Thirty-Day Mortality and Morbidity in COVID-19 Positive vs. COVID-19 Negative Individuals and vs. Individuals Tested for Influenza A/B: A Population-Based Study

  1. Vardan Nersesjan
  2. Moshgan Amiri
  3. Hanne K. Christensen
  4. Michael E. Benros
  5. Daniel Kondziella

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Abstract

Background: As of October 2020, COVID-19 has caused 1,000,000 deaths worldwide. However, large-scale studies of COVID-19 mortality and new-onset comorbidity compared to individuals tested negative for COVID-19 and individuals tested for influenza A/B are lacking. We investigated COVID-19 30-day mortality and new-onset comorbidity compared to individuals with negative COVID-19 test results and individuals tested for influenza A/B.

Methods and findings: This population-based cohort study utilized electronic health records covering roughly half ( n = 2,647,229) of Denmark's population, with nationwide linkage of microbiology test results and death records. All individuals ≥18 years tested for COVID-19 and individuals tested for influenza A/B were followed from 11/2017 to 06/2020. Main outcome was 30-day mortality after a test for either COVID-19 or influenza. Secondary outcomes were major comorbidity diagnoses 30-days after the test for either COVID-19 or influenza A/B. In total, 224,639 individuals were tested for COVID-19. To enhance comparability, we stratified the population for in- and outpatient status at the time of testing. Among inpatients positive for COVID-19, 356 of 1,657 (21%) died within 30 days, which was a 3.0 to 3.1-fold increased 30-day mortality rate, when compared to influenza and COVID-19-negative inpatients (all p < 0.001). For outpatients, 128 of 6,263 (2%) COVID-19-positive patients died within 30 days, which was a 5.5 to 6.9-fold increased mortality rate compared to individuals tested negative for COVID-19 or individuals tested positive or negative for influenza, respectively (all p < 0.001). Compared to hospitalized patients with influenza A/B, new-onset ischemic stroke, diabetes and nephropathy occurred more frequently in inpatients with COVID-19 (all p < 0.05).

Conclusions: In this population-based study comparing COVID-19 positive with COVID-19 negative individuals and individuals tested for influenza, COVID-19 was associated with increased rates of major systemic and vascular comorbidity and substantially higher mortality. Results should be interpreted with caution because of differences in test strategies for COVID-19 and influenza, use of aggregated data, the limited 30-day follow-up and the possibility for changing mortality rates as the pandemic unfolds. However, the true COVID-19 mortality may even be higher than the stated 3.0 to 5.5-fold increase, owing to more extensive testing for COVID-19.

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  1. Version published to 10.3389/fmed.2020.598272
  2. ScreenIT

    SciScore for 10.1101/2020.07.25.20162156: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Ethics Committee of the Capital Region of Denmark waives approval for register-based studies on aggregated anonymized data (Section 14.2 of the Committee Act.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Chi-squared statistics were used to calculate odds ratio (OR), RR and 95% confidence intervals (CI) using SPSS (version 25; IBM, Armonk, NY, USA).
    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:
    As to limitations, we were unable to adjust for confounding factors such as socioeconomic, lifestyle and ethnicity, owing to the use of aggregated EHR data which depend on correct coding by physicians. Selection bias might be considerable because individuals were tested in hospital settings (even as outpatients), and the testing strategy of COVID-19 in Denmark has been much more comprehensive compared to influenza. Conclusions: In this first population-based study comparing COVID-19 with influenza, COVID-19 was associated with substantially higher mortality, which is likely even higher than the stated 3.0-5.5-fold increase owing to more extensive testing for COVID-19, and we observed higher rates of new-onset ischemic stroke, diabetes and renal failure. Next, middle- and long-term follow-up data are required to investigate mortality trajectories in COVID-19 versus influenza populations, and molecular and genetic studies will have to elucidate the specific biological mechanisms behind COVID-19’s higher mortality and morbidity compared to influenza.

    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. ScreenIT

    SciScore for 10.1101/2020.07.25.20162156: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementThe Ethics Committee of the Capital Region of Denmark waives approval for register-based studies on aggregated anonymized data (Section 14.2 of the Committee Act.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Chi-squared statistics were used to calculate odds ratio (OR), RR and 95% confidence intervals (CI) using SPSS (version 25; IBM, Armonk, NY, USA).
    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:

    As to limitations, we were unable to adjust for confounding factors such as socioeconomic, lifestyle and ethnicity, owing to the use of aggregated EHR data which depend on correct coding by physicians. Selection bias might be considerable because individuals were tested in hospital settings (even as outpatients), and the testing strategy of COVID-19 in Denmark has been much more comprehensive compared to influenza. Conclusions In this first population-based study comparing COVID-19 with influenza, COVID-19 was associated with substantially higher mortality, which is likely even higher than the stated 3.0-5.5-fold increase owing to more extensive testing for COVID-19, and we observed higher rates of new-onset ischemic stroke, diabetes and renal failure. Next, middle- and long-term follow-up data are required to investigate mortality trajectories in COVID-19 versus influenza populations, and molecular and genetic studies will have to elucidate the specific biological mechanisms behind COVID-19’s higher mortality and morbidity compared to influenza.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. ScreenIT

    SciScore for 10.1101/2020.07.25.20162156: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementThe Ethics Committee of the Capital Region of Denmark waives approval for register-based studies on aggregated anonymized data (Section 14.2 of the Committee Act.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Chi-squared statistics were used to calculate odds ratio (OR), RR and 95% confidence intervals (CI) using SPSS (version 25; IBM, Armonk, NY, USA).
    SPSS
    suggested: (SPSS, SCR_002865)

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. Version published to 10.1101/2020.07.25.20162156v1 on medRxiv