study in the highly urbanized Ruhr area in western Germany (Schmermund et al. 2002). In total, 4,814 men and women 45–75 years of age participated at baseline. The study was approved by the local institutional review board. All study participants gave informed consent. Environmental data. We applied a vali- dated dispersion and chemistry transport model [the European Air Pollution Dispersion (EURAD) model system] to model daily mass concentrations of PM with an aerodynamic diameter ≤ 2.5 μm (PM 2.5) and ≤ 10 μm (PM10) on a grid of 1 km2 (Ebel et al. 2007; Memmesheimer et al. 2004). Official emis- sion inventory data on a scale of 1 km2, hourly meteorology, and regional topography were used in the model. The primary model output was then calibrated with measured PM10 con- centrations from routine monitoring stations. The daily surface concentrations of PM and ozone (O3) for the period from November 2000 through July 2003 were assigned to the participants’ addresses using a geographic information system (ArcView, version 9.2; ESRI, Redlands, CA, USA). We calculated the mean of the 365 daily PM values before the baseline examination date for each participant. For participants examined before November 2001 [n = 1,162 of 4,814 (24.1%)], we used the mean of 365 daily values starting from the first available PM measurement. Traifc proximity. We assessed residential proximity to the nearest major road using digi- talized maps (ArcView) and daily traffic counts for 2000 [obtained by request from North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection (LANUV), Recklinghausen, Germany]. A major road was defined as a road section in the upper quartile of the daily traffic count in the study area (> 22,980 vehicles/day for general traffic, > 756 vehicles/day for heavy-duty traf- fic). We categorized proximity to the road as ≤ 50 m, 51–100 m, 101–200 m, and > 200 m (reference category). Road trafic noise. Long-term road noise was modeled according to the Directive 2002/49/EC of the European Parliament and of the Council (European Commission 2002) as a weighted day-evening-night 24-hr aver- age sound level (Lden). The following parame- ters were considered for noise level modeling: small-scale topography of the area, dimen- sions of buildings, noise barriers, street axis, vehicle-type–specific traffic density, speed limit, and type of street surface. The following formula was used: Lday Lden = 10 # lg 214 a12 # 10 10 + 4 # 10 Lnight+10 10 k, + 8 # 10 Levening+5 10 where Lday, Levening, and Lnight are the A-weighted long-term average sound level (in decibels) determined over all the day, evening, and night periods of a year, respectively. Exposure to long-term road traffic noise was categorized as ≤ 55 dB (reference), > 55 to ≤ 60 dB, > 60 to ≤ 65 dB, and > 65 dB. Outcome. BP was measured with an auto- mated oscillometric device (model HEM- 705CP; OMRON Corp., Hoofddorp, the Netherlands), using standardized record- ing protocols as described previously (Stang et al. 2006). For the analyses, we calculated the mean of the second and third of three measurements [see Supplemental Material (http://dx.doi.org/10.1289/ehp.1103564)]. Medication use during last 7 days was catego- rized according to the Anatomical Therapeutic Chemical Classification System of the World Health Organization Collaborating Centre for Drug Statistics Methodology (2006). We defined antihypertensive medication as cur- rent intake of the following classes of drugs or any combination of them: diuretics, β-blockers, angiotensin-converting enzyme inhibitors, angiotensin-receptor antagonists, calcium-channel blockers, α-blockers, centrally active antihypertensive drugs, and hydrala- zine. We defined hypertension as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg, or current use of antihypertensive therapy (Chobanian et al. 2003). Other covariates. We assessed current and previous smoking, the lifetime cumula- tive exposure in pack-years, and passive smok- ing exposure. Amount of alcohol intake was given as the number of drinks per week (one drink defined as 0.25 L beer, 0.1 L wine, or 0.02 L spirits), categorized as 0, 1–3, > 3 drinks per week. Participants’ physical activity was expressed as weekly energy expenditure in metabolic equivalents. We defined diabetes mellitus as prior physician diagnosis of diabetes mellitus, use of antidiabetic drugs, random blood glucose ≥ 11.1 mmol/L, or fasting blood glucose ≥ 7 mmol/L. Individual-level socio- economic status (SES) was assessed as years of formal education (United Nations Educational, Scientific, and Cultural Organization 1997), and economic activity (i.e., employed, retired, unemployed, or economically inactive). Statistical methods. We included partici- pants with nonmissing information on expo- sure, outcome, and covariates. Correlations between PM exposures, traffic proximity, road traffic noise, and short-term environmen- tal variables were assessed with Spearman’s rank correlation coefficient. We assessed the association of PM with BP and hyperten- sion using generalized additive models and logistic models, respectively. PM2.5 and PM10 were included as linear terms, based on prior evidence of a linear exposure–response rela- tionship between PM and cardiovascular out- comes. We identified possible confounders using causal graphs (Glymour and Greenland 2008). We specified the most likely temporal Environmental Health Perspectives • volume 119 | number 12 | December 2011 Long-term air pollution and blood pressure relations between variables based on prior biological and epidemiologic knowledge and derived adjustment sets [for additional information on model specifications, see Supplemental Material, Figure 1 and pp. 6–7 (http://dx.doi.org/10.1289/ehp.1103564)]. Specifically, we included the main exposure (1-year mean PM2.5 or PM10), short-term environmental exposures (spatially resolved 3-day lag of PM, 3-day mean of temperature, season, and time trend), traffic proximity, and area-level variables (city, area) in the basic model. The main model additionally included sex, age, and individual-level covariates [body mass index (BMI), alcohol consumption, smoking, passive smoking, and physical activ- ity] instead of area-level variables. In extended models we also included traffic noise and individual SES, antihypertensive medication, diabetes mellitus, and short-term O3 expo- sure (4-day mean). To study the between- and within-area effects, we conducted an extended analysis with a model that included both area- level and individual-level confounders, thereby reflecting the within-city effect of the basic model in a fully adjusted model. To investi- gate the effect of long-term PM exposure in participants without antihypertensive medica- tion, we conducted a stratified analysis accord- ing to intake of antihypertensive medication. Continuous covariates that did not display departure from a linear relationship with the outcome were entered as linear terms. Time trend, age, BMI, and pack-years of smoking (in the analyses of diastolic BP) were han- dled as penalized cubic splines (Hastie and Tibshirani 2007). For the exposure–response analyses, PM was divided into quartiles, with the lowest as the reference category. We explored pos- sible exposure effect modification by covari- ates using product terms. For this, continuous variables were dichotomized at the median. In further sensitivity analyses, we esti- mated the PM effect after excluding 500 par- ticipants exposed to high noise levels (Lden > 65 dB) from the analysis. Statistical analyses were performed using SAS software (version 9.2; SAS Institute Inc., Cary, NC, USA). All effects are given per interquartile range (IQR) of the exposure if not specified otherwise. Results We included 4,291 participants with non- missing values on exposure, outcome, and covariates. The 523 excluded partici- pants did not differ substantially from those included, although they had slightly higher rates of alcohol consumption and higher prevalence of current smoking and environ- mental tobacco exposure [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ ehp.1103564)]. Mean systolic and diastolic BP levels of the study participants were 1707