A spatial model to predict the incidence of neural tube defects.
A spatial model to predict the incidence of neural tube defects.
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DOI：
10.1186/1471-2458-12-951
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发表时间：
2012-11-07
影响因子：
4.5
通讯作者：
Wu J
中科院分区：
文献类型：
--
作者：
Li L;Wang J;Wu J
环境暴露可能在出生缺陷中神经管缺陷（NTD）的发生中起重要作用。它们对NTD的影响可能是非线性的;很少有研究在估计NTD风险时考虑残差的空间自相关。本研究旨在建立一个基于广义加性模型（GAM）和协克里格（cokriging）的空间模型，以检验和模拟NTD的预期发病率，并对发病风险进行推断。我们开发了一个空间模型来预测中国山西省和顺县（NTD高发地区）村级NTD的预期发病率。GAM用于建立局部协变量与预期NTD发生率之间的线性和非线性关系。我们在模型中检查了以下村级协变量：投影坐标，土壤类型，岩性类，到分水岭的距离，河流，断层和主要道路，年平均肥料使用量，水果和蔬菜产量，国内生产总值和医生数量。从GAM的残差被假定为空间自相关和cokriged与区域残差，以提高预测。我们的方法进行了比较，与其他三个模型，泛克里金，广义线性回归和GAM。进行交叉验证以进行验证。我们的模型很好地预测了NTD的预期发生率，CV R2为0.80。重要的预测因素包括肥料使用，每个村庄的质心的位置，最短的距离河流和断层和岩性类具有显着的空间自相关的残差。我们的模型比其他三种方法的R2高出16%或更多。我们的模型解释的方差约为80%。这种建模方法是有用的NTD流行病学研究和干预计划。
Environmental exposure may play an important role in the incidences of neural tube defects (NTD) of birth defects. Their influence on NTD may likely be non-linear; few studies have considered spatial autocorrelation of residuals in the estimation of NTD risk. We aimed to develop a spatial model based on generalized additive model (GAM) plus cokriging to examine and model the expected incidences of NTD and make the inference of the incidence risk. We developed a spatial model to predict the expected incidences of NTD at village level in Heshun County, Shanxi Province, China, a region with high NTD cases. GAM was used to establish linear and non-linear relationships between local covariates and the expected NTD incidences. We examined the following village-level covariates in the model: projected coordinates, soil types, lithodological classes, distance to watershed, rivers, faults and major roads, annual average fertilizer uses, fruit and vegetable production, gross domestic product, and the number of doctors. The residuals from GAM were assumed to be spatially auto-correlative and cokriged with regional residuals to improve the prediction. Our approach was compared with three other models, universal kriging, generalized linear regression and GAM. Cross validation was conducted for validation. Our model predicted the expected incidences of NTD well, with a good CV R2 of 0.80. Important predictive factors included the fertilizer uses, locations of the centroid of each village, the shortest distance to rivers and faults and lithological classes with significant spatial autocorrelation of residuals. Our model out-performed the other three methods by 16% or more in term of R2. The variance explained by our model was approximately 80%. This modeling approach is useful for NTD epidemiological studies and intervention planning.
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基于GIS的人类出生缺陷环境成因地质分析
影响因子：
1.8
作者：
Li, Xinhu;Wang, Jinfeng;Zheng, Xiaoying
通讯作者：
Zheng, Xiaoying
影响因子：
5
作者：
Croen, LA;Todoroff, K;Shaw, GM
通讯作者：
Shaw, GM
DOI：
10.1186/1476-069x-4-11
发表时间：
2005-06-14
期刊：
Environmental health : a global access science source
影响因子：
--
作者：
Vieira, Veronica;Webster, Thomas;Ozonoff, David
通讯作者：
Ozonoff, David
高水平的溴化消毒副产品的澳大利亚社区出生缺陷的风险。
影响因子：
10.4
作者：
Chisholm, Kimberley;Cook, Angus;Bower, Carol;Weinstein, Philip
通讯作者：
Weinstein, Philip
使用地理信息系统进行环境流行病学研究中的暴露评估。
影响因子：
10.4
作者：
Nuckols, John R;Ward, Mary H;Jarup, Lars
通讯作者：
Jarup, Lars