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Bayesian importance parameter modeling of misaligned predictors i soil metal measures related to residential history and intellectual disability in children

机译:贝叶斯重要参数预测中的贝叶斯重要性参数建模,即与儿童居住史和智力障碍有关的土壤金属测量

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In this paper, we propose a novel spatial importance parameter hierarchical logistic regression modeling approach that includes measurement error from misalignment. We apply this model to study the relationship between the estimated concentration of soil metals at the residence of mothers and the development of intellectual disability (ID) in their children. The data consist of monthly computerized claims data about the prenatal experience of pregnant women living in nine areas within South Carolina and insured by Medicaid during January 1, 1996 and December 31, 2001 and the outcome of ID in their children during early childhood. We excluded mother-child pairs if the mother moved to an unknown location during pregnancy. We identified an association of the ED outcome with arsenic (As) and mercury (Hg) concentration in soil during pregnancy, controlling for infant sex, maternal race, mother's age, and gestational weeks at delivery. There is some indication that Hg has a slightly higher importance in the third and fourth months of pregnancy, while. As has a more uniform effect over all the months with a suggestion of a slight increase in risk in later months.
机译:在本文中,我们提出了一种新的空间重要性参数分层逻辑回归建模方法,该方法包括来自未对准的测量误差。我们应用该模型研究母亲居住地土壤金属的估计浓度与孩子中智障(ID)的发展之间的关系。该数据包括每月计算机化的索赔数据,这些数据涉及居住在南卡罗来纳州九个地区的孕妇的产前经历,并在1996年1月1日和2001年12月31日接受了Medicaid的保险,以及他们的孩子在幼儿期的ID结果。如果母亲在怀孕期间搬到不明地点,​​我们将排除母子对。我们确定了怀孕期间ED结果与土壤中砷(As)和汞(Hg)浓度的关联,并控制了婴儿性别,母体种族,母亲的年龄和分娩时的孕周。有迹象表明,在怀孕的第三个月和第四个月,汞的重要性略高。在所有月份中,随着效果更均匀,建议以后几个月的风险略有增加。

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