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

Charlotte Wang (), Shu-Ju Lin, Chuhsing Kate Hsiao and Kuo-Chen Lu
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Charlotte Wang: Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 106319, Taiwan
Shu-Ju Lin: Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
Chuhsing Kate Hsiao: Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 106319, Taiwan
Kuo-Chen Lu: Weather Forecast Center, Central Weather Bureau, Taipei 100006, Taiwan

IJERPH, 2023, vol. 20, issue 2, 1-10

Abstract: 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.

Keywords: Bayesian conditional logistic regression; case-crossover study design; air pollutants; meteorological factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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