The prevalence of SARS-CoV-2 antibodies within the community of a private tertiary university in the Philippines: a serial cross sectional study

  1. Lourdes Bernadette Sumpaico-Tanchanco
  2. Jenica Sy
  3. Angel Belle C. Dy
  4. Myla Levantino
  5. Arianna Maever L Amit
  6. John Wong
  7. Kirsten Angeles
  8. John Paul Vergara

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Abstract

The antibody testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was used to detect the presence of antibodies in a private university setting. This serial cross-sectional study determined the seroprevalence of SARS-CoV-2 antibodies using qualitative and quantitative tests. Between June 2021 to December 2021, samples from 1,318 participants were tested, showing 47.80% of the study population yielding IgG antibodies to SARS-CoV-2 virus. A general increase in seroprevalence was observed from June to December 2021. However, a decreasing trend in IgG reactivity was found in vaccinated individuals over time. IgG antibody formation was observed across all brands of vaccines.

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  1. ScreenIT

    SciScore for 10.1101/2022.04.25.22274280: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was reviewed and approved by the Institutional Review Board (IRB) from the School of Medicine and Public Health Panel of the AdMU Research Ethics Committee.
    Consent: Informed consent was secured prior to inclusion to the study through the digital data collection tool used in the study.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data was analyzed using SPSS software (version 28.0.1.0) and Google Sheets.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: 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 10.1101/2022.04.25.22274280v1 on medRxiv