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Open AccessArticle

Spatial Hurdle Models for Predicting the Number of Children with Lead Poisoning

1
School of Forestry, Northeast Forestry University, Harbin 150040, China
2
Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(9), 1792; https://doi.org/10.3390/ijerph15091792
Received: 19 July 2018 / Revised: 11 August 2018 / Accepted: 18 August 2018 / Published: 21 August 2018
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. View Full-Text
Keywords: overdispersion; zero-inflated count data; negative binomial Hurdle model; generalized linear mixed models; random effects; spatial effects overdispersion; zero-inflated count data; negative binomial Hurdle model; generalized linear mixed models; random effects; spatial effects
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Zhen, Z.; Shao, L.; Zhang, L. Spatial Hurdle Models for Predicting the Number of Children with Lead Poisoning. Int. J. Environ. Res. Public Health 2018, 15, 1792.

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