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Whether ambient air pollution is associated with childhood atopic eczema is controversial. In this paper, we selected 3358 preschool children who had not altered residences since pregnancy from a cross-sectional study during 2011–2012 in Shanghai, China, and obtained parent-reported data regarding childhood atopic eczema using an improved ISAAC questionnaire. We recorded daily concentrations of SO 2 , NO 2 , and PM 10 throughout the child's lifetime (2006–2012), and calculated period-averaged concentrations for each district where the child lived to represent the child's exposure levels of these pollutants during different periods. In the multiple logistic regression analyses adjusted for potential confounders as well as for the other pollutants in the same periods, childhood atopic eczema was significantly associated with increments of NO 2 in the approximate interquartile range (20 μg/m 3 ) during gestational period (adjusted OR, 95% CI for eczema lifetime-ever: 1.80, 1.29–2.49; for eczema in the year prior to the survey: 2.32, 1.57–3.43) and during the first year of life (2.00, 1.40–2.84; 2.16, 1.43–3.28). Exposure to elevated NO 2 in the first two years, three years and total lifetime, as well as exposure to mixtures containing NO 2 in each of these periods, were consistently associated with increased likelihood of childhood eczema. The highest odds ratios were found between exposure to a mixture of SO 2 and NO 2 during total lifetime (increment: 35 μg/m 3 ) and childhood eczema (adjusted OR, 95% CI: 2.80, 1.75–4.48; 3.50, 1.98–6.19). No significant associations were found between childhood eczema and ambient SO 2 and PM 10 individually or in mixtures. This study indicates that gestational and lifetime exposures to ambient NO 2 are risk factors for atopic eczema in childhood. Exposure to ambient SO 2 and PM 10 may enhance the effect of NO 2 exposure on childhood eczema. [ABSTRACT FROM AUTHOR]

Spatio-temporal models of ambient air pollution can be used to predict pollutant levels across a geographical region. These predictions may then be used as estimates of exposure for individuals in analyses of the health effects of air pollution. Integrated nested Laplace approximations is a method for Bayesian inference, and a fast alternative to Markov chain Monte Carlo methods. It also facilitates the SPDE approach to spatial modelling, which has been used for modelling of air pollutant levels, and is available in the R-INLA package for the R statistics software. Covariates such as meteorological variables may be useful predictors in such models, but covariate misalignment must be dealt with. This paper describes a flexible method used to estimate pollutant levels for six pollutants in Suzhou, a city in China with dispersed air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data. [ABSTRACT FROM AUTHOR]