Studies have suggested an association between particulate matter (PM) air pollution and certain congenital anomalies (CAs). However, most studies assumed a linear concentration-response relation and were based on anomalies that were ascertained at birth or up to 1 year of age. We investigated associations between exposures to PM during the first trimester of pregnancy and CAs in 9 organ systems using birth and childhood follow-up data from a leading health care provider in Israel. We conducted a retrospective population-based cohort study among 396,334 births, 2004-2015. Daily PM data at a 1 × 1 km spatial grid were obtained from a satellite-derived prediction models and were linked to the mothers' residential addresses at birth. Adjusted odds ratios (ORs) were estimated with logistic regression models using exposure levels as either continuous or categorical variables. We captured 57,638 isolated CAs with estimated prevalence of 96 and 136 anomalies per 1000 births in the first year of life and by age 6 years, respectively. Analysis of continuous PM with diameter < 2.5 μm (PM2.5) indicated a supra-linear relation with anomalies in the circulatory, respiratory, digestive, genital and integument systems (79 % of CAs). The slope of the concentration-response function was positive and steepest for PM2.5 lower than the median concentration (21.5 μg/m3) and had a less steep or negative slope at higher levels. Similar trends were observed for PM2.5 quartiles. For example, for cardiac anomalies, the ORs were 1.09 (95 % confidence interval: 1.02, 1.15), 1.04 (0.98, 1.10) and 1.00 (0.94, 1.07) for births in the second, third and fourth quartiles, respectively, when compared to the first quartile. In sum, this study adds new evidence for adverse effects of air pollution on neonatal health even with low-level air pollution. Information on late diagnosis of children with anomalies is important in evaluating the burden of disease.
Keywords: Air pollution; Congenital malformation; Cumulative prevalence; Exposure-response curve; First trimester; Satellite-based PM prediction.
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