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

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.

Keywords: COVID-19; New York City; SARS-CoV-2; health disparities; infectious disease; social determinants of health; social epidemiology.

Figure 1

New York City reduction in subway use in zip code tabulation areas during the coronavirus disease 2019 outbreak on the week of A) February 29, 2020; B) March 7, 2020; C) March 14, 2020; D) March 21, 2020; and E) April 11, 2020. Reductions were calculated as the change in subway use relative to the pre-shutdown period and standardized by the pre-shutdown standard deviation. B–D) Maps correspond to key New York City executive orders, as follows: B) local state of emergency, restricted gatherings exceeding 500 persons; C) city school closures; and D) stay-at-home order, nonessential businesses closure (20, 21).

Figure 2

Segmented regression for subway use and log of cumulative cases, by borough, between February 22, 2020, and April 11, 2020. Opaque lines represent the fitted regression estimates, and transparent loess smoothed lines denote empirical case data. Vertical dashed lines indicate the breakpoints of subway use (i.e., date of onset of decline in subway use) and of log of cumulative reported cases (end date of exponential growth period) for each borough.

Figure 3

Change in subway use by median income quantiles between February 22, 2020, and April 11, 2020. Loess smoothed lines and associated 95% confidence intervals were fitted over each income group. Vertical lines indicate timing of policies implemented in New York City; the dotted line indicates local state of emergency, the dashed line represents city schools closure, and the dotted-dashed line indicates stay-at-home order (20, 21).

Figure 4

Associations among sociodemographic variables, mobility, and coronavirus disease 2019 (COVID-19) rate per 100,000 population. All COVID-19 models were single-predictor models adjusted for testing to account for differential testing within zip code tabulation areas. The COVID-19 case data were reported as of April 26, 2020, and mobility data were reported the week of April 11, 2020. The subway outcomes were also from single-predictor models (with no adjustments). The estimate for the rate of COVID-19 is a risk ratio (RR) with a null of 1, and the estimate for subway use is a slope (β) with a null of 0. See associated Web table 1 for more details. CI, confidence interval.