UBC Faculty Research and Publications

Maternal exposure to particulate air pollution and term birth weight : a multi-country evaluation of… Dadvand, Payam; Parker, Jennifer; Bell, Michelle L.; Bonzini, Matteo; Brauer, Michael; Darrow, Lyndsey A.; Gehring, Ulrike; Glinianaia, Svetlana V.; Gouveia, Nelson; Ha, Eun-hee; Leem, Jong Han; van den Hooven, Edith H.; Jalaludin, Bin; Jesdale, Bill M.; Lepeule, Johanna; Morello‑Frosch, Rachel; Morgan, Geoffrey G.; Pesatori, Angela Cecilia; Pierik, Frank H.; Pless‑Mulloli, Tanja; Rich, David Q.; Sathyanarayana, Sheela; Seo, Juhee; Slama, Rémy; Strickland, Matthew; Tamburic, Lillian; Wartenberg, Daniel; Nieuwenhuijsen, Mark J.; Woodruff, Tracey J. Mar 31, 2013

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Environmental Health Perspectives • volume 121 | number 3 | March 2013 367Research | Children’s HealthThe developing fetus is known to be  susceptible to environmental insults (Stillerman et al. 2008). A growing body of evidence has asso-ciated maternal exposure to ambient air pol-lution with a range of adverse pregnancy outcomes including low birth weight (LBW), intra-uterine growth retardation, preterm birth, stillbirth, and congenital anomalies (Glinianaia et al. 2004; Sapkota et al. 2010; Šrám et al. 2005; Vrijheid et al. 2011). However, nota-ble inconsistencies among the findings of these studies (Parker and Woodruff 2008; Parker et al. 2011; Woodruff et al. 2009) have hindered the ability of policy makers to  incorporate the research evidence into policy.Discrepancies among previous studies may reflect genuine differences in the study settings, may be a consequence of specific biases, or may arise from differences in study designs and exposure assessments (Parker and Woodruff 2008; Woodruff et al. 2009). Study setting characteristics that may con-tribute to variation in reported associations include the demographic characteristics of the study population, the major sources of pollutants, the size distribution of particu-late pollutants [e.g., PM2.5:PM10 ratio (par-ticulate matter ≤ 2.5 and 10 µm)], maternal time–activity patterns, the study period, the degree of confounding by socioeconomic sta-tus (SES), and the underlying prevalence of adverse pregnancy outcomes. Relevant study design characteristics include the sources of Address correspondence to P. Dadvand, CREAL, Barcelona Biomedical Research Park, Dr. Aiguader, 88, 08003 Barcelona, Spain. Telephone: 34 93 214 7317. E-mail: pdadvand@creal.catSupplemental Material is available online (http://dx.doi.org/10.1289/ehp.1205575).We are grateful to D. Martinez, J. Grellier, and P. Grillo for their contributions to the analytical parts of the paper. This  project  was supported by the U.S. Environmental Protection Agency under contract number EP-W-05-022 and by the National Institute of Environmental Health Sciences (R01ES016317, R01ES019587). For the Vancouver analysis, the British Columbia (BC) Ministry of Health, the BC Vital Statistics Agency, and the BC Reproductive Care Program approved access to and use of the data facili-tated by Population Data BC. The U.K. PAMPER study was supported by the Wellcome Trust (grant 072465/Z/03/Z). P.D. is funded by a Juan de la Cierva fellowship (JCI-2011-09937) awarded by the Spanish Ministry of Science and Innovation.The authors declare they have no actual or potential competing financial interests.Received 5 June 2012; accepted 28 December 2012.Maternal Exposure to Particulate Air Pollution and Term Birth Weight: A Multi-Country Evaluation of Effect and HeterogeneityPayam Dadvand,1,2,3 Jennifer Parker,4 Michelle L. Bell,5 Matteo Bonzini,6 Michael Brauer,7 Lyndsey A. Darrow,8 Ulrike Gehring,9 Svetlana V. Glinianaia,10 Nelson Gouveia,11 Eun-hee Ha,12 Jong Han Leem,13 Edith H. van den Hooven,14,15 Bin Jalaludin,16,17,18 Bill M. Jesdale,19 Johanna Lepeule,20,21,22 Rachel Morello-Frosch,19,23 Geoffrey G. Morgan,24,25 Angela Cecilia Pesatori,26 Frank H. Pierik,15 Tanja Pless-Mulloli,10 David Q. Rich,27 Sheela Sathyanarayana,28 Juhee Seo,12 Rémy Slama,21,22 Matthew Strickland,8 Lillian Tamburic,29 Daniel Wartenberg,30 Mark J. Nieuwenhuijsen,1,2,3 and Tracey J. Woodruff 311Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; 2Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain; 3CIBER Epidemiologia y Salud Pública (CIBERESP), Spain; 4National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA; 5School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA; 6Department of Clinical and Experimental Medicine, University of Insubria, Varese, Italy; 7University of British Columbia, School of Population and Public Health, Vancouver, British Columbia, Canada; 8Department of Environmental Health, Emory University, Atlanta, Georgia, USA; 9Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; 10Institute of Health & Society, Newcastle University, Newcastle upon Tyne, United Kingdom; 11Department of Preventive Medicine, School of Medicine of the University of São Paulo, São Paulo, Brazil; 12Department of Preventive Medicine, Ewha Womans University, Seoul, Republic of Korea; 13Department of Occupational and Environmental Medicine, Inha University, Incheon, Republic of Korea; 14Generation R Study Group, Erasmus Medical Center, Rotterdam, the Netherlands; 15Urban Environment and Safety, TNO, Utrecht, the Netherlands; 16Centre for Research, Evidence Management and Surveillance, Sydney, Australia; 17South Western Sydney Local Health Districts, Sydney, Australia; 18School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia; 19Department of Environmental Science, Policy, and Management, University of California–Berkeley, Berkeley, California, USA; 20Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 21Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, INSERM, and 22Grenoble University, U823, Institut Albert Bonniot, Grenoble, France; 23School of Public Health, University of California–Berkeley, Berkeley, California, USA; 24North Coast Area Health Service, Lismore, New South Wales, Australia; 25University Centre for Rural Health–North Coast, University of Sydney, Sydney, Australia; 26Department of Occupational and Environmental Health, Università di Milano, Milan, Italy; 27Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA; 28Seattle Children’s Research Institute, University of Washington, Seattle, Washington, USA; 29Centre for Health Services and Policy Research, University of British Columbia, Vancouver, Canada; 30UMDNJ–Robert Wood Johnson Medical School, Piscataway, New Jersey, USA; 31Center for Reproductive Health and the Environment, University of California–San Francisco, San Francisco, California, USABackground: A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent.oBjectives: We aimed to quantify the association between maternal exposure to particulate air pol-lution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association.Methods: Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continu-ous birth weight associated with PM10 and PM2.5 (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates.results: In random-effects meta-analyses, term LBW was positively associated with a 10-µg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM2.5 (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-µg/m3 increase in PM10 exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (–8.9 g; 95% CI: –13.2, –4.6 g). Meta-regressions revealed that centers with higher median PM2.5 levels and PM2.5:PM10 ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations.conclusion: Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment meth-odology that appeared to contribute to the variation in associations reported by centers.key words: air pollution, fetal growth, heterogeneity, ICAPPO, low birth weight, meta-analysis, meta-regression, multi-center study, particulate matter, pregnancy. Environ Health Perspect 121:367–373 (2013). http://dx.doi.org/10.1289/ehp.1205575 [Online 6 February 2013]Dadvand et al.368 volume 121 | number 3 | March 2013 • Environmental Health Perspectivesdata for feto-maternal characteristics (e.g., birth certificates, questionnaires, or hospital records), inclusion/exclusion criteria, outcome definitions (e.g., birth weight as a continu-ous variable, LBW, or small for gestational age), and the analysis of potential confounders and/or effect modifiers. Differences in expo-sure assessment include applied methods for assessing the exposure (e.g., proximity-based,  monitor-based, or model-based methods), exposure time windows, exposure contrasts (e.g., spatial, temporal, or spatiotemporal), the availability of data for specific pollutants, and the analysis of associations with combinations of pollutants (Parker and Woodruff 2008).The International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) was established to investigate the association between maternal exposure to ambient air pollution and pregnancy out-comes across multiple centers and to under-stand how differences in study settings and methods contribute to variations in findings (i.e., between-center heterogeneity) (Parker et al. 2011; Woodruff et al. 2009, 2010). To achieve this goal, we previously discussed methodological differences in the published studies (Woodruff et al. 2009), described the collaborative centers (Woodruff et al. 2010), and presented preliminary estimates of effects for each center (Parker et al. 2011).The overarching aim of this analysis was to evaluate the association between maternal exposure to particulate air pollution and term birth weight and LBW. Toward this aim, we combined effect estimates of the individual ICAPPO centers and assessed the between-center heterogeneity in these associations. The application of a common analysis protocol across ICAPPO centers provided a unique opportunity to separate the contribution of the analytical design to between-center hetero-geneity, and enabled us to assess the impact of center characteristics and exposure assessment methods on the variation in reported effect estimates by each center.MethodsOverview. Our study was based on estimates of effects [odds ratio (OR) for LBW and regression coefficients for birth weight] that were uniformly generated and reported by each ICAPPO center according to a com-mon protocol (Parker et al. 2011). Ambient levels of PM10 and PM2.5 were used as indica-tors of particulate air pollution. Our analysis was focused principally on the association between maternal exposure to PM10 during the entire pregnancy and term LBW (birth weight < 2500 g at 37–42 completed weeks of gestation) because this was reported by most ICAPPO centers. We also conducted additional analyses of data from subsets of ICAPPO centers that stratified PM10–term LBW analyses by the exposure time window (i.e., the first, second, and third trimester), analyzed birth weight as a continuous out-come variable, and estimated the associa-tion between maternal exposure to PM2.5 and term LBW. We synthesized the effect estimates across the centers by applying a meta-analysis framework. The effects of cen-ter characteristics and exposure assessment methods on between-center heterogeneity in effect estimates were explored using a meta-regression framework. The meta-analyses and meta-regressions were conducted using the R statistical package (http://cran.r-project.org/), libraries meta, rmeta, and metafor.ICAPPO. Our analysis relied on effect esti-mates provided by fourteen ICAPPO centers from nine countries with more than three mil-lion singleton term births (Table 1). For the ICAPPO analysis, the centers reanalyzed exist-ing data sets that had been created to evaluate the impacts of maternal exposure to air pol-lution on pregnancy outcomes. The centers relied on outcome data available from rou-tinely collected administrative records (birth certificates) or data collected for a specific study (Bell et al. 2007, 2008; Brauer et al. 2008; Darrow et al. 2011; Gehring et al. 2011; Glinianaia et al. 2008; Gouveia et al. 2004; Ha et al. 2004; Jalaludin et al. 2007; Lepeule et al. 2010; Mannes et al. 2005; Morello-Frosch et al. 2010; Pesatori et al. 2008; Rich et al. 2009; Slama et al. 2009; van den Hooven et al. 2009). More detailed description of the ICAPPO centers has been previously published (Parker et al. 2011; Woodruff et al. 2010).Participating centers were initially asked to provide information on their study location and period, available air pollutants, number of births, prevalence of term LBW, exposure assessment method, and available covari-ate data (Parker et al. 2011; Woodruff et al. 2010). Based on this information, ICAPPO participants developed a common analyti-cal protocol detailing the inclusion criteria, outcomes and covariates of interest, statistical models, and sensitivity analyses. This protocol also specified a standardized way of reporting the results (Parker et al. 2011). Each center was asked to reanalyze its existing data set according to this protocol. The analyses were limited to live-born, singleton, term births with known birth weight, maternal education (or another measure of SES), dates of birth and conception, and ambient PM10 or PM2.5 concentrations during the entire pregnancy.Primary meta-analysis. According to the common protocol, ICAPPO centers initially estimated the association between term LBW and maternal exposure to PM10 averaged over the entire pregnancy. Each center constructed three logistic regression models to estimate the odds of term LBW associated with each 10-µg/m3 increase in average PM10 exposure levels during the entire pregnancy: a) without any adjustment, b) with adjustment only for maternal SES, and c) with adjustment for maternal SES and center-specific covariates (e.g., maternal age, maternal ethnicity, mater-nal smoking, parity, and infant sex). For these center-specific covariates, there was no recom-mendation in the ICAPPO protocol and the centers had the flexibility to independently choose the suitable ones according to their settings. This selection of extra covariates used by each center has been reported elsewhere (Parker et al. 2011). The construction of these three models was to evaluate the effect of adjustment for the maternal SES and other covariates on the combined effect estimates.ICAPPO participants chose maternal edu-cation as a common indicator of maternal SES (Parker et al. 2011). If maternal education data were not available, area-level measures of SES were used [for the PAMPER (Particulate Matter and Perinatal Events Research) study: Townsend Deprivation Score; for the Sydney study: Index of Relative Socioeconomic Disadvantage; and for the Vancouver study: percentage of women with postsecondary edu-cation]. The PAMPER study (Newcastle upon Tyne, UK) provided exposure data only for black smoke, which approximates particulate matter with an aerodynamic diameter ≤ 4 µm (PM4) and has been shown to be a reasonable surrogate for PM10 (Muir and Laxen 1995). The estimates of effect for the PAMPER study were therefore analyzed alongside the studies with PM10 measures.We used meta-analysis to estimate com-bined ORs across the centers. Between-center heterogeneity was quantified using the between-center variance of effect estimates, τ2. The statistical significance of between-center heterogeneity was tested by Cochran’s Q test. The I2 statistic with 95% confidence intervals (CI) was used to estimate the proportion of total variation in effect estimates across centers that was attributable to the between-center heterogeneity (τ2) rather than within-center error (Higgins 2008). If there was significant between-center heterogeneity (i.e., Cochran’s Q test p-value < 0.05), DerSimonian–Laird random-effects models were used for meta-analysis (DerSimonian and Laird 1986); oth-erwise, fixed-effects models were conducted using the Mantel–Haenszel method (Mantel and Haenszel 1959). Associations with a Cochran’s Q test p-value between 0.05 and 0.10 were estimated using both fixed- and random-effects models (Higgins et al. 2002).Additional meta-analyses. Exposure win-dow period. Nine centers analyzed the asso-ciation between maternal exposure to PM10 and term LBW stratified by the trimester of exposure and adjusted for maternal SES. We carried out meta-analyses using these reported stratified ORs to evaluate the impact of the Particulate air pollution and fetal growthEnvironmental Health Perspectives • volume 121 | number 3 | March 2013 369exposure window period on the association between PM10 exposure and term LBW.Birth weight as a continuous  variable. A subset of 11 centers (all centers but Connecticut and Massachusetts, New Jersey, and Seattle, Washington) estimated the change in term birth weight as a continuous outcome variable (grams) associated with each 10-µg/m3 increase in PM10 exposure levels averaged over the entire pregnancy using three sets of linear regression models with predictors as described for the primary analysis. The combined effect estimates across the centers were calculated using meta-analysis, as described above.PM2.5 exposure. Seven centers had data on PM2.5 (Table 1) and reported the odds of term LBW associated with each 10-µg/m3 increase in average PM2.5 exposure levels during the entire pregnancy, using three sets of logistic regression models as described for the primary analysis. We synthesized the effect estimates across these centers using meta- analysis as described before.Sensitivity analyses. We checked the robustness of all meta-analyses results to the omission of influential centers that were iden-tified using DFBETAS, which indicates the change in estimated coefficients after excluding a center from the meta-analysis (Viechtbauer and Cheung 2010). Centers with an abso-lute DFBETAS value > 1 (i.e., that resulted in ≥ 1-SD change in the estimated coefficient when omitted) were considered influential (Viechtbauer and Cheung 2010).Meta-regressions. To assess between-center heterogeneity, we hypothesized that the fol-lowing characteristics of the centers could have an impact on the estimated risk by each center: study area (square kilometers), length of the study (years), number of births, continent of the study location, latitude of the study loca-tion (degrees), percentage of term LBW births, median PM10 exposure levels (micrograms per cubic meter), median PM2.5 exposure levels (micrograms per cubic meter), interquartile range of PM10 exposure levels (micrograms per cubic meter), and PM2.5:PM10 ratio. In addition, we hypothesized that the use of model-based versus monitor-based expo-sure assessment, and temporal versus spatio-temporal exposure contrasts could influence center-specific estimates. Temporal exposure contrasts account for temporal variation in pollutant levels by assigning measurements from a single monitoring station to all study subjects, whereas spatiotemporal contrasts account for both spatial and temporal compo-nents of variation in air pollution levels when estimating exposure.For associations between maternal PM10 exposure during the entire pregnancy and term LBW that showed between-center heteroge-neity, we performed separate univariate meta- regressions using center-specific log- transformed ORs as outcome and each of the center or exposure assessment characteristics listed above as predictors. In effect, these meta-regressions quantified the potential impact of these factors on the estimates of effects (ORs) reported by each center. We excluded the Generation R study (Table 1) from the analysis comparing temporal and spatiotemporal exposure con-trasts because its exposure assessment was based on a dispersion model, which is essentially a spatial approach without any temporal adjust-ment. Analyses of the influence of PM2.5 and PM2.5:PM10 ratios were based on the seven centers with PM2.5 data.ResultsCharacteristics of ICAPPO centers. Of the 14 ICAPPO centers included in our analy-ses (Table 1), 6 were North American, 5 European, 1 South American, 1 Asian, Table 1. Exposure assessment methodologies and characteristics of the ICAPPO centers.Center, location Reference Study periodStudy area (km2)No. of birthsMeasure of SESTerm LBW (%)Median PM10 (μg/m3)PM10 IQR (μg/m3)Median PM2.5 (μg/m3)PM2.5: PM10 ratioaExposure assessmentExposure contrastAtlanta, GA, USA Darrow et al. 2011 1996–2004 4,538 325,221 Maternal education2.62 23.5 3.1 15.8 0.67 Monitor TemporalCalifornia, USA Morello-Frosch et al. 20101996–2006 423,970 1,714,509 Maternal education2.43 28.9 16.1 16.5 0.57 Monitor SpatiotemporalConnecticut and Massachusetts, USABell et al. 2007, 20081999–2002 41,692 173,042 Maternal education2.16 22 7.4 20 0.91 Monitor SpatiotemporalEDEN, Poitiers and Nancy, France Lepeule et al. 2010 2003–2006 480 1233 Age at completion of education2.11 19 3 — — Monitor SpatiotemporalLombardy, Italy Pesatori et al. 2008 2004–2006 23,865 213,542 Maternal education2.71 49 10 — — Monitor SpatiotemporalPAMPER, Newcastle upon Tyne, UK Glinianaia et al. 20081962–1992 63 81,953 Area-level indicatorb3.19 32.8c 87.8c — — Model SpatiotemporalNew Jersey, USA Rich et al. 2009 1999–2003 22,592 87,281 Maternal education2.75 28 6.9 13.7 0.49 Monitor SpatiotemporalPIAMA, North, West, and Center of the Netherlands Gehring et al. 2011 1996–1997 12,000 3,471 Maternal education1.15 40.5 6.7 20.3 0.50 Model SpatiotemporalGeneration R, Rotterdam, Netherlands van den Hooven et al. 20092002–2006 150 7,296 Maternal education2.26 32.8 1.1 — — Model SpatialSão Paulo, Brazil Gouveia et al. 2004 2005 1,500 158,791 Maternal education3.77 40.3 2.9 — — Monitor TemporalSeattle, WA, USA Sathyanarayana S, Karr C, unpublished data1998–2005 17,800 301,880 Maternal education1.56 — 10.2 — Monitor SpatiotemporalSeoul, South Korea Ha et al. 2004 1998–2000 605 372,319 Maternal education1.45 66.5 10.9 — — Monitor TemporalSydney, Australia Jalaludin et al. 20071998–2004 12,145 279,015 Area-level indicatord1.62 16.5 8.2 — — Monitor TemporalVancouver, BC, CanadaBrauer et al. 2008 1999–2002 3,300 66,467 Area-level indicatore1.35 12.5 1.4 3.98 0.32 Monitor SpatiotemporalaRatio of PM10 and PM2.5 median levels. bThe Townsend Deprivation Score is an area-based measure of material deprivation calculated for each enumeration district (~ 200 house-holds) based on 1971, 1981, and 1991 census data. cBlack smoke (~ PM4) was used as a measure of particulate air pollution. dThe Australian Bureau of Statistics (2003) Index of Relative Socio-economic Disadvantage uses a range of census factors and is assigned to each census collection district (~ 200 households). eThe percentage of women with postsecondary education.Dadvand et al.370 volume 121 | number 3 | March 2013 • Environmental Health Perspectivesand 1 was Oceanian. Our analysis included > 3 million births (ranging from a little more than 1,000 to almost 2 million) generally occurring between late 1990s and mid-2000s.Primary meta-analysis. Thirteen centers provided estimates for the association between maternal exposure to PM10 and term LBW [Figure 1A; see also Supplemental Material, Figure S1, for forest plots (http://dx.doi.org/10.1289/ehp.1205575)]. There was sta-tistically significant (Cochran’s Q test p-value < 0.05) between-center heterogeneity in effect estimates (ORs) reported by these centers, with τ2 ranging between 0.0003 and 0.0004 (Table 2). Therefore we used random-effects models to estimate combined ORs across the centers, which indicated a positive associa-tion between term LBW and average maternal exposure to PM10 during the entire preg-nancy before adjustment (OR = 1.04; 95% CI: 1.01, 1.06) and after adjustment for SES (OR = 1.03; 95% CI: 1.01, 1.05) and SES plus center-specific covariates (OR = 1.02; 95% CI: 1.01, 1.04) (Table 2).Additional meta-analyses. Exposure window period. Nine centers reported esti-mates for the association between PM10 and term LBW stratified by the trimester of exposure and adjusted for maternal SES [see Supplemental Material, Figure S2, for forest plots (http://dx.doi.org/10.1289/ehp.1205575)]. All combined trimester-spe-cific ORs for term LBW in association with a 10-µg/m3 increase in PM10 were positive and comparable in magnitude based on fixed-effects models adjusted for maternal SES, with ORs of 1.01 (95% CI: 1.01, 1.01), 1.01 (95% CI: 1.01, 1.02), and 1.02 (95% CI: 1.01, 1.02) for the first, second, and third tri-mesters, respectively (Table 3). Corresponding random-effects ORs were smaller, with a com-bined OR for the first trimester of 1.0 (95% CI: 1.00, 1.01) (Cochran’s Q test p = 0.06; I2 = 45.8%; 95% CI: 0, 74.9%) (Table 3).Birth weight as a continuous variable. Eleven centers estimated the change in term birth weight as a continuous outcome varia-ble (grams) associated with each 10-µg/m3 increase in PM10 exposure levels [Figure 1B; see also Supplemental Material, Figure S3, for forest plots (http://dx.doi.org/10.1289/ehp.1205575)]. The Cochran’s Q test of het-erogeneity was significant (p < 0.05) for all meta-analyses of PM10 and birth weight (data not shown). Random-effects meta- analyses indicated a negative association between term birth weight and a 10-µg/m3 increase in PM10, with unadjusted, SES-adjusted, and SES- plus center-specific covariate–adjusted estimated decreases of –2.7 g (95% CI: –7.2, 1.7 g, p = 0.23), –3.0 g (95% CI: –6.9, 0.9 g, p = 0.13), and –8.9 g (95% CI: –13.2, –4.6 g, p < 0.01), respectively.PM2.5 exposure. Seven centers reported the odds of term LBW associated with each 10-µg/m3 increase in PM2.5 exposure levels [Figure 1C; see also Supplemental Material, Figure S4, for forest plots (http://dx.doi.org/10.1289/ehp.1205575)]. All meta-analyses showed statistically significant between-study heterogeneity (Table 2). The random-effects meta-analyses demonstrated positive associations with term LBW, with sta-tistically significant ORs based on  unadjusted and SES-adjusted models, but not the model adjusted for maternal SES and center-specific covariates (Table 2).Sensitivity analyses. The studies that were most frequently classified as influential were the California study (the largest cen-ter with more than 1.7 million births), the PAMPER study (covering a long time period with wide variation in sources and levels of exposure to black smoke, which was used as a surrogate for PM10), and the Lombardy study [a relatively large study of a hetero-geneous region including a metropolitan area Figure 1. Forest plots for the random-effects meta-analysis of the SES-adjusted OR (95% CI) for the associations between term LBW and PM10 exposure during the entire pregnancy (A), term birth weight and PM10 exposure during the entire pregnancy (B), and between term LBW and PM2.5 exposure during the entire pregnancy (C).Odds ratioOdds ratioBirth weight (g)00 315 0–115 1151 Regression coefficientCenter (95% CI)Atlanta 21.00 (13.60, 28.30)California –8.10 (–8.80, –7.50)EDEN –15.10 (–73.90, 43.80)Lombardy 8.90 (6.60, 11.30)PAMPER –2.00 (–2.50, –1.60)PIAMA –20.50 (–58.60, 17.50)Generation R –42.20 (–147.30, 62.90)São Paulo –15.60 (–25.30, –5.90)Seoul –2.18 (–4.08, –0.28)Sydney –19.90 (–26.60, –13.20)Vancouver –7.00 (–10.50, –3.60)Overall –3.00 (–6.85, 0.85)Center OR (95% CI)Atlanta 1.11 (1.01, 1.22)California 1.01 (1.01, 1.02)Connecticut and Massachusetts 1.13 (1.06, 1.20)EDEN 0.97 (0.38, 2.49)Lombardy 0.98 (0.95, 1.01)PAMPER 1.02 (1.01, 1.02)New Jersey 0.92 (0.85, 1.01)PIAMA 0.63 (0.30, 1.35)Generation R 0.89 (0.21, 3.73)São Paulo 1.13 (1.00, 1.27)Seoul 1.10 (1.06, 1.14)Sydney 1.10 (0.98, 1.23)Vancouver 1.15 (0.61, 2.18)Overall 1.03 (1.01, 1.04)Center OR (95% CI)Atlanta 1.05 (0.92, 1.21)California 1.06 (1.03, 1.08)Connecticut and Massachusetts 1.45 (1.23, 1.71)New Jersey 1.40 (1.08, 1.82)PIAMA 0.51 (0.16, 1.56)Seattle 0.99 (0.98, 1.01)Vancouver 1.63 (1.13, 2.36)Overall 1.10 (1.03, 1.18)Particulate air pollution and fetal growthEnvironmental Health Perspectives • volume 121 | number 3 | March 2013 371(Milan), a large mainly agricultural area (Po valley), and a northern mountainous area] [see Supplemental Material, Table S1 (http://dx.doi.org/10.1289/ehp.1205575)]. The meta-analyses were generally robust to the exclusion of influential studies with regard to the magnitude of the estimated associa-tions (data not shown), but there were some exceptions. For example, the unadjusted OR for LBW with a 10-µg/m3 increase in PM2.5 increased from 1.17 (95% CI: 1.08, 1.26) to 1.20 (95% CI: 1.00, 1.45) after California was excluded, and decreased to 1.06 (95% CI: 0.99, 1.13) after exclusion of Connecticut and Massachusetts. In addition, the aver-age estimated reduction in SES-adjusted term birth weight with a10-µg/m3 increase in PM10 (random-effects model) increased from –3.0 g (95% CI: –6.9, 0.9 g) based on all centers (Figure 1B) to –5.5 g (95% CI: –9.3, –1.6 g) after excluding Atlanta, and to –4.9 g (95% CI: –8.6, –1.1 g) after exclud-ing Lombardy. Similarly, the reduction in unadjusted mean birth weight increased from –2.7 g (95% CI: –7.2, 1.7 g) based on all cen-ters (see Supplemental Material, Figure S3A) to –6.6 g (95% CI: –11.7, –1.5 g) and –5.0 g (95% CI: –10.0, 0.0 g) after removing Atlanta and Lombardy, respectively.Meta-regressions. Of the characteristics we evaluated, median PM2.5 exposure  levels, PM2.5:PM10 ratio, and exposure contrast (temporal vs. spatiotemporal approach) influ-enced the between-center heterogeneity in the reported PM10-term LBW associations, with centers that had higher median PM2.5 exposure levels and PM2.5:PM10 ratios, and that used a temporal exposure contrast, reporting stronger associations in most cases (Table 4). The results of leave-one-out sensitivity analyses for these three meta- regressions were generally consis-tent with those of meta-regressions including all centers (data not shown). However, remov-ing Connecticut and Massachusetts almost doubled the meta-regression coefficient for the PM2.5:PM10 ratio and nullified the associa-tion for median PM2.5 exposure levels, and the meta-regression estimate for median PM2.5 exposure levels was null after New Jersey was excluded.Meta-regressions for the exposure con-trast showed statistically significant hetero-geneity in residuals (from both unadjusted and SES-adjusted models), whereas meta- regressions of the median PM2.5 exposure lev-els and PM2.5:PM10 ratios did not (Table 4). Heterogeneity was reduced when ORs were adjusted for center-specific selection of covari-ates in addition to SES. Associations between center-specific log-ORs and other center or study characteristics were not statistically  significant (data not shown).DiscussionTo our knowledge, this study is the largest multicenter study so far reporting on the asso-ciation between air pollution and fetal growth using a common analytical protocol. We syn-thesized reported estimates of effects by 14 ICAPPO centers around the globe and for the first time quantified the impacts of study settings and aspects of applied exposure assess-ment methods on between-center heterogene-ity in the reported effect estimates (ORs) for this association. We found that maternal expo-sures to PM10 and PM2.5 during the entire pregnancy were positively associated with term LBW. For PM10, all trimester-specific expo-sures were associated with slightly increased odds of term LBW. Furthermore, term birth weight was reduced in association with aver-age PM10 exposure over the entire pregnancy. Most associations showed between-center hetero geneity in the center-specific estimates of associations. Meta-regression assessments of factors possibly affecting PM10-term LBW associations indicated that heterogeneity was influenced by median PM2.5 exposure levels, the PM2.5:PM10 ratio, and the applied expo-sure contrast (temporal vs. spatiotemporal).Primary meta-analyses. Making decisions about the risks from environmental expo-sure in the policy or clinical setting requires Table 2. Combined random-effects ORs (95% CIs) for term LBW in association with a 10-μg/m3 increase in average maternal exposure to PM10 and PM2.5 during pregnancy, and corresponding indicators of between-center heterogeneity across ICAPPO centers.Meta-analysisCombined estimate HeterogeneityOR (95% CI) p-Value τ2 p-Valuea I 2 (95% CI)PM10 (13 centers)Unadjusted 1.04 (1.01, 1.06) < 0.01 0.0004 < 0.01 76.5% (59.9%, 86.2%)Adjusted for maternal SES 1.03 (1.01, 1.05) < 0.01 0.0003 < 0.01 79.4% (65.4%, 87.7%)Adjusted for maternal SES and center-specific covariates1.02 (1.01, 1.04) 0.01 0.0003 0.01 54.3% (14.5%, 75.6%)PM2.5 (7 centers)bUnadjusted 1.17 (1.08, 1.26) < 0.01 0.0055 < 0.01 92.3% (86.7%, 95.6%)Adjusted for maternal SES 1.10 (1.03, 1.18) < 0.01 0.0039 < 0.01 89.7% (81.3%, 94.3%)Adjusted for maternal SES and center-specific covariates1.04 (0.99, 1.09) 0.09 0.0013 < 0.01 68.5% (30.4%, 85.7%)ap-Value for Cochran’s Q test for heterogeneity. bIncluding Atlanta, California, Connecticut and Massachusetts, New Jersey, PIAMA, Seattle, and Vancouver.Table 3. Combined adjusteda ORs (95% CIs) of term LBW in association with a 10-μg/m3 increase in aver-age of PM10 exposure levels during each trimester of exposure.bTrimester of exposureCombined risk HeterogeneityOR (95% CI) p-Value I 2 (95% CI) p-Valuec1st trimester (fixed-effects model) 1.01 (1.01, 1.01) < 0.001 45.8% (0.0%, 74.9%) 0.0641st trimester (random-effects model) 1.00 (1.00, 1.01) 0.3252nd trimester (fixed-effects model) 1.01 (1.01, 1.02) < 0.001 25.7% (0.0%, 65.2%) 0.2132nd trimester (random-effects model) 1.01 (1.01, 1.02) < 0.0013rd trimester (fixed-effects model) 1.02 (1.01, 1.02) < 0.001 42.7% (0.0%, 73.6%) 0.0753rd trimester (random-effects model) 1.01 (1.00, 1.02) 0.001aAdjusted for maternal SES. bORs from nine centers were included in the meta-analysis. cp-Value for Cochran’s Q test for heterogeneity.Table 4. Meta-regression coefficients (95% CIs) from separate models estimating the effect of a 1-μg/m3 increase in the center median PM2.5 level, a 100% increase in PM2.5:PM10 ratio, or the use of a temporal versus spatiotemporal exposure contrast on SES-adjusted center-specific log-ORs for the association between a 10-μg/m3 increase in mean PM10 during pregnancy and term LBW.Meta-regressionTemporal vs. spatiotemporal approacha,b Median PM2.5 levels PM2.5:PM10 ratioRegression coefficient  (95% CI)Residual  heterogeneity (τ2)  (95% CI)Regression coefficient  (95% CI)Residual heterogeneity (τ2) (95% CI)Regression coefficient  (95% CI)Residual heterogeneity (τ2) (95% CI)Unadjusted 0.07 (0.03, 0.11) 0.0033 (0.0007, 0.0259)* 0.03 (0.01, 0.05) 0.0105 (0.0, 0.3901) 0.47 (0.13, 0.82) 0.0019 (0.0, 0.3808)Adjusted for maternal SES 0.08 (0.05, 0.12) 0.0015 (0.0001, 0.0116)* 0.02 (0.00, 0.04) 0.0077 (0.0, 0.3405) 0.39 (0.15, 0.64) 0.0010 (0.0, 0.3776)Adjusted for maternal SES and center-specific covariates0.06 (0.02, 0.10) 0.0006 (0.0, 0.0079) 0.01 (0.00, 0.03) 0.0 (0.0, 0.0608) 0.19 (0.01, 0.37) 0.0 (0.0, 0.1421)aRegression coefficients for using temporal approach compared with the spatiotemporal approach. bGeneration R cohort was excluded because its exposure assessment was based on a dispersion model which is a spatial approach. *Cochran’s Q test p < 0.05.Dadvand et al.372 volume 121 | number 3 | March 2013 • Environmental Health Perspectivessynthesizing and interpreting the available epidemiologic evidence, which may include evaluating a number of relevant studies with different designs. A recent systematic review and meta-analysis of the available evidence on the association between particulate pollution and pregnancy outcomes reported by Sapkota et al. (2010) estimated a combined OR of 1.02 (95% CI: 0.99, 1.05) for LBW in association with a 10-µg/m3 increase in average maternal PM10 exposure during pregnancy. Our esti-mates, which were based on center-specific ORs estimated using a common protocol, were slightly more precise than reported by Sapkota et al. (e.g., OR = 1.03; 95% CI: 1.01, 1.05 for a 10-µg/m3 increase in average mater-nal PM10 exposure based on SES-adjusted estimates); but both point estimates support a comparable deleterious effect of PM10 on term birth weight, an indicator of fetal growth. This comparable finding supports not overrelying on statistical significance nor using it as decid-ing factor in the evaluation of the evidence, but rather describing the degree of confidence and precision of the estimate, which is con-sistent with other scientific writings on this topic. Bacchetti et al. (2005, 2008), for exam-ple, caution against inappropriate reliance on statistical significance and rather support the decision-making process evaluating the degree of confidence in the findings and the effect size. As we have shown by comparing our results to those of Sapkota et al. (2010), even though we were able to improve the signal-to-noise ratio using our approach, the interpreta-tion of the relationship between air pollution and fetal growth remained similar.The consistency of our findings with those of Sapkota et al. (2010), our observed consistency of combined ORs and between-center heterogeneity (τ2 values) for meta-analyses of ORs adjusted for center-specific covariates, and our meta-analyses of unadjusted or SES-adjusted ORs give us confidence that synthesizing effect estimates for the air pollu-tion–pregnancy outcomes associations reported by studies with different designs is informative. Further, experience from this study could be important for policy makers in incorporating research evidence into policy—for example, by including estimates of fetal growth in the future reviews of air quality standards.ICCAPO centers conducted unadjusted and SES-adjusted analyses according to a com-mon analysis protocol, as well as analyses that allowed center-specific selection of covariates. In combining results across centers, there were clear trade-offs to the two approaches. Strictly specifying the models and covariates to be con-sistent across centers was one way to reduce methodological differences between centers. However, there might be important center-specific considerations that required inclusion of certain covariates for validity. For example, the Atlanta results relied entirely on temporal contrasts of exposure over an 11-year time period, and failing to control for long-term time trends, as was done in the unadjusted and SES-adjusted analyses, may have led to tempo-ral confounding. This might explain why ORs adjusted for center-specific selection of covari-ates were different from those unadjusted or adjusted for SES only in some cases.Additional meta-analyses. In their meta-analysis, Sapkota et al. (2010) estimated the association between LBW and maternal PM10 exposure during the first trimester (five stud-ies) and third trimester (seven studies), but did not detect associations with exposure in either trimester (OR = 1.00; 95% CI: 0.97, 1.03, and OR = 1.00; 95% CI: 0.99, 1.01, respectively). We estimated slightly increased relative risks of term LBW in association with PM10 exposure during all trimesters, consis-tent with other previous reports (Parker and Woodruff 2008; Parker et al. 2005).Because Cochran’s Q test of heterogeneity p-values for the trimester-specific analyses of PM10 and LBW were between 0.05 and 0.10, we reported results of both fixed-effects and ran-dom-effects meta-analyses (Higgins et al. 2002). For the second and third trimesters, there were no notable differences in estimates between the two models, whereas the random-effects OR for the association with first-trimester PM10 was null. Given the evidence of heterogeneity, fixed-effects model estimates for this trimester should be interpreted with caution.Combined ORs for term birth weight as a continuous variable indicated a reduc-tion in term birth weight associated with a 10-µg/m3 increase in average PM10 exposure during the entire pregnancy (–2.7 g; 95% CI: –7.2, 1.7 g). The association was stronger when based on ORs adjusted for maternal SES and center-specific covariates (–8.9 g; 95% CI: –13.2, –4.6 g). This may be partly explained by adjustment for gestational age at delivery by nine centers. Gestational age at delivery could confound the association between maternal exposure to air pollution and fetal growth. Although the outcome was term birth weight or LBW, there could be a 6-week difference (between 37 and 42 weeks of gestation) in ges-tational age at delivery. Confounding by gesta-tional age might have had a stronger effect on the continuous birth weight analyses than on the LBW analyses.LBW was associated with maternal PM2.5 exposure (combined random-effects OR = 1.10; 95% CI: 1.03, 1.18 for a 10-µg/m3 increase in average PM2.5 based on SES-adjusted ORs). The strength and direction of this association was comparable with the meta-analysis OR reported by Sapkota et al. (2010) (OR = 1.09; 95% CI: 0.90, 1.32). Meta-regressions. To our best knowl-edge, the impact of study characteristics and exposure assessment methods on estimated associations between adverse birth weight and maternal exposure to air pollution has not been evaluated previously. Median PM2.5 exposure levels, PM2.5:PM10 ratios, and temporal versus spatiotemporal exposure contrasts appeared to influence the estimates of effects reported by the study centers. Parker and Woodruff (2008) suggested that variation in the composition of particulate matter may have contributed to dif-ferences in associations between maternal expo-sure to particulate pollution and fetal growth among seven different regions in the United States (Parker and Woodruff 2008). However, we did not have data on the composition of particulate pollutants, and therefore were not able to investigate its impact.In our analysis, meta-regressions of the influence of temporal versus spatiotemporal exposure contrasts on associations between PM10 and LBW generally showed strong evi-dence of residual heterogeneity, in contrast to meta-regressions of median PM2.5 exposure levels and PM2.5:PM10 ratios (Table 4). These findings could suggest that at least part of the effect of the exposure contrast on PM10–term LBW associations might have been secondary to the effect of PM2.5:PM10 ratios and median PM2.5 levels at each center.Limitations .  Our meta-regressions were based on effect estimates (ORs) from 13 ICAPPO centers. As a rule of thumb, it has been suggested that 10 effect estimates are required in order to include a covariate in meta-regression (Borenstein et al. 2009). We therefore had to limit our analysis to univari-ate rather than multivariate meta- regressions and test the effects of each covariate separately. As a result, we could not evaluate whether our univariate results were confounded by other factors. Furthermore, other center character-istics that could have affected center-specific effect estimates, such as the prevalence of maternal smoking, were not included in our meta-regression analyses.The ICAPPO protocol did not include data that might have been used to better characterize personal exposure, such as direct measurements of personal exposure levels (e.g., personal moni-tor data), pollutant levels at microenvironmen-tal levels (e.g., indoor, outdoor, commuting) or maternal time–activity patterns. Some expo-sure misclassification would have resulted from the use of effect estimates based on associations with ambient levels of pollutants as a surrogate for personal exposure levels.ConclusionOur combined effect estimates, which were based on effect estimates generated by 14 ICAPPO centers across the globe using a common analytical protocol, support an adverse impact of maternal exposure to particu late pollution on fetal growth. The Particulate air pollution and fetal growthEnvironmental Health Perspectives • volume 121 | number 3 | March 2013 373estimated combined associations, although relatively small, could be of major public health importance considering the ubiquitous nature of particulate air pollution exposure and therefore the potential for considerable population attributable risks, particularly given evidence of both perinatal and lifelong effects of LBW on health (Balci et al. 2010; Gibson 2007). 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