Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study

  1. Claudio Zanettini
  2. Mohamed Omar
  3. Wikum Dinalankara
  4. Eddie Luidy Imada
  5. Elizabeth Colantuoni
  6. Giovanni Parmigiani
  7. Luigi Marchionni

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Abstract

The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.

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  1. Version published to 10.3390/vaccines9050427
  2. ScreenIT

    SciScore for 10.1101/2020.06.24.20129817: (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
    MAPLE) and obtained and pre-processed from Wu et. al24.
    MAPLE
    suggested: (Maple, RRID:SCR_014449)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We regard ours as an urgent, but preliminary, study and acknowledge several limitations. Important potentially confounding variables include the socioeconomic levels, quality of healthcare, and the lack of uniform death reporting approaches. While our covariates allow to control for some of these confounding factors, more systematic and accurate data collection approaches would improve the reliability of analysis such as ours. An important limitation is that we could only control for the number of tests performed at the state level, since testing data were not available for each county. Furthermore, testing availability and recommendations, especially in the initial phase of the pandemic, varied between states. We tried to mitigate this issue by using the total population in the county as the offset in our models, rather than the total number of confirmed cases. Since the number of performed tests can have an important impact on the number of reported cases, we decided not to explore the effect of influenza vaccination on SARS-CoV-2 reported infection rates. We were also unable to stratify and analyze COVID-19 death rate by age group, because this information was not available. Similarly, in our study, we could not take into account the variability in vaccine formulation and in vaccine efficacy, which depends on the predominant circulating strains, because this kind of data was not reported. Based on the information currently available, it is unclear whether the observed nega...

    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 10.1101/2020.06.24.20129817: (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 variableDemographic variables: a) % of people 65-year of age or older, b) median age, c) sex ratio (males per 100 females), and d) child dependency ratio (ratio between population under 18 years and population 18-to-64). 6.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    MAPLE) and obtained and pre-processed from Wu et. al24 .
    MAPLE
    suggested: (Maple, SCR_014449)

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    • We regard ours as an urgent, but preliminary, study and acknowledge several limitations.
    • Important potentially confounding variables include the socioeconomic levels, quality of healthcare, and the lack of uniform death reporting approaches.
    • While our covariates allow to control for some of these confounding factors, more systematic and accurate data collection approaches would improve the reliability of analysis such as ours.
    • An important limitation is that we could only control for the number of tests performed at the state level, since testing data were not available for each county.
    • Furthermore, testing availability and recommendations, especially in the initial phase of the pandemic, varied between states.
    • We tried to mitigate this issue by using the total population in the county as the offset in our models, rather than the total number of confirmed cases.

    Results from OddPub: Thank you for sharing your code and data.

    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. Version published to 10.1101/2020.06.24.20129817v1 on medRxiv