• Kuo-Liang Chiang, Jen-Yu Lee, Yu-Mei Chang, Fang-Chuan Kuo, and Chin-Yin Huang.
• Department of Pediatric Neurology, Kuang-Tien General Hospital, Taichung, Taiwan; Department of Nutrition, Hungkuang University, Taichung, Taiwan; Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.
• Epilepsy Behav. 2021 Feb 1; 115: 107487.

ObjectiveThe objective of the study was to explore the influences of seasonality, meteorological conditions, and air pollution exposure on the number of patients who visit the hospital due to seizures.MethodsOutpatient and inpatient data from the National Health Insurance Database of Taiwan from 2009 to 2013, meteorological data from the Meteorological Bureau, and air pollution exposure data from the Taiwan Air Quality Monitoring Stations were collected and integrated into daily time series data. The following data processing and analysis results are based on the mean of the 7 days' lag data of the 18 meteorological condition/air pollution exploratory factors to identify the critical meteorological conditions and air pollution exposure factors by executing univariate analysis. The average hospital visits for seizure per day by month were used as an index of observation. The effect of seasonality has also been examined.ResultsThe average visits per day by month had a significant association with 10 variables. Overall, the number of visits due to these factors has been estimated to be 71.529 (13.7%). The most obvious factors affecting the estimated number of visits include ambient temperature, CH4, and NO. Six air pollutants, namely CH4, NO, CO, NO2, PM2.5, and NMHC had a significantly positive correlation with hospital visits due to seizures. Moreover, the average daily number of hospital visits was significantly high in January and February (winter season in Taiwan) than in other months (R2 = 0.422).ConclusionThe prediction model obtained in this study indicates the necessity of rigorous monitoring and early warning of these air pollutants and climate changes by governments. Additionally, the study provided a firm basis for establishing prediction models to be used by other countries or for other diseases.Copyright © 2020 Elsevier Inc. All rights reserved.

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