McDermott Suzanne, Bao Weichao, Aelion C Marjorie, Cai Bo, Lawson Andrew B
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Discovery 1, 915 Greene Street, Columbia, SC, 29208, USA,
Environ Geochem Health. 2014 Dec;36(6):1191-7. doi: 10.1007/s10653-014-9617-4. Epub 2014 Apr 26.
Low birth weight (LBW) is associated with a number of maternal environmental exposures during pregnancy. This study explored the association between soil metal concentrations around the home where the mother lived during pregnancy and the outcome of LBW. We used a retrospective cohort of 9,920 mother-child pairs who were insured by Medicaid during pregnancy and lived in ten residential areas, where we conducted soil sampling. We used a grid that overlaid the residential areas and collected soil samples at the grid intersections. The soil was analyzed for the concentration of eight metals [arsenic (As), barium (Ba), chromium (Cr), copper (Cu), lead (Pb), manganese (Mn), nickel (Ni), and mercury (Hg)], and we then used Bayesian Kriging to estimate the concentration at the actual maternal addresses, since we had the GIS coordinates of the homes. We used generalized additive modeling, because the metal concentrations had nonlinear associations with LBW, to develop the best fitting multivariable model for estimating the risk of LBW. The final model showed significant associations for female infants, maternal smoking during pregnancy, non-white mothers, Cu, and As with LBW. The As variable was nonlinear in relation to LBW, and the association between higher concentrations of As with LBW was strong (p = 0.002). We identified a statistically significant association between soil concentrations of arsenic around the home of pregnant women and an increased risk of LBW for her infant.
低出生体重(LBW)与孕期母亲接触的多种环境因素有关。本研究探讨了孕期母亲居住房屋周围土壤金属浓度与低出生体重结局之间的关联。我们采用了一个回顾性队列,其中包括9920对母婴,这些母亲在孕期参加了医疗补助计划,并居住在十个居民区,我们在这些居民区进行了土壤采样。我们使用了一个覆盖居民区的网格,并在网格交叉点采集土壤样本。对土壤进行了八种金属[砷(As)、钡(Ba)、铬(Cr)、铜(Cu)、铅(Pb)、锰(Mn)、镍(Ni)和汞(Hg)]浓度的分析,由于我们掌握了房屋的地理信息系统(GIS)坐标,随后我们使用贝叶斯克里金法来估算母亲实际居住地址处的金属浓度。我们使用广义相加模型,因为金属浓度与低出生体重存在非线性关联,以建立最佳拟合多变量模型来估算低出生体重的风险。最终模型显示,女性婴儿、孕期母亲吸烟、非白人母亲、铜和砷与低出生体重存在显著关联。砷变量与低出生体重的关系呈非线性,较高浓度的砷与低出生体重之间的关联很强(p = 0.002)。我们确定了孕妇房屋周围土壤中砷的浓度与婴儿低出生体重风险增加之间存在统计学显著关联。