conference proceedings in the ISI Web of Science (Thomson Reuters 2010) for abstracts of other unpublished studies, using the same search terms as above, but we found no other studies that fall into this category. Meta­analysis. We conducted meta- analyses to obtain summary risk estimates for the association between congenital anomaly groups and ambient air pollutant concentra- tions. Meta-analyses were conducted only if at least four studies had risk estimates [usu- ally odds ratios (ORs)] for the same pollutant and comparable anomaly group. Although no guidelines exist as to the minimum number of studies for meta-analysis, we considered four the minimum number to justifiably run a meta-analysis; this corresponded to having a minimum of 500 cases of congenital anomaly included in each analysis. A list of the specific outcome groups used in the different studies, and the congenial anomaly groups considered comparable for the purposes of these meta- analyses, is available in Supplemental Material, Table 1 (doi:10.1289/ehp.1002946). Meta- analyses calculated two types of summary risk estimates: a) for the comparison of congenital anomaly risk at high versus low exposure level in each study and b) for congenital anomaly risk per unit increase in continuous pollutant concentration. For each of these, we selected risk estimates from the main, confounder- adjusted models presented in each study, not those of sensitivity analyses. If both single- and multiple-pollutant models were pre- sented, we selected the single-pollutant model. If studies presented results for more than one pregnancy period (Hwang and Jaakkola 2008; Ritz et al. 2002), we selected the period most appropriate for the development of congenital anomalies and most used in the other stud- ies: month 2 or weeks 3–8 of gestation. The Strickland et al. (2009) study used two groups of ventricular septal defects (VSDs): muscular and perimembranous type. We calculated the combined risk estimate for these two groups and then entered the combined estimate in meta-analyses. Two studies (published in three reports) were located in the same study area, covered overlapping time periods, and analyzed some of the same anomaly groups (VSDs, coarctation of the aorta, tetralogy of Fallot) in relation to SO2 exposure (Dadvand et al. 2011a, 2011b; Rankin et al. 2009). We entered risk estimates from these publica- tions separately in the SO2 meta-analyses, never at the same time. Because the Dadvand et al. (2011b) substudy had risk estimates for other air pollutants and covered a more recent time period, we used this as the main analy- sis for SO2 and considered the inclusion of the Dadvand et al. (2011a) and Rankin et al. (2009) studies to be sensitivity analyses. In the high- versus low-exposure meta- analysis, we selected from the published reports risk estimates for the fourth compared with the first quartile of exposure, if categori- cal risk estimates were presented (Dadvand et al. 2011a, 2011b; Gilboa et al. 2005; Marshall et al. 2010; Rankin et al. 2009; Ritz et al. 2002). Some studies presented risk estimates only for continuous exposure (Hansen et al. 2009; Hwang and Jaakkola 2008; Strickland et al. 2009); we converted these to the risk estimate per interquartile increase in exposure, using the interquartile ranges (IQRs) given in the reports, because these were considered to compare most closely with estimates from the categorical studies. Information to calculate midquartile points was not given in these articles, so we could not compare the 12.5th and 87.5th percen- tiles. One study (Dolk et al. 2010) presented risk estimates for the 90th versus 10th percen- tile exposure, calculated from a continuous pollutant model, and this estimate was used for the high versus low comparison. Meta-analyses for continuous exposure summarized risk estimates per unit increase in pollutant concentration, assuming that the natural logarithm of the relative risk of congenital anomaly varied linearly with the ambient air pollutant concentration. From each publication we selected the risk esti- mate per continuous unit increase in pol- lutant concentration, if available (Dadvand et al. 2011a; Dolk et al. 2010; Hansen et al. 2009; Hwang and Jaakkola 2008; Ritz et al. 2002; Strickland et al. 2009). Three articles described continuous exposure estimates in the text but did not show the risk estimates (Dadvand et al. 2011b; Gilboa et al. 2005; Marshall et al. 2010); for these we obtained the continuous estimates directly from the authors. To allow comparison of effects among the different studies, units were con- verted to 10 gμ/m 3 for particulate matter with diameter ≤ 10 mμ (PM 10), 10 ppb for ozone (O3) and nitrogen dioxide (NO2), 1 ppm for CO, and 1 ppb for SO2. Exposure estimations that were expressed in a mass per volume unit (e.g., micrograms per cubic meter) instead of parts per million, billion, or hundred million (pphm) were converted to parts per billion using the general equation at 1 atm and 25°C: 24.45 × concentration (micrograms per cubic meter)/molecular weight. An exception was made for PM10, which was always expressed in micrograms per cubic meter. We obtained summary risk estimates in meta-analyses using fixed- or random-effects models. For each pollutant–outcome analysis we first tested for heterogeneity in the risk estimates using the Q-test (Cochran 1954). When the result of the Q-test showed evi- dence for heterogeneity (p < 0.1), we used a random effect analysis, following the method of DerSimonian and Laird (1986). Otherwise, a fixed-effect analysis was conducted using Environmental Health Perspectives • volume 119 | number 5 | May 2011 Air pollution and congenital anomalies the Mantel–Haenszel method (Mantel and Haenszel 1959). Meta-analyses calculated summary risk estimates (ORs) weighted by the inverse variance of each study, taking into account whether a fixed or random model was used. We used the R statistical software pack- age for all analyses (version 2.11.0; R Project for Statistical Computing 2010). We also produced forest plots to show ORs from each of the individual studies included in the meta-analyses and the estima- tion of the summary OR (Light and Pillemer 1984). The sizes of the markers of each OR in the plots represent the relative weight each study contributed to the summary estima- tion. To analyze potential for publication bias, we conducted a weighted Egger test, a linear regression in which the response is the estimated effect and the explanatory variable is a precision term (1/SE) (Egger et al. 1997). A large deviation from zero of the slope term suggests publication bias. Results We identified 10 studies, one divided into two substudies (Dadvand et al. 2011a, 2011b), published between 2002 and 2011 (Table 1). Four studies were conducted in the United States (in California, Texas, Georgia, and New Jersey), three in England (two in the northern region and one in four English regions including the northern region), and one each in Australia, Taiwan, and South Korea. The studies focused mainly on car- diac anomalies (Dadvand et al. 2011a, 2011b; Gilboa et al. 2005; Hansen et al. 2009; Ritz et al. 2002; Strickland et al. 2009) and/or oro- facial clefts (Gilboa et al. 2005; Hansen et al. 2009; Hwang and Jaakkola 2008; Marshall et al. 2010; Ritz et al. 2002). Only two stud- ies (Dolk et al. 2010; Rankin et al. 2009) included the full spectrum of major structural anomalies. The South Korean study was a pro- spective birth cohort study that included all congenital anomalies as one group (14 cases) (Kim et al. 2007). Most other studies used a registry-based case–control design, select- ing cases from routine congenital anomaly registries and controls from birth registries. One study was a registry-based cohort, using anomaly registries as source of case ascertain- ment and birth registry data for denominators (Dolk et al. 2010). Strickland et al. (2009) used a time-series design to link daily expo- sure estimates to daily congenital anomaly rates (for a given conception date); again, data on the congenital anomalies came from a routine register. Major differences are apparent in the diagnostic coding systems, congenital anom- aly grouping methods, and case definitions among the studies [see Supplemental Material, Table 1 (doi:10.1289/ehp.1002946)]. The studies by Gilboa et al. (2005) and Hansen 599