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Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis
Together with the growing availability of data from electronic records from healthcare providers and healthcare systems, an assessment of associations between different environmental parameters (e.g., pollution levels and meteorological data) and hospitalizations, morbidity, and mortality has become...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546668/ https://www.ncbi.nlm.nih.gov/pubmed/30929168 http://dx.doi.org/10.1007/s11356-019-04781-3 |
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author | Slama, Alessandro Śliwczyński, Andrzej Woźnica, Jolanta Zdrolik, Maciej Wiśnicki, Bartłomiej Kubajek, Jakub Turżańska-Wieczorek, Olga Gozdowski, Dariusz Wierzba, Waldemar Franek, Edward |
author_facet | Slama, Alessandro Śliwczyński, Andrzej Woźnica, Jolanta Zdrolik, Maciej Wiśnicki, Bartłomiej Kubajek, Jakub Turżańska-Wieczorek, Olga Gozdowski, Dariusz Wierzba, Waldemar Franek, Edward |
author_sort | Slama, Alessandro |
collection | PubMed |
description | Together with the growing availability of data from electronic records from healthcare providers and healthcare systems, an assessment of associations between different environmental parameters (e.g., pollution levels and meteorological data) and hospitalizations, morbidity, and mortality has become possible. This study aimed to assess the association of air pollution and hospitalizations using a large database comprising almost all hospitalizations in Poland. This time-series analysis has been conducted in five cities in Poland (Warsaw, Białystok, Bielsko-Biała, Kraków, Gdańsk) over a period of almost 4 years (2014–2017, 1255 days), covering more than 20 million of hospitalizations. The hospitalizations have been extracted from the National Health Fund registries as daily summaries. Correlation analysis and distributed lag nonlinear models have been used to investigate for statistically relevant associations of air pollutants on hospitalizations, trying by various methods to minimize potential bias from atmospheric parameters, days of the week, bank holidays, etc. A statistically significant increase of respiratory disease hospitalizations has been detected after peaks of particulate matter concentrations (particularly PM(2.5), between 0.9 and 4.5% increase per 10 units of pollutant increase, and PM(10), between 0.9 and 3.5% per 10 units of pollutant increase), with a typical time lag between the pollutant peak and the event of 2 to 6 days. For other pollution parameters and other types of hospitalizations (e.g., cardiovascular events, eye and skin diseases, etc.), a weaker and ununiform correlations were recorded. Ambient air pollution exposure increases are associated with a short-term increase of hospitalizations due to respiratory tract diseases. The most prominent effect was recorded with the correlation of PM(2.5) and PM(10). There is only weak evidence indicating that such short-term associations exist between peaks of air pollution concentrations and increased hospitalizations for other (e.g., cardiovascular) diseases. The obtained information could be used to better predict hospitalization patterns and costs for the healthcare system and perhaps trigger additional vigilance on particulate matter pollution in the cities. |
format | Online Article Text |
id | pubmed-6546668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-65466682019-06-19 Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis Slama, Alessandro Śliwczyński, Andrzej Woźnica, Jolanta Zdrolik, Maciej Wiśnicki, Bartłomiej Kubajek, Jakub Turżańska-Wieczorek, Olga Gozdowski, Dariusz Wierzba, Waldemar Franek, Edward Environ Sci Pollut Res Int Research Article Together with the growing availability of data from electronic records from healthcare providers and healthcare systems, an assessment of associations between different environmental parameters (e.g., pollution levels and meteorological data) and hospitalizations, morbidity, and mortality has become possible. This study aimed to assess the association of air pollution and hospitalizations using a large database comprising almost all hospitalizations in Poland. This time-series analysis has been conducted in five cities in Poland (Warsaw, Białystok, Bielsko-Biała, Kraków, Gdańsk) over a period of almost 4 years (2014–2017, 1255 days), covering more than 20 million of hospitalizations. The hospitalizations have been extracted from the National Health Fund registries as daily summaries. Correlation analysis and distributed lag nonlinear models have been used to investigate for statistically relevant associations of air pollutants on hospitalizations, trying by various methods to minimize potential bias from atmospheric parameters, days of the week, bank holidays, etc. A statistically significant increase of respiratory disease hospitalizations has been detected after peaks of particulate matter concentrations (particularly PM(2.5), between 0.9 and 4.5% increase per 10 units of pollutant increase, and PM(10), between 0.9 and 3.5% per 10 units of pollutant increase), with a typical time lag between the pollutant peak and the event of 2 to 6 days. For other pollution parameters and other types of hospitalizations (e.g., cardiovascular events, eye and skin diseases, etc.), a weaker and ununiform correlations were recorded. Ambient air pollution exposure increases are associated with a short-term increase of hospitalizations due to respiratory tract diseases. The most prominent effect was recorded with the correlation of PM(2.5) and PM(10). There is only weak evidence indicating that such short-term associations exist between peaks of air pollution concentrations and increased hospitalizations for other (e.g., cardiovascular) diseases. The obtained information could be used to better predict hospitalization patterns and costs for the healthcare system and perhaps trigger additional vigilance on particulate matter pollution in the cities. Springer Berlin Heidelberg 2019-03-30 2019 /pmc/articles/PMC6546668/ /pubmed/30929168 http://dx.doi.org/10.1007/s11356-019-04781-3 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Article Slama, Alessandro Śliwczyński, Andrzej Woźnica, Jolanta Zdrolik, Maciej Wiśnicki, Bartłomiej Kubajek, Jakub Turżańska-Wieczorek, Olga Gozdowski, Dariusz Wierzba, Waldemar Franek, Edward Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title | Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title_full | Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title_fullStr | Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title_full_unstemmed | Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title_short | Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
title_sort | impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546668/ https://www.ncbi.nlm.nih.gov/pubmed/30929168 http://dx.doi.org/10.1007/s11356-019-04781-3 |
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