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Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions

Background: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have...

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Autores principales: Wang, Charlotte, Lin, Shu-Ju, Hsiao, Chuhsing Kate, Lu, Kuo-Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858713/
https://www.ncbi.nlm.nih.gov/pubmed/36673795
http://dx.doi.org/10.3390/ijerph20021039
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author Wang, Charlotte
Lin, Shu-Ju
Hsiao, Chuhsing Kate
Lu, Kuo-Chen
author_facet Wang, Charlotte
Lin, Shu-Ju
Hsiao, Chuhsing Kate
Lu, Kuo-Chen
author_sort Wang, Charlotte
collection PubMed
description Background: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. Method: We defined people aged 51–90 years who were free from ischemic heart disease (ICD9: 410–414) in 1996–2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). Results: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. Conclusions: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.
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spelling pubmed-98587132023-01-21 Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions Wang, Charlotte Lin, Shu-Ju Hsiao, Chuhsing Kate Lu, Kuo-Chen Int J Environ Res Public Health Article Background: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. Method: We defined people aged 51–90 years who were free from ischemic heart disease (ICD9: 410–414) in 1996–2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). Results: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. Conclusions: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health. MDPI 2023-01-06 /pmc/articles/PMC9858713/ /pubmed/36673795 http://dx.doi.org/10.3390/ijerph20021039 Text en © 2023 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
Wang, Charlotte
Lin, Shu-Ju
Hsiao, Chuhsing Kate
Lu, Kuo-Chen
Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title_full Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title_fullStr Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title_full_unstemmed Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title_short Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions
title_sort bayesian approach to disease risk evaluation based on air pollution and weather conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858713/
https://www.ncbi.nlm.nih.gov/pubmed/36673795
http://dx.doi.org/10.3390/ijerph20021039
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