Clinical characteristics of 4499 COVID‐19 patients in Africa: A meta‐analysis

  1. Testimony Jesupamilerin Olumade
  2. Leonard Ighodalo Uzairue

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Abstract

The novel coronavirus disease‐2019 (COVID‐19) pandemic that started in December 2019 has affected over 95 million people and killed over 2 million people as of January 19, 2021. While more studies are published to help us understand the virus, there is a dearth of studies on the clinical characteristics and associated outcomes of the severe acute respiratory syndrome coronavirus 2 on the African continent. We evaluated evidence from previous studies in Africa available in six databases between January 1 and October 6, 2020. Meta‐analysis was then performed using Open‐Meta Analyst and Jamovi software. A total of seven studies, including 4499 COVID‐19 patients, were included. The result of the meta‐analysis showed that 68.8% of infected patients were male. Common symptoms presented (with their incidences) were fever (42.8%), cough (33.3%), headache (11.3%), and breathing problems (16.8%). Other minor occurring symptoms included diarrhea (7.5%) and rhinorrhea (9.4%). Fatality rate was 5.6%. There was no publication bias in the study. This study presents the first description and analysis of the clinical characteristics of COVID‐19 patients in Africa. The most common symptoms are fever, cough, and breathing problems.

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  1. Version published to 10.1002/jmv.26848
  2. ScreenIT

    SciScore for 10.1101/2020.10.20.20215905: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search databases and search strategyy: PubMed, Google scholar, Scopus, The Cochrane Library, EMBASE, and Africa journal online (AJOL) were electronically searched to collect clinical studies on the clinical characteristics of COVID-19 from January 1, 2020 to October 6, 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Data extraction and quality assessment: Two reviewers, using the inclusion and exclusion criteria, independently selected the literature, and extracted data to an Excel database and any disagreement was resolved by consensus.
    Excel
    suggested: None

    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:
    This study is not without its strengths and weaknesses. The strengths of this study include its large sample size and the high-quality score of studies included in the analysis. Some limitations were also conducting our meta-analysis. First, most studies included studies were retrospective and conducted at single centers. This may have introduced admission bias and selection bias, so we cannot rule out the influence of other confounding factors. The sample sizes for two studies9,18 were relatively small, so the test efficiency may be insufficient. Also, data collected in most of the included studies did not include laboratory findings, hence, it was difficult to analyze the clinicopathological characteristics of the disease in patients across Africa. This meta-analysis showed a significant heterogeneity between the studies, due to too many outcomes, and there was no subgroup analysis, which could affect the accuracy of the results presented.

    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.

    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 10.1101/2020.10.20.20215905v1 on medRxiv