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Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates
Ambient particulate matter (PM(2.5)) pollution is an important threat to human health. The aim of this study is to estimate the environmental burden of disease (EBD) for the German population associated with PM(2.5) exposure in Germany for the years 2010 until 2018. The EBD method was used to quanti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602590/ https://www.ncbi.nlm.nih.gov/pubmed/36293778 http://dx.doi.org/10.3390/ijerph192013197 |
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author | Tobollik, Myriam Kienzler, Sarah Schuster, Christian Wintermeyer, Dirk Plass, Dietrich |
author_facet | Tobollik, Myriam Kienzler, Sarah Schuster, Christian Wintermeyer, Dirk Plass, Dietrich |
author_sort | Tobollik, Myriam |
collection | PubMed |
description | Ambient particulate matter (PM(2.5)) pollution is an important threat to human health. The aim of this study is to estimate the environmental burden of disease (EBD) for the German population associated with PM(2.5) exposure in Germany for the years 2010 until 2018. The EBD method was used to quantify relevant indicators, e.g., disability-adjusted life years (DALYs), and the life table approach was used to estimate the reduction in life expectancy caused by long-term PM(2.5) exposure. The impact of varying assumptions and input data was assessed. From 2010 to 2018 in Germany, the annual population-weighted PM(2.5) concentration declined from 13.7 to 10.8 µg/m(3). The estimates of annual PM(2.5)-attributable DALYs for all disease outcomes showed a downward trend. In 2018, the highest EBD was estimated for ischemic heart disease (101.776; 95% uncertainty interval (UI) 62,713–145,644), followed by lung cancer (60,843; 95% UI 43,380–79,379). The estimates for Germany differ from those provided by other institutions. This is mainly related to considerable differences in the input data, the use of a specific German national life expectancy and the selected relative risks. A transparent description of input data, computational steps, and assumptions is essential to explain differing results of EBD studies to improve methodological credibility and trust in the results. Furthermore, the different calculated indicators should be explained and interpreted with caution. |
format | Online Article Text |
id | pubmed-9602590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96025902022-10-27 Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates Tobollik, Myriam Kienzler, Sarah Schuster, Christian Wintermeyer, Dirk Plass, Dietrich Int J Environ Res Public Health Article Ambient particulate matter (PM(2.5)) pollution is an important threat to human health. The aim of this study is to estimate the environmental burden of disease (EBD) for the German population associated with PM(2.5) exposure in Germany for the years 2010 until 2018. The EBD method was used to quantify relevant indicators, e.g., disability-adjusted life years (DALYs), and the life table approach was used to estimate the reduction in life expectancy caused by long-term PM(2.5) exposure. The impact of varying assumptions and input data was assessed. From 2010 to 2018 in Germany, the annual population-weighted PM(2.5) concentration declined from 13.7 to 10.8 µg/m(3). The estimates of annual PM(2.5)-attributable DALYs for all disease outcomes showed a downward trend. In 2018, the highest EBD was estimated for ischemic heart disease (101.776; 95% uncertainty interval (UI) 62,713–145,644), followed by lung cancer (60,843; 95% UI 43,380–79,379). The estimates for Germany differ from those provided by other institutions. This is mainly related to considerable differences in the input data, the use of a specific German national life expectancy and the selected relative risks. A transparent description of input data, computational steps, and assumptions is essential to explain differing results of EBD studies to improve methodological credibility and trust in the results. Furthermore, the different calculated indicators should be explained and interpreted with caution. MDPI 2022-10-13 /pmc/articles/PMC9602590/ /pubmed/36293778 http://dx.doi.org/10.3390/ijerph192013197 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tobollik, Myriam Kienzler, Sarah Schuster, Christian Wintermeyer, Dirk Plass, Dietrich Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title | Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title_full | Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title_fullStr | Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title_full_unstemmed | Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title_short | Burden of Disease Due to Ambient Particulate Matter in Germany—Explaining the Differences in the Available Estimates |
title_sort | burden of disease due to ambient particulate matter in germany—explaining the differences in the available estimates |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602590/ https://www.ncbi.nlm.nih.gov/pubmed/36293778 http://dx.doi.org/10.3390/ijerph192013197 |
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