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| Year : 2013 | Volume
: 15
| Issue : 67 | Page : 388--397 |
Road traffic noise, air pollution components and cardiovascular events
Yvonne de Kluizenaar1, Frank J van Lenthe2, Antoon J.H. Visschedijk3, Peter Y.J. Zandveld4, Henk M.E. Miedema5, Johan P Mackenbach2,
1 Urban Environment and Safety, The Netherlands Organization for Applied Scientific Research (TNO), P.O. Box 49, 2600 AA, Delft; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands
2 Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands
3 Climate, Air and Sustainability, The Netherlands Organization for Applied Scientific Research (TNO), P.O. Box 80015, 3508 TA Utrecht, The Netherlands
4 Urban Environment and Safety, The Netherlands Organization for Applied Scientific Research (TNO), P.O. Box 80015, 3508 TA Utrecht, The Netherlands
5 Urban Environment and Safety, The Netherlands Organization for Applied Scientific Research (TNO), P.O. Box 49, 2600 AA, Delft, The Netherlands
Correspondence Address:
Yvonne de Kluizenaar
TNO, Postbus 49, 2600 AA, Delft
The Netherlands
Abstract
Traffic noise and air pollution have been associated with cardiovascular health effects. Until date, only a limited amount of prospective epidemiological studies is available on long-term effects of road traffic noise and combustion related air pollution. This study investigates the relationship between road traffic noise and air pollution and hospital admissions for ischemic heart disease (IHD: International Classification of Diseases (ICD9) 410-414) or cerebrovascular disease (cerebrovascular event [CVE]: ICD9 430-438). We linked baseline questionnaire data to 13 years of follow-up on hospital admissions and road traffic noise and air pollution exposure, for a large random sample (N = 18,213) of inhabitants of the Eindhoven region, Netherlands. Subjects with cardiovascular event during follow-up on average had higher road traffic noise day, evening, night level (L den) and air pollution exposure at the home. After adjustment for confounders (age, sex, body mass index, smoking, education, exercise, marital status, alcohol use, work situation, financial difficulties), increased exposure did not exert a significant increased risk of hospital admission for IHD or cerebrovascular disease. Relative risks (RRs) for a 5 th to 95 th percentile interval increase were 1.03 (0.88-1.20) for L den; 1.04 (0.90-1.21) for particulate matter (PM 10 ); 1.05 (0.91-1.20) for elemental carbon (EC); and 1.12 (096-1.32) for nitrogen dioxide (NO 2 ) in the full model. While the risk estimate seemed highest for NO 2 , for a 5 th to 95 th percentile interval increase, expressed as RRs per 1 μg/m 3 increases, hazard ratios seemed highest for EC (RR 1.04 [0.92-1.18]). In the subgroup of study participants with a history of cardiovascular disease, RR estimates seemed highest for noise exposure (1.19 [0.87-1.64] for L den); in the subgroup of elderly RR seemed highest for air pollution exposure (RR 1.24 [0.93-1.66] for NO 2 ).
How to cite this article:
de Kluizenaar Y, van Lenthe FJ, Visschedijk AJ, Zandveld PY, Miedema HM, Mackenbach JP. Road traffic noise, air pollution components and cardiovascular events.Noise Health 2013;15:388-397
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de Kluizenaar Y, van Lenthe FJ, Visschedijk AJ, Zandveld PY, Miedema HM, Mackenbach JP. Road traffic noise, air pollution components and cardiovascular events. Noise Health [serial online] 2013 [cited 2023 Oct 1 ];15:388-397
Available from: https://www.noiseandhealth.org/text.asp?2013/15/67/388/121230 |
Full Text
Introduction
Road traffic is a major source of both environmental noise and air pollution exposure in urban areas. Both road traffic noise and air pollution have been related to cardiovascular health effects. [1],[2],[3],[4] The continuing urbanization, the large number of people exposed and cardiovascular morbidity as a major cause of mortality in modern societies, create a need for better insight in the long-term effects of road traffic on cardiovascular morbidity.
Noise exposure
Noise exposure can cause annoyance and sleep disturbance. [5],[6] During the last decades, it has been studied if and how noise exposure may cause further adverse health effects. The pathway from noise exposure to cardiovascular health effects is hypothesized to involve stress reactions that may cause adverse health effects in the long-term. Exposure to noise may-directly or indirectly through disturbance of sleep, communication or activities-affect the autonomic nervous system and the endocrine system, resulting in biological responses such as changes in heart rate and levels of stress hormones. [7],[8],[9] This may, possibly in combination with other factors, lead to increases in biological risk factors (e.g., blood lipids, blood pressure, blood glucose, blood viscosity), which then may eventually result in manifest diseases such as arteriosclerosis and ischemic heart disease (IHD). [8],[10] Currently, there is increasing evidence that long-term exposure to environmental noise can induce stress related health effects including cardiovascular diseases. [1],[2],[9],[11],[12],[13],[14],[15],[16],[17],[18],[19]
Air pollution
A large body of epidemiological studies show associations between particulate matter (PM 10 ) and a wide range of adverse health effects, including cardiovascular health effects. [4],[20],[21] Different pathways to cardiovascular endpoints have been hypothesized for PM 10 , including (1) pulmonary oxidative stress and inflammatory responses that by "spill over" lead to systemic oxidative stress and inflammatory responses, (2) perturbation of autonomic nervous system balance and (3) particles or particle constituents passing through the lungs, thus entering the systemic circulation and provoking "direct" extra-pulmonary effects. [4] A majority of studies linking air pollution with cardiovascular effects focus on PM 10 air pollution. Epidemiological studies into the effects of long-term exposure have shown associations with cardiovascular morbidity and mortality. [22],[23],[24],[25],[26],[27]
Road traffic noise and air pollution share road traffic as a source; therefore, they are to some extent related. [28],[29],[30] In recent years efforts have been made to study the association between cardiovascular morbidity and road traffic noise and air pollution exposure in combination. Studies have focused on various cardiovascular outcomes, including blood pressure, hypertension, myocardial infarction, stroke and cardiovascular mortality. [15],[18],[22],[31],[32],[33],[34],[35],[36],[37] Available studies carefully suggest independent effects of road traffic noise and air pollution.
Until date, however, only a small number of prospective epidemiological studies are available into effects of long-term exposure of road traffic noise and air pollution. With regard to air pollution, particles originating from combustion sources (including traffic) are of specific interest as it has been suggested that these particles are particularly relevant for human health. [3],[21],[38],[39] However, to date, only a limited number of epidemiological studies is available that have studied the impact of long-term exposure to combustion related particle fractions, of which elemental carbon (EC) is an indicator. [3] Previous studies have shown that subjects with pre-existing disease and the elderly may be susceptible groups for the effects of exposure. [4],[40] This study investigates the relationship between road traffic noise and air pollution components PM 10 , nitrogen dioxide (NO 2 ) and EC and hospital based incidence of IHD or cerebrovascular disease.
Methods
Study population
The GLOBE study is a prospective cohort study carried out in the Netherlands, with a primary aim of explaining socio-economic inequalities in health. GLOBE is the Dutch acronym for Health and Living Conditions of the Population of Eindhoven and surroundings. Baseline data were collected in 1991. Details of the study protocol have been described elsewhere [41] and will only be briefly summarized here.
In 1991, a sample (stratified by age, degree of urbanization and socio-economic position) of 27,070 non-institutionalized subjects (aged 15-74 years), was drawn from 18 municipal population registers in the south-eastern part of the Netherlands and asked to participate in the study. With a response of 70.1%, baseline information was collected from 18,973 individuals using a postal questionnaire. The area of study included the city of Eindhoven, which was the 5 th largest city of the Netherlands in 1991.
Health outcome and covariates
The data collection comprised a broad range of potential confounders including socio-demographic variables (age, gender, marital status and education), life-style factors (smoking, alcohol use, physical activity, body mass index [BMI]) and living conditions (employment status, financial problems). A history of cardiovascular disease at baseline (1991) was defined by a positive answer to one of the following questions in the questionnaire: "Did you have a heart disease or infarction during the last 5 years?" and "Did you have a cerebrovascular accident-or experience its consequences - during the last 5 years?"
Health outcome data were obtained from the national database on hospital admissions. The study population was tracked annually through municipal population registers in and outside the study area from the start of the study. These registers virtually completely cover the population and are maintained continuously with respect to deaths and changes of address. It allowed us to link our database to the National Medical Registry, a national database on hospital admissions, available for the period from 1991 to 2003, to obtain information on the incidence of hospital-based IHD (International Classification of Diseases [ICD9] 410-414) and cerebrovascular disease (ICD9 430-438) in the study population. In case of re-admission during follow-up, the first admissions were selected. The five hospitals in the area where the GLOBE participants lived at baseline gave their permission to use their data from the national data set. Record linkage was carried out on the key variables zip-code, gender and date of birth. Approval for the record linkage was received from the Medical Ethical Commission. Details on the record linkage procedure have been described elsewhere. [42]
Age was entered as a continuous variable while gender, BMI, smoking, education, physical activity, marital status, alcohol use, employment status and financial problems were entered as categorical variables. BMI (body weight divided by height squared) was categorized into four groups (underweight [BMI < 20], normal weight range [BMI 20-25], overweight [BMI 25-30], obese [BMI > 30]). Smoking was coded in three categories (current smoker, former smoker and never smoker). Highest attained level of education was distinguished into four different categories (primary education; lower professional and intermediate general education; intermediate professional and higher general education; higher professional education and university). Physical activity was available in four categories (none, little, moderate and much physical activity). Marital status was categorized into four groups (married or living together, unmarried, divorced, widow/widower). Alcohol use was categorized into three groups (moderate, abstainer, excessive). Employment status was categorized in three categories, including unemployed, otherwise not gainfully employed nor studying (incl. e.g., house wife/house man, pensioner, etc.), working (incl. studying, military service). Three categories of financial problems were distinguished (no difficulty, some difficulty, large difficulty). Missing values in potential confounding variables (the percentage of missing values for all confounding variables was below 5.6 %) were imputed, replacing the missing values with the most common category.
Environmental exposure
The road traffic noise exposure of the subjects was calculated at the most exposed faηade of the dwelling with the standard method SKM2 ("Standaard Karterings Methode 2") in accordance with requirements of the EU Environmental Noise Directive (END). For the analyses, we used the EU standard noise metric day, evening, night level (L den). L den is an "average" sound level over 24 h in which sound levels during the evening and the night are increased by 5 dB(A) and 10 dB(A), respectively. SKM2 is the Netherlands' standard method for noise modeling and producing noise maps in compliance with the END. [43] SKM2 is implemented in Urbis [44] that was used here for the exposure calculations. Noise calculations are carried out in two steps calculating first the emission and then the transmission. The emission calculations take into account traffic characteristics, including traffic intensities, traffic composition, speed, road height and road surface type. The transmission calculations take into account the distance between source (road) and dwelling faηade, air attenuation, effects of (yearly) meteorologic conditions, ground attenuation, object screening, reflection of objects opposite the dwelling and statistical diffraction for transmission. Noise exposure is calculated at the height of the center of the dwelling faηade of the exposed subject. Very low noise exposure levels (L den below 45 dB[A]) were recoded as 45 dB(A) since this can be considered to be a lower limit of the ambient noise in urban surrounding.
Air pollution exposure was also assessed at the most exposed faηade. The annual average exposure concentration at a certain location is defined by the sum of the regional and urban background concentration, supplemented with the calculated contribution of the local road traffic, to account for the small scale spatial variation within the city. Background concentrations are estimated annually by the National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), based on measurement data of the Dutch National Air Quality Monitoring Network. Combining these monitoring data with nationwide air pollution modeling, they each year generate a national map (1 km Χ 1 km) of annual average concentrations for the most important air pollution components, including PM 10 and NO 2 . The empirical relationship proposed by Schaap and Denier van der Gon [45] has shown that black smoke (BS) may act as a suitable indicator of EC concentrations. Here, this relationship was employed to derive background EC concentrations from the background BS concentrations measured by two regional monitoring stations within the study area (as part of the National Air Quality Monitoring Network). The local traffic-related EC emission contributions were estimated on the basis of the fuel-specific EC content of exhaust PM 10 emission. [46] These data were input for the calculation of local EC concentrations, assuming an absence of other relevant local EC sources. An inventory on the near-by industrial activities indicated that traffic is indeed the dominating EC source in the Eindhoven region, although some minor contributions may originate from e.g., wood combustion. The dispersion dynamics of EC are implicitly assumed to be identical to PM 10 . PM 10 , NO 2 and EC concentration gradients were obtained using the Netherlands' standard models for local air pollution calculations: CAR II for gradients in a street caused by the contribution of that street and "Pluim Snelweg" for gradients caused by the contributions of highways. [47] Concentration levels were calculated for the year 2004, the end of follow-up, to represent the long-term average spatial variation in air pollution concentrations.
Input data for emission calculations consisted of a detailed digital map describing the geographic location of roads and the traffic characteristics for each road segment (including traffic intensities for each vehicle category, speed and road surface type), provided by the local authorities of Eindhoven for the year 2004. Traffic data was attached as attributes to the road segments for a dense network of roads, including highways, arterial roads, main streets and principal residential streets.
Input for the noise transmission calculations consisted of digital maps with precise information on the geographic location of buildings and ground characteristics (Topographic Service data [TOP 10]) provided by the Netherlands Ministry of Housing, Spatial Planning and the Environment (Ministerie van Volkshuisvesting, Ruimtelijke Ordening en Milieubeheer (VROM) /DGR). Building height was derived from the Actual Height Information Netherlands (AHN), a 5 m Χ 5 m grid with height information based on laser altimetry. The geographic location of noise screens with their height was provided by the local authorities of Eindhoven. The geographical location of dwellings within the building contours (Topographic Service data [TOP 10]) was identified with the use of address coordinates.
In a recent study, the performance of a similar dispersion modeling approach for calculation of air pollution concentrations in Rotterdam, The Netherlands was evaluated. This study showed a good agreement between annual average NO 2 concentrations estimated by dispersion modeling and measured NO 2 concentrations at eighteen sites in the Rotterdam area (Pearson correlation coefficient ρ = 0.77). [48]
Statistical analysis
Cox proportional hazard analysis was performed to investigate the association between residential road traffic exposure (road traffic noise and air pollution) and hospital based incidence of cardiovascular diseases (IHD: ICD9 410-414 or cerebrovascular disease: ICD9 430-438). Results are expressed as relative risks (RRs) with the corresponding 95% confidence intervals (CI) for the time to the first cardiovascular event. For participants who were admitted to a hospital for IHD or cerebrovascular disease, who died, or moved away from the 1991 baseline address, time in the study was calculated as the difference between the start of study and date of the event. For those who had no event, time in the study was calculated as the difference between the start and end of follow-up. Median follow-up time was 8.7 years.
We used different models: (1) Unadjusted model; (2) model adjusted for age and sex; (3) full model, where factors were included that were hypothesized a priori to potentially confound the relationship between traffic exposures and cardiovascular disease, including: Age, sex, BMI, smoking, level of education (as a measure of social economic position), physical activity, marital status, alcohol use, employment status and financial problems; and (4) models that in addition, adjusted for road traffic noise respectively air pollution. All analyze were carried out for the full population and for three specific subgroups of the population: (1) Subjects without a history of cardiovascular disease, (2) subjects with a history of cardiovascular disease and (3) elderly subjects (age 65 and over). Analyses were performed with IBM SPSS Statistics version 20.
Results
[Table 1] shows the characteristics of the study sample by the event of hospital admission for (IHD: ICD9 410-414) or cerebrovascular disease (cerebrovascular event [CVE]: ICD9 430-438) during follow-up. Table shows that subjects who had an event during follow-up on average have a higher age, more often are males, obese (BMI > 30), smokers and lower educated, less often exercise much, more often are married or living together, more often are unemployed and exposure to road traffic noise (L den) and air pollution (PM 10 and EC) at home is higher. In addition, subjects in this group more often reported a history of cardiovascular disease.{Table 1}
[Table 2] shows the RRs for hospital admission for IHD or cerebrovascular disease for road traffic noise exposure, for a 10 dB increase of L den [Table 2]a and for a 5 th to 95 th percentile interval change [Table 2]b. Results are presented for the full population and for the three specific subgroups, for the four different models.{Table 2}
[Table 2]a and b shows that in the GLOBE study sample, in the unadjusted models, with increasing road traffic noise level, a significantly elevated risk for the incidence of IHD or cerebrovascular disease was found (RR 1.12 [95% CI: 1.04-1.21]) and RR 1.27 (95% CI: 1.09-1.47), for a 10 dB increase in L den and a 5 th to 95 th percentile interval increase, respectively). However, after adjustment for confounders in the full model these relationships were smaller and not significant (RR 1.01 [95% CI: 0.94-1.09]) and RR 1.03 (0.88-1.20), for a 10 dB increase in L den and a 5 th to 95 th percentile interval increase, respectively). The RR seemed highest in the subgroup with a history of cardiovascular disease, although not significant (RR 1.09 [95% CI: 0.93-1.27]) for and RR 1.19 (95% CI: 0.87-1.64) for a 10 dB increase in L den and a 5 th to 95 th percentile interval increase, respectively). However, CIs are overlapping. The association seemed not affected by additional adjusting for PM 10 .
[Table 3], [Table 4], [Table 5] show RRs for different components of air pollution: PM 10 , EC and NO 2 respectively. [Table 3]a and b shows a significant association between PM 10 concentration and hospital admission of IHD or cerebrovascular disease (RR 1.06 [95% CI: 1.01-1.11]) and RR 1.20 (95% CI: 1.04-1.38) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase respectively) before adjustment for potential confounders. After adjustment for covariates in the full model, this association was not significant (RR 1.01 [95% CI: 0.97-1.06]) and RR 1.04 (95% CI: 0.90-1.21) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase in PM 10 respectively). The RR seemed highest, although not significant, in the subgroup of elderly subjects (RR 1.04 [95% CI: 0.96-1.13]) and RR 1.13 (95% CI: 0.87-1.46) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase respectively).{Table 3}{Table 4}{Table 5}
In the sub group with a history of cardiovascular disease, a RR was found of (RR 1.03 [95% CI: 0.94-1.12]) and RR 1.09 (95% CI: 0.83-1.43) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase in PM 10 respectively). After additional adjustment for road traffic noise, this changed to RR 1.00 (95% CI: 0.89-1.12) and RR 0.99 (95% CI: 0.70-1.41) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase in PM 10 respectively).
[Table 4]a and b show associations found for EC. Comparison of [Table 3]a and [Table 4]a shows that RR estimates per μg/m 3 increase seem higher for EC than for PM 10 (RR for EC per μg/m 3 increase: 1.16 (95% CI: 1.04-1.30) (unadjusted model), 1.04 (95% CI: 0.92-1.18) (full model) and RR for PM 10 per mg/m 3 increase: 1.06 (95% CI: 1.01-1.11) (unadjusted model), 1.01 (95% CI: 0.97-1.06) (full model). As shown in [Table 3]b and [Table 4]b, associations found for a 5 th to 95 th percentile interval increase are comparable in magnitude (RR for EC per 5 th to 95 th percentile increase: 1.18 (95% CI: 1.04-1.35) (unadjusted model) and 1.05 (95% CI: 0.91-1.20) (full model); RR for PM 10 : 1.20 (95% CI: 1.04-1.38) (unadjusted model) and 1.04 (0.90-1.21) (full model).
[Table 5]a and b shows associations found for NO 2 . With increasing NO 2 concentration, in the unadjusted model, a significantly elevated risk for the incidence of IHD or cerebrovascular disease was found (RR 1.02 [95% CI: 1.01-1.03]) and RR 1.29 (95% CI: 1.10-1.51) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase respectively). However, as for the other air pollution components, these associations were not significant in the full model (RR 1.01 [95% CI: 1.00-1.02]) and 1.12 (0.96-1.32) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase respectively). The RR seemed highest, although not significant, in the subgroup of elderly subjects (RR 1.02 [95% CI: 0.99-1.04] and RR 1.24 (95% CI: 0.93-1.66) for a 1 μg/m 3 and a 5 th to 95 th percentile interval increase respectively).
Discussion
In the present study, we only found significant associations between road traffic noise (L den), various components of air pollution (PM 10 , EC and NO 2 ) and hospital admission for IHD or cerebrovascular disease in the unadjusted models. However, these associations became substantially smaller and non-significant after adjustment for confounders. While the risk estimates seemed highest for NO 2 , when comparing risk estimates for different exposures for a 5 th to 95 th interval increase, with a RR of 1.12 (0.96-1.32), expressed as RRs per μg/m 3 increase, risk estimates seemed highest for EC with an RR of 1.04 (0.92-1.18) in the full model.
For road traffic noise exposure, RRs seemed highest for the subgroup with a history of cardiovascular disease. For air pollution, RRs seemed highest in the subgroup aged 65 and over. However, associations for subgroups of the study population were not significant.
Noise exposure and cardiovascular disease
During the last decades studies have investigated the association between transportation noise exposure and blood pressure changes and hypertension. In a recent meta analyses, Van Kempen and Babisch [2] report a small but significant association between road traffic noise and hypertension (odds ratio [OR] 1.034; 1.011-1.056) per 5 dB increase in L Aeq,16h , based on 24 studies carried out between 1970 and 2010. A limited number of studies have investigated the relationship between IHD, cerebrovascular disease and long-term exposure to transportation noise, with varying results. Early population studies include the Caerphilly [49] and Speedwell [50] studies. In the Caerphilly study, no association was found between road traffic noise and the prevalence of IHD. However, associations were found between noise and a broad range of potential risk factors for IHD. [49] In pooled analyses of the Caerphilly and Speedwell cohorts a marginal risk increase was suggested (RR 1.1 and 1.2 for IHD incidence and prevalence, respectively), for the highest noise category (L eq,6-22h = 66-70 dB[A]) versus the lowest noise category (L eq,6-22h = 51-55 dB[A]). These associations however, were not significant. [50] Babisch et al, [11] reported an association between road traffic noise and the incidence of myocardial infarction (adjusted OR 1.3 [0.88-1.8]), for men exposed to a road traffic noise level exceeding 70 dB(A), compared to those exposed under 60 dB(A), in a case control study in Berlin. In a subsample of men who lived for at least 10 years at their present address, the OR was higher (OR 1.8 [1.0-3.2]). In a review and meta-analysis Babisch [1] found no increase in risk below 60 dB(A) (L day) while risk increase was found with increasing noise levels above 60 dB(A). More recently, Selander et al, [33] reported an association between road traffic noise and myocardial infarction, with an adjusted odds ratio for an exposure above 50 dB(A) of 1.12 (0.95-1.33) in the full population and 1.38 (1.11-1.71) for a subsample excluding persons with hearing loss, or noise exposure from other sources. Sørensen et al, [19] reported a significant association between road traffic noise and incidence of myocardial infarction, with an incidence rate ratio (IRR) of 1.12 (1.02-1.22) per 10 dB (L den) increase in exposure. Gan et al, [18] 2012 reported interquartile range increases of respectively community noise exposure associated with a 6% (1-11%) and black carbon with a 4% (1-8%) increase in coronary heart disease (CHD) mortality. They conclude their findings suggest an independent effect of traffic related noise and air pollution on CHD mortality. Huss et al, [17] reported an adjusted RR of 1.3 (0.96-1.7) for mortality from myocardial infarction, for subjects exposed to aircraft noise ≥60 dB(A) versus <45 dB(A) and 1.5 (1.0-2.2) in a subsample of subjects who had lived at the same place for at least 15 years. However, they found however no association between aircraft noise and cerebrovascular disease mortality. Sorensen et al, [35] reported an IRR of 1.14 (1.03-1.25) for stroke per 10 dB higher level of road traffic noise (L den), with stronger associations in the elderly. [35] Our study, with risk estimates for a 5 th to 95 th percentile increase in road traffic noise (L den) of 1.03 (0.88-1.20) (full sample, full model) and 1.19 (0.87-1.64) for the subgroup with a history of cardiovascular disease, falls within the range of reported risk estimates.
Air pollution and cardiovascular health
A number of epidemiological studies have studied the effects of long-term air pollution exposure and cardiovascular morbidity and mortality. Studies have considered different air pollution components and various endpoints. A limited number of studies have also investigated effects of combustion related fractions (black carbon particles; EC). In a recent review and meta analyses Janssen et al, [3] report a pooled effect estimate of 1.06 (1.04-1.09) for EC and 1.007 (1.004-1.009) for PM 2.5 per 1 μg/m 3 increase in concentration, for all-cause mortality.
In the individual studies, generally increased risks were reported with increasing concentration; however, like in this study CI often included 1. Only few studies into long-term effects on cardiovascular morbidity and mortality have been carried out in Europe. In a large Dutch cohort study, Beelen et al, [51] found RRs of 1.04 (0.95-1.13) for a 10 μg/m 3 increase in BS cardiovascular mortality and similar results for PM 2.5 . In another study, Beelen et al, [22] found a RR of 1.39 (0.99-1.94) for cerebrovascular mortality and 1.01 (0.83-1.22) for IHD mortality for a 10 μg/m 3 increase in BS. They concluded results found in their study were not explained by traffic noise exposure. Our risk estimates with an RR of 1.04 (0.92-1.18) for IHD or cerebrovascular disease for a 1 μg/m 3 increase in EC concentration and 1.01 (0.97-1.06) for a 1 μg/m 3 increase in PM 10 (full model) fall within the range of previous findings. Most studies into long term exposure to PM 10 and mortality were carried out in the US, often reporting somewhat stronger associations. [4] E.g., Laden et al, [52] found an association between PM 2.5 exposure and cardiovascular mortality (RR 1.28 [1.13-1.44]) for a 10 μg/m 3 increase in concentration.
In our study, although estimated risk estimates for a 1 μg/m 3 increase seemed higher for EC than for PM 10 , risk estimates for a 5 th to 95 th percentile increase were similar. This is in line with the results of the recent systematic review by Janssen et al. [3]
When comparing air pollution components for a 5 th to 95 th percentile interval change, RRs seemed highest for NO 2 with an RR of 1.12 (0.96-1.32) in the full model. NO 2 is commonly used as an indicator of combustion related air pollution. Although NO 2 is itself toxic, in toxicological studies, typically effects are only found at levels far exceeding ambient levels. [53] Therefore, at current ambient levels it is unclear if NO 2 itself plays a major role, or if (more commonly assumed) NO 2 is an indicator of other toxic components in the air pollution mixture. [53] (World Health Organization, 2006). In epidemiological studies, associations between long term exposure to NO 2 and adverse health outcomes have been reported. [54] Rosenlund et al, [54] reported a RR for incidence in coronary events per 10 μg/m 3 increase in NO 2 of 1.03 (1.00-1.07), with stronger associations for fatal cases (1.07; 1.02-1.12). Hoek et al, [55] reported a RR of 1.81 (0.98-3.34) for cardiopulmonary mortality for concentration changes from the 5 th to the 95 th percentile (approximately 30 μg/m 3 ) in a random sample of 5000 people from the full cohort of the Netherlands Cohort study. In the full cohort, however, Beelen et al., [51] reported a RR of 1.07 (0.94-1.21) for cardiovascular mortality for a 30 μg/m 3 increase in NO 2 concentration. The risk estimates found in this study fall within the range of previously reported risk estimates.
Studies into short term effects have also reported independent associations between NO 2 and cardiovascular mortality that remained significant after adjustment for ambient particles or sulfur dioxide. [56] Similarly, Chiusolo et al, [40] found statistically significant associations between short-term changes in NO 2 and cardiac mortality, independent of PM 10 and O 3 . They state that the role of NO 2 as a surrogate of unmeasured pollutants cannot be ruled out and suggest that NO 2 may act as a surrogate of ultrafine PM.
Methodological considerations
Strength of our study is that exposure assessment incorporated both road traffic noise and air pollution exposure. As these factors are both identified as possible pathogenic traffic related stressors, it is valuable that in this study we were able to study the effects of both exposures to road traffic noise and air pollution. In addition, we were able to pay specific attention to the combustion related fraction of PM 10 . Until date, only a limited amount of epidemiological studies have considered black carbon particles, such as EC. [3] Second, the prospective design of this large population study, provides a powerful basis for studying long-term effects as compared with cross-sectional studies.
In this study, a large number of potential confounders were available. Nevertheless, the possibility of residual confounding due to unavailable variables cannot be fully excluded. Even after adjustment for confounders, there may still be some residual confounding that may to some extent explain the associations found. However, we minimized any potential residual confounding by being able to adjust for a large range of potential risk factors in the model, including demographic, socio-economic and life-style characteristics. Information on the history of disease was available from a question in the questionnaire, which referred to the previous 5 years. No information was available on disease history further back in time.
As outdoor air pollution is a mixture of a large variety of components that are often related as many of these components share the same sources (e.g., traffic), identification of effects of single components remains complicated: The possibility that one component acts as a surrogate for (a mixture of) other pollutants, cannot be ruled out.
While there was substantial spatial variation in road traffic noise (L den) and NO 2 , the spatial variation in PM 10 and EC in terms of μg/m 3 was relatively small. This may have limited ability to detect associations between air pollution and cardiovascular events. However, since the study area (the Eindhoven region) is quite a large urban area, the long-term spatial pattern in exposure may be assumed to be reasonably representative for a within city contrast.
Exposure levels were estimated at the home address; therefore, there may still be some misclassification of air pollution exposure. No information was available on indoor or occupational exposure or on commuting or on time-activity patterns of the respondents. However, people tend to spend the largest percentage of their time at home.
The effects of high noise exposure may be partly masked by selection mechanisms (e.g., noise sensitive subjects moving away from high exposure areas), better sound insulation measures to the home of most exposed dwellings, choice of bedroom location or changed window opening behavior in reaction to exposure. Some previous studies have shown stronger associations, when information on exposure modifying factors (e.g., bedroom position, window opening behavior, insulation) [13],[57],[58] or effect modifying factors (e.g., hearing loss) [33] could be taken into account. However, in this study no data were available to take these factors into account.
Furthermore, there are indications that effects on cardiovascular morbidity mainly start to occur at very high exposure levels (exceeding 60 dB). [1] In an average city, only a small percentage of the population is exposed to such high levels, which may have limited ability to detect associations for noise.
In summary, in this study no significant association between road traffic noise (L den), various components of air pollution (PM 10 , EC and NO 2 ) and hospital admission for IHD or cerebrovascular disease was found after adjustment for confounders. When comparing risk estimates for different exposures for a 5 th to 95 th interval increase, NO 2 seemed to have the highest risk estimate for events while when expressed per μg/m 3 increase in concentration risk estimates seemed highest for EC. For noise, risks estimates seemed highest for the subgroup with a history of cardiovascular disease. For air pollution, risks estimates seemed highest for the subgroup aged 65 and over.
Acknowledgments
This work was funded by the Netherlands Ministry of Housing, Spatial Planning and the Environment (VROM). The GLOBE study is carried out by the Department of Public Health of the Erasmus University Medical Center in Rotterdam, in collaboration with Municipal Public Health Service in the study region (GGD Brabant-Zuidoost). The study has been and is supported by grants of the Ministry of Public Health, Welfare and Sport, the Sick Fund Council, The Netherlands Organization for Advancement of Research, Erasmus University and the Health Research and Development Council. We thank the Municipality Eindhoven for kindly providing data. We thank Mauricio Avendano Pabon for data linkage of hospital admissions.
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