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Abstract：
The 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.
Outpatient 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.
The 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).
The 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.
Outpatient 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.
The 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).
The 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.
摘要：
这项研究的目的是探索季节性的影响，气象条件,以及空气污染对因癫痫发作而去医院的患者人数的影响。
2009年至2013年台湾国家健康保险数据库的门诊和住院数据，气象局的气象数据，收集台湾空气质量监测站的空气污染暴露数据，并将其整合到每日时间序列数据中。以下数据处理和分析结果是根据18个气象条件/空气污染勘探因素的7天滞后数据的平均值，通过执行单变量分析来识别关键气象条件和空气污染暴露因素。每月平均每天的癫痫发作住院次数被用作观察指标。还研究了季节性的影响。
每月每天的平均访问与10个变量有显著关联。总的来说,由于这些因素的访问次数估计为71.529次(13.7%)。影响估计访视次数的最明显因素包括环境温度,CH4，不。六种空气污染物,即CH4，NO，CO,NO2，PM2.5和NMHC与癫痫发作导致的住院次数呈显着正相关。此外，1月和2月(台湾冬季)的平均每日住院次数明显高于其他月份(R2=0.422).
在这项研究中获得的预测模型表明了政府对这些空气污染物和气候变化进行严格监测和预警的必要性。此外,这项研究为建立其他国家或其他疾病的预测模型提供了坚实的基础。
2009年至2013年台湾国家健康保险数据库的门诊和住院数据，气象局的气象数据，收集台湾空气质量监测站的空气污染暴露数据，并将其整合到每日时间序列数据中。以下数据处理和分析结果是根据18个气象条件/空气污染勘探因素的7天滞后数据的平均值，通过执行单变量分析来识别关键气象条件和空气污染暴露因素。每月平均每天的癫痫发作住院次数被用作观察指标。还研究了季节性的影响。
每月每天的平均访问与10个变量有显著关联。总的来说,由于这些因素的访问次数估计为71.529次(13.7%)。影响估计访视次数的最明显因素包括环境温度,CH4，不。六种空气污染物,即CH4，NO，CO,NO2，PM2.5和NMHC与癫痫发作导致的住院次数呈显着正相关。此外，1月和2月(台湾冬季)的平均每日住院次数明显高于其他月份(R2=0.422).
在这项研究中获得的预测模型表明了政府对这些空气污染物和气候变化进行严格监测和预警的必要性。此外,这项研究为建立其他国家或其他疾病的预测模型提供了坚实的基础。
