index (BMI), socioeconomic status, current smoking, exposure to environmental tobacco smoke (ETS), occupational exposure to tem- perature (heat and cold) and dust, as well as heating with fossil fuels were included as covariates in all models to adjust for potential confounding. BMI as covariate in regression analyses was calculated as the mean of BMI at baseline and at follow-up. The correlation between both these determinations was high (r = 0.77). In exploratory analyses, the study population was subdivided along the median of C3c serum levels (100.40 mg/dL) of all participants with complete data on C3c, all covariables at baseline and available follow-up information; HRs (95% CI) were calculated separately for both C3c subgroups. Whether or not C3c levels modified the association of the exposure variables with type 2 diabetes risk was tested by including an interaction term in the models. All statistical analyses were carried out with SAS for Windows (ver- sion 9.1; SAS Institute Inc., Cary, NC, USA). ArcView (version 9.1; ESRI, Redlands, CA, USA) was used for the geographic data. Results Characteristics of study population. The study was based on 1,775 women who participated in both baseline and follow-up investigations, who had data for all covariables, and who had no diagnosis of diabetes before 1990. Study participants differed from nonparticipants who were still alive at follow-up (n = 1,933) and participants who died during follow-up (n = 585) mainly by a higher level of educa- tion, a slightly lower BMI, a lower prevalence of hypertension, and lower C3c levels [see Supplemental Material, Table 1 (doi:10.1289/ ehp.0901689)]. Of the women who reported no diag- nosis of diabetes before 1990, 187 (10.5%) reported being newly diagnosed with diabe- tes after 1990. In 1990, 54 women already had a diagnosis of diabetes and were therefore excluded from the analysis. Table 1 shows the characteristics of women with newly diag- nosed diabetes and those without a diagnosis of diabetes. Those with incident diabetes were slightly older, had a higher BMI, had higher C3c values, suffered more often from hyper- tension, and more frequently had a history of myocardial infarction or stroke (Table 1). Furthermore, women who had been diag- nosed with diabetes were less educated, heated their rooms more often with ovens using fos- sil fuels, had higher workplace dust exposure, and were more likely to be smokers than were those who had never been diagnosed with diabetes. Slightly more women with incident diabetes (20.9%) changed their address than did women without diabetes (15.4%). The majority of these women did not change their post-code area when they moved. Air pollution and type 2 diabetes We evaluated the reliability of the questionnaire answers concerning a diabe- tes diagnosis. In the follow-up interview in 2008–2009, 386 women answered the ques- tions pertaining to diabetes. Two women reported a diagnosis of diabetes in the 2006 questionnaire but not in the interview. In the interview, two women who had claimed an onset before 1990 now reported an onset after 1990. Therefore, 382 replies in the interview regarding the diagnosis of diabetes between 1990 and 2006 were concordant with the questionnaire from 2006 (99% concordance). Exposure to ambient air pollution. Exposure to ambient air pollution was higher among women who developed type 2 dia- betes in the 16-year follow-up (Table 2). This finding was demonstrated for all chosen indicators and for all spatial scales. It is note- worthy that mean PM emissions at the place of residency of women who lived near busy streets and who had a low-education level was significantly higher than those of women with a higher education level who lived near busy streets (2.01 t/year/km2 compared with 1.83 t/year/km2). Such a socioeconomic Table 1. Characteristics of women in the SALIA study population stratified by type 2 diabetes status.a No diabetes Incident diabetes Variable (n = 1,588) (n = 187) p-Value At baseline Age (years) 54.4 ± 0.70 54.6 ± 0.51 0.0003 BMI (kg/m2)b 26.7 ± 3.8 30.0 ± 4.5 < 0.0001 C3c (mg/dL)b,c 89.7 (1.32) 108.3 (1.31) < 0.0001 Hypertension (%) 20.1 43.5 < 0.0001 Education < 10 years (%)d 19.8 27.3 0.0174 Single-room heating with fossil fuels (%) 16.5 27.3 0.0003 Workplace exposure Dust/fumes (%)e 9.0 12.8 0.0898 Extreme temperatures (%)f 6.4 10.2 0.0551 Smoking (%) 0.1184 Never, without ETSg 45.7 39.1 Never, with ETSg 33.7 32.6 Ex-smoker 8.1 12.8 Current; 0–15 pack-years 5.0 4.8 Current; > 15–30 pack-years 4.7 5.9 Current; > 30 pack-years 2.8 4.8 At follow-up Age (years) 71.0 ± 3.4 71.7 ± 2.8 0.0116 BMI (kg/m2) 26.5 ± 4.0 29.8 ± 4.9 < 0.0001 Hypertension (%) 50.2 72.4 < 0.0001 Hyperlipidemia (%) 42.1 49.7 0.0563 History of myocardial infarction (%) 3.2 9.7 < 0.0001 History of stroke (%) 3.5 7.6 0.0079 Change in address (%) 15.4 20.9 0.0524 Without change in postcode (%) 8.9 9.1 0.9463 With change in postcode (%) 6.4 11.8 0.0067 aData are given as mean ± SD or proportions unless indicated otherwise; the proportion of missing values was < 10% for all variables except C3c [n = 466 missing (29%) among those without diabetes, 58 missing (31%) among those with diabetes]. bMeasurements at day of baseline investigation; all other variables are based on questionnaires. cGeometric mean (geometric SD). dMaximum number of years of education of study participant or husband. eQuestionnaire‑ based information: self‑ reported long‑ lasting exposure at workplace to dust, gases, or fumes. fQuestionnaire‑ based informa‑ tion: self‑reported long‑lasting exposure at workplace to heat, chilliness, or wetness. gETS, environmental tobacco smoke exposure (workplace or home). Table 2. Exposure of women in the SALIA study population to ambient air pollution, especially traffic‑ related air pollutants, assessed on four spatial scales and stratified by type 2 diabetes status.a No diabetes Incident diabetes All (n = 1,588) (n = 187) (n =1,775) p-Value Variable P50 (P25–P75)b P50 (P25–P75) P50 (P25–P75) Wilcoxon test Monitoring stations PM10 (µg/m3) 46.9 (44.0–54.1) 48.6 (44.0–54.1) 46.9 (44.0–54.1) 0.0002 NO2 (µg/m3) 41.7 (23.3–48.2) 46.5 (23.3–48.2) 41.7 (23.3–48.2) 0.0003 Emission inventory PM from traffic (tons/year/km2) 0.54 (0.20–1.06) 0.66 (0.34–1.46) 0.54 (0.22–1.09) 0.0002 NO2 from traffic (tons/year/km2) 11.7 (5.2–23.6) 12.9 (8.2–31.4) 12.0 (5.4–24.4) 0.0022 Land-use regression Soot (10–5 m) 1.88 (1.66–2.05) 1.96 (1.72–2.14) 1.89 (1.67–2.06) < 0.0001 NO2 (µg/m3) 33.9 (23.8–38.6) 36.7 (26.3–39.4) 34.5 (23.8–38.8) 0.0001 Distance < 100 m from busy road (%)c 15.6 17.7 — 0.4549d aFive‑year averages for the stationary sources were used. The proportion of missing values was < 10% for all variables. bP50 (P25–P75): median and the 25th and 75th percentiles. cMore than 10,000 cars/day. d p‑Value χ2‑test Environmental Health Perspectives • volume 118 | number 9 | September 2010 1275