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1000 Titel
  • The association between different timeframes of air pollution exposure and COVID-19 incidence, morbidity and mortality in German counties in 2020
1000 Autor/in
  1. Hermanns, Sophie |
  2. von Schneidemesser, Erika |
  3. Caseiro, Alexandre |
  4. Koch, Susanne |
1000 Verlag BioMed Central
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-12-26
1000 Erschienen in
1000 Quellenangabe
  • 23(1):112
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12940-024-01149-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269463/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Ambient air pollution is a known risk factor for several chronic health conditions, including pulmonary dysfunction. In recent years, studies have shown a positive association between exposure to air pollutants and the incidence, morbidity, and mortality of a COVID-19 infection, however the time period for which air pollution exposure is most relevant for the COVID-19 outcome is still not defined. The aim of this study was to analyze the difference in association when varying the time period of air pollution exposure considered on COVID-19 infection within the same cohort during the first wave of the pandemic in 2020.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We conducted a cross-sectional study analyzing the association between long- (10- and 2-years) and short-term (28 days, 7 days, and 2 days) exposure to NO<jats:sub>2</jats:sub> and PM<jats:sub>2.5</jats:sub> on SARS-CoV-2 incidence, morbidity, and mortality at the level of county during the first outbreak of the pandemic in spring 2020. Health data were extracted from the German national public health institute (Robert-Koch-Institute) and from the German Interdisciplinary Association for Intensive Care and Emergency Medicine. Air pollution data were taken from the APExpose dataset (version 2.0). We used negative binomial models, including adjustment for risk factors (age, sex, days since first COVID-19 case, population density, socio-economic and health parameters).</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We found that PM<jats:sub>2.5</jats:sub> and NO<jats:sub>2</jats:sub> exposure 28 days before COVID-19 infection had the highest association with infection, morbidity as well as mortality, as compared to long-term or short-term (2 or 7 days) air pollutant exposure. A 1 μg/m<jats:sup>3</jats:sup> increase in PM<jats:sub>2.5</jats:sub> was associated with a 31.7% increase in incidence, a 20.6% need for ICU treatment, a 23.1% need for mechanical ventilation, and a 55.3% increase in mortality; an increase of 1 μg/m<jats:sup>3</jats:sup> of NO<jats:sub>2</jats:sub> was associated with an increase for all outcomes by 25.2 – 29.4%.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Our findings show a positive association between PM<jats:sub>2.5</jats:sub> and NO<jats:sub>2</jats:sub> exposure and the clinical course of a SARS-CoV2 infection, with the strongest association to 28 days of exposure to air pollution. This finding provides an indication as to the primary underlying pathophysiology, and can therefore help to improve the resilience of societies by implementing adequate measures to reduce the air pollutant impact on health outcomes.</jats:p> </jats:sec><jats:sec> <jats:title>Trial registration</jats:title> <jats:p>Not applicable.</jats:p> </jats:sec>
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Aged [MeSH]
lokal Environmental Exposure/adverse effects [MeSH]
lokal Particulate matter
lokal Particulate Matter/adverse effects [MeSH]
lokal Germany/epidemiology [MeSH]
lokal COVID-19/mortality [MeSH]
lokal Mortality
lokal Male [MeSH]
lokal Nitrogen Dioxide/analysis [MeSH]
lokal Nitrogen dioxide
lokal Air Pollutants/analysis [MeSH]
lokal Mechanical ventilation
lokal COVID-19/epidemiology [MeSH]
lokal SARS-CoV-2 [MeSH]
lokal SARS-CoV-2
lokal Female [MeSH]
lokal Adult [MeSH]
lokal Humans [MeSH]
lokal Intensive care medicine
lokal Incidence [MeSH]
lokal Particulate Matter/analysis [MeSH]
lokal Middle Aged [MeSH]
lokal COVID-19
lokal Cross-Sectional Studies [MeSH]
lokal Time Factors [MeSH]
lokal Long-term exposure
lokal Short-term exposure
lokal Air Pollution/adverse effects [MeSH]
lokal Morbidity [MeSH]
lokal Research
lokal Air pollution
lokal Air Pollutants/adverse effects [MeSH]
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/SGVybWFubnMsIFNvcGhpZQ==|https://frl.publisso.de/adhoc/uri/dm9uIFNjaG5laWRlbWVzc2VyLCBFcmlrYQ==|https://frl.publisso.de/adhoc/uri/Q2FzZWlybywgQWxleGFuZHJl|https://orcid.org/0000-0001-5663-7447
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  1. Charité – Universitätsmedizin Berlin |
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    1000 Förderer Charité – Universitätsmedizin Berlin |
    1000 Förderprogramm -
    1000 Fördernummer -
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