Franco Suglia et al. Our results are comparable with other research relating traffic exposures to lung function among women. Recently, the Atherosclerosis Risk in Communities study, a community-based cohort, showed that increased trafﬁc density and distance to major roadways were associated with reduced FVC and FEV1 among women (Kan et al. 2007). In other studies also focusing in women, increased exposure to PM ≤ 10 µm in aerody- namic diameter was associated with reduced FEV1 and FVC and increased risk of chronic obstructive pulmonary disease (COPD) (Schikowski et al. 2005). Sekine et al. (2004) reported decreased lung function among women living in Tokyo, Japan, who were exposed to automobile exhaust compared with women living in other cities with lower trafﬁc exposures. To estimate trafﬁc exposure, studies generally have relied on measures of traffic density and residence distance from roadway (Kan et al. 2007; Schikowski et al. 2005; Sekine et al. 2004), whereas other stud- ies estimate PM exposure using area-level monitors (Schikowski et al. 2005). Our study goes beyond this by using estimated exposure to traffic particles from a validated model. Because these exposure estimates are both spe- cific for traffic particles and based on moni- toring data, this gives greater assurance that the association we observed is not entirely attributable to some other attribute of the GIS trafﬁc estimates. In stratified analyses, we noted stronger effects among former smokers than among cur- rent smokers. Other studies have suggested an interaction between smoking and air pollution exposure (Nyberg et al. 2000). We found no signiﬁcant associations among current smokers. A “healthy smoker” effect has previously been noted for trafﬁc-related air pollution (Nyberg et al. 2000). Although former smokers may have quit smoking because of respiratory symptoms and/or health concerns, current smokers may be less sensitive to the effects of tobacco smoke and, subsequently, to air pollu- tion. Furthermore, physiologic changes among smokers (i.e., thickening of bronchial mucosa) may make them less susceptible to additional pollutants (O’Neill et al. 2003). The lack of an effect on lung function among current smokers could also be attributed to factors related to race and SES; for example, of the current smokers in our study, 93% self-identified as white and had lower BC exposures than did their Hispanic/black counterparts. Lower SES can contribute to exposure differentials in traffic-related pollution as well as influence other factors (i.e., stress, nutrition, access to health care), which can enhance susceptibility to air pollution (O’Neill et al. 2003; Romieu 2005; Wright et al. 2005). As in any epidemiologic study, we acknowledge a number of limitations. As is 1336 typical with longitudinal studies, there was sig- niﬁcant reduction in the sample available from the original cohort over time. The nonpartici- pation of subjects from the longitudinal study may be seen as a limitation, although we noted no differences based on race/ethnicity, age, education, or smoking status between those who had lung function assessed and those who did not. Thus, this likely did not inﬂuence our ﬁndings. Although we are able to adjust for a number of factors associated with lung func- tion and air pollution, it is still possible that the associations found in this study can be attribut- able to unmeasured or residual confounding, perhaps most notably due to SES. In addition to adjusting for mother’s education level, the present study is somewhat restricted regarding SES given that all families were recruited from one neighborhood health center in Boston, restricting the variability of individual-level SES in this population. This limited variability reduces the potential for confounding. Moreover, when we further adjusted for neigh- borhood-level indicators of SES, the observed effects were actually strengthened. Although we attempted to capture BC exposure from all residential addresses, we may be missing exposures at work and/or other locations where the participants spend a por- tion of their time. However, this potential mis- classiﬁcation of exposure is nondifferential with respect to the outcome, so it is unlikely to account for the associations found in this study. Furthermore, exposure studies using personal monitors indicate that home expo- sures are the most important in predicting per- sonal exposure (Liu et al. 2003; Rohas-Bracho et al. 2000). Other studies (Sarnat et al. 2002) have shown that home indoor concentrations of PM of outdoor origin are highly correlated with outdoor concentrations. In another study (Brown 2006), the personal exposures of the working spouses of persons with chronic ill- nesses have been shown to be highly correlated with their spouse’s personal exposures. Taken together, we believe these studies indicate that personal exposures to ambient particles are dri- ven primarily by exposures at home. Moreover, we attempted to capture BC exposure from all residential addresses when participants moved. Another limitation of this study is the use of predicted exposure, rather than observed measurements taken outside the residences of the study participants. Because the latter is not practical in a large community-based study, we decided to use all available exposure data and advanced modeling approaches to predict the missing exposure at the residences of the par- ticipants. This approach has become very pop- ular in recent years. A potential statistical issue that arises when using spatiotemporal predic- tions of exposure is that these quantities are uncertain rather than measured quantities, which could bias the resulting health effect estimates. In a previous study (Gryparis et al. 2006), we found that the use of predictions from spatial exposure models induces a Berkson-type measurement error. It is well known that in the Berkson-type measurement error framework, the use of the error-prone covariate results in unbiased regression parame- ter estimates, but the associated standard errors are no longer valid. Gryparis et al. (2006) showed that the use of predictions from spatial exposure models results in unbiased parameter estimates for the association between the pre- dicted exposure and the observed health out- come. The issue with this approach is that the standard errors for the parameter of interest might be incorrect. In such a case, we would expect larger standard errors for the parameter of interest. Last, we have no information on other spatially variable pollutants (i.e., nitrogen dioxide, ozone) to compare with our results. In summary, we found evidence of an association between BC, a marker of traffic pollution, and lung function among women. 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Am J Epidemiol 167(3):280–286. Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D, Berhane K, et al. 2004. The effect of air pollution on lung development from 10 to 18 years of age. N Engl J Med 351(11):1057–1067. Gauderman WJ, Vora H, McConnell R, Berhane K, Gilliland F, Thomas D, et al. 2007. Effect of exposure to trafﬁc on lung development from 10 to 18 years of age: a cohort study. Lancet 369(9561):571–577. VOLUME 116 | NUMBER 10 | October 2008 • Environmental Health Perspectives