SARS-CoV-2 Antibody Prevalence and Population-Based Death Rates, Greater Omdurman, Sudan

  1. Wendelin Moser
  2. Mohammed Ahmed Hassan Fahal
  3. Elamin Abualas
  4. Shahinaz Bedri
  5. Mahgoub Taj Elsir
  6. Mona Fateh El Rahman Omer Mohamed
  7. Abdelhalim Babiker Mahmoud
  8. Amna Ismail Ibrahim Ahmad
  9. Mohammed A. Adam
  10. Sami Altalib
  11. Ola Adil DafaAllah
  12. Salahaldin Abdallah Hmed
  13. Andrew S. Azman
  14. Iza Ciglenecki
  15. Etienne Gignoux
  16. Alan González
  17. Christine Mwongera
  18. Manuel Albela Miranda

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Abstract

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  1. Version published to 10.3201/eid2805.211951
  2. ScreenIT

    SciScore for 10.1101/2021.08.22.21262294: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics: Ethical approval was obtained from the National Health Review Ethics Committee (No. 3–1–21)
    Consent: For the mortality survey, verbal consent was obtained from the head of the household.
    Sex as a biological variablenot detected.
    RandomizationOmdruman, the largest among the three cities was chosen as study site, which included two surveys: i) a retrospective mortality survey using a two–stage cluster sampling methodology based on random geo–points and ii) a nested SARS-CoV-2 antibody prevalence survey.
    Blindingnot detected.
    Power AnalysisSample size calculation: The sample size calculations were performed using the software ENA 2020 (version: Jan/11).

    Table 2: Resources

    Antibodies
    SentencesResources
    The primary objective of the survey was to estimate death rate for individuals ≥50 years and the overall seroprevalence of anti–SARS-CoV-2 antibodies in Omdurman.
    anti–SARS-CoV-2
    suggested: None
    All participants either positive for IgM, positive for IgG or positive for IgM and IgG, based on the RST were considered positive for anti–SARS-CoV-2 antibodies.
    IgG
    suggested: None
    Based on the manufacturer the ELISA assay has a sensitivity of 94.4% and a specificity of 99.6% for detecting previous anti-SARS-CoV-2 antibodies.
    anti-SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical Analysis: The analysis of the data was performed using R (R Core Team, 2020) and Stata V15 (StataCorp. 2017).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    These discrepancies between Sudan and Omdurman figures could also be due to the potential limitation of over two years long recall period for mortality estimates, possibly introducing a bias for deaths occurring at the beginning of the recall period. Surveyors were trained to be aware of this factor to mitigate potential recall bias. The crude seroprevalence shows how widespread the SARS-Cov-2 infection was, affecting all age groups, especially individuals aged 50 years and older. However, the estimates based on the RST might have underestimated the seroprevalence due to several limitations. Firstly, as our survey was undertaken one year after the first SARS-Cov-2 case was detected in Sudan, one can expect a varying degree of antibody decay over time.20,21 Second, when antibodies remain present in the blood, their detection is limited by the performance of the RST.22 To overcome these limitations, two adjustments of the seroprevalence based on the RST were done. After the first adjustment an almost 10% increase (34.3 to 44.0%) in the overall seroprevalence was observed. An even higher increase of 20% in the overall seroprevalence (34.2% to 54.6%), resulted from the second adjustment. This highlights the importance of considering the limitations of the tests used in seroprevalence surveys and the necessity to consider existing performance data for the tests used. Despite the second adjustment included more information than the first one and should be considered as most accurat...

    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 10.1101/2021.08.22.21262294v1 on medRxiv