Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Fig. 1

The Los Angeles Census tracts of this study. The black lines of one-dash-three-dots style indicate the boundaries for the Census tracts

Fig. 2

Two-stages Bayesian modeling framework a. Stage One; b. Stage Two. The circles or ellipses represent the random variables; the arrow lines indicate the influential relationship (association) from the staring node to the ending node of the line

Fig. 3

Non-linear influence of the tract-level factors on effects of air pollutants on term birth weight. The gray dash lines indicate the approximate intervals of thresholds where the influential factors start to take pronouncedly attenuating (a, c and d) or aggravating (b, e, f) influence on effects of air pollutants. The shades around the curve indicate the 95 % pointwise confidence limits of the estimate acquired by the hierarchical models