Spatially varying associations between community-level predictors and COVID-19 outcomes in NYC

Abstract Background The cumulative impacts of COVID-19 on hospitalization and mortality were not uniformly distributed across New York City (NYC). To better understand potential drivers of this observed geospatial disparity, we explored the associations between community-level sociodemographic characteristics and cumulative COVID-19 hospitalizations and mortality using geographically weighted Poisson regression (GWPR). Methods Cumulative COVID-19 hospitalization and mortality rates in 177 NYC modified ZIP code tabulation areas as of Dec 2022 were taken from the NYC Department of Health and Mental Hygiene, and sociodemographic predictors were taken from the 2018 American Community Survey. GWPR was applied using both non-multiscale (single bandwidth) and multiscale (flexible bandwidth) models to assess which predictors were significant and which associations varied spatially (allowing them to potentially act as both risk and protective factors, depending on the location). Results Multiscale GWPR models outperformed non-multiscale models in rendering residual spatial autocorrelation insignificant. Although multiscale GWPR allowed flexible bandwidths for each predictor, most yielded global bandwidths, suggesting geographically consistent effects. For mortality, the percent of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percent of residents with a disability acted solely as a risk factor. The percent of residents with a bachelors degree or higher acted solely as a protective factor against both outcomes. Conclusions Even when associations were allowed to vary spatially in GWPR models, we still found geographically consistent associations between many sociodemographic predictors and cumulative COVID-19 outcomes in NYC. Consistent risk factors, such as prevalence of disability, or protective factors, such as prevalence of higher education, highlight potential areas for city-wide policies to reduce the burden of future epidemics. Key messages • For cumulative COVID-19 outcomes across NYC communities, geographically consistent risk factors include disability prevalence for hospitalization and lack of health insurance for mortality. • Increasing prevalence of higher education acted as a geographically consistent protective factor against both hospitalization and mortality due to COVID-19.