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|Year : 2012
: 14 | Issue : 59 | Page
|Traffic noise and cardiovascular health in Sweden: The roadside study
Charlotta Eriksson1, Mats E Nilsson2, Saskia M Willers3, Lars Gidhagen4, Tom Bellander1, Göran Pershagen1
1 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
2 Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Psychology, Stockholm University, Stockholm, Sweden
3 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
4 Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
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|Date of Web Publication||18-Aug-2012|
Long-term exposure to traffic noise has been suggested to increase the risk of cardiovascular diseases (CVD). However, few studies have been performed in the general population and on railway noise. This study aimed to investigate the cardiovascular effects of living near noisy roads and railways. This cross-sectional study comprised 25,851 men and women, aged 18-80 years, who had resided in Sweden for at least 5 years. All subjects participated in a National Environmental Health Survey, performed in 2007, in which they reported on health, annoyance reactions and environmental factors. Questionnaire data on self-reported doctor's diagnosis of hypertension and/or CVD were used as outcomes. Exposure was assessed as Traffic Load (millions of vehicle kilometres per year) within 500 m around each participant's residential address. For a sub-population (n = 2498), we also assessed road traffic and railway noise in L den at the dwelling façade. Multiple logistic regression models were used to assess Prevalence Odds Ratios (POR) and 95% Confidence Intervals (CI). No statistically significant associations were found between Traffic Load and self-reported hypertension or CVD. In the sub-population, there was no association between road traffic noise and the outcomes; however, an increased risk of CVD was suggested among subjects exposed to railway noise ≥50 dB(A); POR 1.55 (95% CI 1.00-2.40). Neither Traffic Load nor road traffic noise was, in this study, associated with self-reported cardiovascular outcomes. However, there was a borderline-significant association between railway noise and CVD. The lack of association for road traffic may be due to methodological limitations.
Keywords: Air pollution, cardiovascular disease, community noise, epidemiology, hypertension
|How to cite this article:|
Eriksson C, Nilsson ME, Willers SM, Gidhagen L, Bellander T, Pershagen G. Traffic noise and cardiovascular health in Sweden: The roadside study. Noise Health 2012;14:140-7
|How to cite this URL:|
Eriksson C, Nilsson ME, Willers SM, Gidhagen L, Bellander T, Pershagen G. Traffic noise and cardiovascular health in Sweden: The roadside study. Noise Health [serial online] 2012 [cited 2020 Nov 26];14:140-7. Available from: https://www.noiseandhealth.org/text.asp?2012/14/59/140/99864
| Introduction|| |
Community noise is a prominent environmental nuisance. Despite efforts to restrict the exposure, a large proportion of the European population is exposed to noise levels exceeding the recommended guideline values.  In recent years, there has been an increased focus on effects of long-term exposure to transportation noise on the cardiovascular system. Because the auditory system is directly connected to the autonomic nervous system as well as the endocrine system, exposure to noise may trigger a stress response. ,, Long-term exposure has been hypothesized to cause an imbalance in the stress regulating mechanism, which could increase the risk for cardiovascular disease (CVD). 
Several studies have investigated the association between road traffic noise and hypertension ,,,,, as well as ischemic heart diseases (IHD), such as myocardial infarction, ,,, but few studies exist on railway noise. , According to a recent evaluation by the World Health Organization,  there is sufficient evidence that road traffic noise increases the risk of high blood pressure as well as IHD. However, the evidence on railway noise is limited. Additional research is needed, for example to estimate source-specific exposure-response relationships and to assess the potentially modifying effects of air pollution.
The primary objective of this study was to investigate the cardiovascular effects of living near noisy roads and railways using a nationwide survey and independent exposure characterizations. We also aimed at elucidating the modifying effects of several lifestyle and environmental characteristics, including air pollution. Additionally, we assessed the associations with general health, noise annoyance and sleep disturbances.
| Methods|| |
This study is based on a National Environmental Health Survey performed in Sweden in 2007.  A questionnaire was sent to 43,905 randomly selected Swedish adults, aged 18-80 years, who had lived in Sweden for at least 5 years. The selection of study participants was performed in two steps. Initially, and in order to assure a good representation of all parts of the country, 500 individuals from each of the 21 counties in Sweden were sampled. The second part consisted of an enriched selection in 10 counties, including a total of 33,405 individuals. In total, the survey was answered by 25,851 subjects (59.4%). In addition to the survey data, information on residential address coordinates, country of birth, income and education was gathered from registers by Statistics Sweden.
Assessment of disease
The National Environmental Health Survey included questions regarding people's health and annoyance in relation to various environmental factors. We used two questions to identify individuals with either hypertension or cardiovascular disease: (1) "Have you been diagnosed with hypertension by a physician?" and (2) "Do you have, or have you had, any of the following diseases: f… cardiovascular disease?". Additional information regarding the year of diagnosis for hypertension and use of anti-hypertensive treatment was also available. A five-grade scale was used to assess general health, ranging from "very good" to "very bad." We classified subjects as having a "poor health" if they reported "bad" or "very bad" health. Noise annoyance was assessed through the five-grade annoyance scale proposed by the International Commission on Biological Effects of Noise in 2001.  We classified subjects as "annoyed" if they reported being "much" or "very much" annoyed by noise in their home environment during the last 12 months. Sleep disturbances were assessed through two four-graded questions regarding difficulties of falling asleep and awakenings during the night (every week, year around; every week, parts of the year; seldom; never). Subjects were classified as "sleep disturbed" if they reported either difficulties of falling asleep or awakenings "every week, year around" or "every week, parts of the year."
Assessment of exposure
Two separate measures of exposure were used in this study. For the total population (n = 25,851), we assessed neighbourhood Traffic Load (TL) within a 500 m radius around each participant's residential address. TL was estimated by the Swedish Metrological and Hydrological Institute (SMHI) and is expressed in millions of vehicle kilometres per year (Mvkm/y). Data on traffic flows and length of road segments were available in the Swedish National Road Database, which includes information on all major private, municipal and state owned roads. The total population was divided into seven regions: Scania, South-East and the island Gotland, West, Mälardalen, Stockholm, Central and North.
For a sub population (n = 2570), we also assessed road traffic and railway noise exposure in L den , calculated by the Nordic prediction method.  L den is the A-weighted 24-h equivalent continuous sound pressure level, with an addition of 5 dB for evening noise events (in Sweden, defined as the period 19.00-23.00 h) and 10 dB for night time noise events (in Sweden: 23.00-07.00 h).  The sub population included residents in the three major cities in Sweden: Stockholm (n = 1242), Gothenburg (n = 1072) and Malmö (n = 256). These cities have conducted strategic noise mappings according to the Directive 2002/49/EC of the European Parliament and the Council of the European Union, commonly known as the Environmental Noise Directive (END). 
Our method for assessing L den exposure has been described in detail elsewhere.  In summary, we obtained END maps, technical reports and additional digital background data, such as information on the location of roads, railways and buildings, from the Environment and Health Administrations and the Offices of Urban Development in each city. By Geographical Information Systems (GIS), the residential coordinates were linked to buildings and superimposed on the END maps. For each participant, we assessed L den exposure at the most exposed façade of the dwelling. To locate dwellings within buildings, we used the following survey question: "Does your residence have a window facing… (a) larger street or traffic route; (b) local street; (c) railway (including subway, trams etcetera); (d) industry or industrial area; (e) inner yard or back yard; (f) garden or park; (g) nature (forest, lake, meadow or open field); (h) other than listed, what?". Valid L den levels were, in this way, obtained for 2498 subjects (97%). These were recorded in 5 dB classes ranging from <45 to ≥75 dB. The noise exposure assessments were performed using MapInfo Professional 9.5.0.
Average concentrations of air pollution at the residence were assessed by the SMHI. Receptor-based local, urban, regional and total concentrations of NO 2 and PM 10 were calculated by dispersion modeling using the SIMAIR system.  The urban contribution was calculated on a grid with a spatial resolution of 1 km × 1 km, while the local traffic NO 2 and PM 10 contributions were simulated directly at the residence address coordinate as the summed impact from all traffic line sources within a radius of 250 m.
Differences in the distribution of background characteristics, diseases and complaints according to the level of exposure were assessed by the Pearson's Chi-square test for categorical variables and the Student's t-test for continuous variables.
Multiple logistic regression models were used to assess Prevalence Odds Ratios (POR) and 95% confidence intervals (95% CI) for the associations between TL and L den levels of road traffic and railway noise, respectively, and hypertension as well as CVD. TL was categorized in quintiles using the lowest quintile as the reference group. In the sub population, road traffic and railway noise were categorized in five groups, <50, 50-54, 55-59, 60-64 and ≥65 dB, using those exposed at <50 dB as reference. Because of a limited number of subjects, the two highest noise categories were collapsed for railway noise. For hypertension, we performed a time-window analysis according to date of diagnosis (diagnose <1987; diagnose between 1987 and 1997; and diagnose between 1997 and 2007). No date of diagnosis was available for CVD.
To assess confounding, we used a manual backward variable selection technique in the logistic regression model (inclusion criteria P < 0.05).  Tested factors included sex (male; female), age (continuous), education (elementary school, upper secondary school, university), country of birth (Sweden; other), smoking (never, former, current), exposure to air pollution (local, regional, urban and total concentrations of NO 2 and PM 10 ) and region (Scania, South East and Gotland, West, Mälardalen, Stockholm, Central, North). Effect modification was assessed through inclusion of interaction terms between the binary exposure variables and the covariates of interest in the multivariate model, including sex, age (18-39; 40-59; ≥60 years), education, smoking, number of years at residence (<5; ≥5 years), noise exposure at the bedroom side (yes; no), noise annoyance (yes; no) and air pollution (quartiles of total NO 2 and PM 10 ).
Finally, we also assessed correlations between TL, concentrations of NO 2 and PM 10 and road traffic noise by Pearson's correlation coefficient (r).
All analyses were carried out using STATA/SE version 11.0.
| Results|| |
TL within 500 m followed an approximate log-normal distribution with mean 3.42 [standard deviation (SD) 6.10] and median 1.24 (inter quartile range 3.56) Mvkm/y. Cut-off points for quintiles of TL were 0.12, 0.74, 1.94 and 4.94 Mvkm/y, with a maximum of 69.28. Mean estimated concentrations of NO 2 and PM 10 were 7.09 (SD 4.94) μg/ m3 and 11.32 (SD 2.48) μg/m3 , respectively. As expected, there were regional differences in mean TL as well as air pollution concentrations; the Stockholm region showing highest averages (followed by the Scania and West regions) and the Central and North regions showing lowest averages [Table 1]. In the sub population from the three cities, road traffic noise followed a normal distribution with mean 57 (SD 9) dB(A); mean railway noise was 43 (SD 11) dB(A).
|Table 1: Distribution of Traffic Load within 500 m and total concentrations of NO2 and PM10 according to region among respondents to the Swedish Environmental Health Survey 2007|
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Among subjects exposed to a moderate to high TL (Quintiles 2-5), there were more females, non-Swedish born and current smokers than in the reference group (Quintile 1); furthermore, these subjects had lived a shorter time at their current address and were more exposed to air pollution [Table 2]. However, in the sub population from the three largest cities, subjects exposed to road traffic noise levels of 50 dB(A) L den or higher generally had higher education, included fewer non-Swedish born and were more exposed to air pollution in comparison with those exposed below 50 dB(A) L den.
|Table 2: Distribution of background characteristics according to Traffic Load within 500 m and Lden levels of road traffic noise in respondents to the Swedish Environmental Health Survey 2007|
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The assessment of prevalence of diseases and complaints in relation to exposure showed clear exposure-response relationships between TL and L den levels of road traffic and railway noise, respectively, and the prevalence of noise annoyance as well as sleep disturbances [Table 3]. However, no such relationships were evident for hypertension, CVD or poor health. Overall, the prevalence of cardiovascular outcomes was lower in the sub population from the three largest cities (17% and 7% for hypertension and CVD, respectively) than in the total population (21% and 10%, respectively).
|Table 3: Prevalence of disease and complaints according to Traffic Load within 500 m and Lden levels of road traffic and railway noise in respondents to the Swedish Environmental Health Survey 2007|
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Age, education, birth country and smoking were included as covariates in the final logistic regression models as they contributed significantly to the model fit. We did not find any statistically significant associations between TL and hypertension or CVD [Table 4]. Similarly, in the sub population, there was no association between road traffic noise and cardiovascular outcomes. For railway noise, we did not find an association with hypertension; however, there was a borderline statistically significant increased risk of CVD in relation to a noise exposure equal to or above 50 dB(A); POR 1.55 (95% CI 1.00-2.40). None of the air pollutants were associated with hypertension or CVD after adjustments and inclusion of these variables in the regression models did not influence the results with regard to noise.
|Table 4: Associations between exposure variables (Traffic Load within 500 m, Lden levels of road traffic and railway noise) and selfreported hypertension and CVD in respondents to the Swedish Environmental Health Survey 2007, based on subjects with complete data|
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Of the investigated variables, only education significantly modified the associations between TL and the cardiovascular outcomes, where those with high education seemed to have a reduced risk (P = 0.009 for both hypertension and CVD). However, the associations for road traffic and railway noise did not seem to be modified by any of the investigated covariates (data not shown).
In general, the correlation between TL and road traffic noise, respectively, and NO 2 and PM 10 was highest for the local (traffic related) contributions, ranging from r = 0.38 to 0.66. Considering the total concentrations of NO 2 and PM 10 , the correlations with TL were r = 0.65 and 0.59, respectively. For road traffic noise, the corresponding figures were r = 0.44 and 0.46. The correlation between TL and road traffic noise was r = 0.39.
| Discussion|| |
The results of this population-based cross-sectional study did not show an association between neighbourhood TL and prevalence of cardiovascular disease. Our results remained unchanged after adjustments for potentially important confounding factors, including air pollution. In the sub population, where a detailed and validated noise exposure assessment method was used,  there was no association between road traffic noise and outcomes. We did, however, observe a tendency toward an increased risk of CVD among subjects exposed to railway noise ≥50 dB(A).
Residence close to high-traffic roads has been reported to increase the risk of CVD. ,, Traffic variables such as distance to or traffic density on the nearest major road were then used as surrogate variables for traffic-related air pollution. Few studies have used traffic variables to assess health effects of noise exposure or to consider the joint effects of noise and air pollution. In a Dutch cohort study,  traffic intensity on the nearest road was associated with an increased risk of IHD [RR 1.11 (95% CI 1.03-1.20)]. Black smoke, which was used as an indicator of traffic-related particles, was associated with cerebrovascular as well as heart failure mortality. Furthermore, noise exposure >65 dB(A) was related to an increased risk of IHD, but this association approached unity after adjustment for black smoke and traffic intensity. In the present study, we used TL within a 500 m buffer zone around the participants' residential address as an indicator of road traffic noise exposure, simultaneously adjusting for modelled concentrations of NO 2 and PM 10 to reduce the mixing of effects with air pollution. Contrary to the Dutch findings, we did not observe an association between TL and CVD. The power to detect a statistically significant effect of TL on hypertension and CVD was high, >0.99 and 0.97, respectively (assuming POR = 1.2 and two-tailed alfa = 0.05).
Several epidemiological studies using validated calculation models and detailed input traffic data to estimate levels of road traffic noise have found increased risks of hypertension among exposed subjects, primarily in men. ,,,, However, two Swedish studies found associations mainly among females. , In the present study, we did not find evidence of an association between road traffic noise and hypertension, neither for men nor for women. Fewer studies have considered other cardiovascular endpoints, such as myocardial infarction (MI), in relation to road traffic noise. ,,, But, in a metaanalysis, it was concluded that there is evidence of a relationship between road traffic noise over 60 dB(A) and risk of MI.  The lack of association between road traffic noise and self-reported CVD in this study may be due to the fact that we have relatively few highly exposed subjects. Two studies have investigated the relationship between railway noise and blood pressure or hypertension, showing no association between railway noise and prevalence of self-reported hypertension or antihypertensive treatment. , However, Dratva et al. found significant effects of railway noise on blood pressure, which remained after adjustments for air pollution.  No previous study seems to exist on railway noise and IHD. Similar to Dratva et al., we did not see an effect of railway noise on prevalence of hypertension; however, our suggested association for CVD supports an adverse effect of railway noise exposure on the cardiovascular system.
Because air pollution and noise may both contribute to the development of CVD, we aimed to investigate their separate effects. However, the correlation between local air pollution contributions and TL and L den levels of road traffic noise, respectively, was found to be fairly high. For TL and air pollution, this can be attributed to the mutual reliance on traffic data from the Swedish National Road Database. The L den levels of road traffic noise were based on local traffic counts in each municipality, resulting in a lower correlation with air pollution. In relation to previous research, the observed correlation between road traffic noise and total concentrations of NO 2 in our study (r = 0.44) were lower than what was reported in a recent study from Spain,  analyzing modelled L 24h and measured NO 2 (r = 0.62), and also lower than the findings from Davies et al.,  who investigated correlations between short-term average noise levels (L eq, 5 min ) and measured NO 2 (r = 0.53). The correlation between road traffic noise and total PM 10 (r = 0.46) was higher than that reported between modelled noise and black smoke in the Dutch study by Beelen et al.,  r = 0.24, but lower than the correlation found in the study by de Kluizenaar et al.,  r = 0.72. It can be concluded that the correlation between noise and air pollution is study specific and also depends on the methods of assessment. Our findings highlight the difficulties in trying to disentangle the effects of noise and air pollution when using modelled rather than measured exposure data.
We found clear exposure-response relationships between TL and L den levels of road traffic or railway noise and annoyance. A detailed analysis of the associations between the L den levels and annoyance has been presented in a previous publication,  and was in good agreement with the already established exposure-response relationships. , This indicates that our method of noise exposure estimation has a reasonable validity, at least for the sub population where data on dwelling location within buildings supplemented the END-maps. Also, sleep disturbances were strongly related to TL and L den levels of road traffic and railway noise. We did not, however, assess night-time noise levels, which precludes comparison with previous exposure-response functions. 
Several methodological problems may have contributed to conceal potentially important associations between noise exposure and cardiovascular outcomes. Like most epidemiological studies published so far on the health effects of noise, our study was of a cross-sectional design, which limits the possibilities to infer causality.  For example, subjects exposed to moderate or high TL within 500 m had lived fewer years at their residence than those with lower TL, possibly indicating a tendency of people moving out of noise-polluted areas, which may lead to selection bias. Neighbourhood TL is a crude surrogate for noise exposure, as it does not take into account noise barriers and shielding from buildings. Future studies on noise should thus preferably use more detailed noise exposure assessments. Despite the detailed assessment of road traffic, there may also be some misclassification of exposure in the sub population. Because the misclassifications are not likely to depend on outcome status, this may have contributed to an attenuation of the associations. Our attempts to reduce the effect of such misclassification by time-window analysis and stratification on duration of residence did not, however, alter the results. No increased risks were seen among those diagnosed with hypertension most recently (1997-2007) or among those with a residence time ≥5 years.
Furthermore, the reliance on self-reported data is likely to have underestimated the prevalence of disease, particularly hypertension. In a comparison of self-reported and biometrical data on hypertension from the Utrecht Health Project, the sensitivity was as low as 34.5%.  Other studies report somewhat higher sensitivity. , In the study by Oksanen et al.,  the sensitivity was 86% for self-reported prevalence of hypertension when compared with data from registers, although it was lower for incident cases (55%). Self-reported CVD has in general relatively high sensitivity; Oksanen et al., for example, reported a sensitivity of 78% for prevalent cases. Although we were not able to examine the accuracy of our outcome data in detail, the prevalence of self-reported hypertension in our study (21%) was lower than what has been estimated for the general population (27%),  indicating some underreporting. This outcome misclassification is, however, not likely to depend on exposure status.
The strengths of this study include the large population-based sample, the independent exposure characterizations and the availability of high-quality modelled air pollution data.
Furthermore, the results for the total population, in which we used TL as a surrogate variable for noise, did not differ from the sub population, where a more detailed exposure characterization was used. The L den level of road traffic and railway noise at the most exposed façade of the dwelling used in the sub population is a good indicator of individual noise exposure as it accounts for apartment orientation within buildings. Additionally, the Swedish END maps are, generally, of higher quality than required by the directive,  including noise exposure assessments on the complete road net and using a threshold of 35 dB (Stockholm and Gothenburg) or 45 dB (Malmö). Yet, it would have been preferable to include data on additional exposure moderating factors, such as floor height and window insulation. Likewise, we had limited possibilities to control for confounding. Data on, for example, diet, body mass index, physical activity and heredity for CVD are lacking and we therefore cannot rule out residual confounding.
In conclusion, neither neighbourhood TL nor road traffic noise were, in this cross-sectional study, associated with self-reported cardiovascular outcomes. However, there was a borderline-significant association between railway noise and CVD. The lack of association for road traffic may be due to methodological limitations.
| Acknowledgments|| |
The authors wish to thank Dag Stenkvist for assisting with management of the Swedish END maps and Rebecka Pershagen for help with the noise exposure assessment in Stockholm, Gothenburg and Malmö.
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Nobels väg 13, Karolinska Institutet,Stockholm, Sweden
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3], [Table 4]
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