Implications of the reverse associations between obesity prevalence and coronavirus disease (COVID-19) cases and related deaths in the United States

  1. Ning Zhang
  2. Lorraine S. Cordeiro
  3. Zhenhua Liu

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Abstract

The Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was most recently discovered, quickly evolved into a global pandemic. Studies suggested that obesity was a major risk factor for its hospitalization and severity of symptoms. This study investigated the associations between obesity prevalence with overall COVID-19 cases and related deaths across states in the United States. General regression and Chi-square tests were used to examine those associations. The analyses indicated that obesity prevalence (%) across states were negatively associated with COVID-19 cases ( p = 0.0448) and related deaths ( p = 0.0181), with a decrease of 158 cases/100K population and 13 deaths/100K for every 5% increase of the obesity prevalence. When the states were divided based on the median of obesity prevalence (30.9%) into a group of states with low obesity prevalence and a group with high obesity prevalence, both the cases (671 vs 416 cases/100k population) and deaths (39 vs 21 deaths/100k population) were significantly different ( p < 0.001) across groups. These findings provided important information for the relationship between the dual pandemic threats of obesity and COVID-19. These results should not currently be considered as an indication that obesity is a protective factor for COVID-19, and would rather be used as a warning of the public advice that obese people is more vulnerable to COVID-19 infection, which may lead to a false safety message probably given to people with normal body weight.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.09.20127035: (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
    The population data for each state was the estimated population by July 1, 2019 and was released by the U.S. Census Bureau, Population Division (https://www.census.gov).
    https://www.census.gov
    suggested: (U.S. Census Bureau, RRID:SCR_011587)
    Statistical analyses were performed using SAS program (Version 9.4, SAS Institute, Cary, NC, USA).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We acknowledge that this study has limitations: a) There are large variations of the COVID-19 cases and related mortalities across the states, which reduced the accuracy of the estimate. b) The number of cases/state is heavily affected by the number of tests, which are unequally performed across the states. However, even with highly varied test rates across states, the higher test rate/state (5745 vs 4574/100K, Fig. 3C) together with a higher positive rate/tests (11.7 vs 9.1%, Fig. 3D) for the states with low obesity prevalence collectively suggested the higher prevalence of COVID-19 in those states, comparing to states with high obesity prevalence. Also, even after adjusting for the test rate, the results remains significant (Model II). c) COVID-19 is a highly transmissible disease, and population density in large cities are a critical risk factor. Even though in our model III we adjusted for the population, we recognized the influence from the New York City as the epicenter. Nevertheless, the present study discovered interesting negative associations between obesity prevalence and COVID-19 cases and related deaths. These findings may possibly be explained by the “obesity paradox” theory and the sedentary lifestyle with limited out-door activity that is potentially protective to COVID-19 infection in obese people. In conclusion, the present study observed interesting negative associations between obesity prevalence with COVID-19 cases and related mortalities. These associati...

    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

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  2. Version published to 10.1101/2020.06.09.20127035v1 on medRxiv