sources which focus on community-dwelling residents of Canada. This study contributes essential data on the prevalence of COVID-19-related symptoms in Canada, and the proportion of symptomatic persons being tested. This information complements public health-reported data on testing numbers ...

    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.

  3. Version published to 10.1101/2020.05.19.20107391 on medRxiv