Li Lianfa, Laurent Olivier, Wu Jun
Program in Public Health, College of Health Sciences, University of California, Anteater Instruction & Research Bldg (AIRB) # 2034, 653 East Peltason Drive, Irvine, CA, 92697-3957, USA.
State Key Lab of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, A11 Datun Road, Anwai, Chaoyang, Beijing, 100101, China.
Environ Health. 2016 Feb 5;15:14. doi: 10.1186/s12940-016-0112-5.
Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability.
We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight.
Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability.
Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.
流行病学研究表明，空气污染与妊娠结局存在不良关联。这些关联可能会受到空间变化因素的影响，包括社会人口特征、土地利用模式和未考虑的暴露因素。然而，很少有研究系统地调查这些因素对空气污染影响的空间变异性的作用。本研究旨在探讨空气污染对普查区足月出生体重影响的空间变异性，以及区域层面因素对这种变异性的影响。
我们获取了美国加利福尼亚州洛杉矶县2001年至2008年期间超过90万份出生记录。使用时空模型在个体层面模拟了二氧化氮（NO2）和氮氧化物（NOx）的空气污染暴露情况。构建了两阶段贝叶斯分层非线性模型，用于（1）量化每个普查区内空气污染暴露与足月出生体重之间的关联；（2）研究社会人口、土地利用和与暴露相关的因素对空气污染与足月出生体重之间关联的区域间变异性的影响。
空气污染暴露水平越高，足月出生体重越低（平均后验效应：NO2每增加10 ppb，降低14.7（95%可信区间：-19.8，-9.7）克；NOx每增加10 ppb，降低6.9（95%可信区间：-12.9，-0.9）克）。普查区之间关联的变化受到区域层面社会人口、与暴露相关和土地利用因素的显著影响。我们的模型捕捉到了这些因素与空气污染和足月出生体重之间复杂的非线性关系：我们观察到了区域层面因素的影响明显加剧或减弱的阈值。加剧因素可能反映了额外的环境暴露或社会经济地位较低且易感性较高，而减弱因素可能表明暴露减少或社会经济地位较高且易感性较低。
我们的贝叶斯模型有效地将先验知识与训练数据相结合，以推断空气污染与足月出生体重的后验关联，并评估区域层面因素对这种关联空间变异性的影响。本研究为社会人口因素、土地利用模式和未考虑的暴露对空气污染影响的空间变异性的非线性影响提供了新的发现。
