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

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  • Lauren Hoskovec
  • Sheena Martenies
  • Tori L. Burket
  • Sheryl Magzamen
  • Ander Wilson

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.

Suggested Citation

  • Lauren Hoskovec & Sheena Martenies & Tori L. Burket & Sheryl Magzamen & Ander Wilson, 2022. "Association between air pollution and COVID‐19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    • Handle: RePEc:wly:envmet:v:33:y:2022:i:7:n:e2751
    DOI: 10.1002/env.2751
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    References listed on IDEAS

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    1. Silvia Comunian & Dario Dongo & Chiara Milani & Paola Palestini, 2020. "Air Pollution and COVID-19: The Role of Particulate Matter in the Spread and Increase of COVID-19’s Morbidity and Mortality," IJERPH, MDPI, vol. 17(12), pages 1-22, June.
    2. Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
    3. A. P. Alencar & B. R. Santos, 2014. "Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil," Environmetrics, John Wiley & Sons, Ltd., vol. 25(3), pages 165-171, May.
    4. Xiangyu Zheng & Bin Guo & Jing He & Song Xi Chen, 2021. "Effects of corona virus disease‐19 control measures on air quality in North China," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
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    1. Mariaelena Bottazzi Schenone & Elena Grimaccia & Maurizio Vichi, 2024. "Structural equation models for simultaneous modeling of air pollutants," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.

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