The International Collaboration on Air Pollution and Pregnancy Outcomes: Initial Results

ArticleinEnvironmental Health Perspectives 119(7):1023-8 · February 2011with46 Reads
Impact Factor: 7.98 · DOI: 10.1289/ehp.1002725 · Source: PubMed
Abstract

The findings of prior studies of air pollution effects on adverse birth outcomes are difficult to synthesize because of differences in study design. The International Collaboration on Air Pollution and Pregnancy Outcomes was formed to understand how differences in research methods contribute to variations in findings. We initiated a feasibility study to a) assess the ability of geographically diverse research groups to analyze their data sets using a common protocol and b) perform location-specific analyses of air pollution effects on birth weight using a standardized statistical approach. Fourteen research groups from nine countries participated. We developed a protocol to estimate odds ratios (ORs) for the association between particulate matter ≤ 10 μm in aerodynamic diameter (PM₁₀) and low birth weight (LBW) among term births, adjusted first for socioeconomic status (SES) and second for additional location-specific variables. Among locations with data for the PM₁₀ analysis, ORs estimating the relative risk of term LBW associated with a 10-μg/m³ increase in average PM₁₀ concentration during pregnancy, adjusted for SES, ranged from 0.63 [95% confidence interval (CI), 0.30-1.35] for the Netherlands to 1.15 (95% CI, 0.61-2.18) for Vancouver, with six research groups reporting statistically significant adverse associations. We found evidence of statistically significant heterogeneity in estimated effects among locations. Variability in PM₁₀-LBW relationships among study locations remained despite use of a common statistical approach. A more detailed meta-analysis and use of more complex protocols for future analysis may uncover reasons for heterogeneity across locations. However, our findings confirm the potential for a diverse group of researchers to analyze their data in a standardized way to improve understanding of air pollution effects on birth outcomes.

Full-text

Available from: Johanna Lepeule
Environmental Health Perspectives
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Children’s Health
Evidence that poor air quality can adversely
affect birth outcomes is increasing. A small
number of review articles have summarized
existing studies and concluded that there is
likely an adverse effect of air pollution on
pregnancy outcome (Glinianaia et al. 2004;
Ritz and Wilhelm 2008;
Šrám
et al. 2005).
However, estimated associations between
these outcomes and air pollutant exposures
over the whole pregnancy and during specific
time windows (e.g., trimester of pregnancy)
have been inconsistent, making definitive
conclusions difficult (Glinianaia et al. 2004;
Slama et al. 2008; Woodruff et al. 2009).
Comparisons of findings across different
geographic locations are hindered, in part,
by differences in research designs. Although
most published studies have reported adverse
pregnancy outcomes in association with pre-
natal exposure to air pollution, inconsistent
findings reported by some studies prompted
a series of workshops to discuss this relatively
new area of investigation (Slama et al. 2008;
Woodruff et al. 2009) and the formation
of the International Collaboration on Air
Pollution and Pregnancy Outcomes (ICAPPO)
(Woodruff et al. 2010). e primary objective
of ICAPPO is to understand how differences
in research design and methods contribute to
variations in findings.
As part of this effort, a feasibility study
was developed to determine whether it would
be possible to use a common protocol to rean-
alyze existing data sets that were created to
answer similar but not identical research ques-
tions. A workshop was held in Dublin (25–29
August 2009) to share and discuss the initial
results of the feasibility study. In this report,
we describe the common research protocol
and participating studies. Throughout this
article, study results from each research group
are referred to by name [e.g., EDEN study
(Etude des Déterminants pré et post natals du
développement et de la santé de l’Enfant)] if
Address correspondence to J.D. Parker, National Center
for Health Statistics, 3311 Toledo Rd., Room 6107,
Hyattsville, MD 20782 USA. Telephone: (301) 458-
4419. Fax: (301) 458-4038. E-mail: jdparker@cdc.gov
Supplemental Material is available online (doi:10.
1289/ehp.1002725 via http://dx.doi.org/).
M.L.B. was supported in part by National Institutes
of Health grant 1R01ES016317. J.L. was supported by
a postdoctoral grant from Institut national de la san
et de la recherche médicale (INSERM). U.G. was sup-
ported by a research fellowship of the Netherlands
Organization for Scientific Research (NWO).
The findings and conclusions in this article are
those of the authors and do not necessarily represent
the views of the National Center for Health Statistics,
Centers for Disease Control and Prevention.
e authors declare they have no actual or potential
competing financial interests.
Received 15 July 2010; accepted 9 February 2011.
The International Collaboration on Air Pollution and Pregnancy Outcomes:
Initial Results
Jennifer D. Parker,
1
David Q. Rich,
2
Svetlana V. Glinianaia,
3
Jong Han Leem,
4
Daniel Wartenberg,
5
Michelle L. Bell,
6
Matteo Bonzini,
7
Michael Brauer,
8
Lyndsey Darrow,
9
Ulrike Gehring,
10
Nelson Gouveia,
11
Paolo Grillo,
12
Eunhee Ha,
13
Edith H. van den Hooven,
14,15
Bin Jalaludin,
16
Bill M. Jesdale,
17
Johanna Lepeule,
18,19
Rachel Morello-Frosch,
17,20
Geoffrey G. Morgan,
21,22
Rémy Slama,
18,19
Frank H. Pierik,
15
Angela Cecilia Pesatori,
23
Sheela Sathyanarayana,
24
Juhee Seo,
13
Matthew Strickland,
9
Lillian Tamburic,
25
and Tracey J. Woodruff
26
1
National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA;
2
Department of Community
and Preventive Medicine, University of Rochester School of Medicine and Dentistry,Rochester, New York, USA;
3
Institute of Health and
Society, Newcastle University, Newcastle upon Tyne, England, United Kingdom;
4
Department of Occupational and Environmental Medicine,
Inha University, Incheon, Republic of Korea;
5
UMDNJ-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA;
6
Yale
University, School of Forestry and Environmental Studies, New Haven, Connecticut, USA;
7
Department of Experimental Medicine, University
of Insubria, Varese, Italy;
8
University of British Columbia, Department of Medicine, Vancouver, British Columbia, Canada;
9
Department of
Environmental Health, Emory University, Atlanta, Georgia, USA;
10
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the
Netherlands;
11
Department of Preventive Medicine, School of Medicine of the University of São Paulo, São Paulo, Brasil;
12
Epidemiology
Unite, “Fondazione IRCCS Ca’Granda—Ospedale Maggiore Policlinico,” Milan, Italy;
13
Department of Preventive Medicine, Ewha Womans
University, Seoul, Republic of Korea;
14
Generation R Study Group, Erasmus Medical Center, Rotterdam, the Netherlands;
15
Department of
Urban Environment, Netherlands Organisation for Applied Scientific Research (TNO), Delft, the Netherlands;
16
Centre for Research, Evidence
Management and Surveillance, Sydney South West Area Health Service, and School of Public Health and Community Medicine, University
of New South Wales, Sydney, Australia;
17
Department of Environmental Science, Policy and Management, University of California–Berkeley,
Berkeley, California, USA;
18
INSERM, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, U823, Institut
Albert Bonniot, Grenoble, France.;
19
University J. Fourier Grenoble, Grenoble, France;
20
School of Public Health, University of California–
Berkeley, Berkeley, California, USA;
21
North Coast Area Health Service, Lismore, New South Wales, Australia;
22
University Centre for Rural
Health–North Coast, University of Sydney, Sydney, New South Wales, Australia;
23
Department of Occupational and Environmental Health,
Università di Milano, Milan, Italy;
24
Seattle Children’s Research Institute, University of Washington, Seattle, Washington, USA;
25
University
of British Columbia, Centre for Health Services and Policy Research, Vancouver, British Columbia, Canada;
26
Center for Reproductive Health
and the Environment. University of California–San Francisco, San Francisco, California, USA
Ba c k g r o u n d : e findings of prior studies of air pollution effects on adverse birth outcomes are
difficult to synthesize because of differences in study design.
oB j e c t i v e s : The International Collaboration on Air Pollution and Pregnancy Outcomes was
formed to understand how differences in research methods contribute to variations in findings.
We initiated a feasibility study to a) assess the ability of geographically diverse research groups to
analyze their data sets using a common protocol and b) perform location‑specific analyses of air pol
lution effects on birth weight using a standardized statistical approach.
Me t h o d s : Fourteen research groups from nine countries participated. We developed a protocol to
estimate odds ratios (ORs) for the association between particulate matter 10 μm in aerodynamic
diameter (PM
10
) and low birth weight (LBW) among term births, adjusted first for socioeconomic
status (SES) and second for additional location‑specific variables.
re s u l t s : Among locations with data for the PM
10
analysis, ORs estimating the relative risk of term
LBW associated with a 10‑μg/m
3
increase in average PM
10
concentration during pregnancy, adjusted
for SES, ranged from 0.63 [95% confidence interval (CI), 0.301.35] for the Netherlands to 1.15 (95%
CI, 0.612.18) for Vancouver, with six research groups reporting statistically significant adverse associa
tions. We found evidence of statistically significant heterogeneity in estimated effects among locations.
co n c l u s i o n s : Variability in PM
10
LBW relationships among study locations remained despite use
of a common statistical approach. A more detailed meta‑analysis and use of more complex protocols
for future analysis may uncover reasons for heterogeneity across locations. However, our findings
confirm the potential for a diverse group of researchers to analyze their data in a standardized way
to improve understanding of air pollution effects on birth outcomes.
ke y w o r d s : air pollution, birth weight, ICAPPO, low birth weight, particulate matter, pregnancy.
Environ Health Perspect 119:1023–1028 (2011). doi:10.1289/ehp.1002725 [Online 9 February 2011]
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available, otherwise by location (e.g., Seattle
study). Additionally, we present estimated
odds ratios (ORs) for the association between
low birth weight (LBW) among term births
and exposure to ambient particulate mat-
ter with an aerodynamic diameter 10 µm
(PM
10
) during pregnancy.
Methods
Through discussion with the larger group of
ICAPPO participants and detailed planning
by a smaller group (J.D.P., D.Q.R., S.V.G.,
J.H.L.), a protocol for the feasibility study was
developed, agreed upon, and distributed to a
geographically diverse group of researchers. To
maximize the number of participating groups,
we deliberately simplified the protocol by
restricting the primary statistical analysis to one
outcome (LBW in term births) and the air pol-
lution exposure (PM
10
) available for the largest
number locations (Woodruff et al. 2010).
Cohort restrictions. We limited the study
to live-born, singleton, term (37–42 com-
plete weeks of gestation) infants with known
birth weight, maternal education [or another
measure of socioeconomic status (SES)], dates
of birth and conception (often based on last
menstrual period), and ambient PM con-
centrations, as described below, during preg-
nancy. e primary outcome was term LBW,
defined as birth weight < 2,500 g.
Air pollution exposure. e primary expo-
sure variable was the ambient concentration
of PM
10
averaged over the entire pregnancy.
PM
10
concentrations were assigned to each
subject using the approach employed by
each research group in their original work.
Although we focused on PM
10
, investigators
also were encouraged to provide results for
fine PM [≤ 2.5 µm in aerodynamic diameter
(PM
2.5
)] if available. Studies without PM
10
data provided effect estimates for PM
2.5
or
black smoke exposures during pregnancy.
Black smoke approximates PM
4
(< 4 µm
in diameter) (Muir and Laxen 1995); results
for black smoke are presented alongside the
PM
10
results for the PAMPER (Particulate
Matter and Perinatal Events Research) study
(Newcastle upon Tyne, UK). The methods
for modeling the PAMPER black smoke
exposures are described elsewhere (Fanshawe
et al. 2008).
Socioeconomic status. ICAPPO partici-
pants identified SES as a potentially important
control variable when assessing pollution and
birth outcomes (Slama et al. 2008; Woodruff
et al. 2009) and agreed to use maternal edu-
cation as the primary measure of SES in the
feasibility study. Maternal education is com-
monly used as an SES measure in perinatal
studies and has been shown to be related,
albeit imperfectly, with other measures of
SES (Kaufman et al. 2008; Parker et al. 1994;
Pickett et al. 2002). If maternal education was
unavailable, using different individual or area-
level SES measures was allowed. Because the
collection and meaning of maternal educa-
tion for these studies differ among the study
locations, its form as an analytic covariate dif-
fered among the study locations.
Other covariates. Participants also were
encouraged to provide estimates adjusted
for additional covariates as described below.
Although additional variables make compari-
sons of results across locations more challeng-
ing, they allowed us to examine how additional
adjustments specific to each location might
influence estimates reported by each study.
Primary statistical analysis. We used
logistic regression, with term LBW as the
dependent variable and PM
10
as a continuous
explanatory variable; black smoke was used
in the PAMPER study, as described above.
Results are reported as ORs per 10-µg/m
3
increase in average concentration during preg-
nancy to facilitate synthesis of results. Results
from two models were examined: Model 1
covariates were PM
10
and study-specific mater-
nal education or other SES measure; model 2
covariates were PM
10
, maternal education or
other SES measure, plus other study location–
specific covariates as described above.
Secondary statistical analyses. For these anal-
yses, we suggested modeling continuous term
birth weight as an outcome (using linear regres-
sion) and/or using PM
2.5
as an exposure meas-
ure. In addition, results from models describing
associations after controlling for different SES
measures were contributed. Secondary analyses
were encouraged but not required for participa-
tion, so results of secondary analyses were not
reported by all investigators.
Table 1. Birth years, number of births, percent term LBW, and measure of SES used in model 1 (adjusted for SES only), by study.
No. of
births
b
Percent
term LBW
SES measure used in model 1 of feasibility study
Study and location
a
Birth years Measure Descriptive statistics
Atlanta, Georgia, USA (Darrow et al. 2009a, 2009b) 1996–2004 325,221 2.62 Attained maternal education Years: 19.8% < 12, 24.7% 12, 55.5% > 12
California, USA (Morello-Frosch et al. 2010) 1996–2006 1,714,509 2.43 Attained maternal education
c
Years: 31.5% < 12, 28.0% 12, 40.5% > 12
Connecticut and Massachusetts, USA (Bell et al.
2007, 2008)
1999–2002 173,042 2.16 Attained maternal education Mean ± SD, 13.6 ± 2.6 years
EDEN, Poitiers and Nancy, France (Lepeule et al.
2010)
2003–2006 1,233 2.11 Age at completion of education Years: 17.7% < 19, 61.7% 19–24, 20.6% > 24
Lombardy, Italy (Pesatori et al. 2008) 2004–2006 213,542 2.71 Attained maternal education Degree: 33.3% < high school, 45.8% high
school, 3.6% bachelor, 17.6% graduate
PAMPER, Newcastle upon Tyne, UK (Glinianaia
et al. 2008; Pearce et al. 2010)
1962–1992 81,953 3.19 Area-level indicator: Townsend
Deprivation Score
d
Quintile cut-points: –1.2, 2.4, 4.7, 6.6
New Jersey, USA (Rich et al. 2009) 1999–2003 87,281 2.75 Attained maternal education Years: 20.6% < 12, 36.5% 12, 42.9% > 12
PIAMA, the Netherlands (Gehring et al. 2011) 1996–1997 3,471 1.15 Attained maternal education Degree: 22.8% low, 41.6% medium, 35.6% high
Generation R, Rotterdam, the Netherlands
(van den Hooven et al. 2009)
2002–2006 7,296 2.26 Attained maternal education Degree: 10.9% none/low, 44.7% secondary,
44.3% higher
São Paulo, Brazil (Gouveia et al. 2004) 2005 158,791 3.77 Attained maternal education Years: 29.3% < 7, 50.7% 8–11, 19.9% > 11
Seoul, Republic of Korea (Ha et al. 2004) 1998–2000 372,319 1.45 Attained maternal education Degree: 4.1% < high school, 52.7% high school,
43.2% ≤ bachelor
Seattle, Washington, USA (Sathyanarayana S,
Karr C, unpublished data)
1998–2005 301,880 1.56 Attained maternal education
c
Years: 12.8% < 12, 26.1% 12, 60.0% > 12
Sydney, Australia (Jalaludin et al. 2007) 1998–2004 279,015 1.62 Area-level indicator: Index
of Relative Socioeconomic
Disadvantage
e
Quartile cut-points: ≤ 945.1, 1010.7, 1072.7
Vancouver, British Columbia, Canada (Brauer et al.
2008)
1999–2002 66,467 1.35 Area level indicator:
percentage of women with
postsecondary education
Quartile cut-points: 28.8, 36.3, 44.1
a
Data sets have been used for other studies, although not necessarily studies of PM
10
or term LBW; cited analyses sometimes used different versions of the data.
b
Births used
in model 1: singleton, term infants with known birth weight, maternal SES, gestational age, and ambient PM
10
or black smoke concentrations.
c
Collection of maternal education
changed during the study period.
d
The Townsend Deprivation Score is an area-based measure of material deprivation (Townsend et al. 1988), calculated for each enumeration district
(~ 200 households) based on 1971, 1981, and 1991 census data.
e
The Australian Bureau of Statistics (2001) Index of Relative Socio-economic Disadvantage uses a range of census fac-
tors and is assigned to each census collection district (~ 200 households).
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Although full meta-analyses were not per-
formed, in our examination of results, initial
tests of homogeneity across study locations
were conducted using fixed-effects models
(Sterne et al. 2001). In these tests, the null
hypothesis of homogeneity was rejected with
p-values < 0.05.
Results
Locations. Fourteen research groups from nine
countries participated (Table 1). Of these,
six reported results for PM
10
only, six for
both PM
10
and PM
2.5
, one for PM
2.5
only
(Seattle study), and one for black smoke only
(PAMPER study). Most data were from the
late 1990s to the mid-2000s. However, the
PAMPER study comprised births from 1962
through 1992. e number of eligible births
ranged from slightly > 1,000 in the EDEN
study, Nancy and Poitiers, France] to > 1 mil-
lion in the California study, although there
was some variability within studies depending
on the exposure measure and covariates. e
percentage of LBW among term births ranged
from 1.15% in the PIAMA (Prevention and
Incidence of Asthma and Mite Allergy) study
(Netherlands) to 3.77% in the São Paulo
study (Table 1).
By design, data sets used in the feasibil-
ity study have been used for previous studies
of pollution and pregnancy outcomes or are
intended for such use. However, these are not
necessarily studies of PM
10
or term LBW, and
previously published results may have been
based on earlier versions of study data sets (Bell
et al. 2007, 2008; Brauer et al. 2008; Darrow
et al. 2009a, 2009b; Gehring et al. 2011;
Glinianaia et al. 2008; Gouveia et al. 2004; Ha
et al. 2004; Jalaludin et al. 2007; Lepeule et al.
2010; Mannes et al. 2005; Pearce et al. 2010;
Pesatori et al. 2008; Rich et al. 2009; Slama
et al. 2009; van den Hooven et al. 2009).
PM concentration estimation. PM con-
centration estimates and estimation methods
differed among the studies (Table 2). Some
research groups relied on temporal variabil-
ity in PM to estimate effects, where exposure
was calculated by averaging all measurements
over the entire study area for the pregnancy
interval; for these studies, exposure estimates
differed for pregnancies occurring at different
times, but not by maternal residence, within
the study area. Other studies estimated effects
based on both temporal and spatial PM con-
trasts, where estimates were calculated for mul-
tiple geographic administrative units or at each
maternal address; in these studies, exposures
differed both by maternal address and by tim-
ing of the pregnancies within the study period.
Most research groups (11 of 14; 79%) used
routinely collected monitoring network data to
estimate exposures (Table 2), although its use
differs among studies [e.g., averages over geo-
graphic areas; nearest monitor measurement,
or inverse distance-weighted (IDW) averages
from multiple monitors, from residence].
Two research groups used models to esti-
mate PM
10
exposure (Table 2), although
modeling methods differed. e Generation R
study (Rotterdam, the Netherlands) used
dispersion modeling (combination of moni-
toring data with modeling techniques)
(Wesseling et al. 2002), whereas the PIAMA
study (Netherlands) used temporally adjusted
land use regression (LUR) (Gehring et al.
2011) and estimated residential PM
10
from
modeled PM
2.5
concentration (Cyrys et al.
2003). PAMPER used modeled estimates, as
described above; the median modeled black
smoke concentration in the PAMPER data set
was 32.8 µg/m
3
with an interquartile range of
17.1–104.9, reflecting, in part, the long time
spanned. The Vancouver study used moni-
toring network data for PM
10
but used both
LUR models and monitoring network data
(IDW) to estimate PM
2.5
exposures (Brauer
et al. 2008); results for both Vancouver PM
2.5
estimates are shown below.
Socioeconomic status. Eleven of the 14
research groups used maternal education as the
indicator of SES for model 1 (Table 1). However,
the maternal education measure varied in form
and meaning across studies. ree studies relied
on contextual information based on neighbor-
hood characteristics to define maternal SES for
model 1 of the primary analysis (Table 1). Some
research groups included additional individual
level socioeconomic measures for model 2 and in
secondary analyses [see Supplemental Material,
Table 1 (doi:10.1289/ehp.1002725)]. For
example, paternal occupation was used in the
Lombardy study. The California study added
area-level socioeconomic measures. Similarly, the
Vancouver study added an additional area-level
income variable. Some research groups included
individual-level characteristics that may correlate
with SES: maternal age, race, ethnicity, indig-
enous status, and country of birth.
Birth weight. Figure 1 shows the relative
odds of term LBW per 10-µg/m
3
increase in
mean PM
10
concentration during pregnancy,
adjusted for SES (model 1) by location.
Associations differed among study locations
(p-value from test for heterogeneity < 0.001).
Table 2. PM
10
distribution, method of exposure estimation, area, and source of exposure variability, by study.
PM
10
distribution (μg/m
3
)
Approximate
area
a
(km
2
)Study Median 25th percentile 75th percentile Method of exposure estimation Exposure contrast
b
Atlanta 23.5 22.3 25.4 Monitoring network; population-weighted spatial average over
city (Ivy et al. 2008)
4,538 Temporal
California 28.9 22.6 38.7 Monitoring network; nearest monitor within 10 km of residence 423,970
a
Spatial and temporal
Connecticut and
Massachusetts
22.0 18.1 25.5 Monitoring network; spatial average over county of residence 41,692 Spatial and temporal
EDEN 19.0 18 21 Monitoring network; nearest monitor within 20 km of residence 480 Spatial and temporal
Lombardy 49 44 54 Monitoring network; average of monitoring stations located in
nine regional areas (Baccarelli et al. 2007)
23,865 Spatial and temporal
PAMPER
c
(PM
10
not available) Spatial-temporal model for black smoke (Fanshawe et al. 2008) 63 Spatial and temporal
New Jersey 28.0 24.8 31.7 Monitoring network; nearest monitor within 10 km of residence 22,592
a
Spatial and temporal
PIAMA 40.5 36.7 43.4 LUR model (Gehring et al. 2011) with temporal adjustment using
air monitoring network data
d
12,000 Spatial and temporal
Generation R 32.8 32.2 33.3 Dispersion model (Wesseling et al. 2002) 150 Spatial
São Paulo 40.3 39.2 42.1 Monitoring network; average from 14 monitors throughout city 1,500 Temporal
Seattle
e
(PM
10
not available) Monitoring network; population-weighted spatial average of
PM
2.5
for monitors within 20 km of residence (Ivy et al. 2008)
17,800 Spatial and temporal
Seoul 66.45 59.63 69.72 Monitoring network; average from 27 monitors throughout city 605 Spatial and temporal
Sydney 16.50 12.8 21.0 Monitoring network; average from eight monitors throughout city 12,145 Temporal
Vancouver 12.5 11.7 13.1 Monitoring network; inverse distance weighting of up to three
monitors within 50 km of residence
f
3,300 Spatial and temporal
a
Approximate geographic area in which mothers reside; in California and New Jersey, the geographic area includes maternal addresses too far from a PM
10
or PM
2.5
monitoring site
to be included in the study.
b
Temporal contrast is used to describe studies where exposure estimates differ among mothers based on the timing of their pregnancy; spatial contrast is
used to describe studies where exposure estimates differ among mothers based on their residence.
c
Only black smoke available (black smoke is a historic measure of airborne PM,
~ PM
4
, shown to be a reasonable predictor of daily average PM
10
) (Muir and Laxen 1995).
d
PM
10
estimated from PM
2.5
LUR model results.
e
Only PM
2.5
available.
f
PM
2.5
exposure also
derived from LUR (see “PM concentration estimation”).
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Six studies indicated a statistically signifi-
cant positive (adverse) association (Atlanta,
California, Connecticut and Massachusetts,
PAMPER, São Paulo, and Seoul), whereas
the Sydney and Vancouver studies indicated
an adverse, albeit not significant, association
(Figure 1). Little or no association was reported
by seven studies; no research group reported
significant inverse (protective) associations.
Figure 2 shows estimated ORs from
model 2 [models fitted with additional cova-
riates; see Supplemental Material, Table 1
(doi:10.1289/ehp.1002725)]. Additional
covariates varied among studies and included
maternal age and transformations of age,
parity, antenatal visits, country of birth, sex,
maternal smoking, maternal alcohol, mater-
nal hypertension, maternal diabetes, season of
conception, year of birth, marital status, race/
ethnicity, indigenous status, gestational age,
and contextual measures of SES. About half
of model 2 ORs suggest slightly stronger asso-
ciations between air pollution and term LBW
compared with model 1 ORs, whereas other
model 2 ORs were either very similar or atten-
uated compared with model 1 [for a direct
comparison of estimates, see Supplemental
Material, Table 2 (doi:10.1289/ehp.1002725).
Associations differed among study locations
(p-value from test for heterogeneity < 0.05).
Figure 3 shows changes in mean term
birth weight associated with each 10-µg/m
3
increase in PM
10
for the 11 locations report-
ing continuous birth weight results. The
mean estimated change ranged from a 42.2-g
decrease (Generation R) to an increase of
about 20 g (the Atlanta study), with most
estimates (9 of 11) indicating a 2- to 20-g
lower birth weight associated with each
10-µg/m
3
increase in PM
10
exposure. Of the
11 studies, six reported a statistically signifi-
cant adverse effect of PM
10
, whereas two (the
Atlanta and Lombardy studies) indicated a
significant protective effect. These associa-
tions differed among study locations (p-value
from test for heterogeneity < 0.001). After
controlling for study-specific factors, model
coefficients often, although not always, sug-
gested larger decreases in birth weight with
increases in PM
10
[see Supplemental Material,
Table 3 (doi:10.1289/ehp.1002725)]. In the
Atlanta study, the estimate changed from an
apparent mean increase of 20 g to a mean
decrease of –28.8 g [95% confidence interval
(CI), –49.6 to8.1], whereas PIAMA’s esti-
mate changed to an apparent increase [47.0 g
(95% CI, –10.5 to 104.6)] after controlling
for location-specific confounders.
Figure 4 shows estimated relative odds of
LBW associated with each 10-µg/m
3
increase
in PM
2.5
concentration, after controlling for
SES, for a subset of studies. As for PM
10
,
some studies indicated a significant increase
Figure 1. ORs (95% CIs) for LBW among term births in association with a 10-μg/m
3
increase in estimated average PM
10
, or black smoke (PAMPER), concentration
during the entire pregnancy, adjusted for SES (model 1), by study.
PIAMA
Generation R
New Jersey
EDEN
Lombardy
California
PAMPER
Seoul
Sydney
Atlanta
Sao Paulo
Connecticut and Massachusettes
Vancouver
0.50.25 1 3 42 5
OR (95% CI)
Figure 2. ORs (95% CIs) for LBW among term births in association with a
10-μg/m
3
increase in estimated average PM
10
, or black smoke (PAMPER), con-
centration during the entire pregnancy, adjusted for SES and study-specific
variables (model 2), by study.
PIAMA
Atlanta
Lombardy
New Jersey
California
PAMPER
EDEN
Seoul
Connecticut and Massachusettes
Generation R
Sao Paulo
Sydney
Vancouver
123450.50.25
OR (95% CI)
Figure 4. ORs (95% CIs) for LBW among term births in association with a
10-μg/m
3
increase in estimated average PM
2.5
concentration during the entire
pregnancy, adjusted for SES, by study. Results for the Vancouver study are from
two different PM
2.5
estimation methods, LUR and IDW of monitor measurements
(see “Methods”).
PIAMA
Vancouver (IDW)
Atlanta
Seattle
California
New Jersey
Connecticut and Massachusettes
Vancouver (LUR)
0.5 1 1.5 2 2.50.25 53 4
OR (95% CI)
Figure 3. Change in mean birth weight (95% CIs) among term births in association
with a 10-μg/m
3
increase in estimated average PM
10
, or black smoke (PAMPER),
concentration during the entire pregnancy, adjusted for SES, by study.
PIAMA
Generation R
Vancouver
Sao Paulo
Sydney
Seoul
EDEN
PAMPER
Lombardy
California
Atlanta
−150 −100 −50 0 50
Change in birth weight [g (95% CI)]
Page 4
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Environmental Health Perspectives
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1027
in the relative odds of LBW, whereas others
indicated no association. e Vancouver study
reported different results using different PM
2.5
estimates. p-Values from separate heterogene-
ity tests, each including one Vancouver esti-
mate, were 0.06 (LUR) and 0.18 (IDW).
Discussion
Despite the deliberately simple protocol and
the heterogeneity in study designs and loca-
tions, we found some consistency across stud-
ies, particularly for the relationships between
PM
10
and mean birth weight and between
PM
2.5
and LBW. After controlling for SES,
the reduction in mean birth weight associ-
ated with a PM
10
increase of 10 µg/m
3
was
between 2 and 20 g for 9 of 11 locations.
Although based on fewer studies than those
for PM
10
, the initial tests of homogeneity for
PM
2.5
results were not statistically significant.
More detailed meta-analysis of the initial
results, considering alternative models, influ-
ential locations, and differences in location-
specific covariates and exposures, may improve
our understanding of these relationships and
lead to improved summary estimates.
Based on a discussion of initial feasibility
study results at the 2009 workshop in Dublin,
Ireland (see Appendix), participants concluded
that the method used to estimate PM
10
expo-
sures may be the most critical design difference
among the studies. Some prior studies from
California (Basu et al. 2004; Wilhelm and
Ritz 2005), Vancouver (Brauer et al. 2008),
Sydney (Mannes et al. 2005), and Atlanta
(Darrow et al. 2009a) have examined the con-
sequences of different methods for calculat-
ing pollution metrics in the same study but
from different perspectives. For example, as in
the results presented in Figure 4, Brauer et al.
(2008) compared PM
2.5
estimates from LUR
and monitor data (IDW) and concluded that
their moderate correlation could be attribut-
able to different aspects of variability being
captured by each method. Basu et al. (2004)
found stronger associations for exposures esti-
mated over larger geographic areas than over
smaller geographic areas but did not speculate
on the reasons for the discrepancy; however,
Basu et al. (2004) cautioned that studies using
different methods for exposure assessment may
not be comparable.
Importantly, there is large variation in
PM
10
levels and concentration ranges among
study locations. In the Vancouver study,
for example, the 10-µg/m
3
increase used to
derive ORs is nearly an order of magnitude
greater than the interquartile range (11.7
13.1; Table 2) of exposures. Similarly, in the
Atlanta study, the 10-µg/m
3
reporting unit
represents nearly the entire range of PM
10
concentrations (18.629.6 µg/m
3
).e ana-
lytical methods used in the common frame-
work assume no threshold level below which
PM is not associated with health. Although
evidence supports the hypothesis that no
threshold exists for PM relationships and
overall population mortality (Daniels et al.
2000), threshold assumptions have not been
fully explored for adverse reproductive out-
comes, including birth weight. We did not
directly examine nonlinear relationships in
this feasibility study, but they may contribute
to heterogeneity among studies; a more fully
coordinated analysis should improve our abil-
ity to assess nonlinear relationships.
Covariates likely to affect the relation-
ship between PM
10
and LBW differ among
study locations for many reasons (Strickland
et al. 2009). For studies that estimate effects
based on spatial contrasts, controlling for
SES can be important because it may be spa-
tially correlated with exposure concentrations
(O’Neill et al. 2003). However, SES measures
and their relationships with both birth out-
comes and air pollution are not consistent.
For example, although mothers with lower
SES generally tend to have poorer birth out-
comes, the strength of the relationship differs
depending on which birth outcome (birth
weight, preterm birth) and which measures
of SES (maternal education, occupation) are
used (Parker et al. 1994; Pickett et al. 2002).
Although in some places mothers with higher
SES live in less-polluted areas (Woodruff
et al. 2003), in others the opposite relation-
ship holds (Slama et al. 2007). Because par-
ticipating studies rely on exposure estimates
with differing spatial and temporal compo-
nents, critical confounders may differ among
studies (Strickland et al. 2009). Changes
between results for the models using SES only
and those using SES plus covariates varied
among studies, suggesting that other statisti-
cal approaches, possibly hierarchical models,
that allow for different types of confounding
factors could be informative for understand-
ing apparent variations among locations.
Finally, other methods of analysis could
be used. Although logistic regression is com-
monly applied, alternative approaches have
considered spatial correlations (Jerrett et al.
2005), time-varying exposures (Suh et al.
2009), generalized additive models (Ballester
et al. 2010), and hierarchical structures (Yi
et al. 2010). Bell et al. (2007) proposed a
method for handling correlated exposures
across trimesters. Because both model-based
and spatially averaged exposure estimates are
calculated with error, considering their preci-
sion would provide more accurate confidence
intervals (Woodruff et al. 2009).
The ICAPPO feasibility project success-
fully coordinated analyses of the association
between ambient PM concentrations and term
LBW, across multiple locations, data sets, and
research teams worldwide. ese initial results
and the participation of multiple research
groups, even without external funding, sup-
port the continuation of this effort to increase
our understanding of the human reproductive
consequences of adverse air quality.
Re f e R e n c e s
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for Areas, Australia 2001. ABS Catalogue no. 2039.0.
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Appendix
We thank Jason Harless for coordinating many aspects of the feasibility study and all of the
participants at the 2009 Dublin, Ireland, ICAPPO workshop who contributed their insights
and ideas: I. Aguilera, F. Ballester, K. Belanger, M.-H.Chang, G. Collman, M. Dostal,
K. Gray, C. Iñiguez, B.-M. Kim, K. Polanska, and J. Rankin.
We thank the principal investigators and scientific teams of the participating centers. For
the PIAMA study: B. Brunekreef (Utrecht University and University Medical Center Utrecht,
the Netherlands); H.A. Smit [National Institute for Public Health and the Environment
(RIVM) and University Medical Center Utrecht, the Netherlands]; A.H. Wijga (RIVM, the
Netherlands); J.C. de Jongste (Erasmus University Medical Center/Sophia Children’s Hospital
Rotterdam, the Netherlands); J. Gerritsen, D.S. Postma, M. Kerkhof, and G.H. Koppelman
(Medical Center Groningen, the Netherlands); and R.C. Aalberse (Sanquin Research,
Amsterdam, the Netherlands). e PIAMA study is supported by the Netherlands Organization
for Health Research and Development; the Netherlands Organization for Scientific Research;
the Netherlands Asthma Fund; the Netherlands Ministry of Spatial Planning, Housing, and the
Environment; and the Netherlands Ministry of Health, Welfare, and Sport. For the PAMPER
study: L. Parker (Dalhousie University, Halifax, Nova Scotia, Canada) and T. Pless-Mulloli
(Newcastle University, Newcastle upon Tyne, United Kingdom). e PAMPER study was sup-
ported by the Wellcome Trust (grant No 072465/Z/03/Z). For the Eden study: M.-A. Charles
and her group (INSERM 1018 and INSERM–INED joint research team).
For the Vancouver analysis, the linked research database was provided by Population
Data BC. Medical services and hospitalization data were provided by the Ministry of Health,
Government of British Columbia; Vital Statistics data, by the British Columbia Vital Statistics
Agency; and perinatal data, by the British Columbia Reproductive Care Program.
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Page 6
    • "Dadvand et al. [10] reported stronger associations of reduction in term birth weight with higher median levels of particular matter (PM) with diameter <2.5 μm (PM 2.5 ) across 14 study centers from North America, Europe, South America and Asia. Parker et al. [11] suggested that the composition of PM may influence the variability of the observed associations between PM mass and term birth weight in seven regions in the US. Williams et al. [12] quantified the spatially varying effects of sulfur dioxide and lead on birth weight across Census tracts in Tennessee. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability. Methods: We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight. Results: Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability. Conclusions: Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.
    Full-text · Article · Dec 2016 · Environmental Health
    • "Several recent studies examined the spatial variation in PM 2.5 effects on TLBW between different countries or between US states. A large collaborative multi-site international study found a substantial degree of heterogeneity in estimates for entire pregnancy exposure–response between study sites, despite the use of similar exposure assessments and statistical models in the studies (Dadvand et al., 2013; Parker et al., 2011). Hao et al. (2015) found substantial differences between states in the U.S. in terms of the magnitude and direction of effects of PM 2.5 on TLBW. "
    [Show abstract] [Hide abstract] ABSTRACT: Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
    Full-text · Article · Jul 2015 · Environmental Research
    • "However, two recent reviews and meta-analyses attempted to summarize the findings of studies focusing on the relations between birth weight and exposure to criteria pollutants [10,22]. Overall, our findings of increases in mean birth weight associated with ambient air pollution concentrations measured by monitoring stations disagree with those of most published studies [10,11] , including those conducted in California [25,40]. A selection effect might account for such differences. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Exposure to air pollution is frequently associated with reductions in birth weight but results of available studies vary widely, possibly in part because of differences in air pollution metrics. Further insight is needed to identify the air pollution metrics most strongly and consistently associated with birth weight. Methods We used a hospital-based obstetric database of more than 70,000 births to study the relationships between air pollution and the risk of low birth weight (LBW, <2,500 g), as well as birth weight as a continuous variable, in term-born infants. Complementary metrics capturing different aspects of air pollution were used (measurements from ambient monitoring stations, predictions from land use regression models and from a Gaussian dispersion model, traffic density, and proximity to roads). Associations between air pollution metrics and birth outcomes were investigated using generalized additive models, adjusting for maternal age, parity, race/ethnicity, insurance status, poverty, gestational age and sex of the infants. Results Increased risks of LBW were associated with ambient O3 concentrations as measured by monitoring stations, as well as traffic density and proximity to major roadways. LBW was not significantly associated with other air pollution metrics, except that a decreased risk was associated with ambient NO2 concentrations as measured by monitoring stations. When birth weight was analyzed as a continuous variable, small increases in mean birth weight were associated with most air pollution metrics (<40 g per inter-quartile range in air pollution metrics). No such increase was observed for traffic density or proximity to major roadways, and a significant decrease in mean birth weight was associated with ambient O3 concentrations. Conclusions We found contrasting results according to the different air pollution metrics examined. Unmeasured confounders and/or measurement errors might have produced spurious positive associations between birth weight and some air pollution metrics. Despite this, ambient O3 was associated with a decrement in mean birth weight and significant increases in the risk of LBW were associated with traffic density, proximity to roads and ambient O3. This suggests that in our study population, these air pollution metrics are more likely related to increased risks of LBW than the other metrics we studied. Further studies are necessary to assess the consistency of such patterns across populations.
    Full-text · Article · Feb 2013 · Environmental Health
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