An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

  1. Richard S. Whittle
  2. Ana Diaz-Artiles

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Abstract

Background

New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate.

Methods

Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion.

Results

Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p =0.021). An increase of 10,000 people per km 2 is associated with a 2.4% (95% CI 0.6 to 4.2%, p =0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p <0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p <0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p <0.001) increase in positivity rate. The percentage of Hispanic ( p =0.718), Asian ( p =0.966), or Other ( p =0.588) populations were not statistically significant factors.

Conclusions

Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.17.20069823: (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 variableDemographic parameters: Four demographic parameters were included in the study: Percentage of dependent population, Dependents; percentage of aged population, Aged; males per 100 females, MFR; and percentage of the population identifying as white, Race.

    Table 2: Resources

    No key resources detected.

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We note four key limitations of the ecological study. First, our dependent variable is the number of detected COVID-19 cases, which may be significantly different from the number of true cases [47]. We believe, however that this does not detract from the validity of the study, since characterization of the detection and prevalance is important for pandemic management [48]. Studies on HIV rates amongst at risk populations suggest that the relationship between predictors and the number of detected cases is likely a complex interaction via at least three pathways: the true number of cases, access to testing (means) [49], and population attitudes to testing (motivation) [50, 51]. Thus, we can still develop valid inferences, even if we cannot elicit with certainty which one (or ones) of these pathways the significant predictors act through. Second, any associations made must be interpreted with caution since, as with any observational study, spurious correlations produced by unstudied confounding factors may be present. Caution is also advised due to the ecological fallacy of making individual inferences from aggregate data. Further verification is required to determine true causative links between predictors and detected cases even when associations are significant. Third, the significant predictors found are likely not the only explanations for different detection rates between different neighborhoods. However this study does provide useful insight into explaining between-neighb...

    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 10.1186/s12916-020-01731-6
  3. ScreenIT

    SciScore for 10.1101/2020.04.17.20069823: (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

    Results from OddPub: Thank you for sharing your code.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.04.17.20069823v1 on medRxiv