Areas with the 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 healthcare workers had more subway use during the pandemic. The positive associations between subway use and median income, and between subway use and percent non-white and/or Hispanic/Latino do not remain when adjusted for the percent of essential workers. This suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR=1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR=1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs.
The United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave. However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/Summary: New York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases.
Our results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.