Pearson et al. (2000), assumes dispersion of motor vehicle exhaust pollutants mimicking a Gaussian probability distribution such that 96% has dissipated at 500 feet (152.4 m). We chose a distance of 150 m for this metric to capture roads with very high traffic vol- ume, such as freeways, that may have been located > 50 or 100 m from the residence. In the tables and text below, the traffic volume derived from this method is referred to as the maximum weighted traffic. c) We estimated the distance in meters from the participant’s residence to the nearest CalTrans-labeled arterial roadway including principal arterial interstates, principal arterial freeways and highways, and minor arterials. Temporal variation. The traffic counts we used to assess the spatial variability in expo- sure are yearly averages; they do not take into account temporal variations in traffic patterns and meteorologic conditions, both of which can affect the ambient levels of traffic-related air pollutants. To incorporate this tempo- ral variation in our exposure model, we also adjusted the traffic exposure counts by the average levels of nitrogen dioxide during the periconceptual period (0–29 days post-LMP) and during the second month of pregnancy (30–59 days post-LMP) (Slama et al. 2007). We chose these two windows of exposure because exposure during this time period was relevant for the outcome and would be measured at the same period of pregnancy for both the SABs and the live births (Slama et al. 2008). There were insufficient surviving cases to examine exposure during the third month. We used NO2 because daily measure- ments were available during the study period from California Air Resources Board (2008), and it has been shown to be a good marker for traffic-related air pollution (Jerrett et al. 2005). For each window of exposure, we cal- culated the average NO2 levels at the air pol- lution monitor closest to the residence of each woman and divided it by the average NO2 level for that monitor during the entire length of the study from 1990 to 1991 to obtain an adjustment factor. For example, if the NO2 level was 30% higher during the periconcep- tual period than the lifetime of the study, the adjustment factor would be 1.3. We then mul- tiplied traffic counts by these adjustment fac- tors and recalculated the quantiles of exposure for each of the traffic metrics. As a sensitivity analysis, we also limited our analysis to study subjects who lived within 10 km of an NO2 monitor (n = 3,787). Statistical analysis. We modeled the effect of each of the traffic metrics on the risk of SAB using logistic regression with SAS version 9.1 statistical software (SAS Institute Inc., Cary, NC). Maternal age, race/ethnicity, SES, ciga- rette smoke exposure, employment status, and stressful life events were controlled for in the final logistic regression analyses because of their impact in changing the effect estimate of traffic on SAB or because of strong a priori hypotheses regarding their confounder status. The logistic regression analysis was performed both with and without the temporal adjustment for NO2 levels. Stratified analyses by air conditioner use, season of first trimester, commute time, region, race/ethnicity, maternal age, smoking status, and employment status were performed to eval- uate possible effect modification using the traffic metric (maximum traffic within 50 m) with the strongest association with SAB. For instances where effects between groups appeared visibly different and the p-value for the likelihood ratio test of interaction was ≤ 0.10, we evaluated the degree of departure of combined effects from additivity by calculating the relative excess risk due to interaction (RERI) and its associ- ated 95% confidence intervals (CIs) (Hosmer and Lemeshow 1992). For example, the RERI for nonsmokers was constructed as follows: [adjusted odds ratio (AOR) for nonsmoker and highest traffic] – (AOR for nonsmoker and lowest traffic) – (AOR for smoker and high- est traffic) + 1, where the reference group was smoker and lowest traffic exposure. Traffic exposure and spontaneous abortion Results The incidence of SAB among the women included in this analysis was 9.6%. Most of the women were non-Hispanic white, although ethnic minorities represented 34% of the sample. Most were either married or living with their partner and reported work- ing at time of interview. Although most women were located in several cities and suburbs east and south of San Francisco and east of Los Angeles, a few were located near the coast, where background levels of air pol- lution are generally lower. The distribution of SABs by demographic and exposure cat- egories is reported in Table 1. Higher rates of SAB were associated with being ≥ 35 years of age, being African American, and having been interviewed at ≤ 8 weeks of gestation. Rates of SAB were also higher in Asians and women who recently experienced two or more stress- ful life events. There were slight but not statis- tically significant increases in risk with highest exposure to some traffic metrics, most notably maximum AADT within 50 m. Distribution of traffic exposure was also examined by potential confounders (Table 2). Women exposed to the highest levels of traffic Table 2. Sociodemographic characteristics and exposures among women by traffic exposure categories (%). Percentile for maximum daily traffic within 50 ma (range in AADT) 0–74th (0–1,089) (n = 3,734) 75th–89th (1,090–15,199) (n = 741) ≥ 90th (≥ 15,200) (n = 504) Mother’s age (years) 18–24 25–34 ≥ 35 Race/ethnicity Black Hispanic/Latina Asian Non-Hispanic white Week of pregnancy at interview 0–8 > 8 Working at interview Working Not working Mother’s education Up to high school grad/GED Some college/tech school College grad and above Cigarette exposure Nonsmoker Smoked at LMP or interview SES Low income/low education High income/high education Other Recent stressful life events 0–1 ≥ 2 Pregnancy history First pregnancy Previous pregnancy, no SAB One previous SAB Two or more previous SABs GED, general education degree. aLocal roads were assigned a value of zero. 26.14 62.88 10.98 6.16 18.03 9.62 66.19 64.50 35.50 74.95 25.05 41.14 31.44 27.42 82.77 17.23 13.99 14.05 71.96 64.78 35.22 25.24 54.45 15.68 4.64 27.26 62.35 10.39 6.76 20.68 6.89 65.68 63.65 36.35 73.65 26.35 44.38 31.66 23.95 78.73 21.27 17.97 11.28 70.75 64.51 35.49 25.37 52.9 17.41 4.32 34.33 54.37 11.31 6.77 19.72 10.16 63.35 62.35 37.65 78.77 21.23 41.47 34.72 23.81 78.37 21.63 17.00 10.12 72.87 56.55 43.45 27.09 52.99 14.34 5.58 Environmental Health Perspectives • volume 117 | number 12 | December 2009 1941