Biostatistical Investigation of Correlation Between COVID-19 and Diabetes Mellitus

  1. Zahra Eynizadeh
  2. Zahra Ameli
  3. Mahdieh Sahranavard
  4. Mobina Daneshparvar
  5. Mobina Abdollahi Dolagh
  6. Mahboobeh Roozkhosh
  7. Mohammad Reza Ejtehadi
  8. Maryam Azimzadeh Irani

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Abstract

COVID-19 is a highly infectious disease. Studies suggest that its severity is amplified in patients diagnosed with Diabetes Mellitus. In this study, the correlation between the prevalence of COVID-19 and Diabetes was analyzed at the regional and global scale using data extracted from WHO and IDF Diabetes Atlas. For the regional investigation data was assorted into ten regions including Central Asia, Middle east and western Asia, Africa, North America and the Caribbean, South east Asia, East Asia, Europe, South and Central America, South Asia and Oceania. The results show a positive correlation coefficient of 0.47 in Middle east and western Asia. While at the global scale analysis all the selected countries were considered together and a correlation coefficient of 0.32 was observed. This number was increased to 0.69 when the top most affected countries by COVID-19 were considered for the analysis. In order to investigate the time dependent relationship of the two diseases, the data was analysed in five windows of 45 days each since the beginning of pandemic. The results show an increasing pattern of the correlation coefficient in the last three windows. Overall, based on this study by increasing the prevalence of Diabetes Mellitus, the prevalence of COVID-19 cases may also increase.

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    SciScore for 10.1101/2020.11.21.20235853: (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
    Calculation of C and D variables was carried out by Microsoft Excel 2016.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Statistical tests and analyses: Microsoft Excel 2016, Minitab 19 Statistical Software [19] and IBM SPSS Statistics [14] were used to analyse the data. 2.7 Graphs: The scatter plots were drawn using Minitab 19 Statistical Software [19].
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    Minitab
    suggested: (Minitab, RRID:SCR_014483)

    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.

    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 10.1101/2020.11.21.20235853v1 on medRxiv