Association between air pollution and COVID-19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes.

ABSTRACT: Recent ecological analyses suggest air pollution exposure may increase susceptibility to and severity of coronavirus disease 2019 (COVID-19). Individual-level studies are needed to clarify the relationship between air pollution exposure and COVID-19 outcomes. We conduct an individual-level analysis of long-term exposure to air pollution and weather on peak COVID-19 severity. We develop a Bayesian multinomial logistic regression model with a multiple imputation approach to impute partially missing health outcomes. Our approach is based on the stick-breaking representation of the multinomial distribution, which offers computational advantages, but presents challenges in interpreting regression coefficients. We propose a novel inferential approach to address these challenges. In a simulation

SUBMITTER: Hoskovec L 

PROVIDER: S-EPMC9353392 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature