Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City

  1. Karla Therese L Sy
  2. Micaela E Martinez
  3. Benjamin Rader
  4. Laura F White

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Abstract

Using data from New York City from January 2020 to April 2020, we found an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of severe acute respiratory syndrome coronavirus 2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership) on the week of April 11, 2020, sociodemographic factors, and coronavirus disease 2019 (COVID-19) incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-White and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of health-care essential workers had more mobility during the pandemic. When adjusted for the percentage of essential workers, these associations did not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percentage of people older than 75 years old and percentage of health-care essential workers) were associated with a higher rate of COVID-19 cases per 100,000 people, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged not only are at an increased risk for COVID-19 infection, they lack the privilege to fully engage in social distancing interventions.

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  1. Version published to 10.1093/aje/kwaa277
  2. ScreenIT

    SciScore for 10.1101/2020.05.28.20115949: (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: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are a number of limitations of this study. One limitation of the subway data is that a swipe represents an individual entering the subway station to take a trip, but the NYC subway, unlike some other underground systems, only require individuals to swipe when entering the subway; therefore, we do not know where trips terminated. Moreover, testing and reporting bias may distort the case counts for ZCTAs and boroughs. We attempted to limit the extent of this distortion by adjusting for tests given, but variability in volume is a function of both resource allocation and response to disease incidence and thus it is impossible to disentangle these biases. Additionally, mild and asymptomatic cases are likely underestimated for all of New York City and it is possible that our findings are related to differential ascertainment rather than true prevalence. However, our study found that income and race/ethnicity were predictors of COVID-19 prevalence in NYC, when adjusting for testing effort, and that lower-income neighborhoods and communities of color were hit hardest. This conclusion is supported by a recent report from the NY State Governor’s office that stated “lower-income New York City communities and communities of color show 27 percent of individuals tested positive for COVID-19 antibodies, compared with 19.9 percent of New York City’s overall population”.52 Further research is needed to clarify these disparities as COVID-19 seems to entrench existing inequalities and hea...

    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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  3. Version published to 10.1101/2020.05.28.20115949 on medRxiv