Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US

  1. Adam Y. Li
  2. Theodore C. Hannah
  3. John R. Durbin
  4. Nickolas Dreher
  5. Fiona M. McAuley
  6. Naoum Fares Marayati
  7. Zachary Spiera
  8. Muhammad Ali
  9. Alex Gometz
  10. JT Kostman
  11. Tanvir F. Choudhri

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Abstract

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  1. Version published to 10.1016/j.amjms.2020.06.015
  2. ScreenIT

    SciScore for 10.1101/2020.04.17.20069708: (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 variableModel 3 added these county demographics and environmental variables to Model 2: percent of population over 65, proportion of Black residents, percent of the population that is female, percent of population living in rural areas, the Food Environment Index (a measure of accessibility and affordability of healthy food), the rate of violent crime per 100,000 people, the average temperature from 10 days before the first case to the end of the study, air quality measured as the average annual ambient concentrations of PM2.5, percent of the population considered to be in fair or poor health, and the poverty rate.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data Sources for Covariates: Race demographics for counties was obtained from the County Health Rankings and Roadmaps Program database.
    Roadmaps Program
    suggested: None
    Specifically these new variables were: diabetes, obesity, physical inactivity, excessive drinking, and smoking were all reported as percent of the population; liver disease, hypertension, coronary heart disease, and chronic respiratory disease were reported as mortality per 100,000 people; and the patient to primary care physician ratio, the percent of people sleeping fewer than seven hours per night, the percent of the population without health insurance and the percent of the population who received the flu vaccine were also included All statistical analyses were performed with Prism 8.0 (GraphPad Software, San Diego, CA).
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: The emerging nature of Covid-19 required inclusion of only counties with sufficient data. Many counties were excluded from analysis for lack of Covid-19 cases or lack of Covid-19 deaths. This limitation is unavoidable and waiting for sufficient data may deter public health response. This study is also limited by the fact the COVID-19 pandemic is still in progress in all of the counties included here and is at different stages. Thus, the number of cases and mortality statistics for the counties provides a snapshot of the current state, but may not, and likely will not, be reflective of the ultimate case and death tolls in these counties. Moreover, most counties are still in the early stages of accumulating deaths which axiomatically lags behind the number of cases. While our results of temperature and Black race on cases per 100,000 have been consistent for a number of weeks, the number of counties with at least 10 deaths has only recently become large enough to perform similar analyses. Temperature also demonstrates this lack of simultaneity, but changes more predictably and slowly than Covid-19 cases and deaths. Additionally, since we relied heavily on publicly available and easily accessible sources to create our database, we used sources that may be outdated or otherwise inconsistent with the actual value of various statistics during the past few months. Confounding effects are also possible, given that it is impossible to fully extricate socioeconomic, demogr...

    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. Version published to 10.1101/2020.04.17.20069708 on medRxiv