A population-based cohort study of electronic tolling, traffic congestion, and adverse birth outcomes

Changes in traffic-related air pollution exposures and associations with adverse birth outcomes over 20 years in Texas

BACKGROUND

Billions of dollars have been spent implementing regulations to reduce traffic-related air pollution (TRAP) from exhaust pipe emissions. However, few health studies have evaluated the change in TRAP emissions and associations with infant health outcomes. We hypothesize that the magnitude of association between vehicle exposure measures and adverse birth outcomes has decreased over time, parallelling regulatory improvements in exhaust pipe emissions.

METHODS

Using birth records in Texas from 1996 to 2016, we calculated residential exposure measures related to TRAP: nitrogen dioxide (NO2, a marker of the TRAP mixture), vehicle miles travelled within 500 m of homes (VMT500), a measure of traffic volume, and highway proximity. Using an accountability study framework, our analysis examined term birthweight, term low birthweight (TLBW) (<2500 g), preterm birth (PTB) (<37 weeks) and very preterm birth (VPTB) (<32 weeks). We implemented linear and logistic regression models to examine overall and time-stratified associations, including trends by race/ethnicity and socioeconomic groups.

RESULTS

Among exposures for 6 158 518 births, NO2 exposures decreased 59% over time but VMT500 remained relatively stable. TRAP-related exposure measures were persistently associated with harmful birth outcomes [e.g. OR1996-2016 of 1.07 (95% CI: 1.04, 1.08) for TLBW comparing the highest vs lowest NO2 quintile]. The magnitude of associations decreased for total VMT500 and TLBW (-60%, OR1996: 1.08 to OR2016: 1.03 for the highest vs lowest quintile) and PTB (-65%) and VTPT (-61%), but not for term birthweight.

CONCLUSIONS

We observed evidence of small improvements in birth outcomes associated with reductions in exhaust pipe emissions over a 20-year period in Texas.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Roadway construction as a natural experiment to examine air pollution impacts on infant health

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300-1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.