Hoskovec Lauren, Martenies Sheena, Burket Tori L, Magzamen Sheryl, Wilson Ander
Department of Statistics Colorado State University Fort Collins Colorado USA.
Department of Kinesiology and Community Health University of Illinois at Urbana-Champaign Urbana-Champaign Illinois USA.
Environmetrics. 2022 Jul 31:e2751. doi: 10.1002/env.2751.
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.
近期的生态分析表明，接触空气污染可能会增加对2019冠状病毒病（COVID-19）的易感性和病情严重程度。需要开展个体层面的研究来阐明空气污染暴露与COVID-19结局之间的关系。我们针对空气污染和天气的长期暴露对COVID-19严重程度峰值进行了个体层面的分析。我们开发了一种贝叶斯多项逻辑回归模型，并采用多重填补方法来填补部分缺失的健康结局数据。我们的方法基于多项分布的折断棒表示法，该方法具有计算优势，但在解释回归系数方面存在挑战。我们提出了一种新颖的推断方法来应对这些挑战。在一项模拟研究中，我们证明了与完整病例分析相比，我们的方法有能力填补缺失的结局数据并改善回归系数的估计。在我们对科罗拉多州丹佛市55273例COVID-19病例的分析中，疫情前一年细颗粒物年暴露量增加与严重COVID-19结局风险增加相关。我们还发现COVID-19疾病严重程度与暴露之间的相互作用有关。我们的个体层面分析填补了文献中的空白，并有助于阐明长期暴露于空气污染与COVID-19结局之间的关联。
