UBC Faculty Research and Publications

The International Collaboration on Air Pollution and Pregnancy Outcomes : initial results Parker, Jennifer D.; Rich, David Q.; Glinianaia, Svetlana V.; Leem, Jong Han; Wartenberg, Daniel; Bell, Michelle L.; Bonzini, Matteo; Brauer, Michael; Darrow, Lyndsey; Gehring, Ulrike; Gouveia, Nelson; Grillo, Paolo; Ha, Eunhee; van den Hooven, Edith H.; Jalaludin, Bin; Jesdale, Bill M.; Lepeule, Johanna; Morello-Frosch, Rachel; Morgan, Geoffrey G.; Slama, Rémy; Pierik, Frank H.; Pesatori, Angela Cecilia; Sathyanarayana, Sheela; Seo, Juhee; Strickland, Matthew; Tamburic, Lillian; Woodruff, Tracey J. Jul 31, 2011

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Environmental Health Perspectives • volume 119 | number 7 | July 2011 1023Research | Children’s HealthEvidence that poor air quality can adversely affect birth outcomes is increasing. A small number of review articles have summarized existing studies and concluded that there is likely an adverse effect of air pollution on pregnancy outcome (Glinianaia et al. 2004; Ritz and Wilhelm 2008; Šrám et al. 2005). However, estimated associations between these outcomes and air pollutant exposures over the whole pregnancy and during specific time windows (e.g., trimester of pregnancy) have been inconsistent, making definitive conclusions difficult (Glinianaia et al. 2004; Slama et al. 2008; Woodruff et al. 2009).Comparisons of findings across different geographic locations are hindered, in part, by differences in research designs. Although most published studies have reported adverse pregnancy outcomes in association with pre-natal exposure to air pollution, inconsistent findings reported by some studies prompted a series of workshops to discuss this relatively new area of investigation (Slama et al. 2008; Woodruff et al. 2009) and the formation of the International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) (Woodruff et al. 2010). The primary objective of ICAPPO is to understand how differences in research design and methods contribute to variations in findings. As part of this effort, a feasibility study was developed to determine whether it would be possible to use a common protocol to rean-alyze existing data sets that were created to answer similar but not identical research ques-tions. A workshop was held in Dublin (25–29 August 2009) to share and discuss the initial results of the feasibility study. In this report, we describe the common research protocol and participating studies. Throughout this article, study results from each research group are referred to by name [e.g., EDEN study (Etude des Déterminants pré et post natals du développement et de la santé de l’Enfant)] if Address correspondence to J.D. Parker, National Center for Health Statistics, 3311 Toledo Rd., Room 6107, Hyattsville, MD 20782 USA. Telephone: (301) 458-4419. Fax: (301) 458-4038. E-mail: jdparker@cdc.govSupplemental Material is available online (doi:10. 1289/ehp.1002725 via http://dx.doi.org/).M.L.B. was supported in part by National Institutes of Health grant 1R01ES016317. J.L. was supported by a postdoctoral grant from Institut national de la santé et de la recherche médicale (INSERM). U.G. was sup-ported by a research fellowship of the Netherlands Organization for Scientific Research (NWO). The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the National Center for Health Statistics, Centers for Disease Control and Prevention.The authors declare they have no actual or potential competing  financial interests.Received 15 July 2010; accepted 9 February 2011.The International Collaboration on Air Pollution and Pregnancy Outcomes: Initial ResultsJennifer D. Parker,1 David Q. Rich,2 Svetlana V. Glinianaia,3 Jong Han Leem,4 Daniel Wartenberg,5 Michelle L. Bell,6 Matteo Bonzini,7 Michael Brauer,8 Lyndsey Darrow,9 Ulrike Gehring,10 Nelson Gouveia,11 Paolo Grillo,12 Eunhee Ha,13 Edith H. van den Hooven,14,15 Bin Jalaludin,16 Bill M. Jesdale,17 Johanna Lepeule,18,19 Rachel Morello-Frosch,17,20 Geoffrey G. Morgan,21,22 Rémy Slama,18,19 Frank H. Pierik,15 Angela Cecilia Pesatori,23 Sheela Sathyanarayana,24 Juhee Seo,13 Matthew Strickland,9 Lillian Tamburic,25 and Tracey J. Woodruff 261National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA; 2Department of Community and Preventive Medicine, University of Rochester School of Medicine and Dentistry,Rochester, New York, USA; 3Institute of Health and Society, Newcastle University, Newcastle upon Tyne, England, United Kingdom; 4Department of Occupational and Environmental Medicine, Inha University, Incheon, Republic of Korea; 5UMDNJ-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA; 6Yale University, School of Forestry and Environmental Studies, New Haven, Connecticut, USA; 7Department of Experimental Medicine, University of Insubria, Varese, Italy; 8University of British Columbia, Department of Medicine, Vancouver, British Columbia, Canada; 9Department of Environmental Health, Emory University, Atlanta, Georgia, USA; 10Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; 11Department of Preventive Medicine, School of Medicine of the University of São Paulo, São Paulo, Brasil; 12Epidemiology Unite, “Fondazione IRCCS Ca’Granda—Ospedale Maggiore Policlinico,” Milan, Italy; 13Department of Preventive Medicine, Ewha Womans University, Seoul, Republic of Korea; 14Generation R Study Group, Erasmus Medical Center, Rotterdam, the Netherlands; 15Department of Urban Environment, Netherlands Organisation for Applied Scientific Research (TNO), Delft, the Netherlands; 16Centre for Research, Evidence Management and Surveillance, Sydney South West Area Health Service, and School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia; 17Department of Environmental Science, Policy and Management, University of California–Berkeley, Berkeley, California, USA; 18INSERM, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, U823, Institut Albert Bonniot, Grenoble, France.; 19University J. Fourier Grenoble, Grenoble, France; 20School of Public Health, University of California–Berkeley, Berkeley, California, USA; 21North Coast Area Health Service, Lismore, New South Wales, Australia; 22University Centre for Rural Health–North Coast, University of Sydney, Sydney, New South Wales, Australia; 23Department of Occupational and Environmental Health, Università di Milano, Milan, Italy; 24Seattle Children’s Research Institute, University of Washington, Seattle, Washington, USA; 25University of British Columbia, Centre for Health Services and Policy Research, Vancouver, British Columbia, Canada; 26Center for Reproductive Health and the Environment. University of California–San Francisco, San Francisco, California, USABackground: The findings of prior studies of air pollution effects on adverse birth outcomes are difficult to synthesize because of differences in study design.oBjectives: The International Collaboration on Air Pollution and Pregnancy Outcomes was formed to understand how differences in research methods contribute to variations in findings. We initiated a feasibility study to a) assess the ability of geographically diverse research groups to analyze their data sets using a common protocol and b) perform location‑specific analyses of air pol‑lution effects on birth weight using a standardized statistical approach.Methods: Fourteen research groups from nine countries participated. We developed a protocol to estimate odds ratios (ORs) for the association between particulate matter ≤ 10 μm in aerodynamic diameter (PM10) and low birth weight (LBW) among term births, adjusted first for socioeconomic status (SES) and second for additional location‑specific variables. results: Among locations with data for the PM10 analysis, ORs estimating the relative risk of term LBW associated with a 10‑μg/m3 increase in average PM10 concentration during pregnancy, adjusted for SES, ranged from 0.63 [95% confidence interval (CI), 0.30–1.35] for the Netherlands to 1.15 (95% CI, 0.61–2.18) for Vancouver, with six research groups reporting statistically significant adverse associa‑tions. We found evidence of statistically significant heterogeneity in estimated effects among locations.conclusions: Variability in PM10–LBW relationships among study locations remained despite use of a common statistical approach. A more detailed meta‑analysis and use of more complex protocols for future analysis may uncover reasons for heterogeneity across locations. However, our findings confirm the potential for a diverse group of researchers to analyze their data in a standardized way to improve understanding of air pollution effects on birth outcomes.key words: air pollution, birth weight, ICAPPO, low birth weight, particulate matter, pregnancy. Environ Health Perspect 119:1023–1028 (2011). doi:10.1289/ehp.1002725 [Online 9 February 2011]Parker et al.1024 volume 119 | number 7 | July 2011 • Environmental Health Perspectivesavailable, otherwise by location (e.g., Seattle study). Additionally, we present estimated odds ratios (ORs) for the association between low birth weight (LBW) among term births and exposure to ambient particulate mat-ter with an aerodynamic diameter ≤ 10 µm (PM10) during pregnancy.MethodsThrough discussion with the larger group of ICAPPO participants and detailed planning by a smaller group (J.D.P., D.Q.R., S.V.G., J.H.L.), a protocol for the feasibility study was developed, agreed upon, and distributed to a geographically diverse group of researchers. To maximize the number of participating groups, we deliberately simplified the protocol by restricting the primary statistical analysis to one outcome (LBW in term births) and the air pol-lution exposure (PM10) available for the largest number locations (Woodruff et al. 2010).Cohort restrictions. We limited the study to live-born, singleton, term (37–42 com-plete weeks of gestation) infants with known birth weight, maternal education [or another measure of socioeconomic status (SES)], dates of birth and conception (often based on last menstrual period), and ambient PM con-centrations, as described below, during preg-nancy. The primary outcome was term LBW, defined as birth weight < 2,500 g.Air pollution exposure. The primary expo-sure variable was the ambient concentration of PM10 averaged over the entire pregnancy. PM10 concentrations were assigned to each subject using the approach employed by each research group in their original work. Although we focused on PM10, investigators also were encouraged to provide results for fine PM [≤ 2.5 µm in aerodynamic diameter (PM2.5)] if available. Studies without PM10 data provided effect estimates for PM2.5 or black smoke exposures during pregnancy.Black smoke approximates PM4 (< 4 µm in diameter) (Muir and Laxen 1995); results for black smoke are presented alongside the PM10 results for the PAMPER (Particulate Matter and Perinatal Events Research) study (Newcastle upon Tyne, UK). The methods for modeling the PAMPER black smoke exposures are described elsewhere (Fanshawe et al. 2008).Socioeconomic status. ICAPPO partici-pants identified SES as a potentially important control variable when assessing pollution and birth outcomes (Slama et al. 2008; Woodruff et al. 2009) and agreed to use maternal edu-cation as the primary measure of SES in the feasibility study. Maternal education is com-monly used as an SES measure in perinatal studies and has been shown to be related, albeit imperfectly, with other measures of SES (Kaufman et al. 2008; Parker et al. 1994; Pickett et al. 2002). If maternal education was unavailable, using different individual or area-level SES measures was allowed. Because the collection and meaning of maternal educa-tion for these studies differ among the study locations, its form as an analytic covariate dif-fered among the study locations.Other covariates. Participants also were encouraged to provide estimates adjusted for additional covariates as described below. Although additional variables make compari-sons of results across locations more challeng-ing, they allowed us to examine how additional adjustments specific to each location might influence estimates reported by each study.Primary statistical analysis. We used logistic regression, with term LBW as the dependent variable and PM10 as a continuous explanatory variable; black smoke was used in the PAMPER study, as described above. Results are reported as ORs per 10-µg/m3 increase in average concentration during preg-nancy to facilitate synthesis of results. Results from two models were examined: Model 1 covariates were PM10 and study-specific mater-nal education or other SES measure; model 2 covariates were PM10, maternal education or other SES measure, plus other study location–specific covariates as described above.Secondary statistical analyses. For these anal-yses, we suggested modeling continuous term birth weight as an outcome (using linear regres-sion) and/or using PM2.5 as an exposure meas-ure. In addition, results from models describing associations after controlling for different SES measures were contributed. Secondary analyses were encouraged but not required for participa-tion, so results of secondary analyses were not reported by all investigators.Table 1. Birth years, number of births, percent term LBW, and measure of SES used in model 1 (adjusted for SES only), by study.No. of birthsbPercent term LBWSES measure used in model 1 of feasibility studyStudy and locationa Birth years Measure Descriptive statisticsAtlanta, Georgia, USA (Darrow et al. 2009a, 2009b) 1996–2004 325,221 2.62 Attained maternal education Years: 19.8% < 12, 24.7% 12, 55.5% > 12California, USA (Morello-Frosch et al. 2010) 1996–2006 1,714,509 2.43 Attained maternal educationc Years: 31.5% < 12, 28.0% 12, 40.5% > 12Connecticut and Massachusetts, USA (Bell et al. 2007, 2008)1999–2002 173,042 2.16 Attained maternal education Mean ± SD, 13.6 ± 2.6 yearsEDEN, Poitiers and Nancy, France (Lepeule et al. 2010)2003–2006 1,233 2.11 Age at completion of education Years: 17.7% < 19, 61.7% 19–24, 20.6% > 24Lombardy, Italy (Pesatori et al. 2008) 2004–2006 213,542 2.71 Attained maternal education Degree: 33.3% < high school, 45.8% high school, 3.6% bachelor, 17.6% graduatePAMPER, Newcastle upon Tyne, UK (Glinianaia et al. 2008; Pearce et al. 2010)1962–1992 81,953 3.19 Area-level indicator: Townsend Deprivation ScoredQuintile cut-points: –1.2, 2.4, 4.7, 6.6New Jersey, USA (Rich et al. 2009) 1999–2003 87,281 2.75 Attained maternal education Years: 20.6% < 12, 36.5% 12, 42.9% > 12PIAMA, the Netherlands (Gehring et al. 2011) 1996–1997 3,471 1.15 Attained maternal education Degree: 22.8% low, 41.6% medium, 35.6% highGeneration R, Rotterdam, the Netherlands (van den Hooven et al. 2009)2002–2006 7,296 2.26 Attained maternal education Degree: 10.9% none/low, 44.7% secondary, 44.3% higherSão Paulo, Brazil (Gouveia et al. 2004) 2005 158,791 3.77 Attained maternal education Years: 29.3% < 7, 50.7% 8–11, 19.9% > 11Seoul, Republic of Korea (Ha et al. 2004) 1998–2000 372,319 1.45 Attained maternal education Degree: 4.1% < high school, 52.7% high school, 43.2% ≤ bachelorSeattle, Washington, USA (Sathyanarayana S, Karr C, unpublished data)1998–2005 301,880 1.56 Attained maternal educationc Years: 12.8% < 12, 26.1% 12, 60.0% > 12Sydney, Australia (Jalaludin et al. 2007) 1998–2004 279,015 1.62 Area-level indicator: Index of Relative Socioeconomic DisadvantageeQuartile cut-points: ≤ 945.1, 1010.7, 1072.7 Vancouver, British Columbia, Canada (Brauer et al. 2008)1999–2002 66,467 1.35 Area level indicator: percentage of women with postsecondary educationQuartile cut-points: 28.8, 36.3, 44.1aData sets have been used for other studies, although not necessarily studies of PM10 or term LBW; cited analyses sometimes used different versions of the data. bBirths used in model 1: singleton, term infants with known birth weight, maternal SES, gestational age, and ambient PM10 or black smoke concentrations. cCollection of maternal education changed during the study period. dThe Townsend Deprivation Score is an area-based measure of material deprivation (Townsend et al. 1988), calculated for each enumeration district (~ 200 households) based on 1971, 1981, and 1991 census data. eThe Australian Bureau of Statistics (2001) Index of Relative Socio-economic Disadvantage uses a range of census fac-tors and is assigned to each census collection district (~ 200 households).Air pollution and low birth weightEnvironmental Health Perspectives • volume 119 | number 7 | July 2011 1025Although full meta-analyses were not per-formed, in our examination of results, initial tests of homogeneity across study locations were conducted using fixed-effects models (Sterne et al. 2001). In these tests, the null hypothesis of homogeneity was rejected with p-values < 0.05.ResultsLocations. Fourteen research groups from nine countries participated (Table 1). Of these, six reported results for PM10 only, six for both PM10 and PM2.5, one for PM2.5 only (Seattle study), and one for black smoke only (PAMPER study). Most data were from the late 1990s to the mid-2000s. However, the PAMPER study comprised births from 1962 through 1992. The number of eligible births ranged from slightly > 1,000 in the EDEN study, Nancy and Poitiers, France] to > 1 mil-lion in the California study, although there was some variability within studies depending on the exposure measure and covariates. The percentage of LBW among term births ranged from 1.15% in the PIAMA (Prevention and Incidence of Asthma and Mite Allergy) study (Netherlands) to 3.77% in the São Paulo study (Table 1).By design, data sets used in the feasibil-ity study have been used for previous studies of pollution and pregnancy outcomes or are intended for such use. However, these are not necessarily studies of PM10 or term LBW, and previously published results may have been based on earlier versions of study data sets (Bell et al. 2007, 2008; Brauer et al. 2008; Darrow et al. 2009a, 2009b; Gehring et al. 2011; Glinianaia et al. 2008; Gouveia et al. 2004; Ha et al. 2004; Jalaludin et al. 2007; Lepeule et al. 2010; Mannes et al. 2005; Pearce et al. 2010; Pesatori et al. 2008; Rich et al. 2009; Slama et al. 2009; van den Hooven et al. 2009).PM concentration estimation. PM con-centration estimates and estimation methods differed among the studies (Table 2). Some research groups relied on temporal variabil-ity in PM to estimate effects, where exposure was calculated by averaging all measurements over the entire study area for the pregnancy interval; for these studies, exposure estimates differed for pregnancies occurring at different times, but not by maternal residence, within the study area. Other studies estimated effects based on both temporal and spatial PM con-trasts, where estimates were calculated for mul-tiple geographic administrative units or at each maternal address; in these studies, exposures differed both by maternal address and by tim-ing of the pregnancies within the study period. Most research groups (11 of 14; 79%) used routinely collected monitoring network data to estimate exposures (Table 2), although its use differs among studies [e.g., averages over geo-graphic areas; nearest monitor measurement, or inverse distance-weighted (IDW) averages from multiple monitors, from residence].Two research groups used models to esti-mate PM10 exposure (Table 2), although modeling methods differed. The Generation R study (Rotterdam, the Netherlands) used dispersion modeling (combination of moni-toring data with modeling techniques) (Wesseling et al. 2002), whereas the PIAMA study (Netherlands) used temporally adjusted land use regression (LUR) (Gehring et al. 2011) and estimated residential PM10 from modeled PM2.5 concentration (Cyrys et al. 2003). PAMPER used modeled estimates, as described above; the median modeled black smoke concentration in the PAMPER data set was 32.8 µg/m3 with an interquartile range of 17.1–104.9, reflecting, in part, the long time spanned. The Vancouver study used moni-toring network data for PM10 but used both LUR models and monitoring network data (IDW) to estimate PM2.5 exposures (Brauer et al. 2008); results for both Vancouver PM2.5 estimates are shown below.Socioeconomic status. Eleven of the 14 research groups used maternal education as the indicator of SES for model 1 (Table 1). However, the maternal education measure varied in form and meaning across studies. Three studies relied on contextual information based on neighbor-hood characteristics to define maternal SES for model 1 of the primary analysis (Table 1). Some research groups included additional individual level socioeconomic measures for model 2 and in secondary analyses [see Supplemental Material, Table 1 (doi:10.1289/ehp.1002725)]. For example, paternal occupation was used in the Lombardy study. The California study added area-level socioeconomic measures. Similarly, the Vancouver study added an additional area-level income variable. Some research groups included individual-level characteristics that may correlate with SES: maternal age, race, ethnicity, indig-enous status, and country of birth.Birth weight. Figure 1 shows the relative odds of term LBW per 10-µg/m3 increase in mean PM10 concentration during pregnancy, adjusted for SES (model 1) by location. Associations differed among study locations (p-value from test for heterogeneity < 0.001). Table 2. PM10 distribution, method of exposure estimation, area, and source of exposure variability, by study.PM10 distribution (μg/m3) Approximate areaa (km2)Study Median 25th percentile 75th percentile Method of exposure estimation Exposure contrastbAtlanta 23.5 22.3 25.4 Monitoring network; population-weighted spatial average over city (Ivy et al. 2008)4,538 TemporalCalifornia 28.9 22.6 38.7 Monitoring network; nearest monitor within 10 km of residence 423,970a Spatial and temporalConnecticut and Massachusetts22.0 18.1 25.5 Monitoring network; spatial average over county of residence 41,692 Spatial and temporalEDEN 19.0 18 21 Monitoring network; nearest monitor within 20 km of residence 480 Spatial and temporalLombardy 49 44 54 Monitoring network; average of monitoring stations located in nine regional areas (Baccarelli et al. 2007)23,865 Spatial and temporalPAMPERc (PM10 not available) Spatial-temporal model for black smoke (Fanshawe et al. 2008) 63 Spatial and temporalNew Jersey 28.0 24.8 31.7 Monitoring network; nearest monitor within 10 km of residence 22,592a Spatial and temporalPIAMA 40.5 36.7 43.4 LUR model (Gehring et al. 2011) with temporal adjustment using air monitoring network datad12,000 Spatial and temporalGeneration R 32.8 32.2 33.3 Dispersion model (Wesseling et al. 2002) 150 SpatialSão Paulo 40.3 39.2 42.1 Monitoring network; average from 14 monitors throughout city 1,500 TemporalSeattlee (PM10 not available) Monitoring network; population-weighted spatial average of PM2.5 for monitors within 20 km of residence (Ivy et al. 2008)17,800 Spatial and temporalSeoul 66.45 59.63 69.72 Monitoring network; average from 27 monitors throughout city 605 Spatial and temporalSydney 16.50 12.8 21.0 Monitoring network; average from eight monitors throughout city 12,145 TemporalVancouver 12.5 11.7 13.1 Monitoring network; inverse distance weighting of up to three monitors within 50 km of residencef3,300 Spatial and temporalaApproximate geographic area in which mothers reside; in California and New Jersey, the geographic area includes maternal addresses too far from a PM10 or PM2.5 monitoring site to be included in the study. bTemporal contrast is used to describe studies where exposure estimates differ among mothers based on the timing of their pregnancy; spatial contrast is used to describe studies where exposure estimates differ among mothers based on their residence. cOnly black smoke available (black smoke is a historic measure of airborne PM, ~ PM4, shown to be a reasonable predictor of daily average PM10) (Muir and Laxen 1995). dPM10 estimated from PM2.5 LUR model results. eOnly PM2.5 available. fPM2.5 exposure also derived from LUR (see “PM concentration estimation”). Parker et al.1026 volume 119 | number 7 | July 2011 • Environmental Health PerspectivesSix studies indicated a statistically signifi-cant positive (adverse) association (Atlanta, California, Connecticut and Massachusetts, PAMPER, São Paulo, and Seoul), whereas the Sydney and Vancouver studies indicated an adverse, albeit not significant, association (Figure 1). Little or no association was reported by seven studies; no research group reported significant inverse (protective) associations.Figure 2 shows estimated ORs from model 2 [models fitted with additional cova-riates; see Supplemental Material, Table 1 (doi:10.1289/ehp.1002725)]. Additional covariates varied among studies and included maternal age and transformations of age, parity, antenatal visits, country of birth, sex, maternal smoking, maternal alcohol, mater-nal hypertension, maternal diabetes, season of conception, year of birth, marital status, race/ethnicity, indigenous status, gestational age, and contextual measures of SES. About half of model 2 ORs suggest slightly stronger asso-ciations between air pollution and term LBW compared with model 1 ORs, whereas other model 2 ORs were either very similar or atten-uated compared with model 1 [for a direct comparison of estimates, see Supplemental Material, Table 2 (doi:10.1289/ehp.1002725). Associations differed among study locations (p-value from test for heterogeneity < 0.05).Figure 3 shows changes in mean term birth weight associated with each 10-µg/m3 increase in PM10 for the 11 locations report-ing continuous birth weight results. The mean estimated change ranged from a 42.2-g decrease (Generation R) to an increase of about 20 g (the Atlanta study), with most estimates (9 of 11) indicating a 2- to 20-g lower birth weight associated with each 10-µg/m3 increase in PM10 exposure. Of the 11 studies, six reported a statistically signifi-cant adverse effect of PM10, whereas two (the Atlanta and Lombardy studies) indicated a significant protective effect. These associa-tions differed among study locations (p-value from test for heterogeneity < 0.001). After controlling for study-specific factors, model coefficients often, although not always, sug-gested larger decreases in birth weight with increases in PM10 [see Supplemental Material, Table 3 (doi:10.1289/ehp.1002725)]. In the Atlanta study, the estimate changed from an apparent mean increase of 20 g to a mean decrease of –28.8 g [95% confidence interval (CI), –49.6 to –8.1], whereas PIAMA’s esti-mate changed to an apparent increase [47.0 g (95% CI, –10.5 to 104.6)] after controlling for location-specific confounders.Figure 4 shows estimated relative odds of LBW associated with each 10-µg/m3 increase in PM2.5 concentration, after controlling for SES, for a subset of studies. As for PM10, some studies indicated a significant increase Figure 1. ORs (95% CIs) for LBW among term births in association with a 10-μg/m3 increase in estimated average PM10, or black smoke (PAMPER), concentration during the entire pregnancy, adjusted for SES (model 1), by study.PIAMAGeneration RNew JerseyEDENLombardyCaliforniaPAMPERSeoulSydneyAtlantaSao PauloConnecticut and MassachusettesVancouver0.50.25 1 3 42 5OR (95% CI)Figure 2. ORs (95% CIs) for LBW among term births in association with a 10-μg/m3 increase in estimated average PM10, or black smoke (PAMPER), con-centration during the entire pregnancy, adjusted for SES and study-specific variables (model 2), by study.PIAMAAtlantaLombardyNew JerseyCaliforniaPAMPEREDENSeoulConnecticut and MassachusettesGeneration RSao PauloSydneyVancouver1 2 3 4 50.50.25OR (95% CI)Figure 4. ORs (95% CIs) for LBW among term births in association with a 10-μg/m3 increase in estimated average PM2.5 concentration during the entire pregnancy, adjusted for SES, by study. Results for the Vancouver study are from two different PM2.5 estimation methods, LUR and IDW of monitor measurements (see “Methods”).PIAMAVancouver (IDW)AtlantaSeattleCaliforniaNew JerseyConnecticut and MassachusettesVancouver (LUR)0.5 1 1.5 2 2.50.25 53 4OR (95% CI)Figure 3. Change in mean birth weight (95% CIs) among term births in association with a 10-μg/m3 increase in estimated average PM10, or black smoke (PAMPER), concentration during the entire pregnancy, adjusted for SES, by study.PIAMAGeneration RVancouverSao PauloSydneySeoulEDENPAMPERLombardyCaliforniaAtlanta−150 −100 −50 0 50Change in birth weight [g (95% CI)]Air pollution and low birth weightEnvironmental Health Perspectives • volume 119 | number 7 | July 2011 1027in the relative odds of LBW, whereas others indicated no association. The Vancouver study reported different results using different PM2.5 estimates. p-Values from separate heterogene-ity tests, each including one Vancouver esti-mate, were 0.06 (LUR) and 0.18 (IDW).DiscussionDespite the deliberately simple protocol and the heterogeneity in study designs and loca-tions, we found some consistency across stud-ies, particularly for the relationships between PM10 and mean birth weight and between PM2.5 and LBW. After controlling for SES, the reduction in mean birth weight associ-ated with a PM10 increase of 10 µg/m3 was between 2 and 20 g for 9 of 11 locations. Although based on fewer studies than those for PM10, the initial tests of homogeneity for PM2.5 results were not statistically significant. More detailed meta-analysis of the initial results, considering alternative models, influ-ential locations, and differences in location-specific covariates and exposures, may improve our understanding of these relationships and lead to improved summary estimates.Based on a discussion of initial feasibility study results at the 2009 workshop in Dublin, Ireland (see Appendix), participants concluded that the method used to estimate PM10 expo-sures may be the most critical design difference among the studies. Some prior studies from California (Basu et al. 2004; Wilhelm and Ritz 2005), Vancouver (Brauer et al. 2008), Sydney (Mannes et al. 2005), and Atlanta (Darrow et al. 2009a) have examined the con-sequences of different methods for calculat-ing pollution metrics in the same study but from different perspectives. For example, as in the results presented in Figure 4, Brauer et al. (2008) compared PM2.5 estimates from LUR and monitor data (IDW) and concluded that their moderate correlation could be attribut-able to different aspects of variability being captured by each method. Basu et al. (2004) found stronger associations for exposures esti-mated over larger geographic areas than over smaller geographic areas but did not speculate on the reasons for the discrepancy; however, Basu et al. (2004) cautioned that studies using different methods for exposure assessment may not be comparable.Importantly, there is large variation in PM10 levels and concentration ranges among study locations. In the Vancouver study, for example, the 10-µg/m3 increase used to derive ORs is nearly an order of magnitude greater than the interquartile range (11.7–13.1; Table 2) of exposures. Similarly, in the Atlanta study, the 10-µg/m3 reporting unit represents nearly the entire range of PM10 concentrations (18.6–29.6 µg/m3).The ana-lytical methods used in the common frame-work assume no threshold level below which PM is not associated with health. Although evidence supports the hypothesis that no threshold exists for PM relationships and overall population mortality (Daniels et al. 2000), threshold assumptions have not been fully explored for adverse reproductive out-comes, including birth weight. We did not directly examine nonlinear relationships in this feasibility study, but they may contribute to heterogeneity among studies; a more fully coordinated analysis should improve our abil-ity to assess nonlinear relationships.Covariates likely to affect the relation-ship between PM10 and LBW differ among study locations for many reasons (Strickland et al. 2009). For studies that estimate effects based on spatial contrasts, controlling for SES can be important because it may be spa-tially correlated with exposure concentrations (O’Neill et al. 2003). However, SES measures and their relationships with both birth out-comes and air pollution are not consistent. For example, although mothers with lower SES generally tend to have poorer birth out-comes, the strength of the relationship differs depending on which birth outcome (birth weight, preterm birth) and which measures of SES (maternal education, occupation) are used (Parker et al. 1994; Pickett et al. 2002). Although in some places mothers with higher SES live in less-polluted areas (Woodruff et al. 2003), in others the opposite relation-ship holds (Slama et al. 2007). Because par-ticipating studies rely on exposure estimates with differing spatial and temporal compo-nents, critical confounders may differ among studies (Strickland et al. 2009). Changes between results for the models using SES only and those using SES plus covariates varied among studies, suggesting that other statisti-cal approaches, possibly hierarchical models, that allow for different types of confounding factors could be informative for understand-ing apparent variations among locations.Finally, other methods of analysis could be used. Although logistic regression is com-monly applied, alternative approaches have considered spatial correlations (Jerrett et al. 2005), time-varying exposures (Suh et al. 2009), generalized additive models (Ballester et al. 2010), and hierarchical structures (Yi et al. 2010). Bell et al. (2007) proposed a method for handling correlated exposures across trimesters. Because both model-based and spatially averaged exposure estimates are calculated with error, considering their preci-sion would provide more accurate confidence intervals (Woodruff et al. 2009).The ICAPPO feasibility project success-fully coordinated analyses of the association between ambient PM concentrations and term LBW, across multiple locations, data sets, and research teams worldwide. These initial results and the participation of multiple research groups, even without external funding, sup-port the continuation of this effort to increase our understanding of the human reproductive consequences of adverse air quality.RefeRencesAustralian Bureau of Statistics. 2001. Socio-economic Indexes for Areas, Australia 2001. ABS Catalogue no. 2039.0. Canberra:Australian Bureau of Statistics.Baccarelli A, Zanobetti A, Martinelli I, Grillo P, Hou L, Lanzani G, et al. 2007. 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