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Article

Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA

1
Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China
2
Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(10), 2300; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15102300
Received: 20 September 2018 / Revised: 15 October 2018 / Accepted: 17 October 2018 / Published: 19 October 2018
(This article belongs to the Section Environmental Health)
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. View Full-Text
Keywords: incidence rate of children’s blood lead poisoning; elevated blood lead level; quantile regression; geographically weighted quantile regression incidence rate of children’s blood lead poisoning; elevated blood lead level; quantile regression; geographically weighted quantile regression
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MDPI and ACS Style

Zhen, Z.; Cao, Q.; Shao, L.; Zhang, L. Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA. Int. J. Environ. Res. Public Health 2018, 15, 2300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15102300

AMA Style

Zhen Z, Cao Q, Shao L, Zhang L. Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA. International Journal of Environmental Research and Public Health. 2018; 15(10):2300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15102300

Chicago/Turabian Style

Zhen, Zhen, Qianqian Cao, Liyang Shao, and Lianjun Zhang. 2018. "Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA" International Journal of Environmental Research and Public Health 15, no. 10: 2300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15102300

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