SARS-CoV-2 and influenza coinfection throughout the COVID-19 pandemic: an assessment of coinfection rates, cohort characteristics, and clinical outcomes

  1. Colin Pawlowski
  2. Eli Silvert
  3. John C O'Horo
  4. Patrick J Lenehan
  5. Doug Challener
  6. Esteban Gnass
  7. Karthik Murugadoss
  8. Jason Ross
  9. Leigh Speicher
  10. Holly Geyer
  11. A J Venkatakrishnan
  12. Andrew D Badley
  13. Venky Soundararajan

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Case reports of patients infected with COVID-19 and influenza virus (“flurona”) have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.

Article activity feed

  1. Version published to 10.1093/pnasnexus/pgac071
  2. ScreenIT

    SciScore for 10.1101/2022.02.02.22270324: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To track geographic trends in SARS-CoV-2, influenza, and co-infection-related hospitalizations in the United States over the course of the pandemic, we used the “COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries” dataset13.
    State Timeseries”
    suggested: None
    SARS-CoV-2 variant prevalence from the NCBI Virus database: We used the NCBI Virus database6 to determine the prevalence of the different SARS-CoV-2 variants over time.
    NCBI Virus
    suggested: (NCBI Virus, RRID:SCR_018253)
    Influenza strain prevalence from the CDC FluView database: Similarly, we used the CDC FluView database7 to determine the prevalences of the two main influenza strains (A(H1N1)pdm09 and A(H3N2)) over time.
    FluView
    suggested: None
    Relative risks and 95% confidence intervals were computed using the “scipy” package (version 1.7.2) in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    There are several important limitations to note for this study. First, this epidemiological analysis only includes data from US hospitals which have reported co-infections to the HHS Protect Public Data Hub. As a result, this dataset does not include all hospitals. Second, laboratory testing rates for influenza co-infections among COVID-19 cases are low, so the confidence interval for the prevalence of COVID-19 and influenza co-infections based on laboratory data alone is large. Third, in the probability model to estimate the expected number of co-infection cases at the Mayo Clinic, we assume that probabilities of COVID-19 infection and influenza infection are independent, but the true underlying probability distributions are most likely more complex. Indeed, given that the 2020-2021 flu season was extraordinarily mild due to the nonpharmaceutical interventions used to curb COVID-19, and compliance with these measures varied according to disease prevalence, there certainly is a complex interaction that cannot be fully modeled here. This is further complicated in the present flu season with re-implementation of nonpharmaceutical interventions in several localities during the present Omicron surge. Fourth, while we control for pre-existing conditions in the propensity matched analysis, currently we are not controlling for other factors such as respiratory symptoms at time of presentation which may impact testing rates for influenza and COVID-19 or the availability of a combined...

    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 10.1101/2022.02.02.22270324 on medRxiv