...
首页> 外文期刊>Environment international >Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County
【24h】

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

机译:与洛杉矶县足月低出生体重相关的多种污染物接触概况

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
机译:研究表明,多种室外空气污染物和不利的邻里条件在空间上相关。然而,与同时暴露于空气污染混合物和聚集的邻里因素相关的健康风险仍未得到充分研究。由于污染物和区域级协变量之间的共线性问题以及协变量维数的增加,评估污染物混合物对健康的影响的统计模型仍然有限。在这里,我们确定了洛杉矶(LA)县内的污染物暴露概况和邻里环境概况。然后,我们将这些资料与低出生体重(TLBW)进行关联。我们使用贝叶斯剖面回归方法,通过土地利用回归来估计人口普查区块组的平均NO2,NO和PM2.5浓度,以生成污染物暴露概况集群和人口普查区块组级别的情景概况集群。污染物分布群风险估算是使用多级层次模型来实现的,可针对个体水平的协变量进行调整,上下文分布群随机效应以及空间结构化和非结构化残余误差的建模。我们的分析发现了13组污染物暴露概况。在13个污染物簇之间,研究污染物之间的相关性差异很大。 NO2,NO和PM2.5浓度升高的污染物簇显示TLBW的对数几率增加,而PM2.5,NO2和NO浓度低的污染物簇的TLBW的对数几率较低。污染物簇对TLBW的空间分布格局,再加上污染物簇内污染物之间的相关性,暗示与交通有关的主要污染物会影响污染物簇TLBW的风险。此外,TLBW的对数几率最大的情境群集具有更大的不利邻里社会经济,人口统计学和住房条件。我们的数据表明,尽管高风险的多种污染物簇的空间格局与不利的环境邻域簇在很大程度上重叠,但两者都有助于TLBW,而对另一方则具有控制作用。 (C)2016作者。由Elsevier Ltd.发布。这是CC BY许可下的开放访问文章(http://creativecommons.org/licenses/by/4.0/)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号