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Objective : The purpose of this study was to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods : Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children's lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children's blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. Results : At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children's lead poisoning. Further, these negative or positive relationships between children's lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). Conclusions : The GWQR models enabled us to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.

Objective The purpose of this study is to identify the high-risk areas of children's lead poisoning in Syracuse, NY, USA, using spatial modeling techniques. The relationships between the number of children's lead poisoning cases and three socio-economic and environmental factors (i.e., building year and town taxable value of houses, and soil lead concentration) were investigated. Methods Spatial generalized linear models (including Poisson, negative binomial, Poisson Hurdle, and negative binomial Hurdle models) were used to model the number of children's lead poisoning cases using the three predictor variables at the census block level in the inner city of Syracuse. Results The building year and town taxable value were strongly and positively associated with the elevated risk for lead poisoning, while soil lead concentration showed a weak relationship with lead poisoning. The negative binomial Hurdle model with spatial random effects was the appropriate model for the disease count data across the city neighborhood. Conclusions The spatial negative binomial Hurdle model best fitted the number of children with lead poisoning and provided better predictions over other models. It could be used to deal with complex spatial data of children with lead poisoning, and may be generalized to other cities.