Comparison of Multimorbidity in COVID-19 infected and general population in Portugal

  1. Miguel Froes
  2. Bernardo Neves
  3. Bruno Martins
  4. Mário J. Silva

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Abstract

Understanding COVID-19 and its risk factors in the Portuguese population is critical to combat this condition. To study the impact of multimorbidity in the population with COVID-19 infection, we performed a descriptive analysis of a dataset extracted from all reported confirmed cases of COVID-19 in Portugal until June 30, 2020. We observed a prevalence of multimorbidity in 6.77% of the 36,244 infected patients. Patients showed an increased risk of hospitalization, ICU admission and mortality with OR 2.22 (CI 95%: 2.13-2.32) for every additional morbidity. Further studies should confirm these findings and special attention should be made on data collection to ensure proper recording of patient comorbidities.

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  1. Version published to 10.1101/2020.07.02.20144378v4 on medRxiv
  2. Version published to 10.1101/2020.07.02.20144378v3 on medRxiv
  3. Version published to 10.1101/2020.07.02.20144378v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.07.02.20144378: (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
    2.3 Statistical analysis: We used Python 3.6 and packages Numpy, Pandas, Seaborn, and SciPy, in combination with Microsoft Excel to evaluate and plot the prevalence of multimorbidity.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Numpy
    suggested: (NumPy, RRID:SCR_008633)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Additionally, IBM SPSS Statistics was used for obtaining age-adjusted risk of hospitalization, ICU admission, and death.
    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: We detected the following sentences addressing limitations in the study:
    Our study also has several important limitations. First of all, the cross-sectional nature of the COVID-19 dataset makes it impossible to account for incomplete outcomes, since several patients could ultimately be hospitalized or die after the end of observation. Reported data on outcomes may therefore be underestimated, so careful interpretation is advised until more data is available. More importantly, despite the fact that no standard set of conditions is established to define multimorbidity, chronic conditions were given on broad groups and there is no specific information on individual conditions. For example, diabetes is given as one group and no distinction is made between type 1 and type 2 diabetes. Therefore, measured morbidities may herald heterogeneous groups of diseases with different degrees of severity, which may influence outcomes. Future datasets should ideally include more accurate information on chronic conditions. Another important concern is related to the risk of under-reporting, which becomes obvious by analyzing reported cardiac diseases. Given that cardiovascular diseases, particularly hypertension, are very prevalent in the Portuguese population [8], the observed prevalence of 8.14% in our study highly suggests that under-reporting may have occurred. In addition, the prevalence of reported cardiac diseases has significantly increased from the April version of the DGS dataset, which showed a much lower cardiac disease prevalence in the general populati...

    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.
    • No funding statement was detected.
    • 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.

  5. Version published to 10.1101/2020.07.02.20144378v1 on medRxiv