A quantile regression approach to examine fine particles, term low birth weight, and racial/ethnic disparities

Lara Schwarz; Tim Bruckner; Sindana D Ilango; Paige Sheridan; Rupa Basu; Tarik Benmarhnia

doi:10.1097/EE9.0000000000000060

A quantile regression approach to examine fine particles, term low birth weight, and racial/ethnic disparities

Environ Epidemiol. 2019 Jul 11;3(4):e060. doi: 10.1097/EE9.0000000000000060. eCollection 2019 Aug.

Authors

Lara Schwarz^{1

2}, Tim Bruckner³, Sindana D Ilango^{1

2}, Paige Sheridan^{1

2}, Rupa Basu⁴, Tarik Benmarhnia^{1

5}

Affiliations

¹ School of Public Health, San Diego State University, San Diego, California.
² Department of Family Medicine and Public Health University of California, San Diego, California.
³ Public Health, University of California, Irvine, California.
⁴ Air and Climate Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California.
⁵ Scripps Institution of Oceanography University of California, San Diego, California.

Abstract

Background: Exposure to fine particulate matter (PM_2.5) during pregnancy has been shown to be associated with reduced birth weight and racial/ethnic minorities have been found to be more vulnerable. Previous studies have focused on the mean value of birth weight associated with PM_2.5, which may mask meaningful differences. We applied a quantile regression approach to investigate the variation by percentile of birth weight and compared non-Hispanic (NH) Black, NH White, and Hispanic mothers.

Methods: Data for singleton births in California from October 24, 2005 to February 27, 2010 were collected from the birth records accessed from the California Department of Public Health. Air pollution monitoring data collected by the California Air Resources Board and interpolated for each zip code using an inverse-distance weighting approach, and linked to maternal zip code of residence reported on the birth certificate. Multilevel linear regression models were conducted with mother's residential zip code tabulation area as a random effect. Multilevel quantile regression models were used to analyze the association at different percentiles of birth weight (5th, 10th, 25th, 50th, 75th, 90th, 95th), as well as examine the heterogeneity in this association between racial/ethnic groups.

Results: Linear regression revealed that a 10 μg/m³ increase in PM_2.5 exposure during pregnancy is associated with a mean birth weight decrease of 7.31 g [95% confidence interval (CI): 8.10, 6.51] and NH Black mothers are the most vulnerable. Results of the quantile regression are not constant across quantiles. For NH Black mothers whose infants had the lowest birthweight of less than 2673 g (5th percentile), a 10 μg/m³ increase in PM_2.5 exposure is associated with a decrease of 18.57 g [95% CI: 22.23, 14.91], while it is associated with a decrease of 7.77 g [95% CI: 8.73, 6.79] for NH White mothers and 7.76 [8.52, 7.00] decrease for Hispanic mothers at the same quantile.

Conclusion: Results of the quantile regression revealed greater disparities, particularly for infants with the lowest birth weight. By identifying vulnerable populations, we can promote and implement policies to confront these health disparities.

Keywords: Air pollution; Health disparities; Low birth weight; Quantile regression.