Franco Suglia et al. model to predict 24-hr measures of traffic- related pollution exposure using data from more than 80 locations in the greater Boston area. Three-quarters of the monitoring sites were residential; the rest were at commercial or government facilities. The data consist of > 6,021 pollution measurements from 2,127 unique exposure days. A detailed description of all exposure data sources is provided by Gryparis et al. (2007). Predictors in the regres- sion were the BC value at a central stationary monitor (to capture average concentrations in the area on that day), meteorologic conditions and other characteristics (e.g., weekday/week- end) of a particular day, and measures of the amount of traffic activity [e.g., geographic information system (GIS)-based measures of cumulative trafﬁc density within 100 m, pop- ulation density, distance to nearest major roadway, percent urbanization] at a given loca- tion. A cumulative traffic density measure, within a 100-m buffer, is recorded once per location. This was the sum of trafﬁc counts on all road segments within 100 m, multiplied by the length of each road segment. We used spline regression methods to allow these fac- tors to affect exposure levels in a potentially nonlinear way. Finally, we used thin-plate splines, a two-dimensional extension of regres- sion splines, to model longitude and latitude and capture additional spatial variability unac- counted for after including our deterministic spatial predictors in the model. This approach is a form of geoadditive model (Kammann and Wand 2003) for daily concentrations of PM levels. We had complete information on all of these factors for 2,114 of the 2,127 Table 1. Sample demographics, environmental exposures, and lung function measures (n = 272). Measure Demographics Age [years (mean ± SD)] Race/ethnicity [no. (%)] Hispanic/black/othera White Education level [no. (%)] Some college/technical school High school graduate Less than high school Exposure and medical history [no. (%)] Tobacco exposure Never smoker Former smoker Current smoker Environmental tobacco exposure Occupational dust exposure Gas stove used for cooking Length of residence > 2 years Asthma or chronic bronchitis Lung function and predicted BC (mean ± SD) FEV1 (L) FVC (L) FEF25–75% (L/sec) BC (μg/m3) average Value 26.9 ± 5.3 154 (56.6) 118 (43.4) 60 (22.1) 100 (36.8) 112 (41.2) 163 (59.9) 61 (22.4) 48 (17.7) 150 (55.2) 68 (25.0) 212 (77.9) 112 (41.2) 23 (8.5) 2.62 ± 0.39 3.10 ± 0.44 3.26 ± 0.82 0.62 ± 0.15 unique exposure days. We ﬁt a separate model for the warm (May–October) and cold (November–April) seasons. The R2 of the model (over both seasons) was 0.82. The model was separable into components varying over time and components varying over space. For long-term average exposure, we took the prediction of the spatial component plus the long-term average of the temporal predictors. For the purposes of these analyses, we used the average of the two seasons as a measure of average BC exposure. If participants moved within a year from the enrollment date, we calculated an average BC measure for all addresses. This model has been previously used to associate BC at home addresses with mortality risk in adults (Maynard et al. 2007) and with cognitive function in children (Franco Suglia et al. 2007). We measured lung function using a Morgan spirometer (P.K. Morgan, Andover, MA, USA) during the enrollment period (March 1986–October 1992), before 20 weeks of gestation. We measured standing height without shoes before spirometry. The lung function protocol was developed by an experi- enced pulmonologist and pulmonary function technologist. A trained pulmonary function technologist instructed the women during test- ing and monitored the ﬂow-volume curve to ensure good effort. Maneuvers were repeated to obtain three acceptable curves; we stored data from each technically acceptable effort fol- lowing established guidelines (American Thoracic Society 1995). We measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and forced mid-expiratory ﬂow rate (FEF25–75%) from the best acceptable blow (American Thoracic Society 1995). We ascertained detailed data on race/eth- nicity and socioeconomic status (SES) based on education level through standardized ques- tionnaires administered at baseline and clinic follow-up visits, as previously described (Hanrahan et al. 1992). We asked participants about their smoking status on a standardized questionnaire and classified them as current smokers, former smokers, or nonsmokers. We obtained a urine specimen to determine creati- nine-corrected cotinine levels, as previously detailed (Hanrahan et al. 1992). We classiﬁed participants as never smokers if they reported on the standardized questionnaire that they had never smoked and if their urine cotinine levels were < 200 ng cotinine/mg creatinine (Hanrahan et al. 1992). If the self-report of nonsmoking was contradicted by the urine cotinine, we classiﬁed the participant as a cur- rent smoker. We also assessed environmental tobacco exposure, deﬁned as a smoker living in the participant’s home. We assessed occupational exposure history by asking participants whether they ever worked in a dusty job (yes/no), for how long they held that job, and the severity of the dust exposure (mild, moderate, or severe). We defined occupational dust exposure as dust exposure at work for > 2 years or dust expo- sure that was reported by participants as being moderate or severe. Participants were also asked about the type of cooking fuel used in their homes (gas or electric) and whether they had lived in their residence for at least 2 years. We ascertained information on respira- tory illness history using the standardized American Thoracic Society Division of Lung Diseases questionnaire (Ferris 1978). We determined history of asthma based on a report of physician-diagnosed asthma. We also asked participants whether they had ever been diagnosed with chronic bronchitis. And ﬁnally, there may be factors operating at the aggregate (i.e., neighborhood or commu- nity level) that may be more important than individual-level factors in predicting exposures to environmental hazards including air pollu- tion (O’Neill et al. 2003). We examined neighborhood-level SES markers, including percent unemployed within census tracts and living in a census tract with > 20% of house- holds below the federal poverty guidelines. Notably, although participants resided in 43 different census tracts, 48% of the participants resided in ﬁve census tracts. Statistical analyses. We report estimated effects on pulmonary function using percent- age of predicted lung function (based on height, weight, age, and race/ethnicity) as our independent variable. To obtain the predicted lung function values for each woman, we regressed the log-transformed lung function measures against log height, age, log weight, and race/ethnicity. Because we used BC as a surrogate for traffic-related PM exposure, which includes more than just carbon particles, it did not make sense to report results on a unit mass basis. Instead, we report estimated effects of predicted BC per interquartile range (IQR). Table 2. Linear regression models of predicted BC (average summer and winter) by lung function meas- ures: effect estimate [% (95% CI)] for change in percent predicted lung function per IQR increase in BC (0.22 μg/m3) (n = 272). BC model Adjust demographicsa Adjust above + tobaccob Adjust above + asthma + bronchitis FEV1 –1.08 (–2.5 to 0.3) –1.10 (–2.5 to 0.3) –1.09 (–2.5 to 0.3) FVC –0.62 (–1.9 to 0.6) –0.62 (–1.9 to 0.6) –0.62 (–1.9 to 0.6) FEF25–75% –2.97 (–5.8 to –0.2) –3.04 (–5.9 to –0.2) –3.03 (–5.8 to –0.3) aIncludes 144 Hispanics, 2 blacks, 1 Asian, and 7 women who self-identiﬁed as other race/ethnicity. aWe adjusted all models for age, race/ethnicity, education level, and year of assessment. bTobacco = current and past smoking. 1334 VOLUME 116 | NUMBER 10 | October 2008 • Environmental Health Perspectives