Community and Socioeconomic Factors Associated with COVID-19 in the United States: Zip code level cross sectional analysis

  1. Avirup Guha
  2. Janice M. Bonsu
  3. Amit Kumar Dey
  4. Daniel Addison

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Abstract

Background

Multiple reports have pointed towards involvement of community and socioeconomic characteristics of people in the United States may be associated with COVID-19 cases and deaths.

Methods

In this study, zip-code level data from 5 major metropolitan areas, was utilized to study the effect of multiple demographic & socioeconomic factors – including race, age, income, chronic disease comorbidity, population density, number of people per household on number of positive cases and ensuing death. Adjusted linear regression analysis using 13 to 16 such variables was performed.

Results

Overall, 442 zip codes reporting 93,170 positive COVID-19 cases and 138 zip codes reporting mortality ranging from 0 to 25 were included in this study. A multivariable linear regression model noted that 1% increase in the proportion of residents above the age of 65 years, proportion of African American residents, proportion of females, persons per household and population density of the zip code increased the proportion of positive cases by 0.77%, 0.23%, 1.64%, 1.83% and 0.46% respectively (P<0.01) with only population density remaining significant in zip codes with greater than median number of cases. In zips with greater than median number of deaths, no community/socio-economic factor contributed significantly to death.

Conclusion

This study gives early signals of gender, and racial inequalities while providing overwhelming evidence of how population density may contribute to an increase in the number of positive cases of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.19.20071944: (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 variableSpecific metrics collected were proportion of Whites, African Americans, and Hispanics, population density reported as people per square mile, proportion of females, median age in years, proportion of those older than 65 years, median household income in dollars, proportion of those below poverty line, persons per household, proportion of married, median house value in dollars, proportion of those with bachelor’s degree, and proportion of English speaking.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Covariates: The zip code level data was obtained from the censusreporter.org website for 446 zip codes spanning the areas listed above.
    Covariates
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are certain limitations which should be considered while considering the aforementioned results. The study results have potentially changed in terms of reported number of cases and deaths however, the models generated, and effects described are expected to potentially remain the same given the overall power of the study and multiple sensitivity analysis performed. Even though multiple socio-demographic variables were utilized, the authors acknowledge that this study should not be interpreted out of context of currently available data. Specifically, chronic conditions increase the risk of acquiring and dying from COVID-19 and in this study such adjustment was limited from such adjustments. Another metric about the number of positive cases may point towards discrepant testing/swab availability. However, the authors feel that socio-demographic factors may be responsible for such discrepancy in testing and would contribute to multicollinearity and not change the effect noted above.

    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.

  2. Version published to 10.1101/2020.04.19.20071944 on medRxiv