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European Heart Journal Advance Access originally published online on September 26, 2006
European Heart Journal 2006 27(22):2696-2702; doi:10.1093/eurheartj/ehl278
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© The European Society of Cardiology 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Residence close to high traffic and prevalence of coronary heart disease

Barbara Hoffmann1,*, Susanne Moebus1, Andreas Stang2, Eva-Maria Beck1, Nico Dragano3, Stephan Möhlenkamp4, Axel Schmermund4, Michael Memmesheimer5, Klaus Mann6, Raimund Erbel4, Karl-Heinz Jöckel1 on behalf of the Heinz Nixdorf RECALL Study Investigative Group

1 Institute for Medical Informatics, Biometry and Epidemiology, University Hospital, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany
2 Institute of Medical Epidemiology, Biometry and Informatics, Medical Faculty, Martin-Luther-University of Halle-Wittenberg, Halle, Germany
3 Institute of Medical Sociology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
4 West German Heart Center Essen, University Hospital, University of Duisburg-Essen, Essen, Germany
5 Rhenish Institute for Environmental Research at the, University of Cologne, Cologne, Germany
6 Department of Endocrinology, University Hospital, University of Duisburg-Essen, Essen, Germany

Received 3 March 2006; revised 7 September 2006; accepted 11 September 2006; online publish-ahead-of-print 26 September 2006.

* Corresponding author. Tel: +49 201 723 4463; fax: +49 201 723 5933. E-mail address: barbara.hoffmann{at}uk-essen.de

See page 2621 for the editorial comment on this article (doi:10.1093/eurheartj/ehl319)


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Acknowledgements
 References
 
Aims Long-term exposure to urban air pollution may accelerate atherogenesis and increase cardiopulmonary mortality. We aim to examine the relationship between the long-term residential exposure to traffic and prevalence of coronary heart disease (CHD).

Methods and results We used baseline data from the German Heinz Nixdorf RECALL study, a population-based, prospective cohort study. For 3399 participants from two cities, we assessed the long-term personal traffic exposure and background air pollution, comparing residents living within 150 m of major roads with those living further away. The principal outcome variable was clinically manifest CHD. We evaluated the association with multivariable logistic regression, controlling for background air pollution and individual level risk factors. Of 3399 participants, 242 (7.1%) had CHD. The crude odds ratio (OR) for prevalence of CHD at high traffic exposure was significantly elevated (1.62, 95%CI 1.12–2.34) and rose to 1.85 (95%CI 1.21–2.84) after adjusting for cardiovascular risk factors and background air pollution. Subgroup analysis showed stronger effects for men (OR 2.33, 95%CI 1.44–3.78), participants younger than 60 years (OR 2.67, 95%CI 1.24–5.74) and never-smokers (OR 2.72, 95%CI 1.40–5.29).

Conclusion This study provides epidemiological evidence that the long-term exposure to traffic-related emissions may be an important risk factor for CHD.

Key Words: Coronary disease • Epidemiology • Risk factors • Air pollution • Traffic emissions


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Acknowledgements
 References
 
Cardiovascular diseases play a leading role as causes of death. Within the last two decades, environmental factors such as air pollution have been shown to contribute to cardiovascular morbidity.1–3 Long-term residential exposure to traffic-related air pollution increases cardiopulmonary mortality almost two-fold.4 Epidemiological studies suggest that chronic exposure to particulate air pollution acts through enhancing atherogenesis,5 leading to subclinical changes that may play an important role in cardiovascular morbidity and mortality later in life. Moreover, transient exposure to traffic may increase the risk of acute myocardial infarction in susceptible persons.6 Even though motorized traffic produces a complex mixture of potentially hazardous emissions for cardiovascular health, subjecting a substantial part of the population in metropolitan areas to these health hazards, studies examining the cardiovascular health of residents living near major motorways are rare.

The aim of this study is to examine the relationship between the long-term residential exposure to traffic, characterized by proximity of the residence to major motorways, and prevalence of CHD.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Acknowledgements
 References
 
The Heinz Nixdorf RECALL (Risk Factors, Evaluation of Coronary Calcium and Lifestyle) study is a population-based, prospective cohort study. The study rationale and design have been described in detail elsewhere.7 It was approved by the relevant Institutional Ethics Committee. In short, the cohort comprises 4814 males and females aged 45–75 years from three large adjacent cities (Essen, Mülheim, Bochum) of the densely populated and highly industrialized Rhine-Ruhr-region in North-Rhine-Westphalia in Germany. Subjects were randomly selected from mandatory lists of residency. The response proportion calculated as recruitment efficacy proportion8 was 55.8%.9 Baseline assessment (December 2000 until July 2003) included a self-administered questionnaire, and personal face-to-face interviews for personal risk-factor assessment, physical examination including anthropometric measurements and comprehensive laboratory tests. The follow-up examination is scheduled to commence 5 years after the initial examination (2006 until 2008).

The present study is based on data of the baseline examination of a subgroup of the study population (n=3384), consisting of the participants residing in Essen (n=1641) and Mülheim (n=1743), for which digitized information on inner city roads were available.

The home address of each participant at baseline was geocoded with the geographic information system (GIS) MapInfo (MapInfo GmbH, Raunheim, Germany) by the local land registry offices. Participants’ long-term personal traffic exposure was assessed by calculating distances between the home address and the median strip of major roads (autobahn: mean daily vehicle count >30 000–110 000; federal highways: >10 000–60 000) with the GIS, using official digitized maps with a precision of at least ±0.5 m.10 Data on the average daily traffic counts on roads were obtained from the city administrations. High personal traffic exposure was defined as at least one major road within a distance of 150 m of the home address.

For regional particulate matter (PM) background concentration, yearly mean values for PM2.5 on a spatial scale of 5 km were estimated with the EURAD model for the year 2002, using data from the local emission inventories and meteorological and topographical data. Measured air pollution data from local monitoring sites were used for model validation, showing a good agreement with the modelled values (correlation coefficient for daily averages of PM2.5 0.86–0.88 depending on season).11 Model-derived PM2.5 surface data were then linked individually to the participants’ residential addresses to control for regional fine particle differences.

The outcome was clinically manifest coronary heart disease (CHD), defined as a self-reported history of a ‘hard’ coronary event (myocardial infarction or application of a coronary stent or angioplasty or bypass surgery) with an obligatory specification of the year of the event.

Statistical data analysis consisted of multivariable logistic regression analysis with the presence of CHD as the dependent variable and traffic exposure at the home address in three ways ([i] dichotomized as ≤150 m or >150 m, [ii] four categories of >200 m, >100 to ≤200 m, >50 to ≤100 m, and ≤50 m, and [iii] continuously as ln(distance)) as the independent variable. PM2.5 background concentrations on a continuous scale were added to the model to control for regional variations in PM air pollution. Possible confounder variables were selected a priori as the most important known causal and conditional cardiovascular risk factors, classified as either indicator variables [sex, diabetes mellitus (DM), hypertension, smoking status, environmental tobacco smoke exposure (ETS), educational level, physical activity, body-mass index (BMI), triglycerides] or continuous variables [age, number of cigarettes smoked per day, waist-to-hip ratio (WHR), LDL, HDL, HbA1c].

DM was defined as self-reported diabetes or taking an anti-diabetic drug or a non-fasting serum glucose >200 mg/dL or a fasting serum glucose >125 mg/dL.12 History of hypertension was assessed as ever diagnosed with hypertension. Smoking variables included indicator variables for current daily smoker, current occasional smoker, and former cigarette smoker (cessation of smoking within last year, cessation of smoking more than 1 year ago but less than 20 years, cessation of smoking at least 20 years ago) and a continuous variable for the amount of daily smoking. ETS exposure was defined as frequent exposure to ETS at home or at work or in other places (yes/no). No physical activity was defined as no regular engagement in sports. Socioeconomic status was assessed through educational attainment as recommended by the German Epidemiological Association.13 Low education was defined as ≤10 years of schooling with or without vocational training, medium educational level as ≥10 years of schooling and technical or vocational school, and high education as >10 years of schooling with a university entrance qualification.

In order to take the different socioeconomic structure and geographic differences of the two cities under study into account, which may act independently from individual level covariates, an indicator variable for each city was defined. To adjust for the social gradient within the cities, an indicator variable for living in the northern part of the cities, which comprises the lower income residential areas, the region with the higher population density and more industrial activity, was created.

Subgroup analyses were performed to identify people at highest risk for adverse cardiovascular effects of traffic exposure. A shortened model was constructed to allow application in smaller sample sizes with smoking status aggregated into the categories smoker, ex-smoker, and never-smoker and area of residence aggregated into Essen-north and other.

All statistical analysis was performed using SAS version 8.2.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Acknowledgements
 References
 
Baseline characteristics of the Heinz Nixdorf RECALL study population are summarized in Table 1. More highly exposed individuals suffered from CHD (10.1%) than non-exposed (6.8), highly exposed individuals were also more likely to have DM or hypertension, have a lower education, live in the northern area, be a regular smoker, and be exposed to ETS. No difference in regional background PM2.5-concentration existed between the two groups.


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  		Table 1 Baseline characteristics of all 3399 participants and of subgroups by exposure status

 
The distribution of distances between the residences and major roads and the background PM2.5-exposure at the home address are shown in Figures 1 and 2.


Figure 2781
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  		Figure 1 Distribution of distances between the home address and major road for 3399 study participants.

 

Figure 2782
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  		Figure 2 Distribution of background PM2.5-exposure at the home address for 3399 study participants.

 
Results of the logistic regression analyses are presented in Table 2. The unadjusted odds ratio (OR) for having CHD at high exposure to traffic emissions is significantly elevated by 62%. Inclusion of background PM-exposure does not affect the estimate of traffic exposure (model 1). Adjustment for age and sex leads to an increase in the estimate (model 2). Further adjustment in the full model does not change the estimate to a large extent (OR 1.85, 95% CI 1.21–2.84) with ETS (23% decrease of the main effect estimate) and LDL (23% increase) exerting the strongest influence on the main effect. Excluding factors that possibly lie on the causal pathway of exposure to traffic (hypertension, lipid status) has only a small effect on the estimate (model 3). Sensitivity analysis shows that the association between traffic exposure and CHD is stronger for more recent events (within last 5 years) (OR 2.10, 95% CI 1.21–3.66) than for events in the more distant past (OR 1.63, 95%CI 0.90–2.95).


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  		Table 2 Crude and adjusted estimates for the association between high traffic and PM2.5 exposure on prevalence of CHD

 
The odds ratio of coronary artery disease (CAD) increase with decreasing distance, but numbers in the distance strata are small and therefore confidence intervals wide (Table 3). In an analysis with distance as a continuous variable using ln(distance) to account for the exponential decay of air pollution with increasing distance from the road, the pattern of association remains the same, showing an estimated cross-sectional increase in CHD prevalence of 21.1% (95% CI 2.8–42.6%) for a reduction in distance by half.


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  		Table 3 Estimates for the association of high traffic exposure on prevalence of CHD for subgroups according to distance

 
Table 4 presents the adjusted ORs in different subgroups. In ex-smokers and never-smokers (including long-time ex-smokers) we observed a substantial increase in CHD-prevalence as compared to no observable association in smokers. Including an interaction term for traffic and smoking in the full model reveals a borderline significant effect modification. The estimates for men and women as well as for the younger age group (<60 years) vs. the older age group (≥60 years) also differ with larger effects for men and the younger age group, but interaction terms in the full model do not reach statistical significance.


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  		Table 4 Subgroup analysis: estimates for the association of high traffic exposure on prevalence of CHD

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Acknowledgements
 References
 
This study demonstrates an association between the long-term residential exposure to traffic and prevalence of CHD. For all subjects combined, the estimated risk increase is about 85%, with evidence for a positive exposure–response relationship. The estimate is robust to the inclusion or exclusion of numerous cardiovascular risk factors, including regional background air pollution with PM2.5. Our results are in agreement with a recent cohort study,4 showing an 1.95-fold increase in cardiopulmonary mortality for residents living in close proximity (≤150 m) to major roads.

Short-term effects of transient traffic exposure on the incidence of acute myocardial infarction were recently demonstrated.6 In addition, chronic mechanisms, such as the enhancement of atherogenesis, may contribute to increased cardiovascular morbidity and mortality associated with air pollution.5 Several different factors or a combination of them, within the mixture of motorized traffic emissions, may account for the observed associations. Motorized traffic is one of the most important sources of ambient PM air pollution, which has been established as an important risk factor for cardiopulmonary disease.3,14 PM encompasses a variety of different substances, including coarse and fine particles as well as ultrafine particles. Owing to the relatively stable nature of PM10 and PM2.5, a variety of sources other than motorized traffic in this area (industry, coal combustion, agriculture, sea salt) and long-range transport mechanisms, PM10 and PM2.5, are more evenly dispersed in contrast to the unstable UFP, which exhibit a higher small-scale variation. UFP are produced mainly by combustion processes like motorized traffic and aggregate quickly to larger particles, resulting in an exponential decline of traffic-related UFP with increasing distance from major motorways.15 This renders distance to major roads a more effective surrogate for personal exposure to traffic-related UFP than for PM2.5. Exposure to traffic-related UFP might therefore be one possible explanation for the association found in our study.

Other factors such as noise and the resulting psychosocial stress can also affect cardiovascular health. A recent study showed that men, exposed to sound levels of more than 70 dB(A) during the day, had a 1.3-fold increase in risk for myocardial infarction.16 Our definition of high exposure may coincide with high traffic-related noise levels, making it difficult to separate effects of air pollution from noise effects. Considering the size of the noise effect and our adjustment for hypertension, the hypothesized link between noise and cardiovascular disease,17 we think that residual confounding by traffic noise is small and presumably does not explain all of the observed association. Nevertheless, better data on noise exposure are necessary to rule out this alternative explanation.

We found some evidence for effect measure modification by gender in our data. Although men suffer a more than two-fold increase in risk associated with high traffic exposure, there is no clear indication that women's prevalence of CHD is affected, contrasting to prior mortality studies where generally higher effects were seen in women.18 The low number of cases among women however make precise effect estimates difficult, as can be seen in a wide confidence interval. Additionally, there is an increasing evidence that mechanisms leading to CHD differ between men and women,19,20 possibly conveying differences in susceptibility to traffic regarding CHD.

The data showed heterogeneity between active smokers and non-smokers. In a study investigating the effects of welding fumes on acute systemic inflammatory responses, current smokers have been found to have increased baseline inflammatory markers with a lack of further increases after exposure.21 A similar biological interaction between smoking and the effect of air pollution was seen in a recent cross-sectional study on the association between the long-term ambient air pollution and atherosclerosis.5 In our study, the dominating effects of active smoking may have contributed to the inability to observe a small effect of traffic exposure, which is thought to act in part through the same pathways.

Unlike the recent mortality study by Pope et al.,18 we did not find evidence for an effect modification by educational status in our study. Several explanations for this are possible. First, the cross-sectional design of our study might have led to a survivor bias with the more critically ill more likely to die before the baseline examination. This might especially be the case in the lower educational status group and the group of highly exposed participants who showed a higher prevalence of factors associated with a bad prognosis. The suggestion of a lower effect in diabetics, hypertensive participants, and elderly, all affected with a higher baseline prevalence of CHD, supports this explanation of survivor bias.

Secondly, the lower educational status group generally lived in the more polluted areas of the study region with more industrial air pollution sources and higher density of inner city roads. Misclassification bias might therefore have been higher in the low-educational status group than in the other groups who generally lived in residential areas further away from industrial sources and with few highly trafficked inner city roads. Thirdly, well-known determinants of non-participation such as low education, older age, and compromised health status9 might have led to a selection bias in this cross-sectional analysis with a non-proportional under-recruitment of CHD-affected participants in the low-educational status group.

As other studies using CHD events as the outcome (incidence, mortality, or prevalence), our study does not allow the biologically important distinction between an addition of short-term effects of transiently increased air pollution, which might trigger acute cardiovascular events, and the contribution of air pollution to the underlying process of atherogenesis. Animal data,22 epidemiological evidence,5 and the size of the observed association however suggest not only an addition of acute effects but an enhancement of atherosclerosis due to the long-term air pollution exposures.

Limitations of this study include the lack of individual level information on occupational exposures and indoor exposures. The latter is not considered to be an important problem, since indoor air is substantially influenced by outdoor air23 and ETS as the most important source for indoor air pollution has been taken into account. Lack of information on occupational exposures only leads to a spurious increase in risk, if these additional exposures are dependent on exposure status. Since we controlled for individual socioeconomic status, which can be regarded as a crude surrogate for occupational exposures, we do not believe residual confounding by occupational exposures to be large enough to bias the effect estimate to the observed extent. However, to rule out this alternative explanation, more information on occupational exposures is needed.

The results are unlikely affected by the typical limitation of a missing temporal relationship between exposure and outcome in cross-sectional studies, since we used the home address, which was very stable among our participants, for long-term exposure assessment. Data on duration of residence at the registered address before the baseline assessment were not available; however, on the basis of the small number of relocations during the follow-up period (<1% of removals per year), we assumed a high residential stability in our study population prior to the baseline examination. This compares well with a recent cross-sectional study on the association between air pollution and lung function.24 Among slightly younger women (mean age of 54.5 years), who lived in the same region as our study sample, the annual rate of relocations was 2%. Differential relocations in relation to disease status (sick people moving closer to health-care providers), which could spuriously increase the association, seems highly unlikely, since the study was performed in an extremely densely populated area with health-care providers easily accessible throughout the region.

A strength of this study includes the availability of precise small-scale information on distance to major roads. In a region with a relatively homogeneous distribution of background PM, as evidenced by a very narrow range of the PM2.5 distribution (Figure 2) compared with a recent evaluation of within-city contrasts in Los Angeles,25 traffic contributes in a major way to the existing air-pollution exposure contrast within our study sample. As can be expected from the small PM2.5 exposure contrast, we were not able to demonstrate a sizable influence of background PM2.5 on CHD morbidity. Another strength is the comprehensive assessment of individual level information on most of the major causal and conditional cardiovascular risk factors, allowing for extensive control of potential confounding.

Sensitivity analysis revealed a stronger association for recent events than for events in the more distant past. Exposure misclassification is likely to be larger for past events (due to changing traffic density, changes in traffic fleet, and relocations of individual subjects over time), biasing the association towards the null.

In conclusion, this study provides epidemiological evidence that long-term exposure to traffic may be an important risk factor for CHD. Considering the paramount role of CHD in industrialized countries and the still rising use of motor vehicles, further investigations of the possible contributing pathogenetic factors, such as UFP and noise, are necessary.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgements
References
 
The authors wish to thank the Heinz Nixdorf Stiftung (Chairman Schmidt) for the generous financial support of the study and R. Krapoth (city administration of Mülheim), F. Knospe, and M. Moldzio (both city administration of Essen) for their valuable support in geocoding the addresses and calulation of distances. We gratefully acknowledge the collaboration with: Prof. D. Grönemeyer (Bochum), Prof. R. Seibel (Mülheim), L. Volbracht, M. Bröcker, S. Münkel, A. Öffner, A. Winterhalder, H. Hirche (Essen), and PD R. Peter (Ulm).

Conflict of interest: none declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgements
 References
 

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