Abstract

Neonatal mortality and morbidity are often caused by preterm birth and lower birth weight. Gestational diabetes mellitus (GDM) and gestational hypertension (GH) are the most prevalent maternal medical complications during pregnancy. However, evidence on effects of air pollution on adverse birth outcomes and pregnancy complications is mixed. Singleton live births conceived between January 1st, 2000, and December 31st, 2015, and reached at least 27 weeks of pregnancy in Kansas were included in the study. Trimester-specific and total pregnancy exposures to nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), and ozone (O3) were estimated using spatiotemporal ensemble models and assigned to maternal residential census tracts. Logistic regression, discrete-time survival, and linear models were applied to assess the associations. After adjustment for demographics and socio-economic status (SES) factors, we found increases in the second and third trimesters and total pregnancy O3 exposures were significantly linked to preterm birth. Exposure to the second and third trimesters O3 was significantly associated with lower birth weight, and exposure to NO2 during the first trimester was linked to an increased risk of GDM. O3 exposures in the first trimester were connected to an elevated risk of GH. We didn’t observe consistent associations between adverse pregnancy and birth outcomes with PM2.5 exposure. Our findings indicate there is a positive link between increased O3 exposure during pregnancy and a higher risk of preterm birth, GH, and decreased birth weight. Our work supports limiting population exposure to air pollution, which may lower the likelihood of adverse birth and pregnancy outcomes.

Details

Title
Effects of air pollution on adverse birth outcomes and pregnancy complications in the U.S. state of Kansas (2000–2015)
Author
Hao, Hua 1 ; Yoo, Sodahm R. 2 ; Strickland, Matthew J. 3 ; Darrow, Lyndsey A. 3 ; D’Souza, Rohan R. 2 ; Warren, Joshua L. 4 ; Moss, Shannon 2 ; Wang, Huaqing 5 ; Zhang, Haisu 1 ; Chang, Howard H. 6 

 Emory University, Gangarosa Department of Environmental Health, Rollins School of Public Health, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502) 
 Emory University, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502) 
 University of Nevada, Depatment of Health Analytics and Biostatistics, Epidemiology and Environmental Health, School of Public Health, Reno, USA (GRID:grid.266818.3) (ISNI:0000 0004 1936 914X) 
 Yale University, Department of Biostatistics, School of Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Utah State University, Department of Landscape Architecture and Environment Planning, College of Agriculture and Applied Sciences, Logan, USA (GRID:grid.53857.3c) (ISNI:0000 0001 2185 8768) 
 Emory University, Gangarosa Department of Environmental Health, Rollins School of Public Health, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502); Emory University, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502) 
Pages
21476
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2898167405
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.