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      Long-term exposure to air pollution and COVID-19 incidence: a prospective study of residents in the city of Varese, Northern Italy

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          Abstract

          Objectives

          To investigate the association between long-term exposure to airborne pollutants and the incidence of SARS-CoV-2 up to March 2021 in a prospective study of residents in Varese city.

          Methods

          Citizens of Varese aged ≥18 years as of 31 December 2019 were linked by residential address to 2018 average annual exposure to outdoor concentrations of PM 2.5, PM 10, NO 2, NO and ozone modelled using the Flexible Air quality Regional Model (FARM) chemical transport model. Citizens were further linked to regional datasets for COVID-19 case ascertainment (positive nasopharyngeal swab specimens) and to define age, sex, living in a residential care home, population density and comorbidities. We estimated rate ratios and additional numbers of cases per 1 µg/m 3 increase in air pollutants from single- and bi-pollutant Poisson regression models.

          Results

          The 62 848 residents generated 4408 cases. Yearly average PM 2.5 exposure was 12.5 µg/m 3. Age, living in a residential care home, history of stroke and medications for diabetes, hypertension and obstructive airway diseases were independently associated with COVID-19. In single-pollutant multivariate models, PM 2.5 was associated with a 5.1% increase in the rate of COVID-19 (95% CI 2.7% to 7.5%), corresponding to 294 additional cases per 100 000 person-years. The association was confirmed in bi-pollutant models; excluding subjects in residential care homes; and further adjusting for area-based indicators of socioeconomic level and use of public transportation. Similar findings were observed for PM 10, NO 2 and NO. Ozone was associated with a 2% decrease in disease rate, the association being reversed in bi-pollutant models.

          Conclusions

          Long-term exposure to low levels of air pollutants, especially PM 2.5, increased the incidence of COVID-19. The causality warrants confirmation in future studies; meanwhile, government efforts to further reduce air pollution should continue.

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          Most cited references37

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy

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              Sensitivity Analysis in Observational Research: Introducing the E-Value.

              Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.

                Author and article information

                Journal
                Occup Environ Med
                Occup Environ Med
                oemed
                oem
                Occupational and Environmental Medicine
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1351-0711
                1470-7926
                January 2022
                7 January 2022
                7 January 2022
                : oemed-2021-107833
                Affiliations
                [1 ]departmentResearch Center in Epidemiology and Preventive Medicine, Department of Medicine and Surgery , University of Insubria , Varese, Italy
                [2 ]departmentDepartment of Medical Sciences and Public Health , University of Cagliari , Cagliari, Italy
                [3 ]departmentNational Heart and Lung Institute , Imperial College London , London, UK
                [4 ]Arianet S.R.L , Milano, Italy
                Author notes
                [Correspondence to ] Professor Giovanni Veronesi, Research Center in Epidemiology and Preventive Medicine, Department of Medicine and Surgery, University of Insubria, Varese 21100, Italy; giovanni.veronesi@ 123456uninsubria.it
                Author information
                http://orcid.org/0000-0002-4119-6615
                http://orcid.org/0000-0001-8256-2661
                http://orcid.org/0000-0003-2741-7124
                Article
                oemed-2021-107833
                10.1136/oemed-2021-107833
                8764713
                35012995
                174064dd-9510-4810-be67-d79c4bb175bc
                © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

                This article is made freely available for personal use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

                History
                : 16 June 2021
                : 09 December 2021
                Categories
                Environment
                1612
                2474
                Original research
                Custom metadata
                press-release
                free

                Occupational & Environmental medicine
                air pollution,covid-19,epidemiology
                Occupational & Environmental medicine
                air pollution, covid-19, epidemiology

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