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

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  • Zhen Zhen

    (School of Forestry, Northeast Forestry University, Harbin 150040, China)

  • Liyang Shao

    (Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA)

  • Lianjun Zhang

    (Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA)

Abstract

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.

Suggested Citation

  • Zhen Zhen & Liyang Shao & Lianjun Zhang, 2018. "Spatial Hurdle Models for Predicting the Number of Children with Lead Poisoning," IJERPH, MDPI, vol. 15(9), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1792-:d:164772
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    References listed on IDEAS

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    1. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    2. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    4. Liyang Shao & Lianjun Zhang & Zhen Zhen, 2017. "Interrupted time series analysis of children’s blood lead levels: A case study of lead hazard control program in Syracuse, New York," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
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    1. Zhen Zhen & Qianqian Cao & Liyang Shao & Lianjun Zhang, 2018. "Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA," IJERPH, MDPI, vol. 15(10), pages 1-19, October.

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