Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

  1. Sara J. Cromer
  2. Chirag M. Lakhani
  3. Deborah J Wexler
  4. Sherri-Ann M. Burnett-Bowie
  5. Miriam Udler
  6. Chirag J. Patel

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities.

Methods and Findings

All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.

Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized.

Conclusions

This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.09.30.20201830: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Racial and ethnic variables were collapsed into non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, Hispanic, and Other categories, and preferred languages were categorized as English, Spanish, or Other.
    non-Hispanic White
    suggested: None

    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:
    LIMITATIONS: These findings must be interpreted in the context of the study design. The number of cases diagnosed depends heavily on testing rates, which were influenced by changing testing criteria which, in turn, were influenced by local government actions, the necessity of ensuring healthcare worker safety, prevalence of community health centers conducting outpatient testing, and awareness of existing outbreaks. For this reason, we adjusted all analyses for census tract-level per capita testing rates. Additionally, this cohort included only patients tested within one large healthcare system, leading to under-representation of neighborhoods in this geographic area which are predominantly served by other institutions, raising the possibility of selection bias. Similarly, while we fully capture test positivity among those tested and death among those hospitalized, an individual may have been tested in the MGB system but later presented to another hospital for admission, leading to incomplete capture of the hospitalization outcome. This may be especially common among those with missing information (e.g. insurance) who may have only interacted with the MGB system briefly for a SARS-CoV-2 test. While deaths which occurred in individuals admitted to other hospitals were not captured, these individuals would have been excluded from the death analysis which only included those hospitalized in our system. Although more than 500 deaths occurred in the cohort, power to detect smaller ...

    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 10.1101/2020.09.30.20201830 on medRxiv