Traffic-related air pollution, preterm birth and term birth weight in the PIAMA birth cohort study

Environmental Research 111 (2011) 125–135

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Traffic-related air pollution, preterm birth and term birth weight in the PIAMA birth cohort study Ulrike Gehring a,n, Alet H. Wijga b, Paul Fischer c, Johan C. de Jongste d, Marjan Kerkhof e, Gerard H. Koppelman f, Henriette A. Smit g, Bert Brunekreef a,g a

Institute for Risk Assessment Sciences, Utrecht University, PO Box 80178, 3508 TD Utrecht, The Netherlands Centre for Prevention and Health Services Research, National Institute of Public Health and the Environment, Bilthoven, The Netherlands c Centre for Environmental Health Research, National Institute for Public Health and the Environment, Bilthoven, The Netherlands d Department of Pediatrics, Division of Respiratory Medicine, Erasmus University Medical Center/Sophia Children’s Hospital, Rotterdam, The Netherlands e Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands f Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, University Medical Center Groningen, University of Groningen, The Netherlands g Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands b

a r t i c l e in f o

abstract

Article history: Received 7 April 2010 Received in revised form 4 October 2010 Accepted 13 October 2010 Available online 9 November 2010

Background: Maternal exposure to air pollution has been associated with adverse pregnancy outcomes. Few studies took into account the spatial and temporal variation of air pollution levels. Objectives: To evaluate the impact of maternal exposure to traffic-related air pollution during pregnancy on preterm birth and term birth weight using a spatio-temporal exposure model. Methods: We estimated maternal residential exposure to nitrogen dioxide (NO2), particulate matter (PM2.5) and soot during pregnancy (entire pregnancy, 1st trimester, and last month) for 3853 singleton births within the Dutch PIAMA prospective birth cohort study by means of temporally adjusted land-use regression models. Associations between air pollution concentrations and preterm birth and term birth weight were analyzed by means of logistic and linear regression models with and without adjustment for maternal physical, lifestyle, and socio-demographic characteristics. Results: We found positive, statistically non-significant associations between exposure to soot during entire pregnancy and during the last month of pregnancy and preterm birth [adj. OR (95% CI) per interquartile range increase in exposure 1.08 (0.88–1.34) and 1.09 (0.93–1.27), respectively]. There was no indication of an adverse effect of air pollution exposure on term birth weight. Conclusions: In this study, maternal exposure to traffic-related air pollution during pregnancy was not associated with term birth weight. There was a tendency towards an increased risk of preterm birth with increasing air pollution exposure, but statistical power was low. & 2010 Elsevier Inc. All rights reserved.

Keywords: Air pollution Traffic Pregnancy Preterm birth Birth weight

1. Introduction There is growing evidence for adverse effects of maternal exposure to ambient air pollution during pregnancy on pregnancy outcomes including fetal growth and preterm delivery. A number of recent reviews (Glinianaia et al., 2004; Lacasana et al., 2005; Maisonet et al., 2004; Sram et al., 2005; Stillerman et al., 2008) summarize the epidemiological evidence. The effects of different air pollutants including particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and sulphur dioxide (SO2) on pregnancy outcomes have been studied. The authors conclude that, although results are not always consistent, the data suggest that adverse effects of air pollution on fetal growth and preterm delivery may occur and recommend additional research on this

n

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0013-9351/$ - see front matter & 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2010.10.004

topic. Evidence was judged to be stronger for low birth weight than for premature birth in one review (Sram et al., 2005). Other reviews, however, indicated the opposite (Maisonet et al., 2004) or were less conclusive (Glinianaia et al., 2004; Lacasana et al., 2005). Although the effects, if any, are probably small, the public health impact may be considerable due to the widespread nature of the exposure and the long-term health impacts of low birth weight and preterm birth (Osmond and Barker, 2000). The mechanisms behind the associations between exposure to ambient air pollution and pregnancy outcomes are not clear yet. Kannan et al. (2006) describe potential biologic pathways including systemic oxidative stress and inflammation, changes in blood coagulation, endothelial function, and hemodynamic responses. Likewise, the crucial window(s) of exposure are not clear, yet. For low birth weight and preterm birth, the first trimester exposures and the exposures during the third trimester and during the last weeks preceding birth have been implicated as having most relevance (Ritz and Wilhelm, 2008).

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Most of the studies performed so far made use of data from birth registers that have been routinely collected and where information on important confounding variables such as maternal smoking during pregnancy is usually either very limited on the individual level or lacking completely. Relying on data from existing birth or pregnancy cohort studies rather than birth registers has the advantage that usually large amounts of individual data are available. Another deficiency of most of the studies that have been done so far involves the assessment of exposure. Individual exposure measurements are not feasible in large study populations. Therefore, exposure was usually estimated using data from a limited number of routine air quality monitoring sites in the study area, which may be located several kilometers from the homes of study subjects. This may lead to considerable measurement error, especially in relation to pollution from local sources such as traffic (Cyrys et al., 1998; Fischer et al., 2000; Lebret et al., 2000). To overcome this problem, Slama et al. (2007) introduced the use of more sophisticated spatio-temporal exposure models using Geographic Information Systems (GIS), measurement data, and stochastic modeling. We adopted this approach and estimated individual exposures during the entire pregnancy, 1st trimester, and the last month of pregnancy in the present study. The objective of the present study is to explore the effect of exposure to traffic-related air pollution during the entire pregnancy, 1st trimester, and the last month of pregnancy on preterm birth and fetal growth in a birth cohort study of almost 3900 children living in the Netherlands with individual information on maternal physical, lifestyle, and socio-demographic characteristics. 2. Materials and methods 2.1. Study population The Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study is a prospective birth cohort study (Brunekreef et al., 2002). Women were recruited in 1996–1997 during their second trimester of pregnancy from a series of communities in the North, West, and Centre of The Netherlands. Non-allergic pregnant women were invited to participate in a ‘‘natural history’’ study arm. Pregnant women identified as allergic through a screening questionnaire were primarily allocated to an intervention arm with a random subset allocated to the natural history arm. The intervention involved the use of mite-impermeable mattress and pillow covers. The study started with 3963 newborns. The parents of 3943 children filled in and returned a standardized questionnaire when the child was 3 months old, which included amongst others information on pregnancy outcomes. After excluding twins (N ¼44), and children with missing information on gestational age (N ¼13) and birth weight (N ¼23), 3863 children remained for the present analysis. The Institutional Review Boards of the participating institutes approved the study protocol, and written informed consent was obtained from all participants. 2.2. Endpoints For studies where both birth weight and gestational data are available, the use of preterm birth and term birth weight as markers of perinatal health has been recommended (Wilcox, 2001). Therefore, preterm birth (o 37 weeks of gestation) and term (Z37 and o 43 weeks of gestation) birth weight were used as endpoints in the present study. Date of birth, birth weight, infant’s sex, and expected birth date, as recorded by the obstetric care providers, were obtained through parental questionnaires when the child was 3 months old. Gestational age was calculated from the expected birth date (ultra-sound based or based on date of last menstrual period) and the actual birth date. No individual information is available about whether or not the expected birth date was based on an ultra-sound examination or the date of the last menstrual period. 2.3. Exposure assessment We used a spatio-temporal exposure model based on land-use regression and a temporal component from routine air quality monitoring data to estimate individual exposures to air pollution during different periods of pregnancy. The rationale behind this is that variability in maternal exposure to air pollution during pregnancy is caused by spatial contrasts related to proximity to sources and by temporal

contrasts due to seasonal variations in air pollution levels (women were recruited into the study over a period of more than one year which means that pregnancies/ trimesters took place during different seasons for different women). The calculation of individual exposure estimates for the entire pregnancy, 1st trimester, and the last month consisted of two steps. First, annual average concentrations of nitrogen dioxide (NO2), fine particles (PM2.5), and ‘‘soot’’ at the birth address of each participant were estimated by landuse regression models developed for this specific study area, i.e. the North, West, and Centre of The Netherlands (Brauer et al., 2003). In brief, for the land-use regression model, between 1 March 1999 and 20 April 2000 four two-week measurements of NO2, PM2.5, and ‘‘soot’’ (determined as the reflectance of the PM2.5 filters) were performed at each of 40 sites within one year. GIS data were also collected regarding traffic, road and population density in the vicinity of each monitoring location. Regression models were developed to relate the annual average concentrations measured at the 40 monitoring sites with the GIS variables. The models explained 73%, 81%, and 85% of the variability in the annual average concentrations for PM2.5, ‘‘soot’’, and NO2, respectively. More information on the regression models is provided in the online supplement. A detailed description of the land-use regression models for PM2.5 and ‘‘soot’’ has been published elsewhere (Brauer et al., 2003). Second, exposure estimates for the entire pregnancy, 1st trimester, and the last month were calculated from the annual average estimates from the spatial land-use regression model by adding a temporal component taking into account differences in dates of conception and dates of birth. We adopted the approach described by Slama et al. (2007) and used daily air pollution concentrations measured at routine air quality monitoring sites in the study area to estimate the temporal component. The underlying assumption is that the temporal variation in air pollution levels at the routine air quality monitoring sites reflects the temporal variation in air pollution levels at the participants’ homes. Since the study area of the present study is much larger and less homogeneous than the study area of the study by Slama et al. (2007), we decided not to rely on data from one single routine air quality monitoring site, but to include all regional, suburban, and urban background sites of the National Air Quality Monitoring Network (NAQMN, http://www.rivm.nl/milieuk waliteit/lucht/) that were located in our study area and were operating during the entire study period. In total, we used data of 23, 18, and 10 different monitoring sites for NO2, PM10, and black smoke, respectively. The median (minimum–maximum) distance between the monitoring sites and the participants’ homes were 18.1 (0.1–36.4), 29.4 (4.9–47.7), and 37.4 (10.3–69.6) kilometers for NO2, PM10, and black smoke, respectively. To estimate pregnancy and pregnancy-period specific exposures from our land-use regression model, we first calculated for every participant and every pollutant, the average concentration during the entire pregnancy, 1st trimester, and the last month of pregnancy from the daily average concentrations at the nearest NAQMN site. The averages for the entire pregnancy, 1st trimester, and the last month were then divided by the average concentration of this pollutant at the same air quality monitoring site during the measurement campaign for the land-use regression model (1 March 1999–20 April 2000). Finally, annual average exposure estimates at the participants’ birth addresses from the land-use regression model were multiplied by the ratio of the average air pollution level during the respective pregnancy period and the average air pollution level during measurement campaign for the land-use regression modeling to obtain pregnancy-period specific exposure estimates. Since no PM2.5 and reflectance data were available from the NAQMN, PM10 and black smoke data, respectively, were used for temporal adjustment of PM2.5 and soot levels assuming similar temporal variations for PM2.5 and PM10 levels and soot and black smoke levels, respectively. 2.4. Covariates Information on maternal physical, lifestyle, and socio-demographic characteristics was obtained from questionnaires completed by the parents during pregnancy and after the child’s birth. Physical characteristics obtained from the questionnaire that was completed 3 months after the child’s birth included age ( r 25, 25–30, 30–35, and Z35 years), pre-pregnancy body mass index (o 18, 18–25, 25–30, and 430 kg/m2), and the presence of older siblings as a proxy for parity. Sociodemographic co-variables included nationality (defined by the mother’s country of birth; from questionnaire when the child was 2 years old) and parental education (defined as the highest attained educational level of mother and father; low: primary school, lower vocational or lower secondary education; medium: intermediate vocational education or intermediate/higher secondary education; and high: higher vocational education and university; from questionnaire when the child was 1 year old). Lifestyle factors included maternal smoking during pregnancy (no; o 5, 5–10, Z10 cigarettes/day; quit; unknown; from questionnaire during third trimester of pregnancy). Information on maternal allergies was available from the screening questionnaire completed during the women’s first visit to the prenatal health clinics, which usually takes place during the first trimester. 2.5. Statistical analysis Since relationships between air pollution levels and pregnancy outcomes were generally linear (data not shown), air pollution concentrations were used as continuous variables without transformation in the regression models. Associations

U. Gehring et al. / Environmental Research 111 (2011) 125–135 between estimated air pollution levels at the birth address and preterm birth and term birth weight were analyzed with logistic and linear regression models, respectively, with and without adjustment for confounding variables. Results are presented as odds ratios and mean differences, respectively, with 95% confidence intervals. Potential confounding variables were the covariates described above, the child’s gender, and gestational age (completed weeks, categorical; for term birth weight only). Confounders were selected separately for the two health outcomes. Variables were included in the final model if adjustment of the crude association for the specific variable resulted in a change in air pollution effect estimate of more than 5% for any pollutant and any pregnancy period. This means that for each outcome we adjusted for the same set of confounders in the models for the different pollutants and the different periods of pregnancy. Participants with missing information for one or more of the selected confounders were excluded from adjusted, but not from crude analyses. We explored the potential impact of maternal nationality and maternal smoking during pregnancy by analyzing the association between air pollution levels and pregnancy outcomes in the subgroups of Dutch and nonsmoking women, respectively, which were both of sufficient size to yield reliable results. Residential mobility during pregnancy might have resulted in exposure misclassification and air pollution concentrations at the birth address might be a less reliable exposure estimate for women who changed residence during pregnancy. We therefore repeated all analyses for the subset of women who did not change residence during pregnancy. Maternal allergy could be an effect modifier of the association between air pollution and pregnancy outcomes. We tested this hypothesis by looking at non-allergic women separately. Calculations were performed using the Statistical Analysis System (SAS 9.2, Cary, NC, USA).

3. Results 3.1. General characteristics and pregnancy outcomes Exposures were successfully estimated for 3853 of the 3863 children (99.7%). Distributions of pregnancy outcomes and covariates for all children are presented in Table 1. Distributions of pregnancy outcomes and most covariates differed between the three study regions (Table 1 of the online supplement). Associations between pregnancy outcomes and covariates are presented in Table 2. 3.2. Exposure to air pollution The range in levels was larger for NO2 and soot than for PM2.5 (Table 3). Correlations between the different pollutant level estimates were moderate to high for all exposure periods (r ¼0.72–0.86, Table 2 of the online supplement). Correlations between pollutants were lower than the correlations that were reported previously for annual average pollution levels at the participants’ birth addresses (Brauer et al., 2002) due to differences in temporal pattern between pollutants. For all three pollutants, estimated exposures for the entire pregnancy were moderately to highly correlated with estimated exposures for the other pregnancy periods; correlations between estimated exposures for the 1st trimester and the last month of pregnancy were moderate to low (Table 3 of the online supplement). 3.3. Associations between air pollution exposure and pregnancy outcomes In crude and adjusted analyses, we found statistically nonsignificant positive associations between preterm birth and soot levels during the entire pregnancy and during the last month of pregnancy (Table 4). No association was found between the other pollutants and preterm birth. Fully adjusted models were not different from nationality adjusted models presented in Table 4 (data not shown). In crude analyses, for all three pollutants and all exposure periods, negative associations were found with term birth weight (Table 4). None of these associations reached statistical significance. After adjustment for confounding variables (gestational age, maternal pre-pregnancy BMI, and nationality were the most important confounders), only the effect estimates for the

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Table 1 Characteristics of the study population (N ¼ 3853). Variable Pregnancy outcomes Term birth weight (g) [median (min–max), N ¼ 3683] Preterm birth (o 37 weeks) Female gender Covariates Maternal age at birth o 25 years 25–30 years 30–35 years Z 35 years Maternal pre-pregnancy BMI o 18 (underweight) 18–25 (normal weight) 25–30 (overweight) 430 (obesity) Unknown Maternal smoking during pregnancy No Yes, o 5 cig./day Yes, 5–10 cig/day Yes, Z 10 cig/day Quit Unclear Maternal allergy Older siblings Parental education Low Medium High Nationality Dutch Non-Dutch Study region North Middle West Changed residence during pregnancy No Yes Unknown

n/N

(%)

3550 (1400–5705) 165/3853 (4.3) 1858/3853 (48.2)

374/3836 1630/3836 1477/3836 355/3836

(9.7) (42.5) (38.5) (9.3)

70/3853 2651/3853 532/3853 136/3853 464/3853

(1.8) (68.8) (13.8) (3.5) (12.0)

3122/3842 172/3842 174/3842 184/3842 145/3842 45/3842 1186/3853 1935/3853

(81.2) (4.5) (4.5) (4.8) (3.8) (1.1) (30.8) (49.9)

484/3724 1371/3724 1869/3724

(13.0) (36.5) (50.5)

3430/3620 190/3620

(94.8) (5.2)

1200/3853 1559/3853 1094/3853

(31.4) (40.4) (28.1)

2956/3853 851/3853 46/3853

(76.7) (22.1) (1.2)

associations with air pollution levels during the last month of pregnancy were negative, but remained statistically nonsignificant. Since the three study regions differed with regard to pregnancy outcomes and a number of covariates, we explored the possibility of regional confounding due to variables that have not been directly measured by additional adjustment for study region. The association between soot levels during the entire pregnancy and preterm birth became marginally statistically significant (p ¼0.0918) after adjustment for region (Table 4). There was no indication for an adverse effect of air pollution on term birth weight from these analyses; on the contrary, after adjustment for study region, we found a positive significant association between NO2 levels during the first trimester and term birth weight (Table 4). Since study region is an important determinant of air pollution levels in the land-use regression models which were used to estimate exposures, the adjustment for region may be an over-adjustment. 3.4. Sensitivity analyses To test the influence of the temporal adjustment of the estimated exposures from the land-use regression model, we also calculated effect estimates for the unadjusted annual average air pollution concentrations form the land-use regression models. Results were very similar to the results for the pregnancy averages

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Table 2 Associationsa between population characteristics and preterm birth and term birth weight. Preterm birth Variable Gender Female Male Maternal age at birth o 25 years 25–30 years 30–35 years Z 35 years Maternal pre-pregnancy BMI o 18 (underweight) 18–25 (normal weight) 25–30 (overweight) 4 30 (obesity) Unknown Maternal smoking during pregnancy No Yes, o 5 cig./day Yes, 5–10 cig/day Yes, Z 10 cig/day Quit Unclear Maternal allergy Yes No Older siblings No Yes Parental education Low Medium High Nationality Dutch Non-Dutch Gestational age 37 weeks 38 weeks 39 weeks 40 weeks 41 weeks 42 weeks

Birth weight (g)

(%)

OR

(95% CI)

Mean

4.3 4.3

1 1.01

(0.74  1.38)

3491 3627

0 136

5.1 4.6 3.8 3.9

1 0.90 0.74 0.77

(0.54–1.51) (0.43–1.25) (0.38–1.55)

3500 3549 3595 3556

0 49 94 56

11.4 4.2 4.1 3.7 4.1

1 0.34 0.33 0.30 0.33

(0.16–0.72) (0.14–0.78) (0.09–0.94) (0.14–0.79)

3380 3546 3632 3700 3554

0 167 253 320 174

4.0 2.3 3.4 8.2 6.2 8.9

1 0.57 0.85 2.11 1.57 2.32

(0.21-1.55) (0.37–1.95) (1.21–3.69) (0.78–3.16) (0.82–6.58)

3595 3476 3293 3270 3639 3525

0  118  302  325 44  70

5.1 3.9

1 0.77

(0.56; 1.06)

3553 3565

0 12

( 22;46)

6.3 2.3

1 0.35

(0.24–0.49)

3474 3645

0 171

(140;202)

5.2 5.2 3.6

1 1.00 0.68

(0.63–1.60) (0.43–1.09)

3509 3555 3585

0 46 76

4.2 5.3

1 1.27

(0.66–2.45)

3572 3449

0  123

3097 3320 3477 3624 3754 3826

0 223 380 527 657 729

– – – – – –

Mean difference

(95% CI)

(105;167)

( 7;105) (38;151) ( 16;128)

(45;289) (125;380) (174;466) (46;303)

( 193;  44) ( 376;  228) ( 399;  251) ( 38;126) ( 217;77)

(-6;97) (27;126)

( 195;  50)

(145;301) (309;452) (457;598) (584;730) (637;821)

a Associations are presented as odds ratios (OR) for preterm birth and mean differences for term birth weight with 95% confidence intervals (CI) defining the first category as the reference category.

Table 3 Estimated NO2, PM2.5 and soot concentrations at the birth address during different pregnancy periods (N ¼3853). Pollutant (period)

Minimum

25th percentile

Median

Mean

75th percentile

95th percentile

Maximum

NO2 (lg/m3) Entire pregnancy 1st trimester Last month before birth

13.0 7.9 7.8

23.8 24.4 21.6

31.3 31.4 27.9

30.4 31.5 28.9

35.0 38.8 35.3

44.5 48.3 48.5

64.9 89.7 112.1

PM2.5 (lg/m3) Entire pregnancy 1st trimester Last month before birth

14.2 13.2 10.7

17.7 17.2 15.3

20.3 20.2 17.9

20.1 21.3 19.1

22.3 25.0 21.8

24.8 30.0 29.0

33.0 45.4 43.1

Soot (10  5 m  1) Entire pregnancy 1st trimester Last month before birth

1.14 0.57 0.42

2.22 1.96 1.35

2.76 3.01 1.95

2.75 3.27 2.33

3.16 4.43 2.81

3.98 6.05 5.47

6.52 11.06 12.10

from the temporally adjusted land-use regression models (data not shown). Spatial correlation of observations (i.e. children living in the same city being more alike than children living in different cities)

may be another source of bias. We investigated this by performing repeated measures logistic and linear regression analyses allowing for correlation between subjects living in the same city. Estimated correlations between subjects that were living in the same city

U. Gehring et al. / Environmental Research 111 (2011) 125–135

Table 4 Crude and adjusted associations

a

129

between air pollution exposure during pregnancy and pregnancy outcomes. Preterm birth

NO2 Entire pregnancy (IQR: 11.2 mg/m3) 1st trimester (IQR: 14.4 mg/m3) Last month before birth (IQR: 13.7 mg/m3) PM2.5 Entire pregnancy (IQR: 4.6 mg/m3) 1st trimester (IQR: 7.8 mg/m3) Last month before birth (IQR: 5.3 mg/m3) soot Entire pregnancy (IQR: 0.94  10  5 m  1) 1st trimester (IQR: 2.46  10  5 m  1) Last month before birth (IQR: 1.47  10  5m  1)

Term birth weight (g)

Crude OR (95% CI) N¼ 3853

Adj. OR (95% CI)b N ¼ 3853

Adj. OR (95% CI)c N¼ 3853

Crude mean difference (95% CI) N ¼3683

Adj. mean difference (95% CI)b N ¼ 3408

Adj. mean difference (95% CI)c N ¼3408

0.97 (0.79; 1.19)

0.94 (0.76; 1.17)

1.08 (0.80; 1.47)

 12.7 (  33.6; 8.1)

5.6 ( 14.1; 25.3)

24.7 ( 4.1; 53.4)

0.94 (0.76; 1.16) 0.98 (0.81;1.19)

0.89 (0.71; 1.12) 1.00 (0.82;1.22)

0.97 (0.73; 1.27) 1.08 (0.86;1.36)

 5.2 (  26.6; 16.2)  16.3 (  35.7; 3.1)

17.5 ( 2.4; 37.5)  5.2 (  23.3; 12.9)

34.3 (9.7; 58.8)  1.7 (  23.4; 20.0)

0.94 (0.73; 1.20) 0.95 (0.75; 1.22) 0.92 (0.76;1.13)

0.88 (0.68; 1.15) 0.91 (0.70; 1.17) 0.93 (0.75;1.15)

1.22 (0.83; 1.80) 0.98 (0.75; 1.29) 1.06 (0.84;1.35)

 16.6 (  41.6; 8.4)  17.8 (  42.0; 6.4)  12.9 (  32.7; 6.9)

3.0 (  20.3; 26.3) 10.9 ( 11.5; 33.2)  14.8 ( 33.2; 3.5)

30.0 (  7.1; 67.1) 17.5 ( 6.8; 41.7)  13.8 (  35.0; 7.4)

1.12 (0.92; 1.37) 1.00 (0.78; 1.27) 1.08 (0.93;1.25)

1.08 (0.88; 1.34) 0.93 (0.72; 1.20) 1.09 (0.93;1.27)

1.27 (0.96; 1.67) 0.94 (0.72; 1.23) 1.12 (0.96;1.32)

 19.0 (  39.8; 1.8)  12.3 (  36.7; 12.1)  12.8 (  28.7; 3.2)

5.7 ( 13.8; 25.2) 15.7 ( 6.8; 38.3)  7.0 (  21.8; 7.8)

20.7 (  6.8; 48.2) 20.5 (  3.5; 44.4)  5.8 (  21.4; 9.7)

a Associations are expressed as odds ratios (OR) for preterm birth and mean differences for term birth weight with 95% confidence intervals (CI). All associations were calculated for an interquartile range (IQR) increase in exposure. b Results for preterm birth were adjusted for nationality; results for term birth weight were adjusted for gender, gestational age (in weeks, categorical), maternal age, maternal pre-pregnancy BMI, maternal smoking during pregnancy, presence of older siblings, maternal education, and nationality. c Results were adjusted for study region in addition to the variables listed above.

were very low and therefore, results remained largely unchanged (data not shown). Results for the subgroups of Dutch, non-smoking, and nonallergic women were largely identical to those for the entire study population (Figs. 1 and 2; results for the entire study population from Table 4 were added to the figures to facilitate comparisons). Likewise results remained basically unchanged for the subset of women who did not change residence during pregnancy (Figs. 1 and 2).

4. Discussion Our findings provide little support to the hypothesis of an adverse effect of maternal exposure to air pollution during pregnancy on term birth weight. Exposure to soot during entire pregnancy and during the last month of pregnancy tended to be associated with a small, statistically non-significant increase in risk of preterm delivery. A number of studies mainly performed in North America and Australia have reported associations between NO2 and PM2.5 exposure during pregnancy and preterm birth. If positive associations were found, effects were usually small with odds ratios in the range of 1.1–1.2 per 10 mg/m3 increase in PM2.5 and NO2 levels (an overview of the results from the present study and other studies for a 10 mg/m3 increment exposure is given in Table 5). In these studies, effects on preterm birth were reported for air pollution exposure during different periods of pregnancy (entire, early, and late pregnancy; Table 5). In the present study, non-significant positive associations were found with soot, which has not been studied in relation to pregnancy outcomes before, but not with PM2.5 levels. A potential explanation might be that the land-use regression model for soot performed better than the land-use regression model for PM2.5 (Brauer et al., 2003). Another explanation could be that soot is a better marker for traffic related pollution and for toxic substances affecting pregnancy outcomes. At present, the number of studies on the association between PM2.5 and NO2 levels and (term) birth weight is still very limited. An

overview of the results of these studies for a 10 mg/m3 increment in exposure is presented in Table 6. Since there were only two studies with data on PM2.5 exposure, studies with PM10 data were also included in this overview. Four studies reported inverse associations between air pollution levels and term birth weight (Chen et al., 2002; Gouveia et al., 2004; Ha et al., 2001; Wang et al., 1997) and three other studies reported inverse associations between air pollution levels and birth weight not restricted to term births (Aguilera et al., 2009; Bell et al., 2007; Mannes et al., 2005). Some of these associations did not reach statistical significance. Decreases in (term) birth weight that were found in other studies were small. Like for preterm births, effects were reported for different periods of pregnancy (entire pregnancy, trimesters, and last month of pregnancy). No association between maternal exposure to trafficrelated air pollution and preterm birth and term birth weight was found in two other Dutch birth cohort studies that have been recently published (Gehring et al., 2010; van den Hooven et al., 2009). However, exposure assessment in one of these two studies was limited to NO2 (Gehring et al., 2010) and to a purely spatial approach using traffic densities in the other (van den Hooven et al., 2009). Slama et al. (2007) used term birth weight o3000 g as an outcome variable. In their cohort of approximately 1000 children they found statistically significant positive associations between exposure to PM2.5 and absorbance during pregnancy and having a baby with a term birth weight of less than 3000g [adj. RR (95% CI) 1.13 (1.00–1.29) per 1 mg/m3 increase in PM2.5 and 1.45 (1.06–1.87) per 0.5  10  5 m  1 increase in absorbance]. This could not be confirmed in the present study. Odds ratios for the association between term birth weight o3000 g and PM2.5 and absorbance in the present study for the increments in exposure used by Slama et al. were all between 0.98 and 1.00. One advantage of relying on data from a prospective birth cohort study is the availability of individual information from the study questionnaires on maternal physical, lifestyle, and socio-demographic characteristics, which might confound the association between air pollution and pregnancy outcomes. Most of the other studies on air pollution and pregnancy outcomes were based on birth records and have limited information on important

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Fig. 1. Associations between air pollution exposure and preterm birth for the total study population, Dutch children, non-smoking mothers, mothers who did not change residence during pregnancy (non-movers), and non-allergic mothers. (Results were adjusted for nationality. All associations are expressed as odds ratios for an interquartile range increase in exposure. Interquartile ranges were 11.2 mg/m3, 14.4 mg/m3, and 13.7 mg/m3 for NO2 during the entire pregnancy, 1st trimester, and the last month, respectively; 4.6 mg/m3, 7.8 mg/m3, and 5.3 mg/m3 for PM2.5 during the entire pregnancy, 1st trimester, and last month, respectively; 0.94  10  5 m  1, 2.46  10  5 m  1, and 1.47  10  5 m  1 for soot during the entire pregnancy, 1st trimester, and last month, respectively.)

confounders such as maternal smoking (Woodruff et al., 2009), which may explain the inconsistencies between these studies and the present study. A disadvantage of using existing birth cohorts to study the effects of air pollution on pregnancy outcomes is that the study populations are usually small – at least compared to the birth certificate based studies – and that statistical power to study infrequent outcomes is limited. We therefore cannot conclude from the absence of an association between air pollution exposure and the health endpoints studied that there is no association. Limited statistical power is more a concern for the dichotomous outcome of preterm birth, which has a low prevalence (4.3%) in our cohort than for continuous outcome of term birth weight, where variation

within our population is large. Treating gestational duration as a continuous outcome may help to (partly) overcome this problem. In the present study, the strongest association between gestational age (continuous) and air pollution exposure was found for the entire pregnancy average of soot [adj. mean difference (95% CI) per interquartile range increase in exposure: 0.46 ( 0.96; 0.05) days, data not shown] which is in line with the small statistically nonsignificant effects of soot exposure on preterm birth. Excluding children that were born pre- and post-term from the analyses for birth weight might have resulted in a reduced variability in birth weight compared to the entire population including pre- and post-term birth and consequently in reduced statistical power. Analyses for the entire population, however, did

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131

Fig. 2. Associations between air pollution exposure and term birth weight for the total study population, Dutch children, non-smoking mothers, mothers who did not change residence during pregnancy (non-movers), and non-allergic mothers. (Results were adjusted for gender, gestational age, maternal age, maternal pre-pregnancy BMI, maternal smoking during pregnancy, presence of older siblings, maternal education, and nationality. Associations are expressed as mean difference for an interquartile range increase in exposure. Interquartile ranges were 11.2 mg/m3, 14.4 mg/m3, and 13.7 mg/m3 for NO2 during the entire pregnancy, 1st trimester, and last month, respectively; 4.6 mg/m3, 7.8 mg/m3, and 5.3 mg/m3 for PM2.5 during the entire pregnancy, 1st trimester, and the last month, respectively; 0.94  10  5 m  1, 2.46  10  5 m  1, and 1.47  10  5 m  1 for soot during the entire pregnancy, 1st trimester, and the last month, respectively.)

not give a stronger signal than analyses for term births only (data not shown). Another advantage of our study is the availability of individual exposure estimates from temporally adjusted land-use regression models that take into account the spatial and temporal variability of air pollution levels. This approach until now has been used only in few other studies (Aguilera et al., 2009; Brauer et al., 2008; Gehring et al., 2010; Slama et al., 2007). In most previous studies, exposure was defined as the air pollution level measured at the nearest stationary air pollution monitor(s) not accounting for small-scale variations in air pollution levels. Air pollution levels could be an explanation for the absence of an effect in the present study. However, effects on birth weight were not exclusively found

in areas with higher levels of air pollution, but also reported from places like Sydney, where air pollution levels are lower than those of the present study (Mannes et al., 2005). Limited variability in air pollution levels may be another explanation for the absence of an effect in the present study. The variation in PM2.5 levels in the present study is relatively low (2–4-fold differences between the minimum and maximum levels for the different pregnancy periods) compared to other studies (Bell et al., 2007; Mannes et al., 2005). However, we also did not find an indication of an adverse effect of NO2, for which the variability was much larger. It has been suggested that maternal asthma might be related to pregnancy outcomes (Bakhireva et al., 2007; Soong Tan and Neil, 2000). We therefore tested whether maternal allergic disease was a

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Table 5 Associations between PM2.5 and NO2 exposure during pregnancy and preterm birth (o 37 weeks of gestation). Comparison with results from other published studies.a Author, year

Study area

Study period

Study population

Exposure assessment

Adj. OR/RR (95% CI) per 10 lg/m3

PM2.5 Present study

The Netherlands

1996–1997

Singleton births, N ¼3853

Temporally adjusted land-use regression

Entire pregnancy: 0.76 (0.43; 1.46) 1st trimester: 0.89 (0.63; 1.22) Last month before birth: 0.93 (0.75; 1.15)

Wilhelm and Ritz, 2005

California, USA

1994–2000

Singleton births delivered vaginally, N ¼ 106,483

Monitoring site

1st trimester: 0.73 (0.67; 0.80) 6 weeks preceding birth: 1.10 (1.00; 1.21)

Huynh et al., 2006

California, USA

1999–2000

Singleton live births, N ¼42,692

Monitoring site

Entire pregnancy: 1.15 (1.15; 1.16) 1st month: 1.13 (1.13; 1.13) 2 weeks preceding birth: 1.06 (1.05; 1.06)

Jalaludin et al., 2007

Sydney, Australia

1998–2000

Singleton births, N ¼123,840

Monitoring site

1st month: 0.83 (0.68; 1.00) 1st trimester: 0.80 (0.60; 1.07) 3 months preceding birth: 0.83 (0.61; 1.12) 1 month preceding birth: 0.85 (0.68; 1.08)

Ritz et al., 2007

Los Angeles, California, USA

2003

Singleton births, N ¼58,316

Monitoring site

1st trimester: 1.00 (0.94; 1.07)

Brauer, 2008

Vancouver, BC, Canada

1999–2002

Singleton births, N ¼70,249

Monitoring site

Entire pregnancy: 1.79 (1.10; 2.84)

Darrow et al., 2009

Atlanta, Georgia, USA

1994–2004

Singleton births Z20 weeks of gestation, N ¼509,776

Monitoring site

1st month: 1.00 (0.96; 1.06) 6 weeks preceding birth: 0.98 (0.90; 1.04) 1 week preceding birth: 0.97 (0.95; 1.00)

Wu et al., 2009

California, USA

1997–2006

Singleton births, N ¼81,186

Monitoring site

Entire pregnancy: 1.24 (1.08; 1.54)

NO2 Present study

The Netherlands

1996–1997

Singleton births, N ¼3853

Temporally adjusted land-use regression model

Entire pregnancy: 0.95 (0.78; 1.15) 1st trimester: 0.92 (0.79; 1.08) Last month before birth: 1.00 (0.82; 1.22)

Maroziene and Grazuleviciene, 2002

Kaunas, Lithuania

1998

Registered singleton N ¼3988

Monitoring site

Entire pregnancy: 1.25 (1.07; 1.46) 1st trimester: 1.67 (1.28; 2.18) 2nd trimester: 1.13 (0.90; 1.40) 3rd trimester: 1.19 (0.96; 1.47)

Liu et al., 2003

Vancouver, BC, Canada

1986–1998

Singleton live births, N ¼229,085

Monitoring site

1st month: 1.02 (0.89; 1.14) Last month: 1.16 (0.98; 1.35)

Hansen et al., 2006

Brisbane, Australia

2000–2003

Singleton live births, N ¼28,200

Monitoring site

1st trimester: 0.77 (0.40; 1.51) 90 days preceding birth: 1.13 (0.53; 2.40)

Jalaludin et al., 2007

Sydney, Australia

1998–2000

Singleton births, N ¼123,840

Monitoring site

1st month: 0.53 (0.44; 0.62) 1st trimester: 0.56 (0.45; 0.68) 3 months preceding birth: 1.12 (0.88; 1.43) 1 month preceding birth: 1.00 (0.83; 1.23)

Ritz et al., 2007

Los Angeles, California, USA

2003

Singleton births, N ¼58,316

Monitoring site

1st trimester: 1.02 (0.99; 1.06)

Darrow et al., 2009

Atlanta, Georgia, USA

1994–2004

Singleton births Z20 weeks of gestation, N ¼509,776

Monitoring site

1st month: 0.96 (0.93; 1.04) 6 weeks preceding birth: 1.00 (0.93; 1.08) 1 week preceding birth: 1.00 (0.95; 1.02)

a Results from single pollutant models. Only studies where air pollution levels were used as continuous exposure variables were included. For NO2 we used the following conversion: 1 mg/m3 ¼1.9 ppb.

potential confounder or effect modifier in the relationship between traffic-related air pollution exposure and preterm birth and term birth weight. No indication was found for a confounding or effect modifying effect of maternal allergies on the relationship between air pollution and pregnancy outcomes. A potential limitation of the exposure assessment may be that air pollution measurements for the land-use regression model were performed in 1999/2000, while the study participants were born in 1996/1997. This was done under the assumption that there were no major changes in the surroundings of the measurement sites between 1996/1997 and 1999/2000 and that the spatial variation of the pollution surface did not change between these two periods. There is no data available for that period to check this assumption. Some indirect support for the stability of the spatial variation in our study area comes from a validation study that was performed in 2007.

We went back to the original TRAPCA sites and performed four oneweek measurements (one measurement per season) of NO2. Results indicated that the original TRAPCA-model was highly predictive (R2 ¼0.80) of NO2 concentrations measured at the same sites almost ten years later. A detailed description of these measurements will be published elsewhere. However, we cannot rule out completely that back-extrapolating estimated exposures from the land-use regression model might have resulted in measurement error. We back-extrapolated estimated exposures from the land-use regression model using the approach described by Slama et al. (2007). Because of our much larger and more diverse study area, we decided to modify the method developed by Slama et al. (2007) and used a number of routine air quality monitoring sites rather than just one site for temporal adjustment. For each participant the temporal trend at the nearest monitoring site was used for temporal adjustment.

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Table 6 Associations between PM10, PM2.5, and NO2 exposure during pregnancy and birth weight (continuous). Comparison with results from other published studies.a Author, year

Study area

Study period

Study population

Exposure assessment

Outcome

Adj. change BW (95% CI) [g per 10 lg/m3]

PM10 Chen et al., 2002

Nevada, USA

1991–1999

Term singleton live births, N ¼ 39,338

Monitoring site

TBW

Entire pregnancy:  7.3 (  16.7; 2.2) 1st trimester:  0.8 (  5.3; 3.6) 2nd trimester:  0.2 (  4.5; 4.1) 3rd trimester:  4.0 (  8.3; 0.4)

Gouveia et al., 2004

Sao Paolo, Brazil

1997

Term singleton live births, N ¼ 196, 978

Monitoring site

TBW

1st trimester:  13.7 ( 27.0; 0.4) 2nd trimester:  4.4 (  18.9; 10.1) 3rd trimester: 14.6 (0.0; 29.2)

Mannes et al., 2005

Sydney, Australia

1998–2000

Singleton births, N¼ 138,056

Monitoring site

BW

1st trimester:  1.4 ( 13.7; 10.9) 2nd trimester:  20.5 (  33.6;  7.4) 3rd trimester:  9.5 (  23.0; 4.0) Last month:  12.1 ( 23.1;  1.1)

Bell et al., 2007

Connecticut and Massachusetts, USA

1999–2002

Registered singleton births with gestational ages of 32–44 weeks, N¼ 358,504

Monitoring site

BW

Entire pregnancy:  11.1 ( 15.0;  7.2)

Hansen et al., 2007

Brisbane, Australia

2000–2003

Term singleton live birth, N ¼26,617

Monitoring site

TBW

1st trimester:  4.0 (  14.7; 6.8) 2nd trimester: 0.5 (  11.6; 12.6) 3rd trimester: 4.4 ( 8.5; 17.3)

PM2.5 Present study

The Netherlands

1996–1997

Singleton births, N¼ 3853

Temporally adjusted land-use regression model

TBW

Entire pregnancy: 6.5 ( 44.1; 57.2) 1st trimester: 14.0 ( 14.7; 42.6) Last month before birth:  12.1 (  31.2; 7.0)

Mannes et al., 2005

Sydney, Australia

1998–2000

Singleton births, N¼ 138,056

Monitoring site

BW

1st trimester: 3.6  22.9; 30.1) 2nd trimester:  41.0 (  67.9;  14.1) 3rd trimester:  9.8 (  37.4; 17.8) Last month:  24.8 ( 45.8;  3.8)

Bell et al., 2007

Connecticut and Massachusetts, USA

1999–2002

Registered singleton births with gestational ages of 32–44 weeks, N¼ 358,504

Monitoring site

BW

Entire pregnancy:  66.8 (  77.7;  55.9)

The Netherlands

1996–1997

Singleton births, N¼ 3853

Temporally adjusted land-use regression model

TBW

Entire pregnancy: 5.0 (  12.6; 22.6) 1st trimester: 12.2 (  1.7 26.0) 1 month preceding birth:  5.5 (  24.4; 13.4)

Gouveia et al., 2004

Sao Paolo, Brazil

1997

Singleton term live births, N ¼ 196, 978

Monitoring site

TBW

1st trimester:  7.0 (  14.3; 0.3) 2nd trimester: 0.3 (  8.6; 9.2) 3rd trimester: 3.6 ( 6.6; 13.7)

Ha et al., 2004

Seoul, Korea

1996–1997

Term Singleton birth with gestational, N¼ 276,763

Monitoring site

TBW

1st trimester:  34.7 ( 44.4;  25.1)

Mannes et al., 2005

Sydney, Australia

1998–2000

Singleton births, N¼ 138,056

Monitoring site

BW

1st trimester:  20.3 ( 39.3;  1.3) 2nd trimester:  18.1 ( 39.3; 3.2) 3rd trimester:  28.1 (  51.3;  4.9) Last month:  14.4 ( 32.7, 3.8)

Bell et al., 2007

Connecticut and Massachusetts, USA

1999–2002

Registered singleton births with gestational ages of 32–44 weeks, N¼ 358,504

Monitoring site

BW

Entire pregnancy:  35.2 (  42.8;  27.7)

Hansen et al., 2007

Brisbane, Australia

2000–2003

Term singleton live birth, N ¼26,617

Monitoring site

TBW

1st trimester: 32.0 ( 8.1; 71.8) 2nd trimester: 17.8 ( 16.9; 52.5) 3rd trimester:  18.4 (  54.7, 17.8)

Aguilera et al., 2009

Sabadell, Spain

2004–2006

Singleton live births, N¼ 570

Temporally adjusted land-use regression model

BW

Entire pregnancy: 9.3 ( 25.0; 43.6) 1st trimester: 2.7 ( 27.1; 32.4) 2nd trimester: 3.1 ( 25.9; 32.0) 3rd trimester: 13.5 (  15.1; 42.0)

NO2 Present study

BW¼ birth weight; TBW¼term birth weight a Results from single pollutant models. Only studies where air pollution levels were used as continuous exposure variables were included. For NO2 we used the following conversion: 1 mg/m3 ¼1.9 ppb.

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The weak correlations in particular between early and late pregnancy exposures are in agreement with the correlations reported by Slama et al. (2007) and are therefore most likely due to the overall seasonal variation in air pollution levels and not due to variation in temporal pattern between routine air quality monitoring sites: Women whose pregnancy started in winter, when air pollution levels are high, were more likely to have higher exposures in the first trimester compared to the third trimester (which was in summer) and vice versa. Brauer et al. (2008) used a slightly different approach for temporal adjustment. They applied monthly adjustment factors to the entire surface. Chen et al. very recently presented alternative methods for back-extrapolation of estimated exposures from land-use regression by multiplying predicted concentrations from the land-use regression model with the ratio of estimated past and current concentrations (Chen et al. 2009, 2010). When the authors applied three different methods to a case-control study on breast cancer they found that extrapolation methods had similar distributions and that the spatial distributions varied slightly between methods (Chen et al. 2010). At the moment, little is known about the accuracy of the different approaches described above and the implications of the differences between the approaches on the results are not clear. The method of using temporally adjusted exposure estimates from land-use regression models for the estimation of pregnancy exposures is relatively novel and so far, there is little validation of different approaches against each other or against personal measurements. Nethery et al. (2008) compared personal short-term measurements with temporally adjusted estimates from land-use regression and more traditional (purely temporal) exposure estimates using routine air quality monitoring sites for 62 Canadian pregnant women. They found that both air pollution concentrations as captured by routine air quality monitoring sites and local-scale concentration differences, as characterized by land-use regression models, contribute to personal exposure to traffic-related air pollutants. Another potential limitation of the exposure assessment might be that no continuous air quality monitoring site data were available for PM2.5 and reflectance. Therefore, PM10 and elemental carbon concentrations, respectively, were used for temporal adjustment. The lack of continuous air quality monitoring data for some pollutants is common to these types of studies. Brauer et al. (2008) used PM2.5 data for temporal adjustment of black carbon concentrations and Slama et al. (2007) used larger PM and NO2 data for temporal adjustment of PM2.5 and reflectance levels, respectively. It is unclear to which extent this might have influenced the results by introducing measurement error. Moreover, we defined exposure as the estimated air pollution level at the mother’s residential address at birth. This might have resulted in measurement error (Canfield et al., 2006; Chen et al., 2010). We were, however, able to classify women into those who did and who did not change residence during pregnancy. Results for non-movers were largely identical to those for the entire population. Another potential limitation might be that no information was available about exposure at non-residential addresses like work addresses, where participants regularly spend part of their time. Likewise no information was available about time-activity patterns during pregnancy. Data from a validation study among 62 pregnant women in Vancouver, Canada (Nethery et al., 2008) and a Spanish birth cohort (Aguilera et al., 2009) suggest that effects may be stronger in the subgroup of women who spent more time at home. In the present study, information on pregnancy outcomes was obtained through parental questionnaires when the child was 3 months old. A potential concern may be that the self-reported information is less accurate than information obtained directly through medical records. Although this might have introduced some ‘‘noise’’, we do not expect this to have resulted in an over- or under-estimation of the air pollution effects. Gestational age was calculated from the expected birth date (ultra-sound based or if unavailable, based on date of last menstrual period) and the actual birth date. In the Netherlands,

ultrasound-examinations during early pregnancy (around 10 weeks of gestation) are common to verify the expected birth date calculated from the last menstrual period. If the difference between the expected birth dates from ultra-sound measurements and last menstrual period differ by more than 7 days, the former is considered to be more accurate. However, if fetal growth during early pregnancy is restricted due to maternal air pollution exposure, correcting gestational age using an early pregnancy ultra-sound measurement may result in an underestimation of a fetuses gestational age and consequently in an underestimation of the air pollution effect. Since no individual information was available about whether or not the expected birth date was based on ultra-sound measurements or the date of the last menstrual period, it remains unclear to what extent our results may have been affected by this. Also for some of the covariates that were included in the present analysis (e.g. maternal pre-pregnancy BMI) data were collected postnatally rather than prenatally, which may have resulted in re-call bias. We do not have data to check this, but we do believe that this is not a serious concern.

5. Conclusions This study provides little evidence for an adverse effect of maternal exposure to traffic-related air pollution during pregnancy on term birth weight. There was a tendency towards an increased risk of preterm birth with increasing air pollution exposure, but statistical power was low.

Review and approval The Institutional Review Boards of the participating institutes approved the study protocol, and written informed consent was obtained from all participants.

Funding The 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. Ulrike Gehring was supported by a research fellowship of the Netherlands Organization for Scientific Research (NWO).

Appendix A. Supplementary materials Supplementary data associated with this article can be found in the online version at doi:10.1016/j.envres.2010.10.004.

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