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

Author

Listed:
  • Zhen Zhen

    (Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China)

  • Qianqian Cao

    (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)

  • Liyang Shao

    (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)

  • Lianjun Zhang

    (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)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2300-:d:176870
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    References listed on IDEAS

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    1. Washerman, G.A. & Staghezza-Jaramillo, B. & Shrout, P. & Popovac, D. & Graziano, J., 1998. "The effect of lead exposure on behavior problems in preschool children," American Journal of Public Health, American Public Health Association, vol. 88(3), pages 481-486.
    2. Heather Moody & Sue C. Grady, 2017. "Lead Emissions and Population Vulnerability in the Detroit (Michigan, USA) Metropolitan Area, 2006–2013: A Spatial and Temporal Analysis," IJERPH, MDPI, vol. 14(12), pages 1-22, November.
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    5. 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.
    6. 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.
    7. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
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    Cited by:

    1. Biao Sun & Shan Yang, 2020. "Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities," IJERPH, MDPI, vol. 17(20), pages 1-23, October.

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