598 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

Review

There is growing epidemiologic evidence for 

adverse effects on the fetus and newborn from 

maternal prenatal exposure to ambient air pollution (Glinianaia et al. 2004b; Maisonet et al. 

2004; Šrám et al. 2005). Air pollutants such as 

carbon monoxide (CO), sulfur dioxide (SO2),

and particulate matter (PM) have been associated with increased infant mortality, particularly postneonatal respiratory mortality, low 

birth weight, and preterm birth (Glinianaia 

et al. 2004a, 2004b; Maisonet et al. 2004; 

Šrám et al. 2005). Inconsistencies and uncertainties remain concerning the effects of specific 

pollutants and pollutant mixtures and critical exposure periods (Ritz and Wilhelm 2008; 

Woodruff et al. 2009). There are new concerns 

that air pollution may also play a role in causing congenital anomalies, and such an effect is 

both biologically plausible and of public health 

importance (Ritz 2010). Congenital anomalies 

are a main cause of infant mortality and an 

important contributor to childhood and adult 

morbidity. Major congenital anomalies in surviving infants often have serious medical and/or 

cosmetic consequences that commonly require 

surgery and lead to reduced survival rates into 

adulthood (Tennant et al. 2010). Major structural congenital anomalies are diagnosed in 

2–4% of births, but in most cases their etiology 

remains unknown (Weinhold 2009).

Recently, there has been a steep increase 

in the number of air pollution studies with 

congenital anomalies as the primary health 

outcome. The first publication appeared in 

2002 (Ritz et al. 2002), the next in 2005 

(Gilboa et al. 2005), and more have followed 

in the last few years (Dadvand et al. 2011a, 

2011b; Dolk et al. 2010; Hansen et al. 2009; 

Hwang and Jaakkola 2008; Kim et al. 2007; 

Marshall et al. 2010; Rankin et al. 2009; 

Strickland et al. 2009). Because the existing studies have few a priori hypotheses and 

tested many different pollutant–outcome 

combinations, a systematic assessment of the 

consistency of associations across studies is 

needed. Furthermore, in this rapidly evolving 

field, timely evaluation of methods and results 

of existing studies can help to inform and 

improve the design of future research. Here 

we therefore provide a systematic review of 

studies on ambient air pollution and congenital anomalies and develop recommendations 

for future research.

There have been few meta-analyses of 

other environmental exposures and risk of 

congenital anomalies (e.g., Nieuwenhuijsen 

et al. 2009) because of great variability in 

exposure assessment, outcome ascertainment, 

data analysis, and result reporting. Air pollution is one area where exposure estimates 

appear reasonably comparable. To illustrate the 

challenges faced by meta-analyses in this field 

and to offer recommendations for improvement, we conducted meta-analyses for a number of air pollutant–anomaly combinations.

Materials and Methods

Search methods. We followed published 

guidelines for the reporting of this review 

and meta-analysis (Moher et al. 2009; Stroup 

et al. 2000). A bibliographic search was carried out in the MEDLINE engine search

(National Library of Medicine 2010). The 

Medical Subject Heading (MeSH) terms 

“congenital abnormalities,” “pregnancy,” 

and “air pollution” and non-MeSH terms 

“birth defect,” “congenital anomalies,” “cardiac anomalies,” “congenital heart disease,” 

and “oral clefts” were used in the syntax. We 

also searched references in published articles 

and reviews on this topic. From this search 

we selected articles that a) were original epidemiologic studies; b) were written in the 

English language; c) defined all or subgroups 

of congenital anomalies, congenital malformations, or birth defects as outcome; and 

d) studied human prenatal exposure to ambient air pollution using measured concentrations of air pollutants. Studies with purely 

ecologic exposure assessments (e.g.,maternal 

residence in a polluted vs. unpolluted area) 

or studies with quantitative traffic density 

data but without pollutant data (e.g., Cordier 

et al. 2004) were not included in the review. 

We identified one article unpublished at the 

time of the last MEDLINE search but now 

published (Dadvand 2011b). We searched 

Address correspondence to M. Vrijheid, Centre for 

Research in Environmental Epidemiology, Barcelona 

Biomedical Research Park, Doctor Aiguader 88, 

08003 Barcelona, Spain. Telephone: 34 932 147 346. 

Fax: 34 932 147 301. E-mail: mvrijheid@creal.cat

Supplemental Material is available online (doi:10.

1289/ehp.1002946 via http://dx.doi.org/).

We thank the following researchers for providing their unpublished risk estimates for continuous 

exposure: E. Marshall, D. Wartenberg, P. Mendola, 

and S. Gilboa. 

J.R. is funded by a Personal Award Scheme Career 

Scientist Award from the U.K. National Institute of 

Health Research. 

The authors declare they have no actual or potential 

competing financial interests.

Received 7 September 2010; accepted 3 December 

2010.

Ambient Air Pollution and Risk of Congenital Anomalies: A Systematic 

Review and Meta-analysis

Martine Vrijheid,1,2,3 David Martinez,1,2,3 Sandra Manzanares,1,2,4 Payam Dadvand,1,2,3 Anna Schembari,1,2

Judith Rankin,5 and Mark Nieuwenhuijsen1,2,3

1Center for Research in Environmental Epidemiology, Barcelona, Spain; 2Hospital del Mar Research Institute (IMIM), Barcelona, Spain; 3CIBER Epidemiología y Salud Pública, Spain; 4Educational Unit of Preventive Medicine and Public Health, Hospital del Mar–Pompeu 

Fabra University–Agencia de Salut Pública, Barcelona, Spain; 5Institute of Health and Society, Newcastle University, Newcastle upon 

Tyne, United Kingdom

Objective: We systematically reviewed epidemiologic studies on ambient air pollution and congenital 

anomalies and conducted meta-analyses for a number of air pollutant–anomaly combinations.

Data sources and extraction: From bibliographic searches we extracted 10 original epidemiologic studies that examined the association between congenital anomaly risk and concentrations of air 

pollutants. Meta-analyses were conducted if at least four studies published risk estimates for the same 

pollutant and anomaly group. Summary risk estimates were calculated for a) risk at high versus low 

exposure level in each study and b) risk per unit increase in continuous pollutant concentration.

Data synthesis: Each individual study reported statistically significantly increased risks for some 

combinations of air pollutants and congenital anomalies, among many combinations tested. In 

meta-analyses, nitrogen dioxide (NO2) and sulfur dioxide (SO2) exposures were related to increases 

in risk of coarctation of the aorta [odds ratio (OR) per 10 ppb NO2 = 1.20; 95% confidence interval 

(CI), 1.00–144; OR per 1 ppb SO2 = 1.04; 95% CI, 1.00–1.08] and tetralogy of Fallot (OR per 

10 ppb NO2 = 1.25; 95% CI, 1.02–1.51; OR per 1 ppb SO2 = 1.04; 95% CI, 1.00–1.08), and PM2

(particulate matter ≤ 10 μm) exposure was related to an increased risk of atrial septal defects (OR 

per 10 μg/m3 = 1.14; 95% CI, 1.01–1.28). Meta-analyses found no statistically significant increase 

in risk of other cardiac anomalies and oral clefts.

Conclusions: We found some evidence for an effect of ambient air pollutants on congenital cardiac 

anomaly risk. Improvements in the areas of exposure assessment, outcome harmonization, assessment of other congenital anomalies, and mechanistic knowledge are needed to advance this field.

Key words: air pollution, congenital abnormalities, pregnancy. Environ Health Perspect

119:598–606 (2011). doi:10.1289/ehp.1002946 [Online 3 December 2010]
Air pollution and congenital anomalies

Environmental Health Perspectives • volume 119 | number 5 | May 2011 599

conference proceedings in the ISI Web of 

Science (Thomson Reuters 2010) for abstracts 

of other unpublished studies, using the same 

search terms as above, but we found no other 

studies that fall into this category. 

Meta-analysis. We conducted metaanalyses to obtain summary risk estimates for 

the association between congenital anomaly 

groups and ambient air pollutant concentrations. Meta-analyses were conducted only if 

at least four studies had risk estimates [usually odds ratios (ORs)] for the same pollutant 

and comparable anomaly group. Although no 

guidelines exist as to the minimum number 

of studies for meta-analysis, we considered 

four the minimum number to justifiably run 

a meta-analysis; this corresponded to having a 

minimum of 500 cases of congenital anomaly 

included in each analysis. A list of the specific 

outcome groups used in the different studies, 

and the congenial anomaly groups considered 

comparable for the purposes of these metaanalyses, is available in Supplemental Material, 

Table 1 (doi:10.1289/ehp.1002946). Metaanalyses calculated two types of summary risk 

estimates: a) for the comparison of congenital 

anomaly risk at high versus low exposure level 

in each study and b) for congenital anomaly 

risk per unit increase in continuous pollutant 

concentration. For each of these, we selected 

risk estimates from the main, confounderadjusted models presented in each study, not 

those of sensitivity analyses. If both singleand multiple-pollutant models were presented, we selected the single-pollutant model. 

If studies presented results for more than one 

pregnancy period (Hwang and Jaakkola 2008; 

Ritz et al. 2002), we selected the period most 

appropriate for the development of congenital 

anomalies and most used in the other studies: month 2 or weeks 3–8 of gestation. The 

Strickland et al. (2009) study used two groups 

of ventricular septal defects (VSDs): muscular 

and perimembranous type. We calculated the 

combined risk estimate for these two groups 

and then entered the combined estimate in 

meta-analyses. Two studies (published in 

three reports) were located in the same study 

area, covered overlapping time periods, and 

analyzed some of the same anomaly groups 

(VSDs, coarctation of the aorta, tetralogy of 

Fallot) in relation to SO2 exposure (Dadvand 

et al. 2011a, 2011b; Rankin et al. 2009). We 

entered risk estimates from these publications separately in the SO2 meta-analyses, 

never at the same time. Because the Dadvand 

et al. (2011b) substudy had risk estimates for 

other air pollutants and covered a more recent 

time period, we used this as the main analysis for SO2 and considered the inclusion of 

the Dadvand et al. (2011a) and Rankin et al.

(2009) studies to be sensitivity analyses.

In the high- versus low-exposure metaanalysis, we selected from the published 

reports risk estimates for the fourth compared 

with the first quartile of exposure, if categorical risk estimates were presented (Dadvand 

et al. 2011a, 2011b; Gilboa et al. 2005; 

Marshall et al. 2010; Rankin et al. 2009; 

Ritz et al. 2002). Some studies presented 

risk estimates only for continuous exposure 

(Hansen et al. 2009; Hwang and Jaakkola 

2008; Strickland et al. 2009); we converted 

these to the risk estimate per interquartile 

increase in exposure, using the interquartile 

ranges (IQRs) given in the reports, because 

these were considered to compare most closely 

with estimates from the categorical studies. 

Information to calculate midquartile points 

was not given in these articles, so we could 

not compare the 12.5th and 87.5th percentiles. One study (Dolk et al. 2010) presented 

risk estimates for the 90th versus 10th percentile exposure, calculated from a continuous 

pollutant model, and this estimate was used 

for the high versus low comparison.

Meta-analyses for continuous exposure 

summarized risk estimates per unit increase 

in pollutant concentration, assuming that 

the natural logarithm of the relative risk of 

congenital anomaly varied linearly with the 

ambient air pollutant concentration. From 

each publication we selected the risk estimate per continuous unit increase in pollutant concentration, if available (Dadvand 

et al. 2011a; Dolk et al. 2010; Hansen et al. 

2009; Hwang and Jaakkola 2008; Ritz et al. 

2002; Strickland et al. 2009). Three articles 

described continuous exposure estimates in 

the text but did not show the risk estimates 

(Dadvand et al. 2011b; Gilboa et al. 2005; 

Marshall et al. 2010); for these we obtained 

the continuous estimates directly from the 

authors. To allow comparison of effects 

among the different studies, units were converted to 10 μg/m3 for particulate matter with 

diameter ≤ 10 μm (PM10), 10 ppb for ozone 

(O3) and nitrogen dioxide (NO2), 1 ppm for 

CO, and 1 ppb for SO2. Exposure estimations 

that were expressed in a mass per volume unit 

(e.g., micrograms per cubic meter) instead of 

parts per million, billion, or hundred million 

(pphm) were converted to parts per billion 

using the general equation at 1 atm and 25°C: 

24.45 × concentration (micrograms per cubic 

meter)/molecular weight. An exception was 

made for PM10, which was always expressed 

in micrograms per cubic meter.

We obtained summary risk estimates in 

meta-analyses using fixed- or random-effects 

models. For each pollutant–outcome analysis 

we first tested for heterogeneity in the risk 

estimates using the Q-test (Cochran 1954). 

When the result of the Q-test showed evidence for heterogeneity (p < 0.1), we used a 

random effect analysis, following the method 

of DerSimonian and Laird (1986). Otherwise, 

a fixed-effect analysis was conducted using 

the Mantel–Haenszel method (Mantel and 

Haenszel 1959). Meta-analyses calculated 

summary risk estimates (ORs) weighted by 

the inverse variance of each study, taking into 

account whether a fixed or random model was 

used. We used the R statistical software package for all analyses (version 2.11.0; R Project 

for Statistical Computing 2010).

We also produced forest plots to show 

ORs from each of the individual studies 

included in the meta-analyses and the estimation of the summary OR (Light and Pillemer 

1984). The sizes of the markers of each OR 

in the plots represent the relative weight each 

study contributed to the summary estimation. To analyze potential for publication 

bias, we conducted a weighted Egger test, a 

linear regression in which the response is the 

estimated effect and the explanatory variable 

is a precision term (1/SE) (Egger et al. 1997). 

A large deviation from zero of the slope term 

suggests publication bias.

Results

We identified 10 studies, one divided into 

two substudies (Dadvand et al. 2011a,

2011b), published between 2002 and 2011 

(Table 1). Four studies were conducted in the 

United States (in California, Texas, Georgia, 

and New Jersey), three in England (two in 

the northern region and one in four English 

regions including the northern region), and 

one each in Australia, Taiwan, and South 

Korea. The studies focused mainly on cardiac anomalies (Dadvand et al. 2011a, 2011b; 

Gilboa et al. 2005; Hansen et al. 2009; Ritz 

et al. 2002; Strickland et al. 2009) and/or orofacial clefts (Gilboa et al. 2005; Hansen et al. 

2009; Hwang and Jaakkola 2008; Marshall 

et al. 2010; Ritz et al. 2002). Only two studies (Dolk et al. 2010; Rankin et al. 2009) 

included the full spectrum of major structural 

anomalies. The South Korean study was a prospective birth cohort study that included all 

congenital anomalies as one group (14 cases) 

(Kim et al. 2007). Most other studies used 

a registry-based case–control design, selecting cases from routine congenital anomaly 

registries and controls from birth registries. 

One study was a registry-based cohort, using 

anomaly registries as source of case ascertainment and birth registry data for denominators 

(Dolk et al. 2010). Strickland et al. (2009) 

used a time-series design to link daily exposure estimates to daily congenital anomaly 

rates (for a given conception date); again,

data on the congenital anomalies came from 

a routine register.

Major differences are apparent in the 

diagnostic coding systems, congenital anomaly grouping methods, and case definitions 

among the studies [see Supplemental Material, 

Table 1 (doi:10.1289/ehp.1002946)]. The 

studies by Gilboa et al. (2005) and Hansen 
Vrijheid et al.

600 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

et al. (2009) based their groupings of cardiac anomalies and orofacial clefts on the anatomic classification used in the first study by 

Ritz et al. (2002); inclusion and exclusion 

of chromosomal, syndromic, and multiple 

anomalies still differed among these studies. 

The Georgia study (Strickland et al. 2009) 

used a more detailed system of diagnostic 

codes for cardiac anomalies. The U.K. studies (Dadvand et al. 2011a, 2011b; Dolk et al. 

2010; Rankin et al. 2009) all used coding and 

grouping system based on the International 

Classification of Diseases, 9th Revision (World 

Health Organization 1975) and the minor 

anomaly exclusion criteria proposed by 

the European Surveillance of Congenital 

Anomalies (EUROCAT 2009).

In all but one study (Dadvand et al. 

2011a), exposure assessments were based on 

the routine measurements of air pollutant 

concentrations at fixed-site air pollution monitoring stations. Most commonly, exposures 

were assigned using the monitoring station 

nearest to the maternal residence at the time 

of the birth. Distances of residence from the 

monitors varied and in some studies inclusion of study subjects was limited by their 

distance to the monitor [e.g., 16 km in the 

Ritz et al. (2002) study, 10 km in the Rankin 

et al. (2009) study]. Number and density of 

monitors also varied, as did the maximum 

distance of subjects from the nearest monitor 

(Table 1). Dadvand et al. (2011a) developed 

a spatiotemporal model in which black smoke 

and SO2 concentrations at the maternal residence were predicted using concentrations 

measured at 56 monitors and data on traffic, 

meteorology, and land cover. The Georgia 

study (Strickland et al. 2009) differed from 

others in that temporal data from one central 

monitoring station in the county were related 

to the vulnerable window of each pregnancy 

in a time-series analysis. Nearly all other studies defined windows of pregnancy susceptible to the development of the congenital 

anomalies under study (usually weeks 3–8 

of gestation) and averaged exposure over 

those windows. The Dolk et al. (2010) study 

assigned mean pollutant concentrations measured in one year (1996) to the census ward 

level of residence and was not able to estimate 

exposure in pregnancy time windows.

Most studies focused on the most commonly monitored air pollutants: PM10 (nine

studies), SO2 (eight studies), NO2 (seven

studies), CO (seven studies), and O3 (seven

studies). Less frequently studied pollutants 

Table 1. Studies on air pollution and risk of congenital anomalies.

Reference Setting Design Case and control definition Exposure assessment

Exposure range 

in second month 

of gestation

Gestational 

period Adjustment variables

Ritz et al. 

2002

California (USA), 

four counties, 

1987–1993; 

754,030 births

Case–control 3,121 cases with cardiac 

or oral facial cleft 

defects; live births 

and fetal deaths > 20 

weeks gestations; 9,357 

controls, randomly 

selected from all births 

and fetal deaths

Average of 24-hr 

measurements of CO, 

NO2, O3, and PM10 at 

nearest monitoring 

station (within 16 km)

p25–p75: 

CO, 1.14–2.39 ppm

O3, 1.07–2.86 pphm

Months 1, 

2, and 3; 

trimesters 

2 and 3; 

3 months 

preconception 

Maternal age, race, 

education, prenatal 

care, infant sex, 

decade of birth, parity, 

birth type, season of 

conception, and other 

air pollutants 

Gilboa 

et al. 

2005

Texas (USA), 

seven 

counties, 

1997–2000; 

607,500 births

Case–control 1,719 cases with cardiac 

defects or oral clefts; live 

births and fetal deaths 

> 20 weeks gestation; 

3,667 controls, live 

births, and fetal deaths, 

frequency matched by 

vital status, year of birth, 

and county

Average hourly or daily 

concentrations of CO, 

NO2, O3, PM10, and SO2, 

measured at the nearest, 

or next nearest, monitor 

in the county (median 

distance to monitor, 

8.6–14.2 km; maximum, 

36–54 km)

p25–p75:

CO, 0.4–0.7 ppm

NO2, 1.3–2.1 pphm

O3, 1.8–3.1 pphm

PM10, 19.5–29 μg/m3

SO2, 1.3–2.7 ppb

Weeks 3–8 Maternal age, 

education, race/

ethnicity, marital 

status, illness, 

tobacco use, season 

of conception, 

plurality, parity, infant 

sex, prenatal care, and 

gravidity

Kim et al. 

2007

Seoul (South 

Korea), 

2001–2004; 

1,514 births

Birth cohort 14 cases with structural 

defects in cohort of 1,514 

births

Average PM10

concentration from 

nearest of 27 monitoring 

stations

Mean: 

PM10, 89.7 μg/m3

Trimesters 1, 2, 

and 3

Infant sex, birth 

order, season of 

birth, maternal age, 

education, alcohol, 

and body mass index

Hwang and 

Jaakkola 

2008

Taiwan, 

2001–2003; 

721,289 births

Case–control 653 cases with cleft lip 

with or without cleft 

palate diagnosed at birth; 

6,530 controls randomly 

selected from all live 

births

Monthly average of 

continuous concentration 

of CO, NOx, O3, PM10, 

and SO2, measured at 

72 monitoring stations 

in Taiwan; inverse 

distance-weighted 

average assigned to 

each residence

p25–p75:

CO, 0.48–0.76 ppm

NOx, 16.0–23.9 ppb

O3, 24.4–30.1 pphm

PM10, 44.8–64.5 μg/m3

SO2, 2.4–5.0 ppb

Months 1, 2, 

and 3

Maternal age, plurality, 

gestational age, 

population density, 

and season of 

conception

Rankin 

et al. 

2009

Northern 

region (UK), 

1985–1990; 

242,268 births

Case–control 2,779 cases with 

congenital anomaly; live 

births and fetal deaths 

≥ 20 weeks gestation; 

15,000 controls randomly 

selected from all live and 

stillbirths

Average of daily black 

smoke and SO2 from 

all monitoring stations 

within 10 km of maternal 

residence

p25–p75:

SO2, 2.7–4.5 μg/m3

Black smoke, 1.1–2.8 

μg/m3

First trimester Birth weight, sex, and 

material deprivation

Strickland 

et al. 

2009

Georgia (USA), 

five counties, 

1986–2003; 

715,500 births

Time series 3,338 cases with 

an indication of 

cardiovascular 

malformation; all live 

births, stillbirths, and 

fetal deaths > 20 weeks

Average of daily 

measurements of CO, 

NO2, O3, PM10, and 

SO2 from one central 

monitoring station; daily 

time-series analysis 

using conception dates

IQR: 

CO, 0.3 ppm

NO2, 5.7 ppb

O3, 3.0 pphm

PM10, 14.2 μg/m3

SO2, 4.0 ppb

Weeks 3–7 Week of year, and 

cubic spline for day of 

follow-up

continued next page
Air pollution and congenital anomalies

Environmental Health Perspectives • volume 119 | number 5 | May 2011 601

were black smoke, nitric oxide (NO), nitrogen oxides (NOx), and particulate matter 

with aerodynamic diameter ≤ 2.5 μm (PM2.5). 

Pollutant concentration distributions showed 

different patterns across studies (Table 1); for 

example, average CO concentrations were 

highest in the California study (Ritz et al. 

2002), but O3 concentrations were highest 

in the Taiwan study (Hwang and Jaakkola 

2008). In most studies where this information was provided, pollutant concentrations did not increase by more than a factor 

of 2 between the 25th and 75th percentile 

(Table 1).

Cardiac anomalies. Cardiac anomalies 

were analyzed in seven studies. Each study 

included at least six separate cardiac anomaly 

groups [see Supplemental Material, Table 1

(doi:10.1289/ehp.1002946)] and tested these 

against three to six pollutant groups. Each 

study reported only one or a few statistically 

significantly increased risks with increased 

exposure among the multiple associations 

tested; few of these occurred in more than 

one study: CO was related to higher risk of 

VSD in two studies, with ORs for fourthversus first-quartile exposure of 2.95 [95%

confidence interval (CI), 1.44–6.05 (Ritz 

et al. 2002)] and 1.66 [95% CI, 1.37–2.02 

(Dadvand et al. 2011b)]. The studies in 

California and Australia both reported raised 

risks of pulmonary artery and valve defects in 

association with O3 exposure [OR quartile 4 

vs. quartile 1 = 2.94; 95% CI, 1.00–6.05 

(Ritz et al. 2002); OR per 5 ppb = 2.96; 95%

CI, 1.34–7.52 (Hansen et al. 2009)]. Various 

inverse associations (decreasing risks with 

increasing exposure) were also observed.

We conducted meta-analyses for 18 combinations of pollutants and cardiac anomaly 

groups for which four or more studies published results [for summary results, see Table 2; 

for full results, see Supplemental Material, 

Table 2 (doi:10.1289/ehp.1002946)]. The 

summary risk estimates from these metaanalyses were generally close to one, with a 

range of summary ORs for continuous exposure from 0.87 to 1.20, and for high versus 

low exposure from 0.80 to 1.23. Heterogeneity 

tests showed evidence for heterogeneity among 

studies (p < 0.10) in fewer than half of the 

analyses conducted, most consistently related 

to analyses of VSDs. Egger test p-values were 

statistically significant for only 3 of the 68 

meta-analyses we conducted (see Supplemental 

Material, Table 2), indicating that wide-scale 

publication bias is unlikely. We found statistically significantly increased summary risk estimates for continuous NO2 exposure and risk 

of coarctation of the aorta (OR per 10 ppb 

= 1.17; 95% CI, 1.01–1.36) and tetralogy 

of Fallot (OR per 10 ppb = 1.20; 95% CI,

1.02–1.42), for continuous PM10 exposure and 

atrial septal defect (ASD; OR per 10 μg/m3 = 

1.14; 95% CI, 1.01–1.28), and for continuous SO2 exposure and risk of coarctation of 

the aorta (OR per 1 ppb = 1.07; 95% CI,

1.01–1.13) and tetralogy of Fallot (OR per 

1 ppb = 1.03; 95% CI, 1.01–1.05) (Table 2,

Table 1. continued

Reference Setting Design Case and control definition Exposure assessment

Exposure range 

in second month 

of gestation

Gestational 

period Adjustment variables

Hansen 

et al. 

2009

Brisbane 

(Australia), 

1997–2004; 

150,308 births

Case–control Cases with cardiac defects 

and clefts, stillbirths and 

live births; five matched 

controls per case

Average of daily 

measurements of CO, 

NO2, O3, PM10, and SO2

at the monitoring site 

nearest to the center of 

small area of residence

IQR, mean: 

CO, 0.6, 1.1 ppm

NO2, 4.0, 8.2 ppb

O3, 0.5, 2.6 pphm

PM10, 4.0, 18.0 μg/m3

SO2, 0.6, 1.5 ppb

Weeks 3–8 Infant sex; matching 

variables: maternal 

age, marital status, 

indigenous status, 

parity, month of 

last menstrual 

period, area-level 

socioeconomic status, 

and distance to 

monitor

Dolk et al. 

2010

Four regions 

of England, 

1991–1999; 

759,993 births

Cohort 9,085 cases with 

chromosomal and 

nonchromosomal 

anomalies, live births and 

fetal deaths ≥ 20 weeks; 

all live and stillbirths

Annual mean 

concentrations in 1996 

of NO2, PM10, and 

SO2 for 1 × 1 km grids; 

population-weighted 

average assigned 

to census ward of 

residence 

p10–p90:

NO2, 21.5–47.8 μg/m3

PM10, 18.8–26.4 μg/m3

SO2, 3.9–15.0 μg/m3

1996 average Maternal age, 

deprivation index, 

region, and hospital 

catchment

Marshall 

et al. 

2010

New Jersey 

(USA), 

1998–2003; 

690,000 births

Case–control 717 cases with cleft lip 

and/or palate, live births; 

random sample of eligible 

nonmalformed births

Average of measurements 

of CO, NO2, O3, PM10, 

PM2.5, and SO2 at 

nearest monitor 

(maximum, 40 km; 

median, 13–20 km)

p25–p75: 

CO, 0.65–1.02 ppm

NO2, 18–30 ppb

O3, 1.5–3.3 pphm

PM10, 22–33.5 μg/m3

SO2, 3–7 ppb

Weeks 3–8 Maternal race, age, 

education, gravidity, 

alcohol use, smoking, 

season of conception, 

and infant sex

Dadvand 

et al. 

2011a

Northern 

region (UK), 

1985–1996; 

449,355 births

Case–control 2,713 cases with cardiac 

defects, live births and 

fetal deaths ≥ 20 weeks 

gestation; 9,975 controls, 

live and stillbirths

Two-stage spatiotemporal 

modeling of weekly 

averages of black smoke 

and SO2 levels (from 

56 monitors) at maternal 

residence using traffic 

data, land cover data, 

and other predictors

p25–p75: 

Black smoke, 5.93–

14.57 μg/m3

SO2, 17.6–31.2 μg/m3

Weeks 3–8 Year of birth, 

socioeconomic status, 

infant sex, season 

of conception, and 

degree of urbanity

Dadvand 

et al. 

2011b

Northern 

region (UK), 

1993–2003; 

356,767 births 

Case–control 2,140 cases with cardiac 

defects, live births and 

fetal deaths > 20 weeks 

gestation; 14,256 

controls, live and 

stillbirths

Weekly average 

measurements of CO, 

NO, NO2, O3, PM10, and 

SO2, at nearest of six 

monitoring stations in 

the region (maximum, 

56–83 km; median, 

8–12 km)

p25–p75: 

CO, 0.39–0.64 mg/m3

NO2, 29.2–38.4 μg/m3

NO, 13.3–32.5 μg/m3

O3, 33.2–42.4 μg/m3

PM10, 20.5–30.2 μg/m3

SO2, 6.8–15.0 μg/m3

Weeks 3–8 Year of birth, 

socioeconomic status, 

infant sex, season 

of conception, and 

degree of urbanity

Abbreviations: p10, 10th percentile; p25, 25th percentile; p75, 75th percentile; p90, 90th percentile.
Vrijheid et al.

602 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

Figure 1A–E). p-Values for heterogeneity in 

these analyses showed limited evidence for 

heterogeneity (p-values between 0.1 and 0.9). 

Sensitivity analyses excluding the study with 

the largest weight from each meta-analysis 

showed that results for NO2 and tetralogy of 

Fallot and for SO2 and coarctation of the aorta 

were robust to this exclusion. The results for 

SO2 were not robust to the inclusion of the 

first Dadvand study (Dadvand et al. 2011a) 

instead of the second (Dadvand et al. 2011b) 

(Table 2); the first study by Dadvand et al. 

(2011a) also introduced significant heterogeneity. High versus low comparisons did not show 

evidence for increased risks for any anomaly–

pollutant combinations. The summary OR 

for high compared with low CO exposure and 

risk of ASDs was significantly reduced (OR per 

1 ppm = 0.86; 95% CI, 0.75–0.99).

Orofacial clefts. Seven studies examined 

the association between air pollutants and 

risk of cleft lip with or without cleft palate 

(Table 1). Statistically significantly increased 

risks of cleft lip with or without cleft palate were observed in relation to SO2 in two 

studies [OR per 0.6 ppb = 1.27; 95% CI,

1.01–1.62 (Hansen et al. 2009); OR quartile 

4 vs. quartile 1 = 1.6; 95% CI, 1.1–2.2 

(Marshall et al. 2010)]. None of the studies 

reported increased risks of cleft palate alone. 

Meta-analyses summarizing risk estimates for 

exposure to five pollutants and risk of cleft 

lip with or without cleft palate and cleft palate alone were all close to one, and none 

reached statistical significance (Table 3). We 

found evidence for significant heterogeneity 

(p < 0.1) in the CO and SO2 analyses. O3

exposure and cleft lip with or without cleft 

palate showed an association of borderline 

statistical significance (OR per 10 ppb = 1.10;

95% CI, 0.99–1.21) (Table 3, Figure 1F).

Other congenital anomalies. Two studies 

examined a range of anomalies other than cardiac anomalies and orofacial clefts (Dolk et al. 

2010; Rankin et al. 2009). They observed statistically significantly increased risks of omphalocele in relation to PM10 concentrations 

[90th vs. 10th percentile: OR = 2.17; 95% CI,

1.00–4.71 (Dolk et al. 2010)] and of nervous 

system anomalies in relation to black smoke 

concentration [OR = 1.10/mg/m3; 95% CI,

1.03–1.18 (Rankin et al. 2009)]. No other 

anomaly groups/subtypes were at an increased 

risk in relation to the pollutants studied.

Discussion

The evidence base for an effect of exposure to 

ambient air pollutants on congenital anomaly risk is small. Individual studies reported 

increased risks for some combinations of air 

pollutants and congenital anomalies, mostly 

cardiac anomalies, but these occurred among 

many associations tested in each study. Metaanalyses suggest that NO2 and SO2 exposures were related to statistically significant 

increases in risk of coarctation of the aorta 

and tetralogy of Fallot, and PM10 exposure 

to an increase in risk of ASDs; we based summary risk estimates on few studies (n = 4), 

but the total numbers of cases included were 

relatively large (between 655 and 951). Metaanalyses found no statistically significant 

increase in risk of other cardiac anomalies 

and orofacial clefts in relation to air pollution 

exposure. This review and its meta-analysis 

raise important issues that may guide both the 

design and presentation of future studies.

A common feature of all the reviewed 

studies was the use of routine monitoring 

stations as the basis for exposure assessment. 

Exposure indices were usually calculated from 

pollutant measurements at the nearest monitoring station or as a distance-weighted average of measurements of all stations in the area; 

these methods apply a similar exposure to a 

relatively large geographic area and thereby 

measure predominantly community-wide variations in air pollution. This approach will be 

more appropriate for pollutants that vary at 

large geographic scale (e.g., SO2, O3) than for 

those that may have a much finer spatial distribution/resolution (e.g., CO, NO2). Traffic 

exhaust fumes are the main source of air pollutants such as NO2 and PM2.5 in urban areas, 

as well as a main source of suspected causative agents for adverse birth outcomes, such 

as polycyclic aromatic hydrocarbons (PAHs) 

(Perera et al. 2003; Ritz and Wilhelm 2008). 

More precise spatial models, based on dispersion or land-use regression models that take 

into account local road networks and other 

predictive variables, are therefore increasingly 

being recommended and used in research on 

adverse birth outcomes (Aguilera et al. 2009; 

Gilliland et al. 2005; Madsen et al. 2010; 

Nethery et al. 2008; Ritz and Wilhelm 2008; 

Slama et al. 2007, 2008; Wu et al. 2009). 

Only one of the reviewed studies (Dadvand 

et al. 2011a) used a spatiotemporal model 

for black smoke and SO2 exposure in which 

concentrations of these pollutants were predicted from monitoring data combined with 

data from traffic, meteorology, and land use. 

Application of these types of models to future 

studies of congenital anomalies would be a 

step toward more accurate exposure assessment for some of the pollutants of interest. 

Characterization and quantification of errors 

in exposure estimates from these models will 

Table 2. Summary of meta-analysis of studies on air pollutant exposures and cardiac anomalies.

Pollutant and anomaly 

combination

Total 

number 

of casesb

Continuous exposurec High versus low exposure

Studies 

includeda

Heterogeneity 

p-value

Summary OR 

(95% CI)d

Heterogeneity 

p-value

Summary OR 

(95% CI)d

CO per 1 ppm

ASDs 1, 2, 5, 6, 9 1,337 0.10 0.87 (0.72–1.05) 0.17 0.86 (0.75–0.99)

VSDs 1, 2, 5, 6, 9 3,710 < 0.001 1.14 (0.66–1.98) < 0.001 1.18 (0.82–1.69)

Conotruncal defects 1, 2, 5, 6 1,156 0.02 0.95 (0.57–1.58) 0.01 0.95 (0.62–1.44)

NO2 per 10 ppb

ASDs 2, 5, 6, 9 952 0.81 1.10 (0.91–1.33) 0.28 1.07 (0.90–1.26)

VSDs 2, 5, 6, 9 3,460 0.002 1.12 (0.87–1.44) 0.03 0.92 (0.77–1.12)

Coarctation of the aorta 2, 5, 7, 9 756 0.31 1.20 (1.00–1.44) 0.13 1.04 (0.86–1.26)

Tetralogy of Fallot 2, 5, 7, 9 704 0.23 1.25 (1.02–1.51) 0.06 1.04 (0.70–1.55)

O3 per 10 ppb

ASDs 1, 2, 5, 6, 9 1,307 0.08 1.10 (0.92–1.32) 0.31 0.99 (0.83–1.19)

VSDs 1, 2, 5, 6, 9 3,557 0.04 0.94 (0.82–1.08) 0.02 0.93 (0.73–1.18)

Conotruncal defects 1, 2, 5, 6 1,164 0.64 1.07 (0.96–1.19) 0.45 1.13 (0.89–1.42)

PM10 per 10 μg/m3

ASDs 2, 5, 6, 9 951 0.10 1.14 (1.01–1.28) 0.02 1.23 (0.91–1.67)

VSDs 2, 5, 6, 9 3,410 0.12 1.03 (0.96–1.10) 0.70 0.93 (0.84–1.02)

Coarctation of the aorta 2, 5, 7, 9 761 0.02 1.10 (0.88–1.39) 0.48 1.00 (0.79–1.26)

Tetralogy of Fallot 2, 5, 7, 9 546 0.37 1.00 (0.87–1.15) 0.02 0.91 (0.53–1.56)

SO2 per 1 ppb

ASDs 2, 5, 6, 9 909 0.01 0.96 (0.86–1.07) 0.01 0.85 (0.57–1.28)

2, 5, 6, 10e 914 0.01 0.97 (0.87–1.08) 0.02 0.92 (0.63–1.33)

VSDs 2, 5, 6, 9 3,217 < 0.001 1.03 (0.97–1.11) < 0.001 0.96 (0.63–1.46)

2, 5, 6, 10e 3,056 < 0.001 1.01 (0.94–1.07) < 0.001 1.08 (0.81–1.44)

2, 4, 5, 6f < 0.001 1.05 (0.76–1.46)

Coarctation of the aorta 2, 5, 7, 9 682 0.71 1.04 (1.00–1.08) 0.29 1.06 (0.89–1.27)

2, 5, 7, 10e 687 0.02 1.01 (0.94–1.07) 0.01 0.89 (0.61–1.32)

2, 4, 5, 7f 0.95 1.10 (0.92–1.31)

Tetralogy of Fallot 2, 5, 7, 9 655 0.10 1.04 (1.00–1.08) < 0.001 0.80 (0.45–1.41)

2, 5, 7, 10e 670 0.05 1.03 (0.97–1.09) 0.06 1.02 (0.75–1.39)

2, 4, 5, 7 0.13 1.13 (0.93–1.36)

aStudies included are different in each pollutant-anomaly meta-analysis depending on the data they published. 

References are as follows: 1, Ritz et al. (2002); 2, Gilboa et al. (2005); 4, Rankin et al. (2009); 5, Strickland et al. (2009); 6,

Hansen et al. (2009); 7, Dolk et al. (2010); 9, Dadvand et al. (2011b); 10, Dadvand et al. (2011a). bNumber of cases included 

in the continuous exposure analysis. cConversion factors: CO, 1 ppb = 1.15 μg/m3; NO2, 1 ppb = 1.88 μg/m3; O3, 1 ppb = 

1.96 μg/m3; SO2, 1 ppb = 2.62 μg/m3. dWhen heterogeneity p-value is < 0.10, the OR from random effect model is shown; 

otherwise, the OR from the fixed-effects model is shown. eSensitivity analysis using Dadvand 2011a instead of Dadvand 

2011b. fSensitivity analysis including Rankin 2009 instead of Dadvand 2011b.
Air pollution and congenital anomalies

Environmental Health Perspectives • volume 119 | number 5 | May 2011 603

be available from validation studies (Nethery 

et al. 2008), and efforts should be made to 

also incorporate these in evaluations of congenital anomaly risk. Furthermore, air pollution studies of other birth outcomes have 

moved toward trying to estimate more specifically the exact pollutants responsible (e.g., 

transition metals contained in PM, PAHs, 

aromatic hydrocarbons) (e.g., Aguilera et al. 

2009; Perera et al. 2003; Slama et al. 2007), 

rather than focusing on the regulated pollutants, and this would be a valuable direction for 

future congenital anomaly studies.

Nearly all the reviewed studies were based 

on registry information. In such studies, residential addresses are available only at birth, 

not in the first trimester of pregnancy, the 

most relevant period for causation of congenital anomalies (Moore and Persaud 1998). 

Furthermore, residential addresses only 

account for exposures near the home, not in 

other situations thought to make an important contribution to personal exposure, such 

as work location, commuting, and indoor air 

pollution sources (Nethery et al. 2008; Setton 

et al. 2010). However, congenital anomaly 

research in this field cannot easily move away 

from routinely registered data. Large case–control studies with good information on residential history and time–activity patterns may be 

a useful step forward, if they can be combined 

with accurate exposure assessments. Pregnancy 

cohort studies in Europe are currently pooling 

resources to study effects of air pollution and 

other birth outcomes using land-use regression 

exposure models [European Study of Cohorts 

for Air Pollution Effects (ESCAPE) Project 

2009], but few such cohorts are large enough 

to conduct meaningful analyses of congenital anomalies or include thorough enough 

ascertainment of congenital anomalies among 

pregnancy terminations as well as live births. 

Information from such studies may, however, 

provide useful information about the effect of 

including information on residential mobility 

and time–activity patterns on risk estimates 

for other birth outcomes (e.g., Aguilera et al. 

2009; Chen et al. 2010; Lupo et al. 2010).

1.0 2 1.5 .0

OR per 10 ppb

OR per 10 μg/m3 OR per 1 ppb

OR per 1 ppb OR per 10 ppb

OR per 10 ppb

2.5 1.0 2 1.5 2 .0 .5

0.8 1 1.0 1.2 .4 1.8 0.90 0.95 1.05 1.15

0.9 1.0 1.1 1.2 0.4 0.6 0.8 1.0 1.4

Study

Gilboa et al. 2005

Strickland et al. 2009

Dolk et al. 2010

Dadvand et al. 2011b

Gilboa et al. 2005

Strickland et al. 2009

Hansen et al. 2009

Dadvand et al. 2011b

Study

Gilboa et al. 2005

Strickland et al. 2009

Dolk et al. 2010

Dadvand et al. 2011b

Study

Study

183

189

379

127

256

951

Gilboa et al. 2005

Strickland et al. 2009

Dolk et al. 2010

Dadvand et al. 2011b

Gilboa et al. 2005

Strickland et al. 2009

Dolk et al. 2010

Dadvand et al. 2011b

275

176

122

756

n cases

n cases Study

Gilboa et al. 2005

Hwang et al. 2008

Hansen et al. 2009

Marshall et al. 2010

Ritz et al. 2002

n cases Study

85

299

146

125

655

109

275

176

122

682

134

299

146

125

704

n cases

n cases

305

653

145

397

1,950

450

n cases

1.05 (0.81–1.36)

1.33 (1.11–1.60)

1.08 (0.87–1.35)

1.16 (0.69–1.93)

0.90 (0.74–1.22)

1.20 (0.78–1.83)

1.28 (0.82–1.99)

1.74 (1.00–2.50)

OR (95% CI)

OR (95% CI)

1.14 (1.01–1.28)

1.09 (0.98–1.20)

0.96 (0.88–1.04)

1.08 (1.02–1.15)

1.00 (0.92–1.08)

OR (95% CI)

1.04 (1.00–1.08)

1.20 (1.00–1.44)

1.25 (0.94–1.67)

0.90 (0.59–1.38)

1.30 (0.78–2.16)

1.74 (1.00–2.50)

OR (95% CI)

OR (95% CI)

1.09 (0.90–1.31)

1.22 (1.03–1.46)

0.67 (0.40–1.12)

0.96 (0.75–1.24)

1.10 (0.99–1.21)

1.13 (0.90–1.40)

OR (95% CI)

1.08 (0.99–1.18)

1.01 (0.93–1.09)

1.03 (0.98–1.10)

1.05 (0.97–1.14)

1.04 (1.00–1.08)

Combined effect (fixed) 1.25 (1.02–1.51)

Combined effect (fixed) 

Combined effect (fixed) 

Combined effect (fixed) 

Combined effect (fixed) 

Combined effect (fixed) 

O3 SO and cleft lip/palate 2 and tetralogy of Fallot

SO2 PM and coarctation of the aorta 10 and ASDs

NO2 NO and tetralogy of Fallot 2

 and coarctation of the aorta

Figure 1. Forest plots showing risk estimates for individual studies and the combined meta-analysis result. Marker sizes represent the relative weight the study 

contributed to the summary estimation. (A) NO2 and coarctation of the aorta. (B) NO2 and tetralogy of Fallot. (C) PM10 and ASDs. (D) SO2 and coarctation of the 

aorta. (E) SO2 and tetralogy of Fallot. (F) O3 and cleft lip with or without cleft palate.
Vrijheid et al.

604 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

The most difficult issue in our attempt to 

combine data from the different studies was 

the use of very different criteria for definition 

and classification of congenital anomaly subgroups. Moreover, we found differences in 

inclusion and exclusion of cases with other 

anomalies in the same and other organ systems, with chromosomal abnormalities, and 

with other syndromic conditions. We compared the different groupings and exclusions 

used [see Supplemental Material, Table 1

(doi:10.1289/ehp.1002946)] but found that 

in some instances it is not possible to deduce 

from the articles which inclusions and exclusions were made. For our meta-analyses we 

selected a few groups of relatively comparable 

anomaly groups. However, even for these, 

studies differed in their approach to classification. VSDs, for example, were treated as one 

anomaly group by some studies (Dadvand et al. 

2011a, 2011b; Gilboa et al. 2005; Hansen 

et al. 2009; Rankin et al. 2009; Ritz et al. 

2002), as four different anomalies by another 

(Strickland et al. 2009), and were excluded in 

yet another (Dolk et al. 2010). Notably, we 

found the largest evidence for heterogeneity 

for VSDs. In general, congenital heart defects 

form a very heterogeneous set of conditions, 

notoriously difficult to classify (Botto et al. 

2007; Strickland et al. 2008). Several classification systems have been proposed (Botto 

et al. 2007; Jacobs et al. 2007), but diagnostics 

information in routine registries may often 

not be specific enough to apply these. Further 

international harmonization efforts in this area 

[e.g., those undertaken as part of EUROCAT 

(2010) and the International Clearinghouse 

for Birth Defects (2010)] will be of great 

value. Classification of orofacial clefts is more 

straightforward than that of cardiac anomalies, 

but even here differences in exclusions have 

resulted in somewhat nonhomogeneous case 

groups for the meta-analyses. We encourage 

future studies to base their classifications and 

exclusions on those of previous studies in the 

same field, where possible.

The meta-analysis results should be interpreted with caution because they are based on 

few studies, and because some were subject to 

some degree of statistical heterogeneity. On 

the other hand, the total numbers of cases 

included in the meta-analyses were large, ranging from 500 to > 3,700, depending on the 

anomaly group. We found significant results 

for some pollutant–cardiac anomaly combinations, but only two of these (NO2 and 

tetralogy of Fallot, and SO2 and coarctation of 

the aorta) were robust to the exclusion of the 

study with the largest weight, and we could 

not confirm significant findings from the continuous exposure analyses with high- versus 

low-exposure analyses. It is not clear which 

of these latter analyses is the most appropriate: In continuous analyses one assumes a 

(log) linear relationship between exposure and 

outcome; in the field of congenital anomaly, 

linearity cannot automatically be assumed, 

because selective survival of more viable fetuses 

related to the same exposure may lead to nonlinearity (Ritz 2010). On the other hand, the 

high versus low analysis combines categories 

of very different exposure levels across studies 

and has lower power because of smaller numbers of cases in these more extreme exposure 

categories. We recommend that studies report 

both types of analyses, in annexes of sensitivity analyses where appropriate, in order to aid 

future meta-analyses.

Heterogeneity in the studies we reviewed 

may arise from inherent differences between 

the study settings, as well as from differences 

in study designs and analysis methods. First, 

the study areas were different with respect to 

exposure levels and ranges, pollutant mixtures,

and underlying anomaly risks; this may have 

given rise to different dose–response relationships. With respect to study design, we considered the assessment of exposure to be relatively 

similar: Studies assessed mostly the same pollutants, same exposure windows, and similar 

distance-based exposure indices. Heterogeneity 

in our analyses was not particularly related to 

studies that deviated from the rest with respect 

to their approach to exposure assessment such 

as the Strickland et al. (2009) study with a 

purely temporal exposure model and the Dolk 

et al. (2010) study with a purely spatial model. 

As described above, ascertainment and classification of congenital anomalies differed greatly 

among studies, and this may well have played a 

role in the observed heterogeneity. Because all 

reviewed studies used routinely registered data, 

selection and recall biases likely did not play 

a large role in most of the reviewed studies. 

However, two of the reviewed studies (Gilboa 

et al. 2005; Hansen et al. 2009) matched controls to cases by, respectively, mother’s county 

of residence and mother’s distance of residence from an air pollution monitor. This may 

introduce bias by reducing exposure contrast 

between cases and controls.

Covariates included in analyses differed 

and residual confounding structures may 

differ among the studies, thus leading to 

heterogeneity in study results. However, the 

number of known risk factors for congenital 

anomalies is extremely small, and none of 

the suspected risk factors, such as smoking, 

alcohol, folate deficiency, or socioeconomic 

status, are likely to explain a large proportion of cases. Maternal smoking, for example, 

has been established as a risk factor for orofacial clefts with relative risk estimates around 

1.2–1.3 (Lie et al. 2008; Little et al. 2004; 

Meyer et al. 2004), but evidence for an association with risk of other congenital anomalies 

is weak (EUROCAT 2004). Only two of the 

reviewed studies controlled for tobacco smoking (Gilboa et al. 2005; Marshall et al. 2010).

Possible mechanisms of teratogenicity of 

air pollutants at this stage remain speculative, 

but several mechanisms have been hypothesized for effects of PM on fetal growth, 

including oxidative stress, placental inflammation, and changes in coagulation, as reviewed 

by Kannan et al. (2006). Congenital anomalies may be induced through similar effects on 

early fetal growth, as well as by air pollutants 

influencing the migration and differentiation 

of neural crest cells, by interactions with the 

metabolism and detoxification of other xenobiotics, or by indirect effects through maternal immunologic reactions, such as infection 

or asthma, or medication related to these 

Table 3. Summary of meta-analysis of studies on air pollutant exposures and orofacial clefts.

Pollutant 

and anomaly 

combination

Total 

number 

of casesb

Continuous exposurec High versus low exposure

Studies 

includeda

Heterogeneity 

p-value

Summary OR 

(95% CI)d

Heterogeneity 

p-value

Summary OR 

(95% CI)d

CO per 1 ppm

Cleft lip/palatee 1, 2, 3, 6, 8 1,498 0.03 0.89 (0.66–1.20) 0.002 0.99 (0.79–1.24)

Cleft palate 1, 2, 6, 8 697 0.009 0.68 (0.36–1.25) 0.06 0.78 (0.55–1.12)

NO2 per 10 ppb

Cleft lip/palate 2, 6, 7, 8 1,423 0.43 1.00 (0.87–1.16) 0.25 1.06 (0.88–1.28)

Cleft palate 2, 6, 7, 8 809 0.12 0.91 (0.76–1.08) 0.23 0.79 (0.62–1.01)

O3 per 10 ppb

Cleft lip/palate 1, 2, 3, 6, 8 1,950 0.20 1.10 (0.99–1.21) 0.22 1.06 (0.96–1.17)

Cleft palate 1, 2, 6, 8 702 0.79 0.99 (0.83–1.18) 0.42 1.00 (0.79–1.26)

PM10 per 10 μg/m3

Cleft lip/palate 2, 3, 6, 7, 8 2,072 0.70 1.01 (0.96–1.06) 0.45 1.02 (0.94–1.11)

Cleft palate 2, 6, 7, 8 803 0.008 0.91 (0.58–1.41) 0.15 0.86 (0.70–1.07)

SO2 per 1 ppb

Cleft lip/palate 2, 3, 6, 7, 8 1,976 0.06 0.99 (0.94–1.05) 0.01 1.06 (0.87–1.29)

2, 3, 4, 6, 7, 8f 0.02 1.06 (0.89–1.27)

Cleft palate 2, 6, 7, 8 764 0.35 1.03 (0.98–1.07) 0.17 0.94 (0.81–1.10)

aReferences are as follows: 1, Ritz et al. (2002); 2, Gilboa et al. (2005); 3, Hwang and Jaakkola (2008); 4, Rankin et al. 

(2009); 6, Hansen et al. (2009); 7, Dolk et al. (2010); 8, Marshall et al. (2010). bNumber of cases included in the continuous 

exposure analysis. cConversion factors: CO, 1 ppb = 1.15 μg/m3; NO2, 1 ppb = 1.88 μg/m3; O3, 1 ppb = 1.96 μg/m3; SO2,

1 ppb = 2.62 μg/m3. dWhen heterogeneity p-value is < 0.10 the OR from random effect model is shown; otherwise, the OR 

from the fixed-effect model is shown. eCleft lip with or without cleft palate. fSensitivity analysis including Rankin 2009 

(no continuous exposure estimate available).
Air pollution and congenital anomalies

Environmental Health Perspectives • volume 119 | number 5 | May 2011 605

conditions (Dolk et al. 2010; Ritz et al. 2002; 

Sram et al. 2005). In animal studies, maternal 

exposure to O3, NO2, and CO has produced 

embryotoxic effects, as well as teratogenic 

effects such as skeletal and neuromuscular 

anomalies (Garvey and Longo 1978; Kavlock 

et al. 1979; Longo 1977; Singh 1988).

Genetic polymorphisms in developmental 

and detoxification genes have been described 

as potentially increasing individual susceptibility to the teratogenic effects of maternal smoking (e.g., Chevrier et al. 2008; Lammer et al. 

2005); evaluation of similar gene–environment 

interactions may aid mechanistic understanding in future studies of air pollution and congenital anomalies. Interactions between PM 

and specific micro- and macronutrients in 

the diet (antioxidants, antiinflammatory factors) have been proposed to play a role in the 

causation of low birth weight, fetal growth 

restriction, and preterm birth, through joint 

oxidative stress and inflammatory mechanisms (Jedrychowski et al. 2010; Kannan et al. 

2006); such interactions also warrant exploration in the causation of congenital anomalies.

Recommendations

This review of 10 studies finds some evidence 

of an effect of air pollutants on congenital 

anomaly risk, so it is worth considering directions for further research in this area. Air pollution is a ubiquitous exposure, and small 

increases in risk may therefore carry a large 

public health implication; congenital anomalies are rare but serious outcomes that have a 

large impact on infant mortality and morbidity and on morbidity in later life. The statistically significant increases in relative risk we 

observed in meta-analyses were between 3%

and 20% per unit of exposure. Exposure units 

used were of the same order of magnitude as, 

or well within, the IQRs of these pollutants in 

most study areas (Table 1). Therefore, relative 

risk increases of this order of magnitude may 

be considered important for public health. 

Existing studies have so far not used some of 

the recent advances in exposure assessment 

used in other areas of air pollution research. A 

case can thus be made for well-designed studies that can make improvements in at least 

some of the following areas.

Exposure assessment. As outlined above, 

exposure indices with better spatial accuracy 

(while maintaining an accurate temporal 

component) should be used to assess exposure 

to traffic-related air pollution, because this 

is the main source of air pollution in urban 

areas. Better identification of the effects of 

specific exposures and exposure mixtures, 

and the integration of mobility and time–

activity patterns, is recommended for congenital anomaly research in the same way as for 

other birth outcomes. Matching of cases and 

controls by exposure-related variables such as 

maternal area of residence should be avoided 

in future studies.

Outcome harmonization. International 

harmonization of coding and classification of 

anomalies would greatly aid future systematic 

evaluations in this field. Any future studies 

should carefully report the exact classifications 

and exclusions used and attempt to use the 

same classifications as previous studies, at least 

in sensitivity analyses.

Other congenital anomalies. Many of 

the above studies focused on cardiac anomalies and orofacial clefts. These are two of the 

largest anomaly groups, and they have been 

suspected to be related to other environmental exposures (Dolk and Vrijheid 2003). 

However, studies with sufficient statistical 

power should also focus on other anomalies 

for which there may be an environmental etiology (e.g., neural tube defects, limb defects, 

gastroschisis) (EUROCAT 2004).

Mechanisms and individual susceptibility.

Improved knowledge on potential mechanisms of teratogenic action of air pollutants is 

needed; this may be achieved by the integration of mechanistic biomarkers. Evaluation 

of interactions with genetic polymorphisms 

and dietary factors may also provide further 

insights into mechanisms.

References

Aguilera I, Guxens M, Garcia-Esteban R, Corbella T, 

Nieuwenhuijsen MJ, Foradada CM, et al. 2009. Association 

between GIS-based exposure to urban air pollution during 

pregnancy and birth weight in the INMA Sabadell Cohort. 

Environ Health Perspect 117:1322–1327.

Botto LD, Lin AE, Riehle-Colarusso T, Malik S, Correa A. 2007. 

Seeking causes: classifying and evaluating congenital 

heart defects in etiologic studies. Birth Defects Res A Clin 

Mol Teratol 79:714–727.

Chen L, Bell EM, Caton AR, Druschel CM, Lin S. 2010. 

Residential mobility during pregnancy and the potential for 

ambient air pollution exposure misclassification. Environ 

Res 110:162–168.

Chevrier C, Bahuau M, Perret C, Iovannisci DM, Nelva A, 

Herman C, et al. 2008. Genetic susceptibilities in the association between maternal exposure to tobacco smoke and 

the risk of nonsyndromic oral cleft. Am J Med Genet A 

146A:2396–2406.

Cochran WG. 1954. The combination of estimates from different 

experiments. Biometrics 10:101–129.

Cordier S, Chevrier C, Robert-Gnansia E, Lorente C, Brula P, 

Hours M. 2004. Risk of congenital anomalies in the vicinity of municipal solid waste incinerators. Occup Environ 

Med 61:8–15.

Dadvand P, Rankin J, Rushton S, Pless-Mulloli T. 2011a. 

Association between maternal exposure to ambient air 

pollution and congenital heart disease: a register-based 

spatio-temporal analysis. Am J Epidemiol 173:171–182.

Dadvand P, Rankin J, Rushton S, Pless-Mulloli T. 2011b. 

Ambient air pollution and congenital heart disease, a registry-based study. Environ Res 111:435–441.

DerSimonian R, Laird N. 1986. Meta-analysis in clinical trials. 

Control Clin Trials 7:177–188.

Dolk H, Armstrong B, Lachowycz K, Vrijheid M, Rankin J, 

Abramsky L, et al. 2010. Ambient air pollution and risk 

of congenital anomalies in England, 1991–1999. Occup 

Environ Med 67:223–227.

Dolk H, Vrijheid M. 2003. The impact of environmental pollution 

on congenital anomalies. Br Med Bull 68:25–45.

Egger M, Davey SG, Schneider M, Minder C. 1997. Bias in 

meta-analysis detected by a simple, graphical test. BMJ 

315:629–634.

EUROCAT (European Surveillance of Congenital Anomalies).

2004. EUROCAT Special Report. A Review of Environmental 

Risk Factors for Congenital Anomalies. Belfast:University 

of Ulster.

EUROCAT (European Surveillance of Congenital Anomalies). 

2009. EUROCAT Guide 1.3. Instructions for the Registration 

of Congenital Anomalies. Belfast:University of Ulster.

EUROCAT (European Surveillance of Congenital Anomalies). 

2010. Malformation Coding Guides. Available: http://

www.eurocat-network.eu/ABOUTUS/DataCollection/ 

GuidelinesforRegistration/MalformationCodingGuides 

[accessed 30 November 2010].

European Study of Cohorts for Air Pollution Effects Project 

(ESCAPE). 2009. Homepage. Available: http://www.

escapeproject.eu/ [accessed 20 November 2010].

Garvey DJ, Longo LD. 1978. Chronic low level maternal carbon 

monoxide exposure and fetal growth and development. 

Biol Reprod 19:8–14.

Gilboa SM, Mendola P, Olshan AF, Langlois PH, Savitz DA, 

Loomis D, et al. 2005. Relation between ambient air quality 

and selected birth defects, seven county study, Texas, 

1997–2000. Am J Epidemiol 162:238–252.

Gilliland F, Avol E, Kinney P, Jerrett M, Dvonch T, Lurmann F,

et al. 2005. Air pollution exposure assessment for epidemiologic studies of pregnant women and children: lessons 

learned from the Centers for Children’s Environmental 

Health and Disease Prevention Research. Environ Health 

Perspect 113:1447–1454.

Glinianaia SV, Rankin J, Bell R, Pless-Mulloli T, Howel D. 2004a. 

Does particulate air pollution contribute to infant death? A 

systematic review. Environ Health Perspect 112:1365–1371.

Glinianaia SV, Rankin J, Bell R, Pless-Mulloli T, Howel D. 2004b. 

Particulate air pollution and fetal health: a systematic review 

of the epidemiologic evidence. Epidemiology 15:36–45.

Hansen CA, Barnett AG, Jalaludin BB, Morgan GG. 2009. 

Ambient air pollution and birth defects in Brisbane, 

Australia. PLoS One 4:e5408.

Hwang B-F, Jaakkola JJK. 2008. Ozone and other air pollutants and risk of oral clefts. Environ Health Perspect 

116:1411–1415.

International Clearinghouse for Birth Defects. 2010. Home page. 

Available: http://www.icbdsr.org [accessed 30 November 

2010].

Jacobs JP, Mavroudis C, Jacobs ML, Maruszewski B, 

Tchervenkov CI, Lacour-Gayet FG, et al. 2007. Nomenclature 

and databases—the past, the present, and the future: a 

primer for the congenital heart surgeon. Pediatr Cardiol 

28:105–115.

Jedrychowski W, Perera F, Mrozek-Budzyn D, Flak E, Mroz E, 

Sochacka-Tatara E, et al. 2010. Higher fish consumption 

in pregnancy may confer protection against the harmful 

effect of prenatal exposure to fine particulate matter. Ann 

Nutr Metab 56:119–126.

Kannan S, Misra DP, Dvonch JT, Krishnakumar A. 2006. 

Exposures to airborne particulate matter and adverse 

perinatal outcomes: a biologically plausible mechanistic 

framework for exploring potential effect modification by 

nutrition. Environ Health Perspect 114:1636–1642.

Kavlock R, Daston G, Grabowski CT. 1979. Studies on the developmental toxicity of ozone. I. Prenatal effects. Toxicol Appl 

Pharmacol 48:19–28.

Kim OJ, Ha EH, Kim BM, Seo JH, Park HS, Jung WJ, et al. 2007. 

PM10 and pregnancy outcomes: a hospital-based cohort 

study of pregnant women in Seoul. J Occup Environ Med 

49:1394–1402.

Lammer EJ, Shaw GM, Iovannisci DM, Finnell RH. 2005. 

Maternal smoking, genetic variation of glutathione 

S-transferases, and risk for orofacial clefts. Epidemiology 

16:698–701.

Lie RT, Wilcox AJ, Taylor J, Gjessing HK, Saugstad OD, Aabyholm 

F, et al. 2008. Maternal smoking and oral clefts: the role of 

detoxification pathway genes. Epidemiology 19:606–615.

Light RJ, Pillemer DB. 1984. Summing Up: The Science of 

Reviewing Research. Cambridge, MA:Harvard University 

Press. 

Little J, Cardy A, Munger RG. 2004. Tobacco smoking and oral 

clefts: a meta-analysis. Bull WHO 82:213–218.

Longo LD. 1977. The biological effects of carbon monoxide on 

the pregnant woman, fetus, and newborn infant. Am J 

Obstet Gynecol 129:69–103.

Lupo PJ, Symanski E, Chan W, Mitchell LE, Waller DK, 

Canfield MA, et al. 2010. Differences in exposure assignment between conception and delivery: the impact of 

maternal mobility. Paediatr Perinat Epidemiol 24:200–208.
Vrijheid et al.

606 volume 119 | number 5 | May 2011 • Environmental Health Perspectives

Madsen C, Gehring U, Walker SE, Brunekreef B, Stigum H, 

Naess O, et al. 2010. Ambient air pollution exposure, residential mobility and term birth weight in Oslo, Norway. 

Environ Res 110:363–371.

Maisonet M, Correa A, Misra D, Jaakkola JJ. 2004. A review 

of the literature on the effects of ambient air pollution on 

fetal growth. Environ Res 95:106–115.

Mantel N, Haenszel W. 1959. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl 

Cancer Inst 22:719–748.

Marshall EG, Harris G, Wartenberg D. 2010. Oral cleft defects 

and maternal exposure to ambient air pollutants in New 

Jersey. Birth Defects Res A Clin Mol Teratol 88:205–215.

Meyer KA, Williams P, Hernandez-Diaz S, Cnattingius S. 2004. 

Smoking and the risk of oral clefts: exploring the impact of 

study designs. Epidemiology 15:671–678.

Moher D, Liberati A, Tetzlaff J, Altman DG. 2009. Preferred 

reporting items for systematic reviews and meta-analyses: 

the PRISMA statement. BMJ 339:b2535; doi: 10.1136/bmj.

b2535 [Online 21 July 2009].

Moore KL, Persaud TVN. 1998. The Developing Human. Clinically 

Oriented Embryology. Philadelphia:W.B. Saunders.

National Library of Medicine. 2010. Medline. Available: http://

www.nlm.nih.gov/databases/databases_medline.html 

[accessed 31 August 2010]. 

Nethery E, Leckie SE, Teschke K, Brauer M. 2008. From measures to models: an evaluation of air pollution exposure 

assessment for epidemiological studies of pregnant 

women. Occup Environ Med 65:579–586.

Nieuwenhuijsen MJ, Martinez D, Grellier J, Bennett J, Best N, 

Iszatt N, et al. 2009. Chlorination disinfection by-products 

in drinking water and congenital anomalies: review and 

meta-analyses. Environ Health Perspect 117:1486–1493.

Perera FP, Rauh V, Tsai WY, Kinney P, Camann D, Barr D, et al. 

2003. Effects of transplacental exposure to environmental 

pollutants on birth outcomes in a multiethnic population. 

Environ Health Perspect 111:201–205.

R Project for Statistical Computing. What is R? Available: http://

www.r-project.org/ (accessed 17 July 2010).

Rankin J, Chadwick T, Natarajan M, Howel D, Pearce MS, 

Pless-Mulloli T. 2009. Maternal exposure to ambient air 

pollutants and risk of congenital anomalies. Environ Res 

109:181–187.

Ritz B. 2010. Air pollution and congenital anomalies. Occup 

Environ Med 67:221–222.

Ritz B, Wilhelm M. 2008. Ambient air pollution and adverse birth 

outcomes: methodologic issues in an emerging field. Basic 

Clin Pharmacol Toxicol 102:182–190.

Ritz B, Yu F, Fruin S, Chapa G, Shaw GM, Harris JA. 2002. 

Ambient air pollution and risk of birth defects in Southern 

California. Am J Epidemiol 155:17–25.

Setton E, Marshall JD, Brauer M, Lundquist KR, Hystad P, 

Keller P, et al. 2010. The impact of daily mobility on exposure to traffic-related air pollution and health effect 

estimates. J Expo Sci Environ Epidemiol; doi: 10.1038/

jes.2010.14 [Online 30 June 2010].

Singh J. 1988. Nitrogen dioxide exposure alters neonatal development. Neurotoxicology 9:545–549.

Slama R, Darrow L, Parker J, Woodruff TJ, Strickland M, 

Nieuwenhuijsen M, et al. 2008. Meeting report: atmospheric pollution and human reproduction. Environ Health 

Perspect 116:791–798.

Slama R, Morgenstern V, Cyrys J, Zutavern A, Herbarth O, 

Wichmann HE, et al. 2007. Traffic-related atmospheric pollutants levels during pregnancy and offspring’s term birth 

weight: a study relying on a land-use regression exposure 

model. Environ Health Perspect 115:1283–1292.

Šrám RJ, Binková B, Dejmek J, Bobak M. 2005. Ambient air 

pollution and pregnancy outcomes: a review of the literature. Environ Health Perspect 113:375–382.

Strickland MJ, Klein M, Correa A, Reller MD, Mahle WT, 

Riehle-Colarusso TJ, et al. 2009. Ambient air pollution 

and cardiovascular malformations in Atlanta, Georgia, 

1986–2003. Am J Epidemiol 169:1004–1014.

Strickland MJ, Riehle-Colarusso TJ, Jacobs JP, Reller MD, 

Mahle WT, Botto LD, et al. 2008. The importance of nomenclature for congenital cardiac disease: implications for 

research and evaluation. Cardiol Young 18(suppl 2):92–100.

Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, 

Rennie D, et al. 2000. Meta-analysis of observational 

studies in epidemiology: a proposal for reporting. Metaanalysis of Observational Studies in Epidemiology 

(MOOSE) group. JAMA 283:2008–2012.

Tennant PW, Pearce MS, Bythell M, Rankin J. 2010. 20-year 

survival of children born with congenital anomalies: a 

population-based study. Lancet 375:649–656.

Thomson Reuters. 2010. ISI Web of Knowledge. Available: 

http://www.isiwebofknowledge.com/ [accessed 31 August 

2010]. 

Weinhold B. 2009. Environmental factors in birth defects: what 

we need to know. Environ Health Perspect 117:A440–A447.

Woodruff TJ, Parker JD, Darrow LA, Slama R, Bell ML, Choi H, 

et al. 2009. Methodological issues in studies of air pollution 

and reproductive health. Environ Res 109:311–320.

World Health Organization. 1975. International Classification of 

Diseases, 9th Revision. Geneva:World Health Organization.

Wu J, Ren C, Delfino RJ, Chung J, Wilhelm M, Ritz B. 2009. 

Association between local traffic-generated air pollution and preeclampsia and preterm delivery in the South 

Coast Air Basin of California. Environ Health Perspect 

117:1773–1779.
ERRATUM

Environmental Health Perspectives • ERRATUM 

NOTE: Vrijheid et al. [Environ Health Perspect 119:598–606 (2011)] have reported errors in their paper, which resulted from errors in 

formulas applied to convert risk estimates for continuous exposure from reviewed studies. These errors affect the summary values in their 

Abstract, Figure 1, Tables 2 and 3, and Supplemental Material, Table 2A–E. None of these corrections affect the statistical significance of the 

pooled estimates or the conclusions of the paper. 

The authors apologize for the errors.

These errors have been corrected in the PDF version of this article and in the Supplemental Material.