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影响因子：5.691

DOI码：10.1111/nyas.13453

发表刊物：Annals of the New York Academy of Sciences

关键字：geographically weighted regression; children’s lead poisoning; disease mapping

摘要：Children’s lead poisoning continues to compromise children’s health and development, particularly in the inner cities of the United States. We applied a global Poisson model, a Poisson with random effects model, and a geographically weighted Poisson regression (GWPR) model to deal with the spatial dependence and heterogeneity of the number of children’s lead poisoning cases in Syracuse, New York. We used three environmental factors—the building year (i.e., the year of construction) of houses, the town taxable value of houses, and the soil lead concentration—averaged at the census block level to explore the spatially varying relationships between children’s lead poisoning and environmental factors. The results indicated that GWPR not only produced better model fitting and reduced the spatial dependence and heterogeneity in the model residuals but also improved the model predictions for the spatial clusters, or hot spots, of children’s lead poisoning across inner city neighborhoods. Furthermore, the spatially varying model coefficients and their associated statistical tests were visualized using geographical information system maps to show the high-risk areas for the impacts of the environmental factors on the response variable. This information can provide valuable insights for public health agencies to make better decisions on lead hazard intervention, mitigation, and control programs.

合写作者：Lianjun Zhang

第一作者：Q1, Liyang Shao

论文类型：期刊论文

通讯作者：Zhen Zhen*

卷号：1404

期号：1

页面范围：49-60

ISSN号：1749-6632

是否译文：否

发表时间：2017-01-01

收录刊物：SCI