Introduction:There is growing epidemiologic evidence of associations
between maternal exposure to ambient air
pollution and adverse birth outcomes, such as
preterm birth (PTB). Recently, a few studies have
also reported that exposure to ambient air pollution
may also increase the risk of some common
pregnancy complications, such as preeclampsia
and gestational diabetes mellitus (GDM). Research
findings, however, have been mixed. These inconsistent
results could reflect genuine differences in
the study populations, the study locations, the specific
pollutants considered, the designs of the study,
its methods of analysis, or random variation.
Dr. Jun Wu of the University of California–
Irvine, a recipient of HEI’s Walter A. Rosenblith
New Investigator Award, and colleagues have
examined the association between air pollution
and adverse birth and pregnancy outcomes in
California women. In addition, they examined the
effect modification by socioeconomic status (SES)
and other factors. Approach:A retrospective nested case–control study was
conducted using birth certificate data from about
4.4 million birth records in California from 2001 to
2008. Wu and colleagues analyzed data on low
birth weight (LBW) at term (infants born between
37 and 43 weeks of gestation and weighing less
than 2500 g), PTB (infants born before 37 weeks of
gestation), and preeclampsia (including eclampsia)
of the mother during the pregnancy. In addition,
they obtained data on GDM for the years 2006–
2008. In the analyses, all outcomes were included
as binary variables.
Maternal residential addresses at the time of
delivery were geocoded, and a large suite of air
pollution exposure metrics was considered, such
as (1) regulatory monitoring data on concentrations
of criteria pollutants NO2, PM2.5 (particulate
matter ≤ 2.5 μm in aerodynamic diameter), and
ozone (O3) estimated by empirical Bayesian kriging;
(2) concentrations of primary and secondary
PM2.5 and PM0.1 components and sources estimated
by the University of California–Davis
Chemical Transport Model; (3) traffic-related ultrafine
particles and concentrations of carbon
monoxide (CO) and nitrogen oxides (NOx) estimated
by a modified CALINE4 air pollution dispersion
model; and (4) proximity to busy roads, road
length, and traffic density calculated for different
buffer sizes using geographic information system
tools. In total, 50 different exposure metrics were
available for the analyses. The exposure of primary
interest was the mean of the entire pregnancy
period for each mother.
For the health analyses, controls were randomly
selected from the source population. PTB controls
were matched on conception year. Term LBW, preeclampsia,
and GDM were analyzed using generalized
additive mixed models with inclusion of a
random effect per hospital. PTB analyses were conducted
using conditional logistic regression, with
no adjustment for hospital. The main results—
adjusted for race and education as categorical variables
and adjusted for maternal age and median
household income at the census-block level—were
derived from single-pollutant models. Main results and interpretation:In its independent review of the study, the HEI
Health Review Committee concluded that Wu and
colleagues had conducted a comprehensive nested
case–control study of air pollution and adverse
birth and pregnancy outcomes. The very large data
set and the extensive exposure assessment were
strengths of the study.
The study documented associations between
increases in various air pollution metrics and
increased risks of PTB, whereas the evidence was
weaker overall for term LBW; in addition, decreases
in many air pollution metrics were associated with
an increased risk of preeclampsia and GDM, an
unexpected result.
The investigators suggested that underreporting
in the registry data, especially in lower-SES
groups, might have caused the many negative associations
found for preeclampsia and GDM. In addition,
poor geocoding was listed as a potential
explanation, affecting in particular the results that
were based on measures of proximity to busy roads
and traffic density in the smallest buffer size (50
m). However, those issues were not fully explored.
In general, the Committee thought that the analysis
of road traffic indicators in the 50 m buffer was
hampered by the lack of contrast and that the
results are therefore difficult to interpret.
Some other issues with the analytical approaches
should be considered when interpreting the results.
Only a subset of controls was used, to reduce computational
demands. Hence, some models did not
converge, especially in the subgroup analyses.
Most of the results in the report were based on
analyses using single-pollutant models, which is a
reasonable approach but ignores that people are
exposed to complex mixtures of pollutants. The
Committee believed that the few two-pollutant
models that were run provided important insights:
these models showed the strongest association for
PM2.5 mass, whereas components and source-specific
positive associations largely disappeared
after adjusting for PM2.5 mass. This study adds to
the ongoing debate about whether some particle
components and sources are of greater public
health concern than others.
