GIS Based Environmental Surveillance System of Low Birth Weight by PM10 in Seven Cities, Korea

Abstract

ISEE-769 Objective: The environmental pollution has been associated with adverse birth outcome in many studies. Environmental health surveillance system will support for estimating the environmental health risk magnitude and environmental health policy making system. We tried to develop the geographical information system (GIS)-based environmental surveillance system of LBW. Materials and Methods: We obtained air pollution data of 7 Korean metro cities from the Ministry of Environment, meteorological data from the Korean Meteorological Administration, exposure assessment from the National Institute of Environmental Research, and birth data from the Korean National Statistical Office, 2003–2004. We used the generalized additive logistic regression model using individual birth data linkage to air pollution monitoring station data. Results: In 7 metro cities by districts using linear regression analysis, LBW risks were especially increased to 2.5% (95% CI = 0.2–4.9%) in Daegu, and 2.6% (95% CI = 0.3–4.9%) in Ulsan for PM10. In individual residents using logistic regression analysis, LBW risks were increased to 25% (95% CI = 2–53%) in Pusan, 20% (95% CI = 4–38%) in Daegu, and 19% (95% CI = 3–38%) in Ulsan for PM10. In individual residents with adjustment for demographic factor further using multiple logistic regression, LBW risks were increased to 24% (95% CI = 2–52%) in Pusan, 19% (95% CI = 4–37%) in Daegu, and 19% (95% CI = 3–38%) in Ulsan for PM10. Conclusions: Environmental health surveillance is systemic, ongoing collection, and analysis of data correlated to environmentally related disease and exposures and the timely dissemination of information to those who need to know about them in order to take action. GIS modeling is very important for this purpose, so we tried to develop the GIS-based environmental surveillance of low birth weight.