Global air pollution exposure and congenital anomalies: an updated systematic review and meta-analysis of epidemiological studies

ABSTRACT

A systematic review and meta-analysis was conducted to evaluate recent epidemiological evidence on the association of air pollution with congenital anomalies (CAs). Of 11,014 records, 49 were finally included in this meta-analysis. Per 10 μg/m³ increase in air pollutant, PM₁₀ exposure during the 1^st month of pregnancy and at the first trimester (T1) was associated with increased overall CAs. Further, exposure to PM₁₀ was associated with congenital heart disease (OR = 1.055, 95% CI: 1.035, 1.074) and patent ductus arteriosus (OR = 1.094, 95% CI: 1.020, 1.168) at T1, with chromosomal anomalies during the entire pregnancy and with nervous system anomalies when exposure occurred 3 months prior to pregnancy, during the 1^st, 2^nd months of pregnancy and at T1. Besides, a significant association with overall CAs was observed for a combined exposure of PM₁₀ and SO₂ during the 1^st month of gestation (OR: 1.101, 95% CI: 1.023, 1.180). A combined exposure of PM₁₀ and CO was also associated with tetralogy of Fallot during 3–8 weeks of gestation (OR: 1.016, 95% CI: 1.005, 1.027). No significant associations were observed between PM_2.5, NO₂, and O₃ exposure and CAs.

KEYWORDS:

• Air pollution
• congenital anomalies
• epidemiological study
• systematic review
• meta-analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution

All authors contributed to the final version of the manuscript and have approved the final article. Yang Feng and Xinxin Liu had the idea for the article. Xiaoan Zhang, Xin Zhao, Hui Chang, and Fan Ouyang performed the literature search and data analysis. Yang Feng drafted the manuscript. Zengli Yu, Zhan Gao, and Huanhuan Zhang critically revised the work.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/09603123.2023.2246383

Additional information

Funding

The work was supported by the [Major Projects of Collaborative Innovation of Zhengzhou #1] under Grant [No.18XTZX12009]; [Special Major Public Welfare Project of Henan Province #2] under Grant [No. 201300310800]; [National Key R&D Program of China #3] under Grant [2018YFA0606200]; and [Key Project of Science and Technology of Henan Province #4] under Grant [222102310165].