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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.

Objectives The number of SARS-CoV-2 infections is underestimated in surveillance data. Various approaches to assess the seroprevalence of antibodies to SARS-CoV-2 have different resource requirements and generalizability. We estimated the seroprevalence of antibodies to SARS-CoV-2 in Denver County, Colorado, via a cluster-sampled community survey. Methods We estimated the overall seroprevalence of antibodies to SARS-CoV-2 via a community seroprevalence survey in Denver County in July 2020, described patterns associated with seroprevalence, and compared results with cumulative COVID-19 incidence as reported to the health department during the same period. In addition, we compared seroprevalence as assessed with a temporally and geographically concordant convenience sample of residual clinical specimens from a commercial laboratory. Results Based on 404 specimens collected through the community survey, 8.0% (95% CI, 3.9%-15.7%) of Denver County residents had antibodies to SARS-CoV-2, an infection rate of about 7 times that of the 1.1% cumulative reported COVID-19 incidence during this period. The estimated infection-to-reported case ratio was highest among children (34.7; 95% CI, 11.1-91.2) and males (10.8; 95% CI, 5.7-19.3). Seroprevalence was highest among males of Black race or Hispanic ethnicity and was associated with previous COVID-19–compatible illness, a previous positive SARS-CoV-2 test result, and close contact with someone who had confirmed SARS-CoV-2 infection. Testing of 1598 residual clinical specimens yielded a seroprevalence of 6.8% (95% CI, 5.0%-9.2%); the difference between the 2 estimates was 1.2 percentage points (95% CI, −3.6 to 12.2 percentage points). Conclusions Testing residual clinical specimens provided a similar seroprevalence estimate yet yielded limited insight into the local epidemiology of COVID-19 and might be less representative of the source population than a cluster-sampled community survey. Awareness of the limitations of various sampling strategies is necessary when interpreting findings from seroprevalence assessments.

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