Spatially Varying Associations between Community Level Sociodemographic Predictors and Cumulative COVID-19 Outcomes in New York City

Nearly three years after onset of the COVID-19 pandemic in New York City (NYC), cumulative hospitalization and mortality rates were not uniformly distributed across the city. To help understand potential drivers of this geospatial disparity, we applied geographically weighted Poisson regression (GWPR), which allows associations between these outcomes and community-level predictors to be location-dependent.Cumulative COVID-19 hospitalization and mortality rates in n  = 177 NYC modified ZIP code tabulation areas as of December 31, 2022 were obtained from the NYC Department of Health and Mental Hygiene, while socioeconomic and demographic predictors were queried from the 2018 American Community Survey. The GWPR model, and its multiscale generalization, yielded better diagnostics than the more conventional global models that assume spatially stationary associations, with the non-multiscale GWPR model performing the best.Several predictors acted as both risk and protective factors for both outcomes, depending on location, which means one common model for the whole city would be misleading. These include the percentage of non-Hispanic whites, foreign born citizens, male, mean commute time and having had at least one vaccination.Other predictors showed more geographically consistent effects. For mortality, the percentage of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percentage of residents with a disability acted solely as a risk factor. The percentage of residents > 24y with a bachelor’s degree or higher acted solely as a protective factor against both outcomes. These community factors could therefore help guide local place-based interventions to reduce disparities and the overall burden of future epidemics.