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Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai
BACKGROUND: Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a single hospital. The aim of this ecological study based on a 12.5-year time series was to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756174/ https://www.ncbi.nlm.nih.gov/pubmed/35027064 http://dx.doi.org/10.1186/s12940-021-00828-6 |
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author | Peng, Wenjia Li, Hao Peng, Li Wang, Ying Wang, Weibing |
author_facet | Peng, Wenjia Li, Hao Peng, Li Wang, Ying Wang, Weibing |
author_sort | Peng, Wenjia |
collection | PubMed |
description | BACKGROUND: Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a single hospital. The aim of this ecological study based on a 12.5-year time series was to investigate the association of short-term exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM(2.5)) and aerodynamic diameter ≤ 10 μm (PM(10)) with hospital admissions for respiratory diseases. METHODS: Daily hospital admissions data were from the Shanghai Medical Insurance System for the period January 1, 2008 to July 31, 2020. We estimated the percentage change with its 95% confidence interval (CI) for each 10 μg/m(3) increase in the level of PM(2.5) and PM(10) after adjustment for calendar time, day of the week, public holidays, and meteorological factors applying a generalized additive model with a quasi-Poisson distribution. RESULTS: There were 1,960,361 hospital admissions for respiratory diseases in Shanghai during the study period. A 10 μg/m(3) increase in the level of each class of PM was associated with increased total respiratory diseases when the lag time was 0 day (PM(2.5): 0.755%; 95% CI: 0.422, 1.089%; PM(10): 0.250%; 95% CI: 0.042, 0.459%). The PM(2.5) and PM(10) levels also had positive associations with admissions for COPD, asthma, and pneumonia. Stratified analyses demonstrated stronger effects in patients more than 45 years old and during the cold season. Total respiratory diseases increased linearly with PM concentration from 0 to 100 μg/m(3), and increased more slowly at higher PM concentrations. CONCLUSIONS: This time-series study suggests that short-term exposure to PM increased the risk for hospital admission for respiratory diseases, even at low concentrations. These findings suggest that reducing atmospheric PM concentrations may reduce hospital admissions for respiratory diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-021-00828-6. |
format | Online Article Text |
id | pubmed-8756174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87561742022-01-13 Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai Peng, Wenjia Li, Hao Peng, Li Wang, Ying Wang, Weibing Environ Health Research BACKGROUND: Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a single hospital. The aim of this ecological study based on a 12.5-year time series was to investigate the association of short-term exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM(2.5)) and aerodynamic diameter ≤ 10 μm (PM(10)) with hospital admissions for respiratory diseases. METHODS: Daily hospital admissions data were from the Shanghai Medical Insurance System for the period January 1, 2008 to July 31, 2020. We estimated the percentage change with its 95% confidence interval (CI) for each 10 μg/m(3) increase in the level of PM(2.5) and PM(10) after adjustment for calendar time, day of the week, public holidays, and meteorological factors applying a generalized additive model with a quasi-Poisson distribution. RESULTS: There were 1,960,361 hospital admissions for respiratory diseases in Shanghai during the study period. A 10 μg/m(3) increase in the level of each class of PM was associated with increased total respiratory diseases when the lag time was 0 day (PM(2.5): 0.755%; 95% CI: 0.422, 1.089%; PM(10): 0.250%; 95% CI: 0.042, 0.459%). The PM(2.5) and PM(10) levels also had positive associations with admissions for COPD, asthma, and pneumonia. Stratified analyses demonstrated stronger effects in patients more than 45 years old and during the cold season. Total respiratory diseases increased linearly with PM concentration from 0 to 100 μg/m(3), and increased more slowly at higher PM concentrations. CONCLUSIONS: This time-series study suggests that short-term exposure to PM increased the risk for hospital admission for respiratory diseases, even at low concentrations. These findings suggest that reducing atmospheric PM concentrations may reduce hospital admissions for respiratory diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-021-00828-6. BioMed Central 2022-01-13 /pmc/articles/PMC8756174/ /pubmed/35027064 http://dx.doi.org/10.1186/s12940-021-00828-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Peng, Wenjia Li, Hao Peng, Li Wang, Ying Wang, Weibing Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title | Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title_full | Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title_fullStr | Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title_full_unstemmed | Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title_short | Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai |
title_sort | effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in shanghai |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756174/ https://www.ncbi.nlm.nih.gov/pubmed/35027064 http://dx.doi.org/10.1186/s12940-021-00828-6 |
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