ABSTRACT
IntroductionThe population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic and racial characteristics play an important role. MethodsWe analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas (ZCTA) with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. ResultsSpatial clustering accounted for approximately 32% of the variation in the data. For every one unit increase in a scaled standardized measure of Chronic Obstructive Pulmonary Disease (COPD) in a community, there was an approximate 8-fold increase in the risk of a positive COVID-19 test in a ZCTA (Incidence Density Ratio = 8.2, 95% Credible Interval 3.7, 18.3). There was a nearly five-fold increase in the risk of a positive COVID-19 test. (IDR = 4.8, 95% Cr I 2.4, 9.7) associated with the proportion of Black / African American residents. Increases in the proportion of residents older than 65, housing density and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, COPD, heart disease, housing density and Black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of Black/African American residents and proportion of older persons. ConclusionsAreas with large proportions of Black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.
