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| Year : 2014
| Volume
: 16 | Issue : 68 | Page
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| Updated exposure-response relationship between road traffic noise and coronary heart diseases: A meta-analysis |
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Wolfgang Babisch
Department of Environmental Hygiene, Federal Environment Agency, Corrensplatz 1, 14195 Berlin, Germany
Click here for correspondence address
and email
| Date of Web Publication | 26-Feb-2014 |
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A meta-analysis of 14 studies (17 individual effect estimates) on the association between road traffic noise and coronary heart diseases was carried out. A significant pooled estimate of the relative risk of 1.08 (95% confidence interval: 1.04, 1.13) per increase of the weighted day-night noise level L DN of 10 dB (A) was found within the range of approximately 52-77 dB (A) (5 dB-category midpoints). The results gave no statistically significant indication of heterogeneity between the results of individual studies. However, stratified analyses showed that the treatment of gender in the studies, the lowest age of study subjects and the lowest cut-off of noise levels had an impact on the effect estimates of different studies. The result of the meta-analysis complies quantitatively with the result of a recent meta-analysis on the association between road traffic noise and hypertension. Road traffic noise is a significant risk factor for cardiovascular diseases. Keywords: Coronary heart disease, heterogeneity, ischemic heart disease, meta-analysis, road traffic noise
How to cite this article:
Babisch W. Updated exposure-response relationship between road traffic noise and coronary heart diseases: A meta-analysis. Noise Health 2014;16:1-9 |
| Introduction | |  |
Environmental noise is a psycho-social stressor that affects subjective well-being and physical health. [1],[2],[3] Noise disturbs communication, concentration, relaxation and sleep. Chronic long-term exposure to transportation noise has been shown to be associated with the prevalence and incidence of cardiovascular diseases, including hypertension, ischemic heart diseases and stroke. [4],[5],[6],[7] [Figure 1] shows an update of an earlier noise reactions scheme from 2002. [8] The evidence of the association is based on experimental work carried out in the laboratory regarding the biological plausibility (coherence), the consistency amongst study results (different study designs, different populations, different noise sources), the presence of an exposure-response relationship and the magnitude of the effect. The question is no longer whether noise causes cardiovascular diseases; it is rather to what extent. This has to do with the slope of the exposure-response relationship and the empirical onset of the risk increase (intercept of the exposure response curve). [9] Risk assessment and risk management relies on established exposure-response relationships.
Regarding hypertension a meta-analysis of 24 cross-sectional studies (45 estimates) on the association between road traffic noise and the prevalence of hypertension revealed a pooled estimate of the relative risk of odds ratio (OR) = 1.07 (95% confidence interval (CI) = 1.02-1.12) for the increase in risk per increase of the average noise level during the day (L Aeq16hr , at the most exposed facade) of 10 dB (A) within the range of approximately 47-77 dB (A) (5 dB-category midpoints). [10] Another meta-analyses on the association between aircraft noise and hypertension based on 4 cross-sectional and 1 cohort studies (10 estimates) had also shown a higher risk with increasing noise level (OR = 1.13 per 10 dB (A), CI = 1.00-1.28, weighted day-night noise level L DN , range approximately 47-67 dB (A) [5 dB-category midpoints]). [11]
Regarding the association between road traffic noise and the incidence of ischemic heart diseases (myocardial infarction) meta-analyses of 3 case-control and 2 cohort studies (5 estimates) revealed a pooled estimate of the relative risk for males of approximately OR = 1.17 (CI = 0.87-1.57) per 10 dB (A) increase of the noise level during the day L Aeq16hr at the most exposed façade of the dwellings (range: approximately 57-77 dB (A) (5 dB-category midpoints). [12] Since 2008 when this curve was published, a couple of new studies have appeared in the literature which are included in the present updated meta-analysis. [13],[14],[15],[16],[17],[18]
| Methods | | |
Selection of studies
The selection of studies is based on the expert knowledge of the author who has followed the respective literature on noise and cardiovascular health effects that has been published in peer-reviewed journals and in conference papers since many decades. The identification of studies was refined by a systematic literature search with the data-bases PubMed (54 hits) and Scopus (34 hits) using the keywords ("road traffic noise" AND ("myocardial infarction" OR "heart disease" OR "coronary," OR "mortality"). Altogether 21 studies were identified that provided detailed information on noise levels and coronary heart disease risk ratios. Of these, 1 study was an ecological study carried out on men where no individual data on noise and confounders were available. [19] Furthermore, 4 studies were not applicable for trend analyses because only dichotomous results were reported. [18],[20],[21],[22] A further 2 studies were not applicable due to the scarce information with respect to ischemic heart diseases which is only given in major technical research reports. [23],[24] The current meta-analysis, which is considered to be comprehensive refers to altogether 14 studies that were published up until the year 2013. It comprises of 5 cohort studies carried out in Caerphilly (United Kingdom), Bristol-Speedwell (United Kingdom), 204 cities of The Netherlands, Vancouver (Canada), and Copenhagen and Ǻrhus (Denmark), [14],[16],[17],[25] 4 case-control studies carried out in Berlin (Germany) and Stockholm County (Sweden) [13],[26],[27],[28] and 5 cross-sectional studies carried out in Caerphilly (United Kingdom), Bristol-Speedwell (United Kingdom), Berlin (Germany), Tokyo (Japan) and Stockholm, Gothenborg and Malmö (Sweden). [15],[26],[29],[30] The studies are listed with their major characteristics in [Table 1]. All studies except one have adjusted their results not only for age and gender, but also for other potential individual confounders. The adjustment for age and gender is considered to be a minimum requirement for inclusion in this meta-analysis similar to a previous meta-analysis. [10]
Noise indicators
The noise level indicators considered in the studies were the annual non-weighted 16 h average noise level during the day L Aeq16hr , the annual non-weighted day-night average noise level L Aeq24hr , the annual weighted (day + 0 dB, evening + 5 dB, night + 10 dB) day-evening-night average noise level L DEN , [31] or similar indicators according to the national requirements (The Netherlands: Maximum of the annual weighted average noise level L Aeq during the day, the evening or night). For the meta-analyses, all studies were treated together with respect to the calculation of a pooled slope of the association between road traffic noise and coronary health because all had considered energy-based average noise levels that are usually highly correlated, correlated due to the rather consistent decline of noise levels during the night in urban streets. [32],[33],[34] Furthermore, in absolute terms the noise level indicators are comparable due to the evening/night weighting. [3],[5] A representative sample of urban roads showed consistent differences of 0.3 dB for L DEN - L DN and 2 dB for L DEN - L Aeq16hr . [3] All noise levels referred to the most exposed façade of the dwellings. Most of the studies provided estimates of the relative risk for separate 5 dB (A) noise categories, others provided trend estimates of the relative risk over the whole range of noise levels. [16],[17] The reference categories or 95% percentile of continuous noise levels were between <50 and <60 dB (A), the upper noise categories or 5% percentiles of noise levels were between >65 and >75 dB (A).
Coronary heart disease endpoints
Different health outcomes of coronary health were considered in the studies. These included the incidence or prevalence of ischemic heart diseases (International Classification of Disease [ICD 9]: 410-414; ICD 10: I20-I25), ischemic heart diseases or sudden cardiac death if caused by myocardial infarction (ICD 10: I21, I46), acute myocardial infarction (ICD 9: 410; ICD 10: I 21), mortality from coronary heart diseases (ICD 9: 410-414, 429.2; ICD 10: I20-I25), and self-reported heart disease. According to the noise effects' hypothesis all cardiovascular endpoints may be associated with the chronic noise (stress) exposure. [4] In the statistical analyses, however, also sub-samples of studies were analyzed with respect to different coronary health endpoints.
Statistical methods
The statistical software package "STATA Intercooled Release 11" was used to carry out all statistical calculations. [35] The command routines "META" (version 5.0) was used to calculate fixed effect and random effect moment-based estimates of the pooled trend of the relative risks of different studies, including Q-test statistics to assess heterogeneity. The STATA command routine "METAREG" (version 7.0) was also applied for the estimation of random effect models using the restricted maximum-likelihood method (REML) for the estimation of the between study variance component and role of covariates on the explanation of possible heterogeneity between studies. Command lines with their system parameters are shown below:
- Command META: Meta RR + CI - CI, ci, eform
- Command METAREG: Metareg B Study, wsse (SEB) eform noconstant z reml,
where - RR = estimate of the relative risk. e.g., OR
- −CI = lower confidence interval
- +CI = upper confidence interval
- B = ln (OR)
- SEB = ((ln(+CI) - ln(−CI))/3.92
- Study = study identifier.
For studies where categorical risk estimates were given in the original publication the trend estimate overall noise categories was calculated using the STATA command routine 'GLST' (version 9.2). The command line with its system parameters is shown below:
- Command GLST: Glst B Noise, se (SEB) cov (N Cases) cc
- (for case-control studies) or
- Command GLST: glst B Noise, se (SEB) cov (N Cases) ci
- (for cohort and cross-sectional studies),
where - N = number of subjects in each noise level category
- Cases = number of cases
- Noise = category midpoints of each noise level category.
For open-ended noise categories the value of the upper bound (respectively lower bound) of the adjacent category interval plus (respectively minus) half of the width of the adjacent category was assigned. (Note: Relative noise level categories were used with the reference category set to 0 dB (A) to comply with the GLST algorithm).
In case separate effect estimates were provided in the original reference for males and females, the sub-samples were considered independently (separate estimates) in the meta-analyses; otherwise the data refers to males and females taken together.
| Results | | |
Individual trend estimates of the relative risk
All studies had calculated either adjusted odds or risk ratios as estimates of the relative risk. For transparency, [Table 2] shows the input data that were used for the calculation of trend estimates of the relative risk of studies that provided only estimates of the relative risk for different noise exposure categories. The studies carried out in Copenhagen/Aarhus and Vancouver (Model 2) provided trend estimates of the relative risk directly. [16],[17] The estimates per 10 dB (A) and 95% CI of all studies that were finally used for the meta-analysis are shown in [Table 3]. Altogether 17 individual observations were considered (males and females separately or males + females taken together if not otherwise reported). | Table 2: Input data of individual studies for the calculation of trend estimates of the relative risk per noise level increment of 10 dB (A)
Click here to view |
 | Table 3: Individual and pooled trend estimates of the relative risk per noise level increment of 10 dB (A)
Click here to view |
Pooled estimates of the relative risk
[Table 3] also shows the results of the meta-analysis, including pooled fixed and random estimates of the relative risk per increase of the traffic noise level of 10 dB (A) and the respective fixed and random weights of the individual study estimates. The fixed and the random effect estimates of the relative risk were very similar: OR 10 dB (A) =1.08 (95% CI = 1.04, 1.13). The Q-test regarding heterogeneity was not significant (P = 0.298) indicating no major residual between-study variance between the studies. [Table 4] shows the results of meta-regression analyses and related stratified results examining the influence of certain study characteristics as possible sources of heterogeneity between study estimates. The Q-test regarding residual between-study variance, again, was never significant in any of the adjusted models. When the residual variation due to heterogeneity (I 2 ) of the adjusted models and the unadjusted model is compared (ΔI 2 ), type of study, publication year, upper age range of subjects, sample size and the assessment method of coronary heart disease do not give an indication of explaining heterogeneity between the studies. Note: negative values of ΔI 2 were set to zero because larger proportions of variation in the adjusted model are possible if the covariate explains less of the heterogeneity than expected by chance. However, the ΔI 2 values of the difference between the residual variation of the unadjusted and the adjusted model suggest that the treatment of gender in the studies, the inclusion of younger subjects and the lower cut-off of noise levels explained at least part of the variation between studies (rule of thumb: ΔI 2 > 5%). The stratified results show that studies that considered males and females together revealed a smaller estimate of the relative risks than studies that considered the sexes separately. Similarly, studies that included younger subjects below 44 years of age showed a smaller relative risk than studies where the subjects were older. Studies where the low cut of noise levels was ≤ 55 dB (A) revealed a smaller estimate of the relative risk than studies where the low cut was ≤60 dB (A). Studies where the occurrence of the disease was assessed only subjectively (self-reported) showed a tendency toward larger effect estimates. | Table 4: Results of meta - regression analyses examining the influence of study characteristics (stratified results)
Click here to view |
[Figure 2] shows the forest plot of the effect estimates per 10 dB (A) increase in noise level for the association between road traffic noise and coronary heart diseases (12 studies, 17 observations). The dotted vertical line corresponds to no effect (relative risk = 1), the squares correspond to the individual effect estimates and the 95% CI of the studies (the size represents the weight given in the meta-analysis). [Figure 3] shows the funnel plot of the effect estimates of the individual observations against their standard error. Since there is only one outlier (the study carried out in Tokyo), [30] there is little reason to assume publication bias or small study bias (note: Only little weight is given to the Tokyo study; its exclusion did not affect the pooled result much) [Figure 4]. [Figure 4] shows the variation of the pooled effect estimate after exclusion of a single study observation from the meta-analysis. The exclusion of the huge studies (in terms of the number of subjects) carried out in The Netherlands [14] (increase), Vancouver [16] and Copenhagen (decreases) [17] had a large influence on the pooled effect estimate (large statistical weights). When these studies were excluded the pooled estimates were 1.11 (95% CI: 1.06, 1.15), 1.06 (95% CI: 1.01, 1.12) and 1.07 (95% CI: 1.02, 1.13), respectively. [Figure 5] shows cumulative pooled effect estimates per 10 dB (A) increase in noise level of the association between road traffic noise and coronary heart diseases (11 studies, 17 observations) sorted by year of publication (oldest 1993 at the top, newest 2012 at the bottom). The graph shows slightly larger pooled estimates until approximately the year 2000 and a decrease thereafter, particularly due to the impact of the large study from The Netherlands. This impact is bit by bit compensated by the inclusion of the larger studies from Vancouver and Copenhagen that appeared later. The graph does not conflict with the stratified analyses shown in [Table 4], because there averaged effect estimates are shown, not cumulative ones. | Figure 2: Forest plot of the relative risk per 10 dB(A) increase in noise level of the association between road traffic noise and coronary heart diseases (11 studies, 17 observations)
Click here to view |
 | Figure 3: Funnel plot of the single effect estimates against their standard error (11 studies, 17 observations)
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 | Figure 4: Pooled relative risks per noise level increment of 10 dB(A) after exclusion of single study observations from the meta-analysis
Click here to view |
 | Figure 5: Cumulative pooled effect estimates per 10 dB(A) increase in noise level of the association between road traffic noise and coronary heart diseases (11 studies, 17 observations) sorted by year of publication (oldest 1993 at the top, newest 2012 at the bottom)
Click here to view |
| Discussion | | |
The present meta-analyses is a continuation of an earlier meta-analyses in the association between road traffic noise and ischemic heart diseases where a pooled estimate of the relative risk per increase of the noise level L DN by 10 dB (A) of OR = 1.17 (95% CI = 0.87, 1.57) was found. [12] The older meta-analysis was based on 5 studies and referred to males only (5 estimates). The present meta-analysis, which is based on 12 studies revealed a smaller pooled estimate of OR = 1.08 (95% CI = 1.04, 1.13), which was significant and included both males and females (17 estimates). However, while the older meta-analysis referred to noise levels from < 60 dB (A) as a reference to > 75 dB (A), the updated meta-analysis considered studies where noise levels ranged from < 50 to > 75 dB (A). Older studies had suggested a threshold of effect [25],[29] or could not assess the effect at lower noise levels due to a lack of data in available noise maps, [26],[27] newer studies - particularly those that were analyzed with respect to continuous noise data (no 5 dB-categories) - do not suggest any biological threshold of effect. [13],[16],[17] The fact that nine of the fourteen studies assessed noise levels down to 55 dB (A) or lower, justifies that the updated exposure-response relationship should be used in practice within the range from L DN or L DEN ≤ 55 dB (A) (category midpoint approximately 52 dB (A)) as a reference (relative risk = 1) to 77 dB (A) (midpoint of category 75 to < 80 dB (A)). This is also validated by [Table 2] where relative risks per 5 dB-category are shown. With respect to public health considerations this means that the large number of subjects within the range between 55 and 60 dB (A) with a relatively small excess risk do also contribute to the total environmental noise burden of disease. [5] Based on the pooled effect estimate across all studies and all sexes the estimated excess risk for subjects in the highest noise category is approximately 20%.
The results of the meta-analysis give no indication of a significant heterogeneity between the individual studies. The pooled relative risk of 1.08 found for different forms of coronary heart disease is very similar to the one, which was recently found in a meta-analysis of 24 studies (45 estimates) for the association between road traffic noise and hypertension (significant OR = 1.07 (95% CI = 1.02-1.12) per increase of the noise level L Aeq16h of 10 dB (A) within the range of 47-77 dB (A) [5 dB-category midpoints]). [10]
The strength of the meta-analyses is the comprehensive quantitative overview of epidemiological studies that have assessed the relationship between road traffic noise and coronary heart disease. Nearly, all of the studies have adjusted their results for established social and behavioral cardiovascular risk factors beyond the minimum requirement of adjustment for age and gender. A limitation is that the studies were carried out with different methodology and quality of the assessment of exposure and outcome and that adjustment was made for different sets of covariates. If the largest study [16] is excluded, the overall effect estimate is no longer significant. This study did not adjust for smoking. However, it seems unlikely that a difference in the excess risk of approximately 50% compared with the pooled estimate is due to a differential impact of smoking among exposed and non-exposed subjects, particularly, since adjustment was made for social class, which tends to be correlated with smoking in population studies. On the other hand, if the second largest study [14] is excluded, the effect estimate goes up considerably. Both large studies that determine the pooled estimate more than smaller studies tend to balance each other. Both studies used established methods for the calculation of noise levels at the most exposed facade. It is the purpose of the meta-analysis - besides the assessment of heterogeneity - not only to rely on single studies when drawing conclusions. The test statistics of the stratified analysis show that no major statistical heterogeneity was found among the studies. If misclassification of disease happened at random it would have reduced the precision of the effect estimates (larger confidence intervals). Misclassification of exposure may have happened, particularly, at the lower end of the noise level range. However, it is more likely that presumably quiet roads of low traffic volume were noisier than expected (e.g., due to bad road surface) - rather than the opposite, because busy streets with a higher traffic volume are likely to be identified as such, including the assessment of other noise determining parameters. This kind of misclassification would dilute the true effect.
Air pollution was not considered as a potential confounder in most of the studies. However, 3 studies that only provided adjusted results for air pollutants do not suggest a major impact on the effect estimates for noise. In fact a slightly larger estimate is derived for these studies in stratified analyses. In a recent review (within study comparisons), the correlation between noise and pollutants was found to be only low or moderate and did not substantially influence the confounding effects (changes in effect estimates < 10%). [36]
Male gender, the restriction of older subjects in the study sample, a higher cut-off of the noise level and only self-reported assessment of coronary heart disease are associated with a larger effect estimate. However, these findings may not be misinterpreted in terms of effect modification. The studies may also differ with respect to other characteristics and methodological aspects. The quantitative impact of gender and age as effect modifiers must be studied within studies (same methods) and not between studies (different methods). Future noise studies should consider age, gender and exposure reducing factors, such as room location, particularly, with respect to the bedroom (sleep disturbance), the sound insulation of windows and window opening habits as potential effect modifiers. For example, the effect estimates may be larger if the shielding of the quiet side is considered in the analysis, presumably yielding to larger relative risks in subjects with rooms (only) on the exposed side of the dwellings. This may be advantageous for significance testing. With regard to the environmental burden of disease calculations, however, a smaller relative risk, which is applied to the total population, should reveal the same number of cases as a larger relative risk, which is applied to a particular risk group. However, one has to bear in mind that most currently available noise maps refer to noise levels the most exposed facade and do not account for room orientation. Therefore, the study design has to account for this constraint. The role of air pollutants on the association between noise and cardiovascular diseases must be further investigated and vice versa. New endpoints, including stroke, diabetes and metabolic syndrome (obesity) have been studied in recent noise effects' research. [37],[38] These outcomes deserve more attention because they add to the total environmental noise burden of disease. [5]
| Conclusions | | |
Road traffic noise is a risk factor for cardiovascular diseases. The evidence of the association has increased during the last decade. Studies of the associations between road traffic noise and the risk of coronary heart diseases show a significant increase in risk with increasing noise level. The current meta-analysis revealed an 8% increase in risk per increase of the weighted day-night noise level L DN of 10 dB (A) within the range of approximately 52-77 dB (A) (5 dB-category midpoints). The exposure-response relationship can be used for a quantitative environmental burden of disease assessment together with disease incidence and mortality data which are routinely assessed in the global burden of disease statistics. [39]
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Correspondence Address:
Wolfgang Babisch
Department of Environmental Hygiene, Federal Environment Agency, Corrensplatz 1, 14195 Berlin
Germany
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/1463-1741.127847
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4] |
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| Harris Héritier, Danielle Vienneau, Maria Foraster, Ikenna C. Eze, Emmanuel Schaffner, Laurie Thiesse, Franziska Ruzdik, Manuel Habermacher, Micha Köpfli, Reto Pieren, Arno Schmidt-Trucksäss, Mark Brink, Christian Cajochen, Jean Marc Wunderli, Nicole Probst-Hensch, Martin Röösli | | International Journal of Hygiene and Environmental Health. 2018; 221(3): 556 | | [Pubmed] | [DOI] | | | 81 |
Glucocorticoid metabolites in newborns: A marker for traffic noise related stress? |
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| Manuella Lech Cantuaria, Jakob Usemann, Elena Proietti, Victoria Blanes-Vidal, Bernhard Dick, Christa E. Flück, Simone Rüedi, Harris Héritier, Jean-Marc Wunderli, Philipp Latzin, Urs Frey, Martin Röösli, Danielle Vienneau | | Environment International. 2018; 117: 319 | | [Pubmed] | [DOI] | | | 82 |
Occupational noise exposure and risk of hypertension in an industrial workforce |
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| Baylah Tessier-Sherman, Deron Galusha, Linda F. Cantley, Mark R. Cullen, Peter M. Rabinowitz, Richard L. Neitzel | | American Journal of Industrial Medicine. 2017; 60(12): 1031 | | [Pubmed] | [DOI] | | | 83 |
Environmental Determinants of Cardiovascular Disease |
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| Aruni Bhatnagar | | Circulation Research. 2017; 121(2): 162 | | [Pubmed] | [DOI] | | | 84 |
Exposure to Road, Railway, and Aircraft Noise and Arterial Stiffness in the SAPALDIA Study: Annual Average Noise Levels and Temporal Noise Characteristics |
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| Maria Foraster, Ikenna C. Eze, Emmanuel Schaffner, Danielle Vienneau, Harris Héritier, Simon Endes, Franziska Rudzik, Laurie Thiesse, Reto Pieren, Christian Schindler, Arno Schmidt-Trucksäss, Mark Brink, Christian Cajochen, Jean Marc Wunderli, Martin Röösli, Nicole Probst-Hensch | | Environmental Health Perspectives. 2017; 125(9) | | [Pubmed] | [DOI] | | | 85 |
Association of Long-Term Exposure to Transportation Noise and Traffic-Related Air Pollution with the Incidence of Diabetes: A Prospective Cohort Study |
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| Charlotte Clark, Hind Sbihi, Lillian Tamburic, Michael Brauer, Lawrence D. Frank, Hugh W Davies | | Environmental Health Perspectives. 2017; 125(8) | | [Pubmed] | [DOI] | | | 86 |
Urban noise functional stratification for estimating average annual sound level |
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| Guillermo Rey Gozalo, Juan Miguel Barrigón Morillas, Carlos Prieto Gajardo | | The Journal of the Acoustical Society of America. 2015; 137(6): 3198 | | [Pubmed] | [DOI] | | | 87 |
Environmental Noise Exposure and Its Effect on Population Health |
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| Alexandra Filová, Martin Samohýl, Lubica Argalášová | | Hygiena. 2015; 60(4): 155 | | [Pubmed] | [DOI] | | | 88 |
Roadway Proximity and Risk of Sudden Cardiac Death in Women |
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| Jaime E. Hart, Stephanie E. Chiuve, Francine Laden, Christine M. Albert | | Circulation. 2014; 130(17): 1474 | | [Pubmed] | [DOI] | | | 89 |
Road traffic noise and hypertension – Accounting for the location of rooms |
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| Wolfgang Babisch,Gabriele Wölke,Joachim Heinrich,Wolfgang Straff | | Environmental Research. 2014; | | [Pubmed] | [DOI] | | | 90 |
Environmental noise and sleep disturbances: A threat to health? |
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| Halperin Demian | | Sleep Science. 2014; | | [Pubmed] | [DOI] | |
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