IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i10p2300-d176870.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/10/2300/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/10/2300/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
    3. Sargent, J.D. & Brown, M.J. & Freeman, J.L. & Bailey, A. & Goodman, D. & Freeman Jr., D.H., 1995. "Childhood lead poisoning in Massachusetts communities: Its association with sociodemographic and housing characteristics," American Journal of Public Health, American Public Health Association, vol. 85(4), pages 528-534.
    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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deniz Yeter & Ellen C. Banks & Michael Aschner, 2020. "Disparity in Risk Factor Severity for Early Childhood Blood Lead among Predominantly African-American Black Children: The 1999 to 2010 US NHANES," IJERPH, MDPI, vol. 17(5), pages 1-26, February.
    2. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    3. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    4. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
    5. Gilpin, Gregory A., 2012. "Teacher salaries and teacher aptitude: An analysis using quantile regressions," Economics of Education Review, Elsevier, vol. 31(3), pages 15-29.
    6. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    7. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    8. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    9. Makowsky, Michael, 2009. "Religious Extremism, Clubs, and Civil Liberties: A Model of Religious Populations," MPRA Paper 14358, University Library of Munich, Germany.
    10. Nicole Schneeweis & Rudolf Winter-Ebmer, 2008. "Peer effects in Austrian schools," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 133-155, Springer.
    11. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    12. Jones, David J., 2012. "Primary prevention and health outcomes: Treatment of residential lead-based paint hazards and the prevalence of childhood lead poisoning," Journal of Urban Economics, Elsevier, vol. 71(1), pages 151-164.
    13. Lin, Ming-Jen, 2009. "More police, less crime: Evidence from US state data," International Review of Law and Economics, Elsevier, vol. 29(2), pages 73-80, June.
    14. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    15. Egon Franck & Stephan NŸesch, 2008. "The Effect of Talent Disparity on Team Performance in Soccer," Working Papers 0021, University of Zurich, Center for Research in Sports Administration (CRSA), revised 2009.
    16. Schmidt, Christoph & Fertig, Michael, 2002. "The Role of Background Factors for Reading Literacy: Straight National scores in the Pisa 2000 Study," CEPR Discussion Papers 3544, C.E.P.R. Discussion Papers.
    17. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    18. Sierra Pugh & Matthew J. Heaton & Jeff Svedin & Neil Hansen, 2019. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 634-650, December.
    19. Heather A. Moody & Sue C. Grady, 2021. "Lead Emissions and Population Vulnerability in the Detroit Metropolitan Area, 2006–2013: Impact of Pollution, Housing Age and Neighborhood Racial Isolation and Poverty on Blood Lead in Children," IJERPH, MDPI, vol. 18(5), pages 1-20, March.
    20. Martin Tingley & Benjamin Shaby, 2015. "Comments on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 47-53, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2300-:d:176870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.