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Environmental health surveillance of low birth weight in Seoul using air monitoring and birth data
- Environmental health surveillance of low birth weight in Seoul using air monitoring and birth data
- Seo J.H.; Ha E.H.; Kim O.J.; Kim B.M.; Park H.S.; Leem J.H.; Hong Y.C.; Kim Y.J.
- Ewha Authors
- 하은희; 김영주; 박혜숙; 김병미
- SCOPUS Author ID
- 하은희; 김영주; 박혜숙; 김병미
- Issue Date
- Journal Title
- Journal of preventive medicine and public health = Yebang Ǔihakhoe chi
- Journal of preventive medicine and public health = Yebang Ǔihakhoe chi vol. 40, no. 5, pp. 363 - 370
- SCOPUS; KCI
- Document Type
- OBJECTIVES: The principal objective of this study was to determine the relationship between maternal exposure to air pollution and low birth weight and to propose a possible environmental health surveillance system for low birth weight. METHODS: We acquired air monitoring data for Seoul from the Ministry of Environment, the meteorological data from the Korean Meteorological Administration, the exposure assessments from the National Institute of Environmental Research, and the birth data from the Korean National Statistical Office between January 1, 2002 and December 31, 2003. The final birth data were limited to singletons within 37-44 weeks of gestational age. We defined the Low Birth Weight (LBW) group as infants with birth weights of less than 2500g and calculated the annual LBW rate by district. The air monitoring data were measured for CO, SO(2), NO(2), and PM(10) concentrations at 27 monitoring stations in Seoul. We utilized two models to evaluate the effects of air pollution on low birth weight: the first was the relationship between the annual concentration of air pollution and low birth weight (LBW) by individual and district, and the second involved a GIS exposure model constructed by Arc View 3.1. RESULTS: LBW risk (by Gu, or district) was significantly increased to 1.113(95% CI=1.111-1.116) for CO, 1.004 (95% CI=1.003-1.005) for NO(2), 1.202(95% CI=1.199-1.206) for SO(2), and 1.077(95% CI=1.075-1.078) for PM(10) with each interquartile range change. Personal LBW risk was significantly increased to 1.081(95% CI=1.002-1.166) for CO, 1.145(95% CI=1.036-1.267) for SO(2), and 1.053(95% CI=1.002-1.108) for PM(10) with each interquartile range change. Personal LBW risk was increased to 1.003(95% CI=0.954-1.055) for NO(2), but this was not statistically significant. The air pollution concentrations predicted by GIS positively correlated with the numbers of low birth weights, particularly in highly polluted regions. CONCLUSIONS: Environmental health surveillance is a systemic, ongoing collection effort including the analysis of data correlated with environmentally-associated diseases and exposures. In addition, environmental health surveillance allows for a timely dissemination of information to those who require that information in order to take effective action. GIS modeling is crucially important for this purpose, and thus we attempted to develop a GIS-based environmental surveillance system for low birth weight.
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