Research

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

Ta-Chien Chan^1,2,7,9 , Mei-Lien Chen³ , I-Feng Lin^4,5 , Cheng-Hua Lee^6,8 , Po-Huang Chiang⁷ , Da-Wei Wang^9,10 and Jen-Hsiang Chuang^1,5,9

¹  National Health Command Center, Centers for Disease Control, Taipei, Taiwan, R.O.C

²  Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Taiwan, R.O.C

³  Institute of Environmental and Occupational Health Sciences & Department of Environmental and Occupational Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

⁴  Department of Social Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

⁵  Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

⁶  Institute of Health Care Administration, National Yang-Ming University, Taipei, Taiwan, R.O.C

⁷  Center for Health Policy Research and Development, National Health Research Institutes, Miaoli County, Taiwan, R.O.C

⁸  Bureau of National Health Insurance, Taipei, Taiwan, R.O.C

⁹  Institute of BioMedical Informatics, School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

¹⁰  Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C

author email corresponding author email

International Journal of Health Geographics 2009, 8:26doi:10.1186/1476-072X-8-26

Published:	7 May 2009

Abstract

Background

Buffer 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).

Results

We 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.

Conclusion

This 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.