Clinical risk, sociodemographic factors, and SARS-CoV-2 infection over time in Ontario, Canada

  1. Jacob A. Udell
  2. Bahar Behrouzi
  3. Atul Sivaswamy
  4. Anna Chu
  5. Laura E. Ferreira-Legere
  6. Jiming Fang
  7. Shaun G. Goodman
  8. Justin A. Ezekowitz
  9. Kevin R. Bainey
  10. Sean van Diepen
  11. Padma Kaul
  12. Finlay A. McAlister
  13. Isaac I. Bogoch
  14. Cynthia A. Jackevicius
  15. Husam Abdel-Qadir
  16. Harindra C. Wijeysundera
  17. Dennis T. Ko
  18. Peter C. Austin
  19. Douglas S. Lee

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

We aimed to determine whether early public health interventions in 2020 mitigated the association of sociodemographic and clinical risk factors with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a population-based cohort study of all adults in Ontario, Canada who underwent testing for SARS-CoV-2 through December 31, 2020. The outcome was laboratory-confirmed SARS-CoV-2 infection, determined by reverse transcription polymerase chain reaction testing. Adjusted odds ratios (ORs) were determined for sociodemographic and clinical risk factors before and after the first-wave peak of the pandemic to assess for changes in effect sizes. Among 3,167,753 community-dwelling individuals, 142,814 (4.5%) tested positive. The association between age and SARS-CoV-2 infection risk varied over time ( P -interaction < 0.0001). Prior to the first-wave peak, SARS-CoV-2 infection increased with age whereas this association reversed thereafter. Risk factors that persisted included male sex, residing in lower income neighborhoods, residing in more racially/ethnically diverse communities, immigration to Canada, hypertension, and diabetes. While there was a reduction in infection rates after mid-April 2020, there was less impact in regions with higher racial/ethnic diversity. Immediately following the initial peak, individuals living in the most racially/ethnically diverse communities with 2, 3, or ≥ 4 risk factors had ORs of 1.89, 3.07, and 4.73-fold higher for SARS-CoV-2 infection compared to lower risk individuals in their community (all P  < 0.0001). In the latter half of 2020, this disparity persisted with corresponding ORs of 1.66, 2.48, and 3.70-fold higher, respectively. In the least racially/ethnically diverse communities, there was little/no gradient in infection rates across risk strata. Further efforts are necessary to reduce the risk of SARS-CoV-2 infection among the highest risk individuals residing in the most racially/ethnically diverse communities.

Article activity feed

  1. Version published to 10.1038/s41598-022-13598-z
  2. ScreenIT

    SciScore for 10.1101/2021.04.28.21256052: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The use of data in this project was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board for use of anonymized data.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: 26 These datasets were linked using unique, encoded identifiers and analyzed at ICES as previously described and validated.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All data were analyzed at ICES using SAS version 9.4 (SAS Institute, Cary, NC).
    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:
    There are important limitations to acknowledge as well. Access to testing for SARS-CoV-2 infection varied over time, including restriction of testing during the earlier periods to the highest risk patients; while some patients with severe COVID-19 illness may also have died before testing. Over time, testing capacity increased and broader testing occurred. As a result, our analysis was focused on individuals that underwent SARS-CoV-2 testing to reduce ascertainment bias. In addition, a small number of hospital-based SARS-CoV-2 test results were not available for this analysis, but this low percentage of missingness would not be expected to materially impact the results of available data. Since we did not have individual-level data on weight, smoking status, income, and race/ethnicity, we relied on community-level variables as proxies. During both time periods, a number of chronic conditions were significantly associated with a lower risk of SARS-CoV-2 infection, likely reflecting collider/screening bias among asymptomatic patients undergoing regular care.37 Despite the dramatic impact of a provincial lockdown, following the peak of the initial wave of the pandemic in early April, the highest likelihood of SARS-CoV-2 infection emerged among clusters of people represented by younger age, male sex, individuals that immigrated to Canada, with hypertension or diabetes residing in the most racially/ethnically diverse, urban, most socioeconomically disadvantaged communities of Ontar...

    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 10.1101/2021.04.28.21256052 on medRxiv