tetano
Editor, Senior Moderator
Environmetrics
. 2022 Jul 31;e2751.
doi: 10.1002/env.2751. Online ahead of print.
Association between air pollution and COVID-19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes
Lauren Hoskovec[SUP] 1 [/SUP], Sheena Martenies[SUP] 2 [/SUP], Tori L Burket[SUP] 3 [/SUP], Sheryl Magzamen[SUP] 4 [/SUP], Ander Wilson[SUP] 1 [/SUP]
Affiliations
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 study, we demonstrate our method's ability to impute missing outcome data and improve estimation of regression coefficients compared to a complete case analysis. In our analysis of 55,273 COVID-19 cases in Denver, Colorado, increased annual exposure to fine particulate matter in the year prior to the pandemic was associated with increased risk of severe COVID-19 outcomes. We also found COVID-19 disease severity to be associated with interactions between exposures. Our individual-level analysis fills a gap in the literature and helps to elucidate the association between long-term exposure to air pollution and COVID-19 outcomes.
Keywords: Pólya‐gamma; SARS‐CoV‐2; categorical regression; multiple imputation.
. 2022 Jul 31;e2751.
doi: 10.1002/env.2751. Online ahead of print.
Association between air pollution and COVID-19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes
Lauren Hoskovec[SUP] 1 [/SUP], Sheena Martenies[SUP] 2 [/SUP], Tori L Burket[SUP] 3 [/SUP], Sheryl Magzamen[SUP] 4 [/SUP], Ander Wilson[SUP] 1 [/SUP]
Affiliations
- PMID: 35945947
- PMCID: PMC9353392
- DOI: 10.1002/env.2751
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 study, we demonstrate our method's ability to impute missing outcome data and improve estimation of regression coefficients compared to a complete case analysis. In our analysis of 55,273 COVID-19 cases in Denver, Colorado, increased annual exposure to fine particulate matter in the year prior to the pandemic was associated with increased risk of severe COVID-19 outcomes. We also found COVID-19 disease severity to be associated with interactions between exposures. Our individual-level analysis fills a gap in the literature and helps to elucidate the association between long-term exposure to air pollution and COVID-19 outcomes.
Keywords: Pólya‐gamma; SARS‐CoV‐2; categorical regression; multiple imputation.