.2020 Sep 14;4(5):e113.

doi: 10.1097/EE9.0000000000000113. eCollection 2020 Oct.

Prenatal exposure to particulate air pollution and gestational age at delivery in Massachusetts neonates 2001-2015: A perspective of causal modeling and health disparities

Xinye Qiu¹, Kelvin C Fong², Liuhua Shi^{1 3}, Stefania Papatheodorou⁴, Qian Di⁵, Allan Just⁶, Anna Kosheleva¹, Carmen Messerlian^{1 4}, Joel D Schwartz^{1 4}

Affiliations

¹ Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
² School of the Environment, Yale University, New Haven, Connecticut.
³ Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.
⁴ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
⁵ School of Medicine, Tsinghua University, Beijing, China.
⁶ Icahn School of Medicine at Mount Sinai, New York City, New York.

PMID:33154990
PMCID: PMC7595249
DOI: 10.1097/EE9.0000000000000113

Prenatal exposure to particulate air pollution and gestational age at delivery in Massachusetts neonates 2001-2015: A perspective of causal modeling and health disparities

Xinye Qiu et al. Environ Epidemiol.2020.

.2020 Sep 14;4(5):e113.

doi: 10.1097/EE9.0000000000000113. eCollection 2020 Oct.

Authors

Xinye Qiu¹, Kelvin C Fong², Liuhua Shi^{1 3}, Stefania Papatheodorou⁴, Qian Di⁵, Allan Just⁶, Anna Kosheleva¹, Carmen Messerlian^{1 4}, Joel D Schwartz^{1 4}

Affiliations

¹ Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
² School of the Environment, Yale University, New Haven, Connecticut.
³ Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.
⁴ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
⁵ School of Medicine, Tsinghua University, Beijing, China.
⁶ Icahn School of Medicine at Mount Sinai, New York City, New York.

PMID:33154990
PMCID: PMC7595249
DOI: 10.1097/EE9.0000000000000113

Abstract

There is a lack of evidence on causal effects of air pollution on gestational age (GA) at delivery.

Methods: Inverse probability weighting (IPW) quantile regression was applied to derive causal marginal population-level GA reduction for GA percentiles associated with increased ambient particulate matter with diameter <2.5 μm (PM_2.5) levels at maternal residential address for each trimester and the month preceding delivery using Massachusetts birth registry 2001 to 2015. Stratified analyses were conducted for neonatal sex, maternal age/race/education, and extreme ambient temperature conditions.

Results: For neonates at 2.5th, 10th, 25th, 50th, 75th, and 97.5th percentiles of GA at delivery, we estimated an adjusted GA reduction of 4.2 days (95% confidence interval [CI] = 3.4, 5.0), 1.9 days (1.6, 2.1), 1.2 days (1.0, 1.4), 0.82 days (0.72, 0.92), 0.74 days (0.54, 0.94), and 0.54 days (0.15, 0.93) for each 5 μg/m3 increment in third trimester average PM_2.5 levels. Final gestational month average exposure yielded a similar effect with greater magnitude. Male neonates and neonates of younger (younger than 35 years) and African American mothers as well as with high/low extreme temperature exposure in third trimester were more affected. Estimates were consistently higher at lower GA percentiles, indicating preterm/early-term births being more affected. Low-exposure analyses yielded similar results, restricting to areas with PM_2.5 levels under US ambient annual standard of 12 μg/m³.

Conclusions: Prenatal exposure to PM_2.5 in late pregnancy reduced GA at delivery among Massachusetts neonates, especially among preterm/early-term births, male neonates, and neonates of younger and African American mothers. Exposure to extremely high/low temperature amplifies the effect of PM_2.5 on GA.

Keywords: Air pollution; Gestational age; Health disparity; Inverse probability weighting; Quantile regression; Temperature.

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Conflict of interest statement

The authors declare that they have no conflicts of interest with regard to the content of this report. This study was supported by the National Institutes of Health (NIH) grant (R01 ES024332-01A1, ES-000002). This publication was made possible also by US Environmental Protection Agency (EPA) grant (RD-835872-01). Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US EPA. Further, US EPA does not endorse the purchase of any commercial products or services mentioned in the publication. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The funding sources support open access publishing.Sponsorships or competing interests that may be relevant to content are disclosed at the end of the article.

Figures

**Figure 1.**
A, Births percentile distribution for GA. The counts of births distributed for percentiles of GA at delivery and its corresponding GA at delivery levels. B, Counterfactual distribution of GA. We used the effect estimates in the third trimester to do counterfactual predictions comparing the distribution of GA within Massachusetts newborns population under ambient PM_2.5 levels 5 μg/m³ lower than current situation and 5 μg/m³ higher than current situation from 2001 to 2015.

**Figure 2.**
Boxplot distribution of average ambient PM_2.5 levels during the third trimester at maternal residential address for various maternal racial groups. Racial groups represent 1: Caucasians, 2: African Americans, 3: Asian and Pacific Islanders, 4: Native Americans, and 5: other nonmissing race, respectively.

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Prenatal exposure to particulate air pollution and gestational age at delivery in Massachusetts neonates 2001-2015: A perspective of causal modeling and health disparities

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Prenatal exposure to particulate air pollution and gestational age at delivery in Massachusetts neonates 2001-2015: A perspective of causal modeling and health disparities

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Conflict of interest statement

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