Background: Although several previous studies have assessed the association of fine particulate matter (PM2.5) exposure during pregnancy with preterm birth, the results have been inconsistent and remain controversial. This meta-analysis aims to quantitatively summarize the association between maternal PM2.5 exposure and preterm birth and to further explore the sources of heterogeneity in findings on this association.
Methods: We searched for all studies published before December 2014 on the association between PM2.5 exposure during pregnancy and preterm birth in the MEDLINE, PUBMED and Embase databases as well as the China Biological Medicine and Wanfang databases. A pooled OR for preterm birth in association with each 10 μg/m(3) increase in PM2.5 exposure was calculated by a random-effects model (for studies with significant heterogeneity) or a fixed-effects model (for studies without significant heterogeneity).
Results: A total of 18 studies were included in this analysis. The pooled OR for PM2.5 exposure (per 10 μg/m(3) increment) during the entire pregnancy on preterm birth was 1.13 (95% CI = 1.03-1.24) in 13 studies with a significant heterogeneity (Q = 80.51, p < 0.001). The pooled ORs of PM2.5 exposure in the first, second and third trimester were 1.08 (95% CI = 0.92-1.26), 1.09 (95% CI = 0.82-1.44) and 1.08 (95% CI = 0.99-1.17), respectively. The corresponding meta-estimates of PM2.5 effects in studies assessing PM2.5 exposure at individual, semi-individual and regional level were 1.11 (95% CI = 0.89-1.37), 1.14 (95% CI = 0.97-1.35) and 1.07 (95% CI = 0.94-1.23). In addition, significant meta-estimates of PM2.5 exposures were found in retrospective studies (OR = 1.10, 95% CI = 1.01-1.21), prospective studies (OR = 1.42, 95% CI = 1.08-1.85), and studies conducted in the USA (OR = 1.16, 95 % CI = 1.05-1.29).
Conclusions: Maternal PM2.5 exposure during pregnancy may increase the risk of preterm birth,but significant heterogeneity was found between studies. Exposure assessment methods, study designs and study settings might be important sources of heterogeneity, and should be taken into account in future meta-analyses.