Spatiotemporal analysis of air pollution and asthma patient visits in Taipei, Taiwan

BackgroundBuffer analyses have shown that air pollution is associated with an increased incidence of asthma, but little is known about how air pollutants affect health outside a defined buffer. The aim of this study was to better understand how air pollutants affect asthma patient visits in a metropolitan area. The study used an integrated spatial and temporal approach that included the Kriging method and the Generalized Additive Model (GAM).ResultsWe analyzed daily outpatient and emergency visit data from the Taiwan Bureau of National Health Insurance and air pollution data from the Taiwan Environmental Protection Administration during 2000–2002. In general, children (aged 0–15 years) had the highest number of total asthma visits. Seasonal changes of PM10, NO2, O3 and SO2 were evident. However, SO2 showed a positive correlation with the dew point (r = 0.17, p < 0.01) and temperature (r = 0.22, p < 0.01). Among the four pollutants studied, the elevation of NO2 concentration had the highest impact on asthma outpatient visits on the day that a 10% increase of concentration caused the asthma outpatient visit rate to increase by 0.30% (95% CI: 0.16%~0.45%) in the four pollutant model. For emergency visits, the elevation of PM10 concentration, which occurred two days before the visits, had the most significant influence on this type of patient visit with an increase of 0.14% (95% CI: 0.01%~0.28%) in the four pollutants model. The impact on the emergency visit rate was non-significant two days following exposure to the other three air pollutants.ConclusionThis preliminary study demonstrates the feasibility of an integrated spatial and temporal approach to assess the impact of air pollution on asthma patient visits. The results of this study provide a better understanding of the correlation of air pollution with asthma patient visits and demonstrate that NO2 and PM10 might have a positive impact on outpatient and emergency settings respectively. Future research is required to validate robust spatiotemporal patterns and trends.