Impacts of gestational age uncertainty in estimating associations between preterm birth and ambient air pollution Open Access

Nealy, Benjamin (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/r494vk192?locale=en
Published

Abstract

Background: Airborne pollutants have known deleterious health effects and pregnant women have been identified as a potential vulnerable population. Previous epidemiologic studies utilizing birth records have shown heterogeneous relationships between air pollution exposure during pregnancy and the risk of preterm birth (PTB, gestational age < 37 weeks). Uncertainty in gestational age at birth may contribute to this heterogeneity.

Methods: We first examined disagreement between clinical and last menstrual period-based (LMP) determination of PTB from individual-level birth certificate data for the 20-county Atlanta metropolitan area during 2002 to 2006. We then estimated associations between five trimester-averaged pollutant exposures and PTB, defined using various methods based on the clinical or LMP gestational age. Finally, using a multiple imputation approach, we incorporated uncertainty in gestational age to determine the impact of this variability on associations between pollutant exposures and PTB.

Results: Odds ratios were most elevated when a more stringent definition of PTB was used. For example, defining PTB only when LMP and clinical diagnoses agree yielded an odds ratio (OR) of 1.09 for first trimester carbon monoxide exposure versus an OR of 1.04 when PTB was defined as either an LMP or clinical diagnosis. Accounting for outcome uncertainty resulted in wider confidence intervals-- between 7.4% and 43.8% wider than those assuming the PTB outcome is without error.  

Conclusions: Despite discrepancies in PTB derived using either the clinical or LMP gestational age estimates, our analyses demonstrated robust positive associations between PTB and ambient air pollution exposures when gestational age uncertainty is present. 

Table of Contents

Introduction..........................................................................................................................1

Methods................................................................................................................................3

            Health and Air Quality Data....................................................................................3

            Statistical Analysis...................................................................................................4

            Multiple Imputation.................................................................................................5

Results..................................................................................................................................7

Discussion............................................................................................................................9

References..........................................................................................................................16

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