Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, New York.
School of Forestry, Northeast Forestry University (NEFU), Harbin, Heilongjiang, People's Republic of China.
Ann N Y Acad Sci. 2017 Sep;1404(1):49-60. doi: 10.1111/nyas.13453. Epub 2017 Aug 22.
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.
儿童铅中毒继续损害儿童的健康和发育，尤其是在美国的内城。我们应用了全局泊松模型、带随机效应的泊松模型和地理加权泊松回归（GWPR）模型来处理纽约锡拉丘兹儿童铅中毒病例数的空间依赖性和异质性。我们使用了三个环境因素——房屋的建造年份（即建造年份）、房屋的城镇应税价值和土壤铅浓度——在普查区块层面进行平均，以探索儿童铅中毒与环境因素之间的空间变化关系。结果表明，GWPR 不仅产生了更好的模型拟合，减少了模型残差的空间依赖性和异质性，而且提高了对儿童铅中毒的空间聚类（或热点）的模型预测，这些聚类横跨内城社区。此外，使用地理信息系统地图可视化了空间变化的模型系数及其相关统计检验，以显示环境因素对因变量的影响的高风险区域。这些信息可以为公共卫生机构提供有价值的见解，以便更好地做出关于铅危害干预、缓解和控制计划的决策。
