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I present three types of applications of generalized additive models (GAMs)to COVID-19 mortality rates in the US for the purpose of advancing methods todocument inequities with respect to which communities suffered disproportionateCOVID-19 mortality rates at specific times during the first three years of thepandemic. First, GAMs can be used to describe the changing relationship betweenCOVID-19 mortality and county-level covariates (sociodemographic, economic, andpolitical metrics) over time. Second, GAMs can be used to performspatiotemporal smoothing that pools information over time and space to addressstatistical instability due to small population counts or stochasticityresulting in a smooth, dynamic latent risk surface summarizing the mortalityrisk associated with geographic locations over time. Third, estimation ofCOVID-19 mortality associations with county-level covariates conditional on asmooth spatiotemporal risk surface allows for more rigorous consideration ofhow socio-environmental contexts and policies may have impacted COVID-19mortality. Each of these approaches provides a valuable perspective todocumenting inequities in COVID-19 mortality by addressing the question ofwhich populations have suffered the worst burden of COVID-19 mortality takinginto account the nonlinear spatial, temporal, and social patterning of disease.

Understanding Complex Patterns in Social, Geographic, and Economic  Inequities in COVID-19 Mortality at the County Level in the US Using  Generalized Additive Models