Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020

The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate condition...
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Autor*in:	Chalita Jainonthee [verfasserIn] Ying-Lin Wang [verfasserIn] Colin W. K. Chen [verfasserIn] Karuna Jainontee [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	air pollution environmental factor health risk influenza particulate matter PM

Übergeordnetes Werk:	In: Tropical Medicine and Infectious Disease - MDPI AG, 2017, 7(2022), 11, p 341
Übergeordnetes Werk:	volume:7 ; year:2022 ; number:11, p 341

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/tropicalmed7110341

Katalog-ID:	DOAJ017822076

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520			\|a The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM<sub<2.5</sub<, and PM<sub<10</sub<). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM<sub<2.5</sub< and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM<sub<2.5</sub< lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM<sub<10</sub< and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
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allfieldsSound	10.3390/tropicalmed7110341 doi (DE-627)DOAJ017822076 (DE-599)DOAJ17f6c2cbfd3b47c98636fab36b109142 DE-627 ger DE-627 rakwb eng Chalita Jainonthee verfasserin aut Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM<sub<2.5</sub<, and PM<sub<10</sub<). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM<sub<2.5</sub< and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM<sub<2.5</sub< lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM<sub<10</sub< and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution. air pollution environmental factor health risk influenza particulate matter PM<sub<2.5</sub< Medicine R Ying-Lin Wang verfasserin aut Colin W. K. Chen verfasserin aut Karuna Jainontee verfasserin aut In Tropical Medicine and Infectious Disease MDPI AG, 2017 7(2022), 11, p 341 (DE-627)102556488X 24146366 nnns volume:7 year:2022 number:11, p 341 https://doi.org/10.3390/tropicalmed7110341 kostenfrei https://doaj.org/article/17f6c2cbfd3b47c98636fab36b109142 kostenfrei https://www.mdpi.com/2414-6366/7/11/341 kostenfrei https://doaj.org/toc/2414-6366 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2022 11, p 341
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author	Chalita Jainonthee
spellingShingle	Chalita Jainonthee misc air pollution misc environmental factor misc health risk misc influenza misc particulate matter misc PM<sub<2.5</sub< misc Medicine misc R Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020
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topic_title	Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020 air pollution environmental factor health risk influenza particulate matter PM<sub<2.5</sub<
topic	misc air pollution misc environmental factor misc health risk misc influenza misc particulate matter misc PM<sub<2.5</sub< misc Medicine misc R
topic_unstemmed	misc air pollution misc environmental factor misc health risk misc influenza misc particulate matter misc PM<sub<2.5</sub< misc Medicine misc R
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title	Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020
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title_full	Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020
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author_browse	Chalita Jainonthee Ying-Lin Wang Colin W. K. Chen Karuna Jainontee
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title_sort	air pollution-related respiratory diseases and associated environmental factors in chiang mai, thailand, in 2011–2020
title_auth	Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020
abstract	The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM<sub<2.5</sub<, and PM<sub<10</sub<). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM<sub<2.5</sub< and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM<sub<2.5</sub< lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM<sub<10</sub< and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
abstractGer	The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM<sub<2.5</sub<, and PM<sub<10</sub<). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM<sub<2.5</sub< and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM<sub<2.5</sub< lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM<sub<10</sub< and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
abstract_unstemmed	The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM<sub<2.5</sub<, and PM<sub<10</sub<). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM<sub<2.5</sub< and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM<sub<2.5</sub< lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM<sub<10</sub< and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
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title_short	Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020
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Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020

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