Who is more susceptible to Covid-19 infection and mortality in the States?

  1. Y Han
  2. V.O.K. Li
  3. J.C.K. Lam
  4. P. Y Guo
  5. R.Q Bai
  6. W.T.W. Fok

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Abstract

Background

A novel coronavirus was detected in Wuhan, China and reported to WHO on 31 December 2019. WHO declared a global pandemic on 11 March 2020. The first case in the US was reported in January 2020. Since mid-March 2020, the number of confirmed cases has increased exponentially in the States, with 1.1 million confirmed cases, and 57.4 thousand deaths as of 30 April 2020. Even though some believe that this new lethal coronavirus does not show any partiality to the rich, previous epidemiological studies find that the poor in the US are more susceptible to the epidemics due to their limited access to preventive measures and crowded living conditions. In this study, we postulate that the rich is more susceptible to Covid-19 infection during the early stage before social distancing measures have been introduced. This may be attributed to the higher mobility (both inter- and intra-city), given their higher tendency to travel for business/education, and to more social interactions. However, we postulate after the lockdown/social distancing has been imposed, the infection among the rich may be reduced due to better living conditions. Further, the rich may be able to afford better medical treatment once infected, hence a relatively lower mortality. In contrast, without proper medical insurance coverage, the poor may be prevented from receiving timely and proper medical treatment, hence a higher mortality.

Method

We will collect the number of confirmed Covid-19 cases in the US during the period of Jan 2020 to Apr 2020 from Johns Hopkins University, also the number of Covid-19 tests in the US from the health departments across the States. County-level socio-economic status (SES) including age, sex, race/ethnicity, income, education, occupation, employment status, immigration status, and housing price, will be collected from the US Census Bureau. State/county-level health conditions including the prevalence of chronic diseases will be collected from the US CDC. State/county-level movement data including international and domestic flights will be collected from the US Bureau of Transportation Statistics. We will also collect the periods of lockdown/social distancing. Regression models are constructed to examine the relationship between SES, and Covid-19 infection and mortality at the state/county-level before and after lockdown/social distancing, while accounting for Covid-19 testing capacities and co-morbidities.

Expected Findings

We expect that there is a positive correlation between Covid-19 infection and SES at the state/county-level in the US before social distancing. In addition, we expect a negative correlation between Covid-19 mortality and SES.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.01.20087403: (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

    No key resources detected.

    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:
    In our regression analysis, we have accounted for the effects of social distancing/lockdown, co-morbidities, and the limitations of COVID-19 testing capacities. Implication: We expect that in the States, the rich are at a higher risk of Covid-19 infection before social distancing/lockdown, but a lower risk of mortality, after controlling other confounding factors, such as social distancing/lockdown, co-morbidities, and the limitations of COVID-19 testing capacities. We expect that in the US, while the rich may have a higher risk of Covid-19 infection before social distancing/lockdown, the poorer may have higher mortality after being infected. This can help guide future public health policy-making, as identifying which particular income groups are most/least susceptible to Covid-19 infection/mortality, should provide insights into how future medical resource should be allocated or redirected, in order to help the most needy in the society. We expect that in the future, studies on Covid-19 infection/mortality modelling should incorporate SES factors which should have a decisive impact on the accuracy of such modeling.

    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 10.1101/2020.05.01.20087403v1 on medRxiv