We investigated the extent to which associations of ambient air pollutant concentrations and birth weight varied across birth weight quantiles.
We analyzed singleton births ≥27 weeks gestation from 20-county metropolitan Atlanta with conception dates between 1 January 2002 and 28 February 2006 (N=273,711). Trimester-specific and total pregnancy average concentrations for 10 pollutants, obtained from ground observations that were interpolated using 12-km Community Multiscale Air Quality model outputs, were assigned using maternal residence at delivery. We estimated associations between interquartile range width (IQRw) increases in pollutant concentrations and changes in birth weight using quantile regression.
Gestational age-adjusted associations were of greater magnitude at higher percentiles of the birth weight distribution. Pollutants with large vehicle source contributions (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon, and total PM2.5 mass), as well as PM2.5 sulfate and PM2.5 ammonium, were associated with birth weight decreases for the higher birth weight percentiles. For example, whereas the decrease in mean birthweight per IQRw increase in PM2.5 averaged over pregnancy was -7.8g (95% CI: -13.6g, - 2.0g), the quantile-specific associations were: 10th percentile -2.4g (-11.5g, 6.7g); 50th percentile -8.9g (-15.7g, -2.0g); and 90th percentile -19.3g (-30.6g, -7.9g). Associations for the intermediate and high birth weight quantiles were not sensitive to gestational age adjustment. For some pollutants we saw associations at the lowest quantile (10th percentile) when not adjusting for gestational age.
Associations between air pollution and reduced birth weight were of greater magnitude for newborns at relatively heavy birth weights. air pollution, birth weight, quantile regression
1School of Community Health Sciences, University of Nevada-Reno, Reno, NV, USA
2Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
3Department of Biostatistics, Yale University, New Haven, CT, USA
4Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Acknowledgments: The results reported herein are from an alternative analysis (quantile regression) for Specific Aim 3 for Project 3 of United States Environmental Protection Agency center grant R834799. This work was also supported by grants from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR000454, UL1TR001863, and KL2TR001862. This publication’s contents are solely the responsibility of the grantee and do not necessarily represent the official view of the US EPA. Further US EPA does not endorse the purchase of any commercial products or services mentioned in the publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Competing financial interest declaration: All authors declare no competing financial interests.
Statement on availability of data and code for replication: The data are not available for replication, because the data use agreement does not allow us to redistribute birth records. Computer code will be made available upon request.
Corresponding author: Matthew J. Strickland, 1664 N Virginia St / 0275, Reno, NV 89557-0275. email@example.com 775-682-7088