196 VOLUME 116 | NUMBER 2 | February 2008 • Environmental Health Perspectives

Research

Although air pollution concentrations have

decreased substantially over the last several

decades, recent studies from the United States

found associations between long-term exposure to air pollution and cardiopulmonary and

lung cancer mortality (Abbey et al. 1999;

Dockery et al. 1993; Jerrett et al. 2005; Laden

et al. 2006; Miller et al. 2007; Pope et al.

1995, 2002). Cohort studies from Europe

have tended to confirm the U.S. findings

(Filleul et al. 2005; Gehring et al. 2006; Hoek

et al. 2002; Nafstad et al. 2004), but the

emphasis has been on different pollutants and

on different exposure assessment methods. The

U.S. studies have used data from single monitoring stations to characterize exposure of subjects living in that city, or spatial interpolation

from multiple monitoring stations. Most

European studies have estimated exposure at

the home address using dispersion or stochastic

modeling and variables such as living close to

busy roads. In a previous Dutch study in

5,000 subjects, a random sample from a full

cohort (n ~ 120,000), cardiopulmonary and

all-cause mortality were associated with living

close to a major road, with relative risks of

1.95 [95% confidence interval (CI), 1.09–3.52]

and 1.41 (95% CI, 0.94–2.12), respectively

(Hoek et al. 2002). In this article we extend

this work to the full cohort with a much larger

number of deaths and with an improved exposure assessment method.

Materials and Methods

Study design. The cohort has been described in

detail elsewhere (van den Brandt et al. 1990a).

Briefly, the Netherlands Cohort Study on Diet

and Cancer (NLCS) was initiated in September

1986 with the enrollment of 120,852 subjects

(58,279 males and 62,573 females) 55–69

years of age living in 204 municipalities located

throughout the country. The study was

designed as a case–cohort study: Cases are

derived from the entire cohort, whereas the person-years at risk are estimated from a random

subcohort (n = ~5,000) (Volovics and van den

Brandt 1997). This approach was selected for

efficiency of baseline questionnaire processing

and avoidance of active follow-up of the entire

cohort. At baseline, all participants completed

an 11-page questionnaire on dietary habits and

other risk factors for cancer. For all participants, data from one machine-readable page of

the questionnaire were entered at baseline (with

information about age, sex, and smoking status). After recruitment, the entire cohort was

followed up for cancer incidence by record

linkage to cancer registries (van den Brandt

et al. 1990b). For the emerging cases and the

randomly selected subcohort, the remaining 10

questionnaire pages (not machine readable)

were manually entered, blinded with respect to

case–subcohort status. The exact residential

address at baseline was available for all study

participants.

Mortality was assessed between 1 January

1987 and 31 December 1996. Mortality data

were obtained from the Dutch Central Bureau

of Genealogy and the Dutch Central Bureau

of Statistics (unpublished data). The cause of

death was coded according to the International

Classification of Diseases, 9th Revision [ICD-9;

World Health Organization (WHO) 1975]

(for 1986–1995) and 10th Revision (ICD-10;

WHO 1993) (for 1996). Causes of death were

grouped into natural cause, cardiopulmonary,

cardiovascular, respiratory, lung cancer, and

mortality other than cardiopulmonary or lung

Address correspondence to B. Brunekreef, Institute

for Risk Assessment Sciences, Utrecht University,

P.O. Box 80178, 3508 TD Utrecht, the Netherlands.

Telephone: 31 30 2539490/94. Fax: 31 30 2539499.

E-mail: b.brunekreef@uu.nl

Supplemental Material is available online at http://

www.ehponline.org/members/2007/10767/suppl.pdf

We are indebted to the participants of this study

and further thank the Dutch Central Bureau of

Genealogy and the Dutch Central Bureau of

Statistics for providing data. We thank S. van de

Crommert, H. Brants, J. Nelissen, C. de Zwart, W.

van Dijk, M. Moll, A. Pisters, M. Jansen, H. van

Montfort, T. van Moergastel, L. van den Bosch, and

R. Schmeitz for assistance; C. van Wiechen

(National Institute of Public Health and the

Environment) for advice on geographic information

system calculations; the National Institute of Public

Health and the Environment for providing air pollution data monitored by the National Air Quality

Monitoring Network in the Netherlands.

The research described in this article was conducted

under contract to the Health Effects Institute (HEI)

(research grant 01-2), an organization jointly funded

by the U.S. Environmental Protection Agency (EPA)

and certain motor vehicle and engine manufacturers.

The Netherlands Cohort Study on Diet and Cancer

was supported by the Dutch Cancer Society. 

The contents of this article do not necessarily

reflect the views of HEI, or its sponsors, nor do they

necessarily reflect the views and policies of the U.S.

EPA or motor vehicle and engine manufacturers.

The authors declare they have no competing

financial interests.

Received 15 August 2007; accepted 9 November

2007.

Long-Term Effects of Traffic-Related Air Pollution on Mortality 

in a Dutch Cohort (NLCS-AIR Study)

Rob Beelen,1 Gerard Hoek,1 Piet A. van den Brandt,2 R. Alexandra Goldbohm,3 Paul Fischer,4 Leo J. Schouten,2

Michael Jerrett,5 Edward Hughes,6 Ben Armstrong,7 and Bert Brunekreef1,8

1Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands; 2Department

of Epidemiology, Maastricht University, Maastricht, the Netherlands; 3TNO Quality of Life, Department of Prevention and Health, Leiden,

the Netherlands; 4Centre for Environmental Health Research, National Institute for Public Health and the Environment (RIVM), Bilthoven,

the Netherlands; 5School of Public Health, University of California, Berkeley, California, USA; 6Edward Hughes Consulting, Ottawa, Ontario,

Canada; 7Public and Environmental Research Unit, London School of Hygiene and Tropical Medicine, London, United Kingdom; 8Julius

Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands

BACKGROUND: Several studies have found an effect on mortality of between-city contrasts in

long-term exposure to air pollution. The effect of within-city contrasts is still poorly understood.

OBJECTIVES: We studied the association between long-term exposure to traffic-related air pollution

and mortality in a Dutch cohort.

METHODS: We used data from an ongoing cohort study on diet and cancer with 120,852 subjects

who were followed from 1987 to 1996. Exposure to black smoke (BS), nitrogen dioxide, sulfur

dioxide, and particulate matter ≤ 2.5 μm (PM2.5), as well as various exposure variables related to

traffic, were estimated at the home address. We conducted Cox analyses in the full cohort adjusting

for age, sex, smoking, and area-level socioeconomic status. 

RESULTS: Traffic intensity on the nearest road was independently associated with mortality.

Relative risks (95% confidence intervals) for a 10-μg/m3 increase in BS concentrations (difference

between 5th and 95th percentile) were 1.05 (1.00–1.11) for natural cause, 1.04 (0.95–1.13) for cardiovascular, 1.22 (0.99–1.50) for respiratory, 1.03 (0.88–1.20) for lung cancer, and 1.04

(0.97–1.12) for mortality other than cardiovascular, respiratory, or lung cancer. Results were similar for NO2 and PM2.5, but no associations were found for SO2. 

CONCLUSIONS: Traffic-related air pollution and several traffic exposure variables were associated with

mortality in the full cohort. Relative risks were generally small. Associations between natural-cause

and respiratory mortality were statistically significant for NO2 and BS. These results add to the evidence that long-term exposure to ambient air pollution is associated with increased mortality.

KEY WORDS: air pollution, cohort, mortality, traffic. Environ Health Perspect 116:196–202 (2008).

doi:10.1289/ehp.10767 available via http://dx.doi.org/ [Online 19 November 2007]
cancer (Table 1). The NLCS study was

approved by institutional review boards from

Maastricht University and the Netherlands

Organization for Applied Scientific Research.

All cohort members consented to participation

by completing a mailed, self-administered

questionnaire.

Air pollution exposure assessment. Details

of the exposure assessment method have been

described previously (Beelen et al. 2007). In

summary, long-term exposure to outdoor air

pollution at the 1986 home address was estimated for all participants as the sum of

regional, urban, and local traffic contributions. The home addresses were geocoded

into standard Dutch geographic coordinates

[Address Coordinates Netherlands (ACN)]

using a database from 2000 consisting of all

registered addresses by the Dutch postal service (unpublished data). The accuracy of

ACN is high, with 93.5% of all coordinates

located at the centroid of the correct building,

6.0% located at the centroid of the correct

parcel, and only 0.5% not located in the correct building or parcel (Kadata 2001). No

information was available about the exact

work addresses of participants.

We estimated regional background concentrations using inverse distance weighed

interpolation of concentrations measured at

regional background sites in the National Air

Quality Monitoring Network (NAQMN).

We estimated the additional urban component using regression models with residual

concentrations for all regional background

and urban monitoring sites in the NAQMN

as dependent variable, calculated as measured

concentration minus estimated regional component concentration using cross-validation.

As predictor variables, we used the number of

inhabitants around a monitoring site and

land-use variables that indicated whether a

site was located in a city center, in a rural

background location, or in an industrial location. The sum of the regional and urban contributions was defined as background

concentration. Background concentrations

were estimated for nitrogen dioxide, black

smoke (BS), and sulfur dioxide. Average concentrations were estimated for 1976–1985

and 1987–1996 (in 1986 the NAQMN was

rearranged, resulting in only limited days with

valid measurements in 1986). We estimated

the background concentration for fine particles < 2.5 μm in diameter (PM2.5) by converting PM10 (particles < 10 μm) concentrations,

measured in the NAQMN from 1992 to

1996, into PM2.5 concentrations using a single ratio, established from monitoring data in

the Netherlands. This was done because

PM2.5 was not monitored in the Netherlands

during the study period.

Local traffic contributions were characterized by traffic variables that we estimated

using a geographic information system (GIS)

from a digital road network to which traffic

intensity data (average total number of motor

vehicles per 24 hr (mvh/24hr), including

weekdays and weekend) from 1986 were

linked. Because traffic intensity data were

available for different years for municipal

roads, we extrapolated traffic intensities to

1986 for roads for which 1986 traffic data

were not available. Extrapolation was based

on trends estimated from traffic intensity data

from municipalities with multiple years of

data (Beelen et al. 2007). Traffic intensities

were linked to the National Road Database

[Nationaal WegenBestand (NWB); unpublished data], based on road name and/or road

number. The NWB includes all roads in the

Netherlands that have a street name and/or

road number. More than 98% of the Dutch

roads have been included. More than 95% of

all road sections in the NWB have a maximum location difference of 10 m compared

with the true location (Beelen et al. 2007).

The digital road network and the coordinate

database for geocoding addresses were from

the same time period, and both used the same

standard Dutch coordinate system; therefore,

substantial error due to geographic differences

between the two databases is unlikely. 

We used the following as traffic variables:

a) traffic intensity on nearest road; b) sum of

traffic intensity in a 100-m buffer around

each residential address; c) traffic intensity on

the nearest major road (with > 10,000 mvh/

24hr) and distance to this road; and d) an

indicator variable “living within 100 m of a

motorway and/or within 50 m of a local road

with traffic intensity > 10,000 mvh/24hr.”

Further, we obtained quantitative estimates

for the local component for NO2, BS, and

PM2.5 from field-monitoring campaigns

(Beelen et al. 2007). We estimated no local

traffic contribution for SO2 because there is

virtually no traffic contribution to this pollutant. These local component concentrations

were added to the background concentrations, producing an overall exposure estimate

for each pollutant. 

Statistical analysis. We analyzed air pollution effects for overall concentrations and for

a combination of background concentrations

and traffic variables to identify effects of living

near busy roads separately.

We calculated relative risks (RRs) for concentration and traffic variable differences

between the 5th and the 95th percentiles of the

distributions. For NO2 this was rounded

to 30 μg/m3, for BS 10 μg/m3, for SO2

20 μg/m3, and for PM2.5 10 μg/m3. For the

traffic variables the differences between the 5th

and the 95th percentile were 10,000 mvh/24hr

for the traffic intensity on the nearest road,

335,000 mvh/100m for the sum of traffic

intensity in a buffer of 100 m, 20,000 mvh/

24hr for the traffic intensity on the nearest

major road, and 2.3 m for the natural logarithm of distance to this road.

We conducted analyses in the full cohort

using Cox proportional hazards models.

Person-years were calculated for all participants from baseline until death or end of follow-up. Person-years for subjects who died

from causes other than those being analyzed

were judged censored at time of death in

cause-specific analyses.

We adjusted for sex, age at baseline, and

smoking status coded as never, ex, and current

smoker separately for cigarette, cigar, and pipe

smoking. We further adjusted for area-level

indicators assessed using GIS data from the

Central Bureau of Statistics (CBS; unpublished data): percentage of persons with a low

and with a high income at the neighborhood

scale and the COROP area scale. COROP

areas have been defined in 1970 by the

Coordination Commission for Regional

Research Program such that each COROP

area consists of a central point (e.g., a city) and

the surrounding economic and social region.

The Netherlands is divided in 40 COROP

areas. We chose two scales for area-level socioeconomic status because life expectancy varies

significantly by COROP area (de Hollander

et al. 2006), and the neighborhood scale does

not capture such regional variations adequately. Low income was defined by the CBS

as below the 40th percentile and high income

as above the 80th percentile of the Dutch

income distribution (Table 2).

We used Cox-Poisson random effects survival software as described by Jerrett et al.

(2005) to incorporate spatial clustering at the

municipal and/or neighborhood scale in the

full cohort analyses. Both one-level (i.e.,

municipality or neighborhood) and two-level

(i.e., municipality and neighborhood) analyses

Long-term exposure to air pollution and mortality

Environmental Health Perspectives • VOLUME 116 | NUMBER 2 | February 2008 197

Table 1. Number of deaths during follow-up.

Cause ICD-9 codes ICD-10 codes No. of deaths

All cause All All 17,610

Natural cause < 800 < V01 17,286

Cardiopulmonary 400–440 or 460–519 I10–I70 or J00–J99 7,153

Cardiovascular 400–440 I10–I70 6,137

Respiratory 460–519 J00–J99 1,016

Lung cancer 162 C33–C34 1,888

Other than cardiopulmonary Not 400–440, not 162, and Not I00–I70, not J00–J99, 8,569

or lung cancer not 460–519, and < 800 not C33–C34, and < V01
were conducted, using independent-clusters

and distance–decay random-effects models. 

We assessed effect modification by sex

and cigarette-smoking status in the full cohort

by conducting separate analyses in subgroups.

We also conducted separate subgroup analyses for the percentage of persons with low

income in a neighborhood and COROP area

(in tertiles).

We tested heterogeneity in pollution relative risks across estimates from different subgroups using Cochran’s Q test (DerSimonian

and Laird 1986).

We also conducted case–cohort analyses,

including only subcohort members and cases

(deaths), which is the standard data analysis

approach in the NLCS. Cases were enumerated from the entire cohort, whereas personyears for the entire cohort were estimated

using the random subcohort of 4,971 participants. We analyzed data with Cox proportional hazards models. To account for

additional variance introduced by sampling

from the cohort, we estimated standard errors

using the robust Huber–White sandwich estimator (Lin and Wei 1989). 

In the case–cohort analyses we adjusted for

variables chosen a priori: sex; age at baseline;

active cigarette, cigar, and pipe smoking coded

as never/ex/current and number of cigarettes/

cigars/pipes and number of years of smoking;

passive smoking defined as whether the partner smoked; educational level in three categories: primary school, lower vocational

education, and high school and higher; the last

occupation of the participant was coded in six

categories: never paid work, blue collar, lower

white collar, upper white collar, other, and

whether the last occupation was > 40 years

ago; occupational exposure of the last occupation to biological dust, mineral dust, and

gases and fumes coded as no, low, and high

exposure assessed using the community-based

ALOHA job exposure matrix (Sunyer et al.

1998); marital status in two categories: married, and never married, divorced, or widowed; body mass index (BMI) in categories

< 20, 20–25, 25–30, and > 30 kg/m2; alcohol

consumption in five categories: none, 0–5,

5–15, 15–30, and > 30 g/day; dietary habits as

continuous variables: intake of vegetables,

fruit, fish, and energy-adjusted intake of fiber,

folate, and saturated, monounsaturated,

polyunsaturated, and trans fat; and for the

area-level indicators of socioeconomic status. 

No methods were available for spatial

autocorrelation analyses in a case–cohort setting. We assessed effect modification by educational level, fruit consumption (in tertiles),

and vegetable consumption (in tertiles) in the

case–cohort by conducting separate analyses

in subgroups.

Information about moving during the

follow-up period was available only in the

case–cohort group. We conducted a separate

analysis for subjects who did not move during

the follow-up period. We also conducted separate analyses in the case–cohort data set for

people who had a paid job and who did not

have a paid job at baseline. 

Data management was done using SPSS

12.0 (SPSS Inc., Chicago, USA), and statistical analyses were conducted using STATA

statistical software, version 8 (StataCorp,

College Station, TX, USA). GIS calculations

were conducted using ArcInfo (ESRI,

Redlands, WA, USA). Spatial analyses were

conducted using the R interface to the software described by Ma et al. (2003).

Results

We identified a geographic coordinate for

the home address for 97% of the subjects

(n = 117,528). About 15% of the subjects

died during follow-up (Table 1). Population

characteristics of subjects for whom a coordinate was available are summarized in Table 2.

Exposure data. Background concentrations estimated for the periods 1976–1985

and 1987–1996 were highly correlated (> 0.9)

for each pollutant. The correlations between

different air pollutants within the same period

were all > 0.8, except for SO2 (> 0.6) (Beelen

et al. 2007). Figure 1 illustrates that there is

considerable contrast of exposure in the full

cohort: 5,784 participants (4.9%) lived within

50 m of a road > 10,000 mvh/24hr and/or

within 100 m of a motorway; 4.4% lived

within 50 m of a road > 10,000 mvh/24hr,

and 0.6% lived within 100 m of a motorway.

Correlations between background BS and traffic intensity on the nearest road and sum of

traffic intensity in a 100 m buffer were modest: 0.12 and 0.28, respectively.

Associations between mortality and air

pollution concentrations. Table 3 shows the

associations between overall air pollution concentrations and mortality in the full cohort

and case–cohort analyses. 

In the full cohort analyses, there was no

association between estimated SO2 and mortality in any of the analyses. BS and NO2

were significantly associated with naturalcause and respiratory mortality. Effect estimates for other mortality were also increased

with RRs similar to the effect estimates for

natural-cause mortality. The effect estimate in

the full cohort for cardiopulmonary mortality

was 1.07 (95% CI, 0.98–1.15) for BS for a

10-μg/m3 increase in concentration.

In the adjusted case–cohort analyses, there

was no association between air pollution and

mortality.

Further analyses showed that age–sex

adjusted results between case–cohort and full

cohort analyses were comparable, especially for

BS and NO2, but adjusted results were not.

This was related to the loss of about 40% of

subjects in the adjusted case–cohort analyses

because of missing values in one or more confounder variables. Further analyses in the

case–cohort sample showed little difference

between the effect estimates adjusted for all

available confounders and adjusted for only the

limited set of confounders available in the full

cohort, when the analysis was restricted to subjects without missing values. Because of these

issues with the case–cohort analysis and evidence that residual confounding in the full

cohort is unlikely to be substantial, here we

focus mainly on the full cohort results.

Appendix 1 of the Supplemental Material

(online at http://www.ehponline.org/members/

2007/10767/suppl.pdf) provides a more

detailed discussion of these differences between

case–cohort and full cohort results. Case–

cohort analyses were, of course, also less precise,

especially for the more frequent outcomes for

Beelen et al.

198 VOLUME 116 | NUMBER 2 | February 2008 • Environmental Health Perspectives

Table 2. Descriptive characteristics of subjects who died and who were alive at end of follow-up in the full

cohort [among subjects for which geographic coordinates of the home address were available (n = 117,528)]. 

Cases Noncases

Characteristic (n = 17,286) (n = 100,242)

Sex (men) 11,317 (65.5) 45,484 (45.4)

Age (years) 64 (60–67) 61 (58–65)

Cigarette-smoking status

Never 4,788 (29.8) 40,113 (42.5)

Ex 5,063 (31.5) 29,899 (31.7)

Current 6,207 (38.7) 24,325 (25.8)

Cigar-smoking status

Never 13,663 (82.7) 84,935 (88.3)

Ex 1,429 (8.6) 6,394 (6.7)

Current 1,438 (8.7) 4,844 (5.0)

Pipe-smoking status

Never 15,227 (91.5) 90,351 (93.6)

Ex 865 (5.2) 4,200 (4.4)

Current 552 (3.3) 1,947 (2.0)

Percent of persons with low income in neighborhood 41 (36–47) 41 (36–46)

Percent of persons with high income in neighborhood 18 (12–24) 19 (13–25)

Percent of persons with low income in a COROP area 41 (36–45) 41 (36–45)

Percent of persons with high income in a COROP area 19 (18–23) 19 (18–23)

Values are number (%) or median (interquartile range).
which the ratio between subcohort size and

number of cases was smaller.

Associations between mortality and traffic variables. Table 4 shows the adjusted

associations between the traffic variables and

cause-specific mortality in the full cohort and

case–cohort analyses. Effect estimates for

traffic variables were independent of background pollutant in the model, so we present

relative risk estimates for traffic variables

from models with BS background concentrations (1987–1996). Effect estimates were not

sensitive to different confounder models for

both full cohort and case–cohort analyses

(data not shown).

In the full cohort analyses, risks for all traffic variables were elevated for all mortality outcomes, except for other mortality. The relative

risk estimate for the association between cardiopulmonary mortality and traffic intensity

on nearest road was 1.06 (95% CI, 1.00–1.12)

for an increase of 10,000 mvh/24hr. No association was found with any of the mortality

outcomes for traffic intensity on the nearest

major road and distance to this road (data

not shown).

The adjusted case–cohort analysis did not

show any association with traffic variables.

However, further analysis showed that this was

not attributed to the additional adjustment (for

the case–cohort analyses a larger number of

confounders was available compared with the

full cohort analyses), but because sampling the

subcohort introduced random error in a

downward direction, probably related to the

small fraction of high exposed subjects and the

skewness of the exposure distribution of the

traffic variables (Figure 1). Appendix 1 of the

Supplemental Material (online at http://www.

ehponline.org/members/2007/10767/

suppl.pdf) provides a more detailed discussion

of the effect of random variability in the

case–cohort analyses.

Spatial analyses, effect modification, and

moving. When spatial autocorrelation was

Long-term exposure to air pollution and mortality

Environmental Health Perspectives • VOLUME 116 | NUMBER 2 | February 2008 199

Table 3. Adjusted RRs (95% CIs) for the association between exposure to BS, PM2.5, NO2, and SO2 (1987–1996) with cause-specific mortality in full cohort and

case–cohort analyses (increment used to calculate RR).a

No. of casesb BS (10 μg/m3) PM2.5 (10 μg/m3) NO2 (30 μg/m3) SO2 (20 μg/m3)

Mortality Full cohort Case cohort Full cohort Case cohort Full cohort Case cohort Full cohort Case cohort Full cohort Case cohort

Natural cause 15,287 10,094 1.05 (1.00–1.11) 0.97 (0.83–1.13) 1.06 (0.97–1.16) 0.86 (0.66–1.13) 1.08 (1.00–1.16) 0.87 (0.69–1.10) 0.97 (0.90–1.05) 0.91 (0.71–1.16)

Cardiovascular 5,397 3,608 1.04 (0.95–1.13) 0.98 (0.81–1.18) 1.04 (0.90–1.21) 0.83 (0.60–1.15) 1.07 (0.94–1.21) 0.88 (0.66–1.17) 0.94 (0.82–1.06) 0.88 (0.65–1.18)

Respiratory 904 574 1.22 (0.99–1.50) 1.29 (0.91–1.83) 1.07 (0.75–1.52) 1.02 (0.56–1.88) 1.37 (1.00–1.87) 1.26 (0.74–2.15) 0.88 (0.64–1.22) 0.88 (0.51–1.50)

Lung cancer 1,670 1,059 1.03 (0.88–1.20) 1.03 (0.77–1.38) 1.06 (0.82–1.38) 0.87 (0.52–1.47) 0.91 (0.72–1.15) 0.80 (0.52–1.23) 1.00 (0.79–1.26) 0.99 (0.62–1.58)

Other 7,603 5,036 1.04 (0.97–1.12) 0.91 (0.78–1.07) 1.08 (0.96–1.23) 0.85 (0.65–1.12) 1.09 (0.98–1.21) 0.83 (0.66–1.06) 1.00 (0.90–1.12) 0.93 (0.72–1.19)

aFull cohort analyses adjusted for age, sex, smoking status, and area level indicators of socioeconomic status. Case–cohort analyses adjusted for age, sex, BMI, active smoking, passive

smoking, education, occupational exposure, marital status, alcohol use, vegetable intake, fruit intake, energy intake, fatty acids intake, folate intake, fish consumption, and area-level

indicators of socioeconomic status. BS, PM2.5, and NO2 are quantitative overall concentrations. SO2 is background concentration (including traffic intensity on nearest road in model).

Number of person-years in full cohort analyses is 984,589, and number of person-years in case–cohort analyses is 28,522. bThe number of cases between full cohort and case–cohort

adjusted analyses differs because the larger confounder model in the case–cohort analyses produces a higher number of subjects not available for analysis due to missing values.

Figure 1. Distribution of estimated NO2 (background and overall estimate), BS (background and overall estimate), SO2 (background), and PM2.5 (overall estimate)

concentrations (1987–1996), and of the traffic intensity on the nearest road and the sum of traffic intensity in a 100-m buffer, at the 1986 home address (n = 117,528).

Abbreviations: Max, maximum; mi, minimum.

12,500

10,000

7,500

5,000

2,500

0

12,500

10,000

7,500

5,000

2,500

0

27,500

25,000

22,500

20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0

27,500

25,000

22,500

20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0

15,000

10,000

5,000

0

30,000

25,000

20,000

15,000

10,000

5,000

0

120,000

100,000

80,000

60,000

40,000

20,000

0

40,000

30,000

20,000

10,000

0

Frequency FrequencyFrequency FrequencyFrequency FrequencyFrequency Frequency

10 20 30 40 50 60 70 10 20 30 40 50 60 70 10 5 15 20 25 30 35 40 10 5 15 20 25 30 35 40

0 20.0 22.5 25.0 27.5 30.0 32.5 35.0 37.5 40.0 0 25,000 50,000 75,000 100,000 125,000 0 5 10 20 30 35 15 25 200,000 400,000 600,000 800,000 1,000,000

Background NO2 (μg/m3) Overall NO2 (μg/m3) Background BS (μg/m3) Overall BS (μg/m3)

Background SO2 (μg/m3) Overall PM2.5 (μg/m3) Traffic intensity on

nearest road (mvh/24hr)

Sum of traffic intensity in

100-m buffer (mvh/24hr)

Mean 34.5 μg/m3

Min 14.6 μg/m3

Max 52.8 μg/m3

SD 7.3 μg/m3

Mean 36.9 μg/m3

Min 14.6 μg/m3

Max 66.7 μg/m3

SD 8.2 μg/m3

Mean 13.9 μg/m3

Min 8.7 μg/m3

Max 19.5 μg/m3

SD 2.2 μg/m3

Mean 16.5 μg/m3

Min 8.7 μg/m3

Max 35.8 μg/m3

SD 3.5 μg/m3

Mean 13.7 μg/m3

Min 4.2 μg/m3

Max 33.8 μg/m3

SD 5.1 μg/m3

Mean 28.3 μg/m3

Min 23.0 μg/m3

Max 36.8 μg/m3

SD 2.1 μg/m3

Mean 2,284 mvh/24hr

Min 1 mvh/24hr

Max 104,275 mvh/24hr

SD 3,767 mvh/24hr

Mean 140,903 mvh/24hr

Min 0 mvh/24hr

Max 893,722 mvh/24hr

SD 116,104 mvh/24hr
taken into account, full cohort results did not

change appreciably (Figure 2). 

In the full cohort analyses, there were no

significant differences in effect estimates

between men and women. The effect estimates for respiratory mortality were higher

among current smokers, whereas there was

suggestive evidence that effect estimates for

natural-cause and lung cancer mortality were

higher among never smokers (Figure 3A, D).

Air pollution effect estimates for natural-cause

mortality were significantly higher for people

living in neighborhoods with the lowest

percentage of persons with a low income.

Differences between subgroups were, however,

inconsistent for the different causes of death;

for example, for cardiovascular mortality the

lowest and medium tertile had the same relative risk, and for respiratory mortality the

highest risk was found in the subgroup with

the highest percentage of low-income subjects.

Subgroup analyses in the case–cohort data set

were adjusted for the limited number of confounders available for the full cohort to avoid

the described selection effect that is present

when the case–cohort analyses are adjusted for

all available confounders. Effect estimates for

the full case–cohort data set adjusted for the

limited confounder model used in the full

cohort analyses were for BS overall concentrations 1.03 (95% CI, 0.91–1.17) for naturalcause mortality, 1.02 (95% CI, 0.88–1.18) for

cardiovascular mortality, 1.16 (95% CI,

0.91–1.48) for respiratory mortality, 0.99

(95% CI, 0.80–1.23) for lung cancer mortality, and 1.03 (95% CI, 0.91–1.16) for other

mortality. Figure 3F shows that the effect

estimates for natural-cause mortality for BS

were higher in those with low education and

in those with low fruit consumption in the

case–cohort analyses. Relative risks for the different groups, however, did not differ significantly. Trends were similar for cardiovascular

and other mortality (data not shown). Effects

estimates for the different mortality outcomes

for BS overall concentrations were not different for different tertiles of vegetable consumption (data not shown).

Approximately 30% of the participants

moved residence between 1986 and end of

follow-up (Beelen et al. 2007). Effect estimates

from case–cohort analyses for the association

between air pollution and mortality were higher

for subjects who did not move during the

follow-up period compared with all subjects,

though not significantly so. Effect estimates for

subjects who did not move during the followup period were for BS overall concentrations

1.13 (95% CI, 0.97–1.31) for natural-cause

mortality, 1.12 (95% CI, 0.94–1.34) for

cardiovascular mortality, 1.39 (95% CI,

1.01–1.90) for respiratory mortality, 1.14 (95%

CI, 0.88–1.48) for lung cancer mortality, and

1.10 (95% CI, 0.94–1.28) for other mortality. 

At baseline approximately 85% of the

participants had no paid job. Effect estimates

of case–cohort analyses for natural-cause

mortality were not different for participants

who had no paid job at baseline (1.05; 95%

CI, 0.91–1.21) and for participants who had

a paid job at baseline (0.97; 95% CI,

0.74–1.29), and did not differ with the results

for the entire case–cohort sample. Results for

the other mortality outcomes were similar.

Discussion

In the full cohort, traffic-related air pollution

and several traffic exposure variables were

associated with mortality. Relative risks were

generally small. Statistically significant associations between NO2 and BS exposure and

natural-cause and respiratory mortality were

found. The highest relative risks were found

for respiratory mortality. Relative risks were

also elevated but not significant for mortality

other than cardiovascular, respiratory, or lung

cancer mortality.

For the first time in Europe, we now

report a relative risk estimate for PM2.5 based

on PM2.5 derived from monitored PM concentrations. Effect estimates for PM2.5 for the

full cohort analyses, although not statistically

significant, were quantitatively comparable

for natural-cause mortality to those of the

American Cancer Society (ACS) study in the

United States (Pope et al. 2002). In the ACS

study, strongest associations were found

between cardiovascular mortality and fine

particulate exposure, whereas we found only

slightly, not significantly elevated risks for

PM2.5 exposure and this subcategory of mortality. The ACS study (Pope et al. 2004) and

Beelen et al.

200 VOLUME 116 | NUMBER 2 | February 2008 • Environmental Health Perspectives

Table 4. Adjusted RRs (95% CIs) for the association between traffic variables with cause-specific mortality

in full cohort and case–cohort analyses.a

Exposure model Full cohort Case cohort

Natural-cause mortality

Traffic intensity on nearest road 1.03 (1.00–1.08) 0.99 (0.88–1.11)

Traffic intensity in a 100-m buffer 1.02 (0.97–1.07) 0.98 (0.85–1.13)

Living near a major road 1.05 (0.97–1.12) 0.92 (0.74–1.15)

Cardiovascular mortality

Traffic intensity on nearest road 1.05 (0.99–1.12) 1.03 (0.90–1.17)

Traffic intensity in a 100-m buffer 1.00 (0.92–1.08) 0.98 (0.82–1.16)

Living near a major road 1.05 (0.93–1.18) 0.93 (0.72–1.21)

Respiratory mortality

Traffic intensity on nearest road 1.10 (0.95–1.26) 0.94 (0.71–1.25)

Traffic intensity in a 100-m buffer 1.21 (1.02–1.44) 1.23 (0.89–1.68)

Living near a major road 1.19 (0.91–1.56) 0.85 (0.50–1.43)

Lung cancer mortality

Traffic intensity on nearest road 1.07 (0.96–1.19) 1.03 (0.87–1.22)

Traffic intensity in a 100-m buffer 1.07 (0.93–1.23) 1.10 (0.85–1.43)

Living near a major road 1.20 (0.98–1.47) 1.07 (0.70–1.64)

Other mortality

Traffic intensity on nearest road 1.00 (0.94–1.06) 0.93 (0.82–1.06)

Traffic intensity in a 100-m buffer 0.99 (0.93–1.06) 0.93 (0.80–1.07)

Living near a major road 0.98 (0.88–1.09) 0.85 (0.68–1.07)

The number of person-years and number of cases for the full cohort and case–cohort analyses are shown in Table 3.

aThe used confounders for the full cohort and case–cohort analyses are described in Table 3. RRs were calculated for differences from the 5th to the 95th percentile: for the traffic intensity on the nearest road: 10,000 mvh/24hr; for the traffic intensity

in a 100-m buffer: 335,000 mvh/100m. RRs for living near a major road were calculated with reference category “not living

near a major road.” All models included BS background concentration (1987–1996) as background concentration.

Figure 2. Adjusted results of spatial analyses for association between cardiopulmonary mortality and

BS background concentration (1987–1996) (A) and traffic intensity on the nearest road in the full cohort

(n = 107,005) (B). RRs and 95% CIs are shown for the original, 1-level neighborhood independent-clusters

(analysis a), 1-level municipality independent-clusters (analysis b), 2-level independent-clusters (analysis

c), 1-level neighborhood distance-decay (analysis d), 1-level municipality distance-decay (analysis e), and

2-level distance-decay (analysis f) analyses (confounders used are age, sex, smoking status, and arealevel indicators of socioeconomic status).

1.30

1.25

1.20

1.15

1.10

1.05

1.00

0.95

0.90

RR (95% CI)

Original a b c d e f Original a b c d e f

Analysis Analysis

1.30

1.25

1.20

1.15

1.10

1.05

1.00

0.95

0.90

RR (95% CI)

A B
the extended follow-up of the Six Cities

Study (Laden et al. 2006) were unable to

address traffic intensity variables for which

we found some associations with especially

respiratory mortality.

Our findings are coherent with other

cohort studies that reported significant associations between respiratory mortality and longterm exposure to air pollution (Abbey et al.

1999; Nafstad et al. 2004). Numerous studies

have also found effects of long-term and shortterm exposure to air pollution on respiratory

morbidity (Brunekreef and Holgate 2002;

Gauderman et al. 2007). Further, time-series

studies have found associations between shortterm changes in air pollution and both cardiovascular and respiratory mortality.

We also found nonsignificantly elevated

risks for the association between mortality

other than cardiopulmonary or lung cancer

mortality and overall air pollution. This

mortality outcome, however, includes several

cancer types and other mortality outcomes that

might also be associated with air pollution.

A study in a subpopulation of the ACS

study in Los Angeles, California, estimated

exposure to PM2.5 at an intraurban scale

(Jerrett et al. 2005). Relative risks associated

with a 10-μg/m3 increase of PM2.5 concentrations were 1.17 (95% CI, 1.05–1.30) for

all-cause, 1.12 (95% CI, 0.97–1.30) for

cardiopulmonary, and 1.44 (95% CI,

0.98–2.11) for lung cancer mortality. The

results suggested that the health effects associated with within-city gradients in PM2.5 concentrations may be larger than previously

found across metropolitan areas (Jerrett et al.

2005). A recent large cohort study on longterm exposure to fine particulate air pollution

and cardiovascular events among postmenopausal women in 36 U.S. metropolitan

areas also found that effect estimates within

cities were larger than effect estimates

between cities. The risk of cardiovascular

death with higher levels of PM2.5 was larger

than the estimates reported in the previous

U.S. cohort studies. A relative risk of 1.76

(95% CI, 1.25–2.47) was found for cardiovascular mortality for an increase of 10 μg/m3

in PM2.5 concentrations (Miller et al. 2007).

In the current study we assessed air pollution

on an even finer spatial scale than the Jerrett

et al. (2005) and Miller et al. (2007) studies,

because we took traffic near the home into

account, but we nevertheless found RR estimates for PM2.5 that were more comparable

to the national ACS study, which assessed

between-city variability of concentrations. 

In the present study, we refined the traffic

exposure variables by using a more precise

road network and adding actual traffic intensity data to the network compared with the

previous study (Hoek et al. 2002). With these

more refined traffic variables, we did observe

associations with cause-specific mortality, but

the effect estimates were smaller than reported

previously for the less refined exposure indicator in a random subgroup of the full cohort.

This is related partly to sampling from the

full cohort and to the difference in follow-up:

Adding an extra 2 years of follow-up reduced

the relative risk for living near a major road in

the subcohort from 1.95 to 1.34. The previous study results were based on about only

500 deaths and a small number of study subjects producing wide confidence intervals. 

In a reanalysis of two U.S. cohort studies,

Krewski et al. (2000) found that persons with

the lowest education experienced the largest

health effects from air pollution. We found in

the case–cohort analyses suggestive evidence

for higher effects for BS in those with low

education and in those with low fruit

consumption. Low fruit consumption occurred

significantly more in low education households, suggesting that the possible modifying

effect of education on air pollution estimates

may result from differences in fruit consumption between subjects with different educational levels. Fruit consumption may protect

against oxidative stress, which is one of the

main pathways from air pollution to health

effects (Kelly 2004). However, this is but one

of a series of differences in potential risk factors

that are related to educational level (e.g., physical activity patterns or other dietary habits).

Although not statistically significantly different, the effect estimates for natural-cause

and lung cancer mortality were higher among

never smokers, whereas the effect estimates for

Long-term exposure to air pollution and mortality

Environmental Health Perspectives • VOLUME 116 | NUMBER 2 | February 2008 201

Figure 3. Association between black smoke overall concentration (1987–1996) and cause-specific mortality in

subgroups for cigarette smoking status in the full cohort data set (A–E), and (F) by education and fruit consumption in the case–cohort data set. (A) Natural-cause (p = 0.15), (B) cardiovascular (p > 0.2), (C) respiratory

(p = 0.11), (D) lung cancer (p = 0.14), and (E) other mortality (p > 0.2). (F) Education of the household coded as

low = only primary school; middle = lower vocational education; and high = junior high school, senior high

school, higher vocational education, and university (p > 0.2). Fruit consumption divided in tertiles: low, 0–96.8

g/day; medium, 96.8–191.8 g/day; and high, > 191.8 g/day. Adjusted for age, sex, smoking status, and arealevel indicators of socioeconomic status (p > 0.2). p-Value, Cochran’s Q test for heterogeneity.

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

RR (95% CI)

Never

Cigarette smoking status

RR (95% CI)

A B

Ex Current

Cigarette smoking status

Never Ex Current

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

Never

Cigarette smoking status

Ex Current

Cigarette smoking status

Never Ex Current

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

RR (95% CI)RR (95% CI)

Never

Cigarette smoking status

Ex Current

Group

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

RR (95% CI)

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

RR (95% CI)

Low education

Middle education

High education

Low fruit

Medium fruit

High fruit

C D

E F
Beelen et al.

202 VOLUME 116 | NUMBER 2 | February 2008 • Environmental Health Perspectives

respiratory mortality were higher among current smokers. We do not have an explanation

for this. Stronger associations between air pollution and lung cancer mortality in neversmokers have been observed in the large ACS

study as well (Pope et al. 2002), whereas for

respiratory mortality the RRs for never-, ex-,

and current smokers were all close to unity

(Pope et al. 2004). 

For subjects who did not move during the

follow-up period, higher effect estimates were

found for the BS overall concentrations than

in all subjects. Such results could be related to

more accurate exposure assessment for subjects who did not move. Because moving is a

time-dependent variable, a limitation of this

analysis is that we restricted the analysis only

to the subjects who did not move during the

follow-up period, ignoring the person-years

that subjects lived at their baseline address

before they moved. This may have created

bias (Pearce 1992). The association between

air pollution and mortality will still probably

not differ for subjects who did not move and

for subjects living at their baseline address

before they moved.

Background concentrations showed a

decrease over time (Beelen et al. 2007), but

the correlations between the periods

1976–1985 and 1987–1996 were high

(> 0.9). Therefore it was difficult to evaluate

which time period was most important in

relation to health effects. The spatial correlation between different pollutants was also

high (Beelen et al. 2007). This makes it very

difficult to isolate the health effects of individual pollutants because they act as indicators of

a mixture of air pollutants coming from the

same sources (Kjellstrom et al. 2002).

We used traffic intensity data only for the

year 1986. Traffic intensities increased over

time, but the correlations between traffic

intensities from 1986 and traffic intensities

from the years during follow-up were all

> 0.92, supporting the use of traffic intensities

from one year to represent a long-term average (Beelen et al. 2007). However, the high

correlations made it difficult to assess which

traffic intensity years are most important in

relation to adverse health effects. 

The results may not necessarily apply to

current situations because emissions per vehicle

have decreased in the last decades due to technical innovations and use of catalytic converters.

However, the number of vehicles has increased

in the same time period, and important

pollutants such as NO2, ultrafine particles, and

(diesel) soot are still being emitted.

A limitation of the exposure assessment

method is that we assessed only outdoor concentrations at the baseline residential address,

not taking into account factors related to infiltration of outdoor air pollution into the home

such as air exchange rate. The questionnaire

did not contain information about the work

address. However, approximately 85% of the

population had no paid job at baseline.

Further, we had no information about the time

participants spent at home or about the time

commuting in traffic. The resulting misclassification is however likely to be nondifferential.

In conclusion, we found that trafficrelated air pollution and several traffic exposure variables were associated with mortality

in the full cohort. Relative risks were generally

small. Associations between natural-cause and

respiratory mortality were statistically significant for NO2 and BS. These findings add to

the evidence found in studies on the health

effects of long-term exposure to air pollution

concentrations. Although the relative risks

were small, the public health impact of exposure to air pollution may be substantial

because the exposed population is large.

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