A Cross-Sectional Analysis of Demographic and Behavioral Risk Factors of Severe Acute Respiratory Syndrome Coronavirus 2 Seropositivity Among a Sample of U.S. College Students

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2021.01.20.21249905: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study protocol #2008293852 received approval from the University’s Institutional Review Board. 2.2 Setting: Study invitation emails were sent to a random sample of 7,499 IUB undergraduate students.
    Randomization2.1 Study Design: The parent study design was a randomized controlled trial (RCT) to test whether receiving SARS-CoV-2 antibody test results alters students’ protective behaviors against infection (12).
    Blindingnot detected.
    Power AnalysisHowever, the power analysis was specific to the RCT aims, and therefore no sample size calculation was conducted for the current cross-sectional analysis of the baseline data.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The SARS-CoV-2 antibody laboratory tests were conducted in-person outdoors on the IUB campus, between September 14 - 30.
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Trained field staff read the antibody test results from the test kit, took a high-quality picture of the kit and uploaded it to a secured cloud drive, and entered the test results into the REDCap data management system.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    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:
    4.3 Limitations and generalizability: In this study, because we used cross-sectional baseline data, we cannot assess temporal ordering between different study variables and outcomes. Even though confounding is usually a limitation in observational studies, adjusting for confounding was not necessary in the current study because our research questions were descriptive and predictive, and they were not about causal inference (26). Lastly, all data, except SARS-CoV-2 antibody laboratory test results, were collected through self-reported surveys. Different sources of bias, such as measurement and recall biases, could affect the quality of self-reported data. However, we found a very strong association between a positive SARS-CoV-2 antibody laboratory test result and a positive self-reported SARS-CoV-2 testing history, suggesting measurement bias may not be a significant concern for the self-reported data. Despite the limitations, our study provides insight into the dynamics of SARS-CoV-2 seropositivity among college students and can help educational administrators and policy makers when developing future strategies for combating the pandemic in these settings. Particularly, as we used random sampling methods in this study to increase the external validity of our results, our findings may be applicable to other large universities in the U.S.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04620798Active, not recruitingLongitudinal COVID-19 Antibody Testing in Indiana University…


    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.
    • Thank you for including a protocol registration statement.

    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.