Several prospective cohort studies suggest that

long-term exposure to outdoor air pollution

is associated with increased mortality from

cardiopulmonary diseases (Abbey et al. 1999;

Chen et al. 2005; Dockery et al. 1993; Filleul

et al. 2005; Miller et al. 2007; Pope et al.

2002, 2004). Road traffic is a major contributor to outdoor air pollution in industrialized

countries, contributing fine particulate matter

(PM), carbon monoxide, oxides of nitrogen,

and other pollutants. Assessment of traffic

exposure can enhance studies of health effects

of outdoor air pollution because local sources

are important and because few people live

close to the monitoring stations, which are

often purposefully located away from local

sources such as busy roads.

Recent studies have shown associations of

long-term and short-term exposure to traffic

air pollution with cardiovascular mortality,

morbidity, and subclinical parameters

(de Paula Santos et al. 2005; Finkelstein et al.

2004; Hoek et al. 2002; Hoffmann et al.

2006, 2007; Lanki et al. 2006; Peters A et al.

2004; Schwartz et al. 2005; Tonne et al. 2007;

Volpino et al. 2004). In contrast, few prospective studies have examined traffic air pollution

and coronary events. A recent study of

survivors of myocardial infarction in Rome

lacked information on smoking, an important

potential confounder (Rosenlund et al. 2008).

Two other prospective studies, one in Canada

(Finkelstein et al. 2004) and the other in the

Netherlands (Hoek et al. 2002), assessed only

mortality. We need more prospective data on

coronary events in healthy general populations,

with detailed data on potential confounders,

including smoking, collected at the individual

level, to address the hypothesis that long-term

traffic exposure influences the development of

coronary heart disease (CHD).

In the present study, we examined the

association between long-term residential

traffic exposure and incident CHD events

among participants in the Atherosclerosis

Risk in Communities (ARIC) study, a

prospective population-based cohort of middle-age men and women. This study included

data on a wide range of risk factors for CHD

collected prospectively at the individual level.

Materials and Methods

Participants. We studied participants from the

ARIC study, which was designed to investigate

the natural history and etiology of atherosclerosis and its sequelae. Details of the design,

objectives, and quality control activities of the

ARIC study have been previously reported

(ARIC Investigators 1989). A probability sample of 15,792 residents 45–64 years of age was

recruited in 1987–1989 from four U.S. communities: Forsyth County, North Carolina;

Jackson, Mississippi; northwest suburbs of

Minneapolis, Minnesota; and Washington

County, Maryland. The Jackson sample was

100% African American, and the other three

were predominantly white. The institutional

review boards of the four participating centers

approved the study, and all participants gave

written informed consent before the study.

Ascertainment of events. Study participants

were followed for incident CHD until

December 2002. Potential events were identified via annual telephone calls, communitywide hospital surveillance, and linkage with

local and national death-certificate registries.

We investigated events and deaths; we validated

events using hospital records, and deaths using

physician records and next-of-kin interviews.

We defined incident CHD on the basis of published criteria as the first definite or probable

myocardial infarction, silent myocardial infarction by electrocardiography, definite CHD

death, or coronary revascularization (White

et al. 1996). We classified events by a combination of computer algorithm and independent

review by one or two physicians of medical

record abstractions and discharge summaries.

Geocoding. We geocoded participant

addresses using a commercial service (Mapping

Analytics LLC, Rochester, NY), which assigned

Environmental Health Perspectives • VOLUME 116 | NUMBER 11 | November 2008 1463

Research

Address correspondence to S.J. London, Epidemiology

Branch, National Institute of Environmental Health

Sciences, P.O. Box 12233, Mail Drop A3-05,

Research Triangle Park, NC 27709 USA. Telephone:

(919) 541-5772. Fax: (919) 541-2511. E-mail:

london2@niehs.nih.gov

Supplemental Material is available online at

http://www.ehponline.org/members/2008/11290/

suppl.pdf

The Atherosclerosis Risk in Communities study is a

collaborative study supported by National Heart,

Lung, and Blood Institute contracts N01-HC-55015,

N01-HC-55016, N01-HC-55018, N01-HC-55019,

N01-HC-55020, N01-HC-55021, and N01-HC55022. This work was also supported by Z01

ES043012 from the Intramural Research Program,

National Institute of Environmental Health Sciences.

The authors declare they have no competing

financial interests.

Received 23 January 2008; accepted 8 July 2008.

Prospective Analysis of Traffic Exposure as a Risk Factor for Incident Coronary

Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study

Haidong Kan,1,2 Gerardo Heiss,3 Kathryn M. Rose,3 Eric A. Whitsel,3,4 Fred Lurmann,5 and Stephanie J. London1

1Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and

Human Services, Research Triangle Park, North Carolina, USA; 2Department of Environmental Health, School of Public Health, Fudan

University, Shanghai, China; 3Department of Epidemiology, School of Public Health, and 4Department of Medicine, School of Medicine,

University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 5Sonoma Technology Inc., Petaluma, California, USA

BACKGROUND: For people living close to busy roads, traffic is a major source of air pollution. Few

prospective data have been published on the effects of long-term exposure to traffic on the incidence of coronary heart disease (CHD).

OBJECTIVES: In this article, we examined the association between long-term traffic exposure and

incidence of fatal and nonfatal CHD in a population-based prospective cohort study.

METHODS: We studied 13,309 middle-age men and women in the Atherosclerosis Risk in

Communities study, without previous CHD at enrollment, from 1987 to 1989 in four U.S. communities. Geographic information system–mapped traffic density and distance to major roads

served as measures of traffic exposure. We examined the association between traffic exposure and

incident CHD using proportional hazards regression models, with adjustment for background air

pollution and a wide range of individual cardiovascular risk factors.

RESULTS: Over an average of 13 years of follow-up, 976 subjects developed CHD. Relative to those

in the lowest quartile of traffic density, the adjusted hazard ratio (HR) in the highest quartile was

1.32 [95% confidence interval (CI), 1.06–1.65; p-value for trend across quartiles = 0.042]. When

we treated traffic density as a continuous variable, the adjusted HR per one unit increase of logtransformed density was 1.03 (95% CI, 1.01–1.05; p = 0.006). For residents living within 300 m of

major roads compared with those living farther away, the adjusted HR was 1.12 (95% CI,

0.95–1.32; p = 0.189). We found little evidence of effect modification for sex, smoking status, obesity, low-density lipoprotein cholesterol level, hypertension, age, or education.

CONCLUSION: Higher long-term exposure to traffic is associated with incidence of CHD, independent of other risk factors. These prospective data support an effect of traffic-related air pollution on

the development of CHD in middle-age persons.

KEY WORDS: air pollution, coronary disease, traffic. Environ Health Perspect 116:1463–1468

(2008). doi:10.1289/ehp.11290 available via http://dx.doi.org/ [Online 8 July 2008]
a latitude and longitude coordinate to each

address. Geocoding was performed with the

Centrus Enhanced Database, which was primarily based on the Topologically Integrated

Geographic Encoding and Referencing

(TIGER) system data.

Traffic exposure. We quantified small-scale

spatial variations of traffic exposure by two

measurements: geographic information system

(GIS)–mapped traffic density assignments at

place of residence, and the distance from place

of residence to nearest roadways of various

types. We used the participant’s address at the

baseline visit (1987–1989) as the basis for

calculating both exposure measures.

Traffic density. We obtained the roadway

locations and annual average daily traffic volumes from Geographic Data Technology

(GDT; now Tele Atlas Global Crossroads,

Boston, MA). We selected GDT roadway

geometry data because they provide 100%

roadway coverage in the four communities

and are the most extensively georeferenced (or

repositioned), using aerial imagery to match

real-world locations. It is estimated that most

GDT roads in populated areas are located

with ±12 m “position accuracy.” GDT bases

the traffic volumes on state and county

agency traffic counts on highways, arterials,

and collector streets with more than approximately 1,000 vehicles per day. They assign

traffic counts to neighboring roadway links

with similar capacity.

We used the link-based traffic volumes to

generate maps of traffic density with 10 × 10 m

resolution using the ARCInfo Spatial Analyst

software (Kan et al. 2007; Peters JM et al.

2004). We created traffic density maps with

300 m circular search radii that produce densities decreasing by approximately 90% between

the edge of the roadway and 300 m away (perpendicular) from the roadways, which is consistent with the characteristics observed by Zhu

et al. (2002, 2006). We used identical mapping procedures in all the communities so that

the results are comparable across communities.

The densities reflect proximity to traffic without consideration of differential exposures

caused by meteorology. This method accounts

for the combined relative influence of several

roadways (and road types) with various traffic

activity levels at different distances from each

residence location. This metric generally

behaves like an inverse-distance–weighted traffic volume, except that it specifically considers

intersections and multiple roadways more

accurately. Therefore, these density values provide a relative indication of which residence

locations are likely to be most exposed to traffic

activity and, as such, are dimensionless indicators of proximity to traffic volume.

Because the available traffic density data

were from 2000, we back-extrapolated to the

study period (1987–1992) based on change in

population density using county-level census

population data. Changes in traffic volumes

over time are correlated with changes in

population density (Polzin 2006).

Distance to major roads. To estimate

qualitatively the distribution of the distance

from residence locations to roadways, we calculated straight-line distances. The distanceto-roadway data include the distance (in

meters) from each unique residence location

to the nearest roadways.

In a previous study, the concentrations of

ultrafine PM from highway traffic became

indistinguishable from the background concentration at distances > 300 m (Zhu et al.

2002). We therefore dichotomized distance to

major roads (interstate and state highways,

major arterials) at 300 m. To conduct analysis

of sensitivity of the results to the choice of cutpoints, we also categorized distance to major

roads as ≤ 150 m and > 150 m (Hoffmann

et al. 2006; Venn et al. 2001).

Background air pollution level. We

acquired data on the background ambient concentrations of PM with aerodynamic diameter

≤ 10 μm (PM10), nitrogen dioxide, and ozone

during the research period from the U.S.

Environmental Protection Agency air quality

data retrieval system. We abstracted 24-hr

average concentrations for PM10 and NO2 and

Kan et al.

1464 VOLUME 116 | NUMBER 11 | November 2008 • Environmental Health Perspectives

Figure 1. Distribution of traffic density at ARIC participant residences (1987–1989). (A) Traffic density

(n = 13,309). (B) PM10 (μg/m3

; n = 13,309). (C) NO2 (ppb; n = 9,902). (D) O3 (ppb; n = 13,309).

8,000

6,000

4,000

2,000

0

0 30 60 90 120 150

Traffic density

Frequency

2,000

1,500

1,000

500

0

25.0 27.5 30.0 32.5 35.0 37.5 40.0

PM10 (μg/m3)

2,000

1,500

1,000

500

0

Frequency

14 16 18 20 22 24 26

NO2 (ppb)

5,000

4,000

3,000

2,000

1,000

0

Frequency

42 44 46 50 52 54 48

O3 (ppb)

A B

C D Frequency

Table 1. Baseline characteristics of the ARIC

participants by the status of incident CHD at end of

follow-up (n = 13,309).a

Incident CHD No incident CHD 

Characteristic (n = 976) (n = 12,333)

Sex (% male) 59.3 41.4

Age (years) 55.8 ± 5.6 53.9 ± 5.8

BMI (kg/m2

) 28.6 ± 5.4 27.6 ± 5.4

Ethnicity (% black) 31.4 28.4

Smoking (%)

Current 38.7 25.1

Former 31.1 31.5

Never 30.0 43.4

Drinking (%)

Current 49.7 57.2

Former 25.4 17.2

Never 24.7 25.6

Hypertension (%) 54.1 34.3

Diabetes (%) 28.4 10.0

Total cholesterol (mmol/L) 5.8 ± 1.2 5.5 ± 1.1

HDL (mmol/L) 1.2 ± 0.4 1.4 ± 0.4

LDL (mmol/L) 3.9 ± 1.1 3.5 ± 1.0

aValues are mean ± SD unless specified as percentage.
8-hr (from 1000 hours to 1800 hours) average

concentrations for O3. We spatially interpolated the average concentrations from air

quality monitoring stations to the cohort residence locations using inverse distance weighting.

Other covariates. We defined hypertension

as systolic blood pressure of ≥ 140 mmHg,

diastolic blood pressure of ≥ 90 mmHg, or use

of antihypertensive medication during the previous 2 weeks. We defined diabetes mellitus as

a fasting glucose level of ≥ 126 mg/dL

(7.0 mmol/L), a nonfasting glucose level of

≥ 200 mg/dL (11.1 mmol/L), or a selfreported history of or treatment for diabetes.

Trained, certified technicians determined

anthropometric measures following a detailed,

standardized protocol. We calculated body

mass index (BMI) as weight (kg)/[height (m)]2.

Blood collection and processing for levels of

total cholesterol, low-density lipoprotein

(LDL), and high-density lipoprotein (HDL)

are described elsewhere (National Heart, Lung,

and Blood Institute 1988). Trained and certified interviewers also collected information on

age, ethnicity, sex, smoking, environmental

tobacco smoke (ETS), alcohol consumption

status, occupation, education, family income,

and family history of CHD. We calculated the

family risk score based on the participant’s

report of parents and five oldest siblings’ history of CHD (Li et al. 2000). Smoking variables included smoking status (never, former,

and current smokers), age at starting to smoke,

years smoked, and cigarettes per day. We

classified never smokers and former smokers as

exposed to ETS if they reported being in close

contact with smokers for more than 1 hr/week

(Howard et al. 1998). Thus, we obtained five

strata for active and passive smoking: current

smoker, former smoker with ETS, former

smoker without ETS, never smoker with ETS,

never smoker without ETS. Neighborhoodlevel socioeconomic status (SES), in addition

to individual level factors, may affect health

status (Geronimus and Bound 1998), so we

included 1990 census-tract–level data on

median employment rate and poverty rate

(U.S. Census Bureau 1992).

Statistical analysis. The end point of interest was incident CHD, so we excluded participants if they had prevalent CHD (n = 762).

We also excluded persons who met the following criteria: ethnicity other than African

American or white (n = 48) and, because of

their small numbers, African Americans from

Minnesota and Maryland field centers (n = 55);

and missing geocoding information (n =

1,724). Exclusions overlapped in some

instances, leaving 13,309 subjects for analysis.

We conducted all analyses using the statistical software package SAS, version 9.1 (SAS

Institute Inc., Cary, NC). We calculated

follow-up time as the time from baseline to an

event, or the last follow-up contact, or through

December 2002, whichever occurred first.

We used Cox proportional hazards regression analyses to assess the associations of traffic

exposure with the risk of incident CHD.

Distributions of traffic density are highly

skewed (Figure 1); therefore, we analyzed traffic density both as quartiles and as a continuous variable after log transformation. We

estimated the hazard ratios (HRs) of incident

CHD for quartiles of traffic density relative to

the lowest quartile, and for one unit increase

of log-transformed density values. For tests for

linear trends across increasing quartiles of traffic density, we used the median value in each

quartile. We also estimated the risk for living

close to major roads (≤ 300 m or ≤ 150 m),

using living farther away as the reference.

Our basic models included age, sex, center,

and ethnicity (Miller et al. 2007). In the

adjusted models, we added factors that we identified a priori as potential confounders: BMI,

physical activity, education, occupation, individual family income, census-tract–based SES

(median employment rate and poverty rate),

smoking status (current smoker, former smoker

with ETS, former smoker without ETS, never

smoker with ETS, never smoker without ETS),

age at starting to smoke (0–15, 15–20, 20–29,

and ≥ 30 years), years smoked, cigarettes per

day, alcohol intake (never, former, and current

drinker), hypertension, diabetes status, family

risk score, HDL, LDL, total cholesterol, fibrinogen, and background air pollution level

(PM10 and O3). NO2 data were missing in

Jackson (Figure 1), so we did not include them

in the adjusted models. Because other concomitants of traffic exposure, such as stress, could

affect cardiovascular health (Williams et al.

2000), we did a sensitivity analysis to examine

the impact of social stress (trait anger), measured at the second cohort examination, on the

estimated effect of traffic exposure.

We also conducted stratified analyses by

sex, smoking status, obesity, LDL level, hypertension, age, and education, to examine

potential modifiers of the association between

traffic exposure and incident CHD. We categorized BMI according to the standard definition: normal/underweight (BMI < 25) and

overweight/obese [BMI ≥ 25 (Centers for

Disease Control and Prevention 2007)]. We

dichotomized LDL level as ≤ 130 mg/dL and

> 130 mg/dL. We defined hypertension as systolic blood pressure of ≥ 140 mmHg, diastolic

blood pressure of ≥ 90 mmHg, or use of antihypertensive medication during the previous

2 weeks. We classified education as low (less

than high school), middle (high school or

vocational school), or high (college or above).

Results

Table 1 presents selected characteristics of participants at baseline, according to whether or

not they developed incident CHD during follow-up. Among the 13,309 study participants

who were free of CHD at baseline, 976 subjects developed CHD (268 fatal and 708 nonfatal) over an average of 13 years of follow-up.

As expected, subjects with incident CHD were

slightly older; more likely to be male, black, or

current smokers; had higher BMI, total cholesterol, and LDL and lower HDL; and had

higher prevalence of hypertension and diabetes.

The estimated traffic density and background air pollutant (PM10, NO2, and O3)

concentrations at the baseline home address

varied greatly (Figure 1). Consistent with previous reports (Hoek et al. 2001), we did not find

a strong correlation between traffic density and

background air pollution level; the Pearson correlation coefficients of traffic density with

PM10, NO2, and O3 were –0.12, –0.04, and

–0.10, respectively. Background PM10 was

moderately correlated with NO2 (Pearson correlation coefficient, r = 0.60) and O3 (r = 0.44);

NO2 was weakly correlated with O3 (r = 0.03).

Greater traffic density was associated with

increased risk of incident CHD in both basic

and adjusted models (Table 2). Relative to

those in the lowest quartile of traffic density,

the adjusted HRs across increasing quartiles

were 1.17 [95% confidence interval (CI),

0.93–1.47], 1.38 (95% CI, 1.11–1.72), and

1.32 (95% CI, 1.06–1.65) (p-value for trend

across quartiles = 0.042). When we treated

Traffic-related air pollution and coronary heart disease

Environmental Health Perspectives • VOLUME 116 | NUMBER 11 | November 2008 1465

Table 2. HRs (95% CIs) for incident CHD associated with traffic density.

Quartile Continuous variable (log-transformed)

Model 1 (lowest) 2 3 4 p-Value for trenda One unit increase p-Value

Median of quartiles 0 2.87 14.97 41.83

Cases 223 228 262 263

Basic modelb 1.00 1.13 (0.94–1.37) 1.31 (1.09–1.57) 1.28 (1.07–1.54) 0.018 1.02 (1.01–1.04) 0.004

Adjusted modelc 1.00 1.17 (0.93–1.47) 1.38 (1.11–1.72) 1.32 (1.06–1.65) 0.042 1.03 (1.01–1.05) 0.006

ap-Values for trend based on quartiles scaled by the quartile medians. bCovariates were age, sex, center, and ethnicity. cCovariates were age, sex, center, ethnicity, BMI, physical activity, education, occupation, individual family income, census-tract–based SES, smoking status, age at starting to smoke, years smoked, cigarettes per day, alcohol intake, hypertension,

diabetes status, family risk score, HDL, LDL, total cholesterol, fibrinogen, and background air pollution level.
traffic density as a continuous variable, the

adjusted HR per one unit increase of logtransformed density was 1.03 (95% CI,

1.01–1.05; p = 0.006) (Figure 2).

For residents living within 300 m of

major roads compared with subjects living

farther away, the adjusted HR (model 2) was

1.12 (95% CI, 0.95–1.32; p = 0.189) for incident CHD (Table 3, Figure 2). In the analysis with alternative cut point of distance to

major roads (150 m), we found similar patterns with incident CHD (Table 3, Figure 2).

We further examined whether sex, smoking status, BMI, LDL level, hypertension,

age, and education modified the association

of traffic density with incident CHD

(Table 4). Although results did not always

achieve statistical significance with the

reduced sample sizes in subgroup analyses,

there were positive associations in most strata

and little evidence of effect modification.

Consistent with the previous study of

Hoek et al. (2002), we did not observe significant effects of background air pollution on

incident CHD; the HRs of incident CHD

per 10-μg/m3 increase of PM10 and O3 were

1.28 (95% CI, 0.76–2.18) and 1.04 (95%

CI, 0.45–2.42), respectively. In addition, the

observed associations between traffic and

CHD remained after we further adjusted for

trait anger (data not shown).

Discussion

Among adults from four U.S. communities

followed prospectively over an average of 13

years, higher residential exposure to traffic, a

major source of air pollution in urban areas,

was associated with an increased risk of incident CHD events. To our knowledge, our

study provides the first prospective evidence

of the association between traffic exposure

and incident cardiovascular morbidity in the

general population.

Traffic emissions result in small-scale spatial

variations and therefore mainly affect residents

living close to busy roads (Roorda-Knape et al.

1999). Thus, air pollution data from fixed

monitoring stations may be inadequate to study

traffic-related air pollution and health outcomes, especially for those living near busy

roads. For example, Hoek et al. (2002) identified a consistent association of cardiopulmonary

mortality with traffic exposure, but not with

estimated ambient background concentration

of the traffic indicator pollutants black smoke

and NO2. Similarly, we did not observe a significant effect of background air pollution on

incident CHD. This is not surprising given

that the ARIC study was not designed to

examine air pollution and was conducted only

in four communities. Furthermore, these four

communities were not well supplied with air

pollution monitors during the study period,

resulting in little variation in measured air pollution within communities. In addition, from

an analytic perspective, any affect of baseline

air pollution on risk would be indistinguishable from differences in risk by community, a

design variable for this study.

Our prospective results support previous

findings from cross-sectional, case–control,

and cohort studies examining the association

between long-term traffic exposure and

cardiovascular morbidity, mortality, or intermediate end points. In a prospective cohort

analysis of myocardial infraction survival,

Kan et al.

1466 VOLUME 116 | NUMBER 11 | November 2008 • Environmental Health Perspectives

Table 3. HRs (95% CIs) for incident CHD by distance to major roads. 

Dichotomized at 300 m Dichotomized at 150 m

Model < 300 m ≥ 300 m p-Value < 150 m ≥ 150 m p-Value

Cases 683 293 408 568

Basic modela 1.13 (0.98–1.30) 1.00 0.085 1.12 (0.99–1.28) 1.00 0.073

Adjusted modelb 1.12 (0.95–1.32) 1.00 0.189 1.09 (0.94–1.26) 1.00 0.264

aCovariates were age, sex, center and ethnicity. bCovariates were age, sex, center, ethnicity, BMI, physical activity, education, occupation, individual family income, census-tract–based SES, smoking status, age at starting to smoke, years

smoked, cigarettes per day, alcohol intake, hypertension, diabetes status, family risk score, HDL, LDL, total cholesterol,

fibrinogen, and background air pollution level.

Table 4. Adjusted HRs for incident CHD associated with traffic density, stratified by sex, smoking status,

BMI, and education.a

Higher traffic density

Characteristic No. (%) cases Adjusted ratiob p-Value for trendc p-Value for interactiond

Sex 

Female 397 (5.2) 1.27 (0.91–1.78) 0.066 0.397

Male 579 (10.2) 1.41 (1.04–1.91) 0.138

Smoking status

Never 293 (5.2) 0.84 (0.56–1.24) 0.785 0.380

Current/former 681 (8.9) 1.61 (1.22–2.12) 0.013

BMI 

< 25 254 (5.7) 1.49 (0.98–2.28) 0.051 0.271

≥ 25 719 (8.2) 1.29 (0.99–1.68) 0.208

LDL (mg/dL)

≤ 130 320 (5.5) 1.38 (0.93–2.03) 0.133 0.567

> 130 618 (8.8) 1.29 (0.98–1.70) 0.171

Hypertension

No 443 (5.2) 1.45 (1.04–2.02) 0.041 0.659

Yes 528 (11.1) 1.30 (0.96–1.77) 0.213

Baseline age (years)

≤ 60 733 (6.7) 1.36 (1.05–1.75) 0.028 0.624

> 60 243 (10.3) 1.10 (0.68–1.78) 0.991

Education

Low 321 (10.7) 1.09 (0.73–1.64) 0.725 0.705

Middle 372 (6.9) 1.74 (1.20–2.52) 0.016

High 283 (5.8) 1.22 (0.80–1.86) 0.420

aCovariates were age, sex, center, ethnicity, BMI, physical activity, education, occupation, individual family income, census-tract–based SES, smoking status, age at starting to smoke, years smoked, cigarettes per day, alcohol intake, hypertension, diabetes status, family risk score, HDL, LDL, total cholesterol, fibrinogen, and background air pollution level.

bComparing the fourth with the first quartiles of traffic density (95% CI). cp-Value for trend based on quartiles scaled by

the quartile medians. dp-Value for interaction between traffic exposure and stratification factors.

Figure 2. Adjusted HRs (and 95% CIs) for incident CHD in relation to traffic density and by distance to major

roads. Covariates were age, sex, center, ethnicity, BMI, physical activity, education, occupation, individual

family income, census-tract–based SES, smoking status, age at starting to smoke, years smoked, cigarettes per day, alcohol intake, hypertension, diabetes status, family risk score, HDL, LDL, total cholesterol,

fibrinogen, and background air pollution level.

1.80

1.60

1.40

1.20

1.00

0.80

0.60

Hazard ratio

1234

Quartiles

Traffic density

One unit increase of logtransformed density

Distance

< 300 m versus

≥ 300 m

< 150 m versus

≥ 150 m
Rosenlund et al. (2008) found that long-term

exposure to traffic-related air pollution

increased the risk of CHD, and the relative risk

for incident coronary events per 10 μg/m3 of

NO2 was 1.03 (95% CI, 1.00–1.07). In a

case–control analysis in Boston, Massachusetts,

an interquartile range increase in cumulative

traffic near the home was associated with a

4% (95% CI, 2–7%) increase in the odds of

acute myocardial infarction, suggesting an

effect of long-term exposure to traffic (Tonne

et al. 2007). In a cross-sectional analysis,

Hoffmann et al. (2006) found that higher

long-term exposure to traffic-related emission,

but not background air pollution, was associated with increased risk of CHD events

(odds ratio = 1.85; 95% CI, 1.21–2.84) in a

German population. In the same study, residents living within 50 m from a major road

had an odds ratio of 1.63 (95% CI,

1.14–2.33), relative to subjects living > 200

m away, for elevated coronary artery calcification, an intermediate cardiovascular end point

(Hoffmann et al. 2007).

In the present study, we examined the

association between traffic exposure at the

baseline residences (visit 1, 1987–1989) and

incident CHD. We found similar associations

of traffic with CHD when we used the firstyear (1987) exposure data. This is comparable

with previous studies of air pollution in relation to mortality that assessed exposure at the

beginning of follow-up (Hoek et al. 2002;

Pope et al. 2002, 2004). Although air pollution levels may vary over time because of

changes in emission or economic activity,

substantial changes are usually slow and affect

the region in the same way.

Various factors may modify the health

effects of air pollution. We did not find significant evidence for effect modification by sex,

smoking status, obesity, LDL cholesterol level,

hypertension, age, or education. The information on modification of long-term effects of air

pollution by educational status is inconsistent

(Hoffmann et al. 2006; Pope et al. 2002).

Additional examination of modifying factors in

future investigations will help in public policy

making, risk assessment, and standard setting.

Some limitations of our analysis should be

noted. We did not predict the air pollutant

concentration based on traffic density data,

and we could not validate our exposure assessment with actual measurements given that the

exposure period was 1987–1989. Likewise,

our traffic density metric does not reflect the

local meteorologic conditions that could

influence the emission, mixing, and transport

of air pollutants. Some studies have suggested

a stronger association with stop-and-go traffic

than with moving traffic and with truck traffic compared with car traffic (Ryan et al.

2005). However, in most studies, including

ours, it was not possible to separate traffic

types. As in some other studies, our exposure

assessment was limited to residential address,

and we lacked information on home exposures to other sources of pollutants, such as

cooking or heating. Because our study outcomes are CHD events, rather than a measure

of CHD pathogenesis, it is possible that some

of the CHD events may be attributable to

short-term exposure to traffic (Lanki et al.

2006; Peters A et al. 2004). However, given

evidence that the association for acute exposure to air pollution is smaller in magnitude

than the associations for long-term exposure

(Künzli et al. 2005), we suspect that any overestimation of effects of long-term exposure to

traffic would be minimal. Also, as in any epidemiologic study, residual confounding is

possible. However, we carefully adjusted for

known and potential confounders, including

demographic characteristics, personal and

neighborhood level socioeconomic characteristics, cigarette smoking, family risk factors,

and background air pollution.

We are not able to rule out the possible

effect of traffic noise, which at high levels may

have adverse effects on cardiovascular physiology (Berglund et al. 1996). However, the

associations between traffic noise and cardiovascular risk are far less consistent than those

between air pollution and cardiovascular disease (Ising and Kruppa 2004). Moreover,

given that noise-related cardiovascular events

(e.g., hypertension) may follow a different

pathway than does air pollution (Jarup et al.

2008), and we found a significant association

between traffic and CHD in nonhypertensive

subjects (Table 4), it seems unlikely that noise

explains the observed effects of traffic.

We lacked assessment of traffic-related air

pollution for the approximately 10.9% of subjects whose addresses could not be geocoded.

Most of the missing geocodes were attributed

to such problems as missing state (most often

military addresses), address left blank, temporary address, address not in the United States,

apartment name without address, or only a post

office box given. This could raise concern about

potential selection bias. However, for missing

geocode data to have created a spurious association between higher traffic exposure and incident CHD, subjects with and without geocodes

would need to differ in both traffic exposure

and incident CHD. Although we have no data

on their traffic exposure, they were similar to

subjects with nonmissing geocodes in sociodemographic characteristics and incident CHD

[see Supplemental Material, Table S1 (online at

http://www.ehponline.org/members/2008/

11290/suppl.pdf)]. Thus, it is unlikely that the

missing geocode data could have created the

observed associations.

Because we obtained the geocodes for participants’ addresses from the TIGER file by

Mapping Analytics, error could result from the

use of an older road network data. To assess

this, we randomly selected 100 participants

from each of the ARIC communities and

regeocoded their residential addresses using the

GDT software, which incorporates a more

recent road network database. Using these new

geocodes, we recalculated the traffic densities

and distances to major roads and compared

them with the original results. The two

geocoding methods resulted in similar estimates for the distance to nearest major roads.

For traffic density, the two methods yielded

quite concordant values for the Forsyth,

Jackson, and Minneapolis communities, but

concordance was lower for Washington

County. This might reflect a renaming of

streets that occurred there, so we repeated our

analyses excluding Washington County from

our analysis. With the less precise exposure

assessment in Washington County excluded,

the association of traffic density with incident

CHD remained [see Supplemental Material,

Table S2 (online at http://www.ehponline.

org/members/2008/11290/suppl.pdf)], suggesting that the association is relatively robust

to geocoding error.

A major strength of our study is the use of

an objective measure of traffic-related air pollution at residential addresses (e.g., GIS-based

assessment of traffic density and distance to

major roads) to capture exposure relevant for

subjects living in close proximity to busy roads.

A further strength of our study is the overall

residential stability of the cohort; approximately

90% of the ARIC participants had lived in the

same community for > 10 years at the baseline

visit. An earlier study of ARIC study participants reported very high concordance between

past decades and visit 1 for county and state of

residence (Rose et al. 2004). Moreover, we

based our analysis on carefully collected incidence data in a large cohort from four U.S.

communities. We prospectively collected data

on exposure, outcome, and a wide range of

potential confounders at the individual levels

using standardized protocols and extensive

quality assurance. In addition to doing detailed

adjustment for individual-level confounders

and evaluating potential effect modification,

we also adjusted for a community-level measure of SES to help account for confounding

(Marmot 2001).

In summary, in this prospective analysis,

higher long-term exposure to traffic, a major

source of air pollution, was related to

increased risk of incident CHD. These findings add to the previous data on mortality and

disease prevalence and suggest that trafficrelated air pollution can influence the development of disease in an ostensibly healthy

middle-age population. Continued emphasis

on the implementation of strategies for reducing traffic-related air pollution is likely to reap

additional public health benefits.

Traffic-related air pollution and coronary heart disease

Environmental Health Perspectives • VOLUME 116 | NUMBER 11 | November 2008 1467
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