Exploring spatially varying relationships between children's lead poisoning and environmental factors.

Abstract

Children's lead poisoning continues to compromise children's health and development, particularly in the inner cities of the United States. We applied a global Poisson model, a Poisson with random effects model, and a geographically weighted Poisson regression (GWPR) model to deal with the spatial dependence and heterogeneity of the number of children's lead poisoning cases in Syracuse, New York. We used three environmental factors-the building year (i.e., the year of construction) of houses, the town taxable value of houses, and the soil lead concentration-averaged at the census block level to explore the spatially varying relationships between children's lead poisoning and environmental factors. The results indicated that GWPR not only produced better model fitting and reduced the spatial dependence and heterogeneity in the model residuals but also improved the model predictions for the spatial clusters, or hot spots, of children's lead poisoning across inner city neighborhoods. Furthermore, the spatially varying model coefficients and their associated statistical tests were visualized using geographical information system maps to show the high-risk areas for the impacts of the environmental factors on the response variable. This information can provide valuable insights for public health agencies to make better decisions on lead hazard intervention, mitigation, and control programs.