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      Ambient Air Pollution in Relation to SARS-CoV-2 Infection, Antibody Response, and COVID-19 Disease: A Cohort Study in Catalonia, Spain (COVICAT Study)

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          Abstract

          Background:

          Emerging evidence links ambient air pollution with coronavirus 2019 (COVID-19) disease, an association that is methodologically challenging to investigate.

          Objectives:

          We examined the association between long-term exposure to air pollution with SARS-CoV-2 infection measured through antibody response, level of antibody response among those infected, and COVID-19 disease.

          Methods:

          We contacted 9,605 adult participants from a population-based cohort study in Catalonia between June and November 2020; most participants were between 40 and 65 years of age. We drew blood samples from 4,103 participants and measured immunoglobulin M (IgM), IgA, and IgG antibodies against five viral target antigens to establish infection to the virus and levels of antibody response among those infected. We defined COVID-19 disease using self-reported hospital admission, prior positive diagnostic test, or more than three self-reported COVID-19 symptoms after contact with a COVID-19 case. We estimated prepandemic (2018–2019) exposure to fine particulate matter [PM with an aerodynamic diameter of 2.5μm ( PM2.5 )], nitrogen dioxide ( NO2 ), black carbon (BC), and ozone ( O3 ) at the residential address using hybrid land-use regression models. We calculated log-binomial risk ratios (RRs), adjusting for individual- and area-level covariates.

          Results:

          Among those tested for SARS-CoV-2 antibodies, 743 (18.1%) were seropositive. Air pollution levels were not statistically significantly associated with SARS-CoV-2 infection: Adjusted RRs per interquartile range were 1.07 (95% CI: 0.97, 1.18) for NO2 , 1.04 (95% CI: 0.94, 1.14) for PM2.5 , 1.00 (95% CI: 0.92, 1.09) for BC, and 0.97 (95% CI: 0.89, 1.06) for O3 . Among infected participants, exposure to NO2 and PM2.5 were positively associated with IgG levels for all viral target antigens. Among all participants, 481 (5.0%) had COVID-19 disease. Air pollution levels were associated with COVID-19 disease: adjusted RRs=1.14 (95% CI: 1.00, 1.29) for NO2 and 1.17 (95% CI: 1.03, 1.32) for PM2.5 . Exposure to O3 was associated with a slightly decreased risk ( RR=0.92 ; 95% CI: 0.83, 1.03). Associations of air pollution with COVID-19 disease were more pronounced for severe COVID-19, with RRs=1.26 (95% CI: 0.89, 1.79) for NO2 and 1.51 (95% CI: 1.06, 2.16) for PM2.5 .

          Discussion:

          Exposure to air pollution was associated with a higher risk of COVID-19 disease and level of antibody response among infected but not with SARS-CoV-2 infection. https://doi.org/10.1289/EHP9726

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          Most cited references48

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          Is Open Access

          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Is Open Access

            Collider bias undermines our understanding of COVID-19 disease risk and severity

            Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.
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              Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

              Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.
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                Author and article information

                Journal
                Environ Health Perspect
                Environ Health Perspect
                EHP
                Environmental Health Perspectives
                Environmental Health Perspectives
                0091-6765
                1552-9924
                17 November 2021
                November 2021
                : 129
                : 11
                : 117003
                Affiliations
                [ 1 ]Barcelona Institute for Global Health , Barcelona, Spain
                [ 2 ]CIBER Epidemiologia y Salud Pública , Madrid, Spain
                [ 3 ]Universitat Pompeu Fabra , Barcelona, Spain
                [ 4 ]Hospital del Mar Medical Research Institute , Barcelona, Spain
                [ 5 ]Genomes for Life–GCAT laboratory, Germans Trias i Pujol Research Institute , Badalona, Spain
                [ 6 ]Banc de Sang i Teixits , Barcelona, Spain
                [ 7 ]Faculty of Health Sciences, Universitat Oberta de Catalunya , Barcelona, Spain
                Author notes
                Address correspondence to: Manolis Kogevinas, Severo Ochoa-ISGlobal, NCDs and Environment Group, Barcelona Institute for Global Health (ISGlobal) – Campus MAR, Barcelona Biomedical Research Park (PRBB) (office 194), 88 Doctor Aiguader Rd., 08003 Barcelona, Spain. Telephone: (34) 93 214 7332. Email: manolis.kogevinas@ 123456isglobal.org
                Author information
                https://orcid.org/0000-0002-9605-0461
                https://orcid.org/0000-0002-3014-0747
                https://orcid.org/0000-0001-7399-3267
                https://orcid.org/0000-0002-8789-067X
                https://orcid.org/0000-0001-6891-7032
                https://orcid.org/0000-0002-0007-5315
                https://orcid.org/0000-0001-9929-5707
                https://orcid.org/0000-0003-2840-7753
                https://orcid.org/0000-0002-0448-3404
                https://orcid.org/0000-0002-3704-9874
                https://orcid.org/0000-0002-2325-1027
                https://orcid.org/0000-0003-3277-3107
                https://orcid.org/0000-0001-5105-9836
                https://orcid.org/0000-0002-6751-4060
                Article
                EHP9726
                10.1289/EHP9726
                8597405
                34787480
                6e726fec-46a7-44fa-aee0-532e0a8bbd72

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                History
                : 27 May 2021
                : 22 October 2021
                : 22 October 2021
                Categories
                Research

                Public health
                Public health

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