Online Appendix for the following JACC article
TITLE: Urban Particulate Matter Air Pollution Is Associated With Subclinical
Atherosclerosis: Results From the Heinz Nixdorf Recall Study
AUTHORS: Marcus Bauer, MD, Susanne Moebus, PhD, MPH, Stefan Möhlenkamp, MD,
Nico Dragano, PhD, Michael Nonnemacher, PhD, Miriam Fuchsluger, Dipl. Soz., Christoph
Kessler, Dipl. Ing., Hermann Jakobs, PhD, Michael Memmesheimer, PhD, Raimund Erbel,
MD, Karl-Heinz Jöckel, PhD, and Barbara Hoffmann, MD, MPH, on behalf of the Heinz
Nixdorf Recall Study Investigative Group
APPENDIX
Exposure assessment
The study area covers a region of approximately 600 km 2. We used a residencebased approach to characterize exposure to urban air pollution. A validated
dispersion and chemistry transport model (EURAD) was applied to model daily mass
concentrations of particulate matter with an aerodynamic diameter less than 10 µm
(PM10) and less than 2.5 µm (PM2.5) on a grid of 1 km2 (Memmesheimer et al. 2004).
The EURAD model uses input data from official emission inventories on a scale of 1
km2, including industrial sources, household heating, traffic and agriculture, data on
hourly meteorology and regional topography. Surface concentrations are calculated
by dispersing emissions in horizontal strata, taking chemical reactivity, mass
transport between horizontal strata, and deposition into account. The primary model
output is then calibrated using measured PM concentrations from routine monitoring
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sites to derive corrected exposure concentrations in each grid cell. The calibration
process takes the nature of the monitoring station (traffic, industrial, urban or regional
background site) into account. Because the value for each grid cell represents the
average air pollutant concentration in this 1 km2 area, these average concentrations
can diverge from the measured concentration at the monitoring site. Primary modelderived PM10 concentrations and corrected PM10 concentrations for every day of the
study period were compared with measured values from routine monitoring sites
which measure the urban or background concentration. Due to lack of regulation,
PM2.5 concentrations were not routinely measured at monitoring stations in Germany
during the study period. The state agency responsible for air quality monitoring
operates only a few PM2.5 monitoring sites and regularly calculates transformation
factors for the conversion of PM10 into PM2.5. These derived PM2.5 values were used
in the model development.
Daily surface concentrations of each pollutant were modelled for the time period from
November 2000 until July 2003 on a scale of 1 km2. The surface concentrations were
then assigned to the addresses of the participants, using a geographic information
system (ArcView 9.2, ESRI, Redlands, CA, USA). For the assessment of long-term
residential exposure, the average of the 365 daily values of the participant’s grid cell
prior to the participant’s examination date was calculated. For participants with
examination dates before November 2001, for which the prior 365 day average could
not be calculated because modeling started only in November 2000, we used the first
available 365 days.
Vehicle-type specific traffic densities for the entire study region for the year 2002
were supplied by the LANUV NRW. Residential exposure to traffic was estimated by
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measuring the distance between residence and next major road (ArcView 9.2, ESRI,
Redlands, CA, USA). Major roads were defined as roads with a traffic density in the
highest quintile of the distribution of traffic densities, irrespective of road type.
Statistical modeling approach and sensitivity analysis
We used generalized additive models with penalized splines to investigate the shape
of the association between continuous covariates and the outcome. For covariates
which showed signs of a departure from a linear relationship with the outcome, we
included appropriate higher order terms (squared or cubed) in the final linear
regression model. In the final, more parsimonious model, we only retained those
covariates which acted as confounders of the air pollution-atherosclerosis association
or improved model fit.
To investigate effect-measure modification, we first estimated associations in specific
subgroups, which have been shown to be more susceptible to air pollution effects.
Then we included product terms of the potential effect modifier with the exposure to
examine departure from an additive relationship for age, gender, city of residence,
socio-economic status, smoking status, and presence or absence of diabetes,
hypertension and coronary artery disease.
Regression diagnostics included the exclusion of outlying values and data points with
high leverage.
In sensitivity analyses we repeated the analysis using maximum CIMT instead of
mean CIMT as the dependent variable. In addition, we dichotomized the continuous
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outcome measure mean CIMT at the 90th percentile and conducted a logistic
regression. To examine the additional adjustment for income, we included equivalent
income in the subgroup of participants for whom this information was available
(n=3,204). To explore the effect of different ways of modeling traffic exposure, we
also included distance to high traffic in categories (0-50 m, 51-100 m, 101-200 m, >
200 m as reference).
Results
Primary modelled and corrected PM10 concentrations were in good agreement with
measured PM10 concentrations from urban and regional background monitoring
stations. In the 3 years of the study period, the correlation coefficients (by year)
between the primary model output and the measured concentrations were 0.43 to
0.46 with a bias ranging from -0.3 to -4.3, and the correlation coefficients between
the corrected values and the measured concentrations were 0.89 to 0.94 with a bias
from 0 to -0.7 µg/m3.
Baseline characteristics of the study population (n=3380) and the 1434 participants
with missing values are presented in the supplemental table 2A.
The sensitivity analysis revealed no major change when using the maximum CIMT of
both sides as the outcome metric (percent change in maximum CIMT per IDR of
PM2.5 4.2%; 95% CI 1.7–6.8%). In the logistic regression analysis, we did not see an
association between PM2.5 or PM10 exposure and being in the top 10 percent of the
CIMT distribution (OR 1.01; 95% CI 0.60–1.67). Because the effect was generally
stronger in younger participants with lower CIMT values, the loss of information when
dichotomizing the outcome can mask the effect. Including equivalent income did not
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change the estimates for PM or traffic exposure. When we included proximity to
traffic in categories, effect estimates were inconsistent without evidence for an
exposure-response relationship within 200 m of a highly trafficked road (0-50 m:
0.7% (95% CI -3.5-5.0%); 51-100 m: -1.9% (-4.8-1.1%); 101-200 m: 1.0% (-1.23.2%), > 200 m reference). Exclusion of 15 observations with studentized residuals >
│3│ led to a reduction of the main estimate for PM2.5 to 3.9%. The estimate for
distance to traffic was not changed.
Acknowledgements
Apart from the authors, the following persons were essentially involved in the Heinz
Nixdorf Recall Study: Baumgart D, Essen, Hirche H, Essen, Peter R, Ulm,
Schmermund A, Frankfurt, Stang A, Halle, K Lauterbach, Cologne.
Previous members of the Study Group: Assert R, Beck EM, Benemann J, Berenbein
S, Ebradlidze T, Freitag N, Gutersohn A, Jaeger B, Karoussos I, Kriener P,
Mankovski J, Münkel S, Öffner A, Pump H, Püttmann P, Ritzel A, Schuldt K, SnyderSchendel E, Weyers S, Winterhalder S.
Advisory board:
Meinertz T, Hamburg (Chair), Bode C, Freiburg, de Feyter PJ, Rotterdam,
Netherlands; Güntert B, Hall I.T. (Austria); Gutzwiller F, Bern, Switzerland; Heinen H,
Bonn; Hess O, Bern, Switzerland; Klein B, Essen; Löwel H, Neuherberg; Reiser M,
Munich; Schmidt G, Essen; Schwaiger M, Munich; Steinmüller C, Bonn; Theorell T,
Stockholm, Sweden; Willich SN, Berlin.
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Table 2A: Summary statistics of 3,380 analyzed participants of the Heinz Nixdorf
Recall Study and the 1434 participants who were excluded due to missing data.
Analyzed
Excluded participants due
participants
to missing data on
(n=3,380)
outcome or covariates
(n=1434)
n missing
Median ± IQR IMT [mm]
0.66 ± 0.16
1035
0.67 ± 0.17
Mean ± SD Age [years]
59.5 ± 7.7
0
59.9 ± 8.0
Male sex, n (%)
1743 (52)
0
652 (45)
Educational attainment, n (%)
16
<10 years
330 (10)
217 (15)
11-13 years
1874 (55)
802 (57)
14-18 years
812 (24)
256 (18)
>18 years
364 (11)
143 (10)
Occupational status, n (%)
15
Employed (≥15 h/week)
1397 (41)
531 (37)
Inactive or home maker
448 (13)
215 (15)
Pensioner
1326 (39)
577 (41)
209 (6)
96 (7)
Unemployed
Smoking status, n (%)
10
Current smoker
776 (23)
352 (25)
Former smoker
1221 (36)
441 (31)
Never smoker
1383 (41)
631 (44)
Mean ± SD Cigarettes/day in current smokers
18.8 ± 10.8
27
19.4 ± 11.1
Median ± IQR pack years in ever smokers
21.0 ± 29.3
35
23 ± 30.9
1094 (32)
0
480 (33)
Environmental tobacco smoke, n (%)
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Alcohol consumption >3 times/week, n (%)
686 (20)
301
216 (19)
584 ± 1353
87
504 ± 1218
Mean ± SD Body mass index [kg/m²]
27.9 ± 4.6
29
28.0 ± 4.8
Mean ± SD total cholesterol [mg/dL]
229 ± 39
22
229 ± 40
202 (6)
0
125 (9)
Coronary calcification score >0, n (%)a
2292 (71)
76
937 (69)
Diabetes mellitus, n (%)
454 (13)
0
202 (14)
Hypertension, n (%)b
1264 (37)
15
199 (42)
Antihypertensive medication, n (%)
1296 (38)
313
449 (40)
Lipid-lowering medication, n (%)
352 (10)
310
125 (11)
Median ± IQR Physical activity [MET
minutes/week]
Coronary heart disease, n (%)
City of residence, n (%)
0
Mülheim
1255 (37)
504 (35)
Essen
1125 (33)
529 (37)
Bochum
100 (30)
401 (28)
Area within city, n (%)
5
North
470 (14)
221 (15)
Middle
1892 (56)
801 (56)
South
1018 (30)
407 (28)
a based
on 3251 participants due to missing values for the coronary artery
calcification score
b
Systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥ 90 mmHg.
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