School Teaching Posture Correlates with COVID-19 Disease Outcomes in Ohio

  1. Cheyenne Ehman
  2. Yixuan Luo
  3. Zi Yang
  4. Ziyan Zhu
  5. Sara Donovan
  6. Annika J. Avery
  7. Jiayi Wang
  8. James Lawler
  9. Rebecca Nugent
  10. Valerie Ventura
  11. Seema S. Lakdawala

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Abstract

At the start of the COVID-19 pandemic, most US K-12 schools shutdown and millions of students began remote learning. By September 2020, little guidance had been provided to school districts to inform fall teaching. This indecision led to a variety of teaching postures within a given state. In this report we examine Ohio school districts in-depth, to address whether on-premises teaching impacted COVID-19 disease outcomes in that community. We observed that counties with on-premises teaching had more cumulative deaths at the end of fall semester than counties with predominantly online teaching. To provide a measure of disease progression, we developed an observational disease model and examined multiple possible confounders, such as population size, mobility, and demographics. Examination of micropolitan counties revealed that the progression of COVID-19 disease was faster during the fall semester in counties with predominantly on-premises teaching. The relationship between increased disease prevalence in counties with on-premises teaching was not related to deaths at the start of the fall semester, population size, or the mobility within that county. This research addresses the critical question whether on-premises schooling can impact the spread of epidemic and pandemic viruses and will help inform future public policy decisions on school openings.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.16.21260464: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

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

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1101/2021.07.16.21260464 on medRxiv