Green et al. Table 1. Proportion of SABs and unadjusted ORs and 95% CIs, by demographic and exposure variable Cases 479 Percent SAB 9.62 categories. Variable Total Mother’s age (years) 18–24 25–34 > 34 Race/ethnicity Hispanic/Latina Black Asian Non-Hispanic white Marital status Married/living together Separated/divorced/widowed Never married Week of pregnancy at interview 0–8 > 8 Working at interview Yes No 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 0–74th (0–1,089) 75–89th (1,090–15,199) ≥ 90th (≥ 15,200) 0–50th (0) 51–74th (1–11,499) 75–89th (11,500–25,799) ≥ 90th (≥ 25,800) Maximum daily traffic within 50 ma [percentile (range)] Maximum daily traffic within 100 ma [percentile (range)]) Maximum daily traffic within 150 ma [percentile (range)]) 0–49th (0–5,269) 50–74th (5,270–17,999) 75–89th (18,000–33,199) ≥ 90th (≥ 33,200) Distance to nearest arterial (m) 0–50 51–100 101–200 201–300 > 300 Maximum weighted traffic within 150 m (percentile) 0–50th 51–74th 75th–89th ≥ 90th 1940 GED, general education degree. aLocal roads were assigned a value of zero. No. 4,979 1,351 3,084 544 314 925 461 3,275 4,566 110 301 3,188 1,781 3,740 1,237 2,072 1,582 1,320 4,056 907 724 644 3,496 3,182 1,797 1,266 2,690 786 233 3,734 741 504 2,530 1,203 746 500 2,488 1,233 757 501 882 783 1,196 868 1,250 2,489 1,245 747 498 105 280 94 40 75 56 307 435 16 28 350 128 382 96 183 157 138 387 92 61 60 348 289 190 109 251 78 41 355 65 59 238 110 77 54 241 122 67 49 92 73 105 85 124 233 122 72 53 7.77 9.08 17.28 12.74 8.11 12.15 9.37 9.53 14.55 9.30 10.98 7.19 10.21 7.76 8.83 9.92 10.45 9.54 10.14 8.43 9.32 9.95 9.08 10.57 8.61 9.33 9.92 17.60 9.51 8.77 11.71 9.41 9.14 10.32 10.80 9.69 9.89 8.85 9.78 10.43 9.32 8.78 9.79 9.92 9.36 9.80 9.64 10.64 Unadjusted OR (95% CI) 1.21 (0.96–1.52) 2.52 (1.88–3.38) 1.44 (1.02–2.02) 0.86 (0.67–1.12) 1.31 (0.97–1.77) 1.58 (0.93–2.72) 0.99 (0.66–1.46) 0.63 (0.51–0.77) 0.74 (0.59–0.93) 1.13 (0.91–1.41) 1.2 (0.95–1.50) 1.09 (0.87–1.38) 0.87 (0.66–1.15) 1.00 (0.75–1.31) 1.18 (0.98–1.43) 1.15 (0.91–1.45) 1.19 (0.88–1.60) 2.49 (1.71–3.63) 0.92 (0.69–1.21) 1.26 (0.94–1.69) 0.97 (0.77–1.23) 1.11 (0.85–1.45) 1.17 (0.85–1.59) 1.02 (0.81–1.29) 0.91 (0.68–1.20) 1.01 (0.73–1.40) 1.06 (0.80–1.41) 0.93 (0.69–1.27) 0.87 (0.67–1.15) 0.99 (0.74–1.32) 1.04 (0.83–1.31) 1.03 (0.78–1.36) 1.15 (0.84–1.58) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5,144 participants available for analysis (499 SABs, 4,613 live births, and 32 stillbirths). Assessment of exposure. Spatial variation. Procedures for geocoding of residences were reported previously (Green et al. 2004; Waller et al. 2001). We had a residential history for each woman. We selected the residence at the time of the LMP if it differed from residence at the time of interview, because we wanted to ensure that exposure would be assessed very early in pregnancy, including the time of implantation. Only 6% of the women moved between LMP and interview. Geocoding of the residential address at LMP was performed using ArcView geographic information system (GIS) software (version 3.0a; Environmental Systems Research Institute, Redlands, CA) with StreetMap extension (Build 7), and Mapquest. We were able to successfully geocode the resi- dences of 4,979 of the 5,144 study subjects. The addresses of the remaining women were not geocoded because of post office boxes, miss- ing data on residence, or inability of the soft- ware to locate the address on the street maps. Those women were, on average, slightly older (by < 1 year) than the women included in the study but did not differ from them with respect to race/ethnicity, smoking status, pregnancy outcome, employment status, and socioeco- nomic status (SES). The geocoded residences were overlaid with a street network layer of traffic data for 1992 provided by the California Department of Transportation (CalTrans) (California Department of Transportation 1993); traffic counts were very close in time to the study period. These data provide the annual average daily traffic (AADT) for all roads of the following functional classes: principal arterial interstates, principal arterial freeways and high- ways, minor arterials, and major and minor collectors (Green et al. 2004). There are no traffic flow data for local residential streets. The AADT represents the average number of vehicles traveling in both directions on a dis- crete road segment. Using ArcView 3.2a, we determined all road segments located within a 300-m radius of each woman’s residence, as well as information regarding functional class and AADT for 1992. Exposure to residential traffic was esti- mated for each woman using each of the fol- lowing three traffic metrics: a) Maximum AADT within a specified buffer was con- structed by taking the largest reported AADT of the road segments within a set radius from the residence (50 m, 100 m, and 150 m). If there were only local residential streets and no CalTrans counted segments in the buffer, we assigned a value of zero to that residence to construct percentiles of traffic volume. b) We estimated the road segment with the highest AADT within 150 m after application of a Gaussian weight based on distance from the residence. The weighting model, developed by ovlume 117 | number 12 | December 2009 • Environmental Health Perspectives