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ARTICLE  
Year : 2013  |  Volume : 15  |  Issue : 65  |  Page : 224-230
Road traffic noise and health-related quality of life: A cross-sectional study

1 School of Population Health, The University of Auckland, Auckland, NewZealand
2 Department of Psychology, Auckland University of Technology, Auckland, NewZealand
3 Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, NewZealand

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Date of Web Publication15-Jun-2013
 
  Abstract 

Evidence is emerging linking environmental noise to health problems. Noise can affect health directly and indirectly: For example, noise sensitivity moderates the effects of noise annoyance, which in turn mediates the effects of noise exposure. An alternative hypothesis is that noise sensitivity marks the presence of susceptibility to health problems in general, including annoyance from noise. Whether noise sensitivity causes poor health or whether it is a marker of susceptibility to health problems was addressed by comparing the results of a community-based survey of people with similar noise sensitivity profiles but different environmental noise exposures. A questionnaire was delivered to people living in two socio-economically-matched areas: One was within 50 m of a motorway and the other was away from any significant source of environmental noise. The questionnaire contained 58 questions comprised of the World Health Organization health-related quality of life questionnaire (WHOQOL), and questions about amenity, neighborhood issues, environmental annoyances, demographics, and noise sensitivity. Noise sensitivity did not vary with proximity to the motorway but annoyance with traffic noise and fumes was greater in those living close to the motorway than in those who were not. Scores on the four WHOQOL domains (physical, psychological, social, and environmental) were lower in those living close to the motorway, and the WHOQOL domain scores correlated negatively with noise sensitivity in those who lived near motorways but not in those who lived in the quieter areas. This suggests that noise sensitivity is related to poor health outcomes rather than being a trait marker of susceptibility to health problems in general.

Keywords: Annoyance, noise, noise sensitivity, traffic, World Health Organization health-related quality of life questionnaire

How to cite this article:
Welch D, Shepherd D, Dirks KN, McBride D, Marsh S. Road traffic noise and health-related quality of life: A cross-sectional study. Noise Health 2013;15:224-30

How to cite this URL:
Welch D, Shepherd D, Dirks KN, McBride D, Marsh S. Road traffic noise and health-related quality of life: A cross-sectional study. Noise Health [serial online] 2013 [cited 2015 Feb 27];15:224-30. Available from: http://www.noiseandhealth.org/text.asp?2013/15/65/224/113513

  Introduction Top


Exposure to unwanted sound is increasing globally due to population growth, urbanization and technological developments. Concurrently, scientific evidence linking environmental noise to health problems is emerging. [1],[2],[3],[4],[5] For example, the World Health Organization (WHO) reports data indicating that chronic exposure to noise can compromise health, and estimates that at least 30% of European citizens are exposed to night-time sound pressure levels that can be considered detrimental to sleep. [1],[2],[6] The major source of environmental noise is from road traffic, [7] followed by aircraft and rail noise. [6] Road traffic noise is caused primarily by cars and trucks, with trucks being approximately 5 dB noisier than cars, and noise output for individual vehicles peaking during the night due to higher average speeds relative to day-time hours. [8]

Based on estimates of disability-adjusted life years (DALYs) for road traffic noise estimated in Europe, the impacts of road traffic noise on health are non-trivial. In contrast with traffic accidents, the number of DALYs due to exposure to road traffic noise increased between the years 1980 and 2000, with projections that this disease burden will continue to grow in the future. In Belgium, the estimated the number of DALYs due to road traffic noise was found to be up to 21.8% of the environmental burden of disease due to environmental pollutants. [9] More recently, the WHO estimate that 587,000 DALYs are lost per year due to noise within European cities, with road traffic noise comprising the major burden. [10]

Road traffic noise can affect health both directly and indirectly. [11] Direct health effects have been found to include hearing loss and cardiovascular effects, while indirect health effects, involving moderating or mediating factors, include annoyance and sleep disturbance. [12] Noise-induced hearing loss, typically occurring when peak exposures exceed 140 dB, is an unlikely outcome given typical road traffic noise levels. [8] However, associations between cardiovascular disease, (e.g., hypertension or ischemia) and exposure to road traffic noise have been reported, [5],[12],[13],[14] though cannot be attributed solely to direct effects as indirect effects can also lead to negative cardiovascular outcomes. Indirect effects such as sleep disruption and annoyance are more commonly the focus of noise health research, and are often used as outcome variables in dose-response research. [15] In Europe, road traffic noise constitutes the dominant source of noise annoyance, [16] where the term annoyance is defined as a scalar property denoting noise-induced stress, either facilitating the development of health effects via endocrine processes or as a health effect in its own right. Estimates of annoyance due to road traffic noise have been obtained internationally using social surveys, with European data suggesting that between 10% and 35% [3],[7],[17],[18] of urban dwellers are annoyed by road traffic noise.

Noise annoyance is a multifactorial construct, determined by a combination of acoustic parameters, (e.g., level, tonal characteristics, dynamics), personal characteristics, (e.g., noise sensitivity) and socio-political factors that influence attitudes toward noise sources and those regulating the noise sources. As a result, dose-response relationships between annoyance and noise levels typically account for only 15-20% of the variability between the two. As such, other factors beyond noise level are required to understand the health impacts of noise.

One independent non-acoustical factor predicting noise annoyance is noise sensitivity. [7],[19],[20] One study identifies two key characteristics of noise sensitive individuals. [21] Firstly, they are more likely to attend to sound and evaluate it negatively, (e.g., find it threatening or annoying) and secondly, they have stronger emotional reactions to noise, and as a consequence, greater difficulty habituating. Noise sensitivity has a large impact on noise annoyance ratings, lowering annoyance thresholds by up to 10 dB. [20] On the other hand, a "third variable" hypothesis has been developed [22] suggesting that noise sensitivity does not moderate the effects of noise annoyance, but rather that it marks the presence of susceptibility to health problems in general, including annoyance from noise.

Whether noise sensitivity causes poor health or whether it is a marker of susceptibility to health problems is a question that remains unanswered. By sampling from the populations in two types of area, close to and far from a motorway, we sought to address this question: if the people who lived in a relatively noise-free environment experienced similar associations between noise sensitivity and health as those living near a motorway, then noise sensitivity could be regarded as a marker, whereas if noise sensitivity is associated with poorer health in those near motorways, then it is moderating the response to noise.


  Methods Top


A cross-sectional study design was employed, comprising five areas in Auckland, New Zealand's largest city. Houses located within 50 m of one of three major motorways (North, South, and West), as determined by satellite images, were classified as "motorway." Using a similar method, houses at least 2 km from any motorway, other major roads, or other major sources of environmental noise, (i.e., industry), were classified as "non-motorway." There were two non-motorway areas within Auckland City (South and West), which were strictly matched to the motorway areas using the New Zealand Deprivation Index 2006. [23] This index assesses socio-economic status based on car and telephone access, receipt of means-tested benefits, unemployment, household income, sole parenting, educational qualifications, home ownership, and home living space.

Estimates of traffic levels on motorways were made based on New Zealand Transport Authority data of average daily total traffic counts for each segment along the motorway network (8 along the South Motorway 6 along the North, and 6 along the West). [24] The percentage of truck traffic averaged at 6%. Estimates of traffic levels in the suburban "low noise" areas were made using data from Auckland Council's transport organization. [25] All data were collected during the period 2004-2009. Due to low traffic counts for the West Non-Motorway area, only traffic data from the main road in the area were available. As such, the traffic flow rates we have assumed (and hence our estimates of noise levels) would tend to be over-estimates for this quiet area. Estimates of noise levels were derived from traffic counts using the online noise calculator. [26] For the purpose of computing the noise levels, it was assumed that vehicles travelled at the legal speed limits of 50 km/h on streets and 100 km/h on motorways, that 6% of the motorway traffic was trucks and that all other vehicles were cars, that the road surfacing was smooth asphalt, and that the distance between traffic and dwellings was 50 m. This allowed a noise level for dwellings on each street with available data from each area to be estimated [Table 1].
Table 1: Median estimated street noise levels for motorway and non-motorway areas*

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Participants

Participants lived in the city of Auckland, New Zealand, and within one of the five target areas. Those aged 18 years or over were invited to participate.

Instruments

The questionnaire was entitled "2010 Wellbeing and Neighborhood Survey" and was designed to disguise the true intent of the study, with potential participants invited to participate in research investigating their place of living and their wellbeing. The questionnaire contained 58 items categorized as (health-related quality of life [HRQOL]: 26 items), amenity (2 items), neighborhood issues (14 items), environmental annoyances (7 items), demographic information (8 items), and 1 item estimating noise sensitivity. To measure health, we employed the short form of the World Health Organizations health-related quality of life (WHOQOL) scale, called the WHOQOL-BREF, which adheres to the WHO's seminal definition of health as "a state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity." The WHOQOL adopts a multi-dimensional profile of HRQOL, dividing it into four domains: Physical health (7 items), psychological wellbeing (6 items), social relationships (3 items), and environmental factors (8 items). Two additional items assess overall quality-of-life and self-rated health. Each item is scored on a 5-point scale, where a low score corresponds to a negative evaluation of that aspect of life and a high score corresponds to a positive evaluation. It has been proposed that the WHOQOL-BREF is better suited for use in public health research compared with more traditional HRQOL instruments. [27] The amenity items asked the respondent how much they agreed with two questions: "I am satisfied with my neighborhood/living environment," and "My neighborhood/living environment makes it difficult for me to relax at home." The neighborhood problem scale consisted of 14 distracter items that were not included in our analysis. Of the seven annoyance items, four asked about air quality, while three asked about annoyance to road traffic, other neighbors, or other sources of noise. The annoyance to noise items were based on recommendations issued by the International Commission on the Biological Effects of Noise. [28] The participant was also asked to rate themselves as either not noise sensitive, moderately noise sensitive, or very noise sensitive. Demographic measures are displayed in [Table 2].
Table 2: Demographic profile of the motorway and non - motorway groups

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Procedure

Two questionnaires and a pre-paid, return-addressed envelope were deposited into the letterboxes of eligible houses. The participants were asked to complete the questionnaires independently at a convenient time, to think about their life in the last 2 weeks, and circle the number on the scale that best reflected their answer to each question. After completion of the questionnaires the participants were instructed to return them in the prepaid envelope provided. No incentives to participate were offered.

Statistical analysis

All analyses were undertaken using the Statistical Package for Social Sciences (SPSS; v. 18), which initially involved a missing data analysis and the recoding of negatively worded items. Total scores for each of the four WHOQOL-BREF domains were then computed. Preliminary exploratory analyses were conducted to compare noise sensitivity scores (Chi-squared test) and WHOQOL domain scores (t-tests) between the motorway and non-motorway groups, and to test for associations between annoyance caused by traffic fumes and noise both overall and in the motorway and non-motorway groups separately (Spearman's Rho).

The main analysis was a repeated-measures analysis of variance (ANOVA) where WHOQOL domain scores were treated as four repeated measures, and area (motorway or non-motorway) and noise sensitivity (not sensitive, moderately sensitive, or very sensitive) were treated as between-subjects factors. Where violations of the sphericity assumption occurred, the Huynh-Feldt (Box) correction was applied. [29] Secondary analyses were conducted to explore the associations between annoyance and HRQOL across the four WHOQOL domains, and annoyance and noise sensitivity, both overall and in the motorway and non-motorway groups separately (Pearson's r).


  Results Top


Overall, questionnaires were delivered to 1250 houses, 750 in high-noise and 500 in low-noise areas. Of these, 257 were returned from the high-noise areas and 245 from the low-noise areas, corresponding to response rates of 34% and 49%, respectively. The demographic profiles of the two sample groups are displayed in [Table 2]. A series of Chi-square and Mann Whitney U-tests revealed no associations between any of the demographic variables and motorway proximity (P > 0.05). In relation to self-reported annoyance to road traffic, [Figure 1] displays the percentage of respondents in each annoyance category for the motorway and non-motorway groups, and for pooled data. Inspection of [Figure 1] reveals different trends across the two groups, with the motorway group exhibiting a quadratic relationship, and the non-motorway group a linear relationship. Of the 493 who responded to the self-report question about noise sensitivity, 184 (37%) were not sensitive, 253 (51%) were moderately sensitive, and 56 (11%) were very sensitive [Table 2]. These proportions did not differ between the people who lived close to motorways and those in the socioeconomically matched control areas that were not near motorways or other major noise sources (χ2 (2) =0.805, P = 0.669).
Figure 1: Percentage of respondents in each of five self - report annoyance categories, for pooled data, and motor and non-motorway groups

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Repeated-measures ANOVA showed an interaction between WHOQOL domain and area of dwelling (F (3,1512) = 8.729, P < 0.001); [Figure 2]. This suggests that while there was an overall reduction in quality of life in those dwelling near a motorway (F (1,504) = 21.238, P < 0.001), this was not constant across WHOQOL domains. [Figure 2] shows that while differences were present in all domains, the effect was least in the social domain. There was also an interaction between area of dwelling and noise sensitivity in terms of overall WHOQOL (F (2,504) = 3.100, P = 0.046); [Figure 3]. This shows that the mean WHOQOL score was lower for more noise-sensitive people who dwell near to motorways, but that noise sensitivity was not associated with WHOQOL score for those who dwell far from motorways. Furthermore, there was a gradual reduction of quality of life for people with increasing noise sensitivity in the motorway group, but not in the non-motorway group.
Figure 2: Mean World Health Organization health - related quality of life questionnaire (WHOQOL) domain scores for those living near and far from a motorway. Error bars represent one standard error of the mean. Since WHOQOL domains consist of different numbers of items, standardized values have been shown to allow comparison of differences across WHOQOL domains

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Figure 3: Mean World Health Organization health - related quality of life questionnaire score for socioeconomically matched people who dwell near or far from motorways plotted as a function of degree of noise sensitivity. Error bars represent one standard error of the mean

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Annoyance caused by traffic fumes and annoyance caused by traffic noise were correlated overall (Spearman's Rho = 0.499, P < 0.001), implying that those who were more annoyed by traffic noise were also more likely to be annoyed by air pollution caused by traffic. The correlation was significantly stronger in the people who lived near motorways (Rho = 0.540, P < 0.001: 95% CI = 0.447-0.621) than in those who did not (Rho = 0.317, P < 0.001: 95% CI = 0.200-0.425). Additionally, there were independent correlations between the four WHOQOL domains and annoyance due to fumes and noise [Table 3]a. Overall, effect sizes were similar for annoyance due to fumes and noise, and weakest for the social domain. There was a difference between the motorway and non-motorway groups in correlations between annoyance due to fumes and noise, and HRQOL [Table 3]b and c. In the motorway group, fumes annoyance and noise annoyance were negatively correlated with all of the WHOQOL domains. In the non-motorway group, the correlations were generally weaker, and often not reaching the significance criterion. There were also differences in the correlations between noise annoyance and noise sensitivity: in the motorway group this was positive (r (256) = 0.213, P = 0.001), but in the non-motorway group there was no relationship (r (246) = 0.054, P = 0.395).
Table 3:

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  Discussion Top


Noise annoyance, or noise-induced stress, is an unwanted health impact that is discouraged by most governing bodies across the world. The WHO suggests that the percentage of highly annoyed respondents be adopted as an estimate of noise annoyance prevalence. [30] Adopting the definition of Paunoviζ et al., 11.9% of our sample indicated that they were highly annoyed by road traffic noise. [7] As expected, our data vary according to context, with 24.9% of the motorway sample, and 4.7% of the non-motorway sample, reporting they were highly annoyed. Paunoviζ et al., comparing noisy (24-h Leq > 65 dbA) to quiet (24-h Leq < 55 dbA) streets, reported high annoyance figures of 37.1% and 23.7% respectively. [1],[7] While the absolute estimates do not concord between the two studies, the overall trend of noise annoyance being higher in higher traffic noise areas is preserved. Studies have shown that annoyance is related to living environment, [28] and differences between the two studies are likely due to differences in the physical environment.

Most participants in the research by Paunoviζ resided in apartment-type dwellings, whereas in our study, respondents mostly lived in single-level detached or semi-detached houses. Our annoyance data do, however, reflect findings from the Swiss cohort study on air pollution and lung disease in adults, where approximately 13% of the sample reported a high level of noise annoyance. [3]

Mean noise sensitivity did not vary with proximity to a motorway, but self-reported annoyance to noise and air pollution due to traffic was greater in those who lived close to motorways than in those who did not. Scores on all four WHOQOL domains were lower in those who lived close to motorways, and correlated more strongly and negatively with annoyance from traffic noise and fumes in those who lived near to motorways than in those who did not. WHOQOL domain scores correlated negatively with noise sensitivity in those who lived near to motorways but not in those who lived far from motorways. An hypothesis put forward by Fhyri and Klaboe implies that a person's noise sensitivity reflects an underlying personality type which influences their health status, and thus leads to spurious findings of correlations between noise sensitivity and health in noise exposed people. [22] By comparing samples of people with similar noise sensitivity profiles but different noise exposures, we were able to test this hypothesis. Our finding of reduced HRQOL in more noise-sensitive people who were exposed to noise but not in those who dwelt in quiet areas is evidence against the hypothesis. Rather, our findings support the idea of trait noise sensitivity which is associated negatively with HRQOL, but only in those exposed to noise.

In the non-motorway group, HRQOL was affected by traffic annoyance to a lesser extent and in a different pattern [Table 3]b than in the motorway group [Table 3]c. In the non-motorway group, traffic noise annoyance was also unrelated to noise sensitivity (r (246) = 0.054, P = 0.395) and noise sensitivity was unrelated to HRQOL (indicated by the similar height of the light grey bars in [Figure 3]). In a cross-sectional study we cannot, however, determine which came first: The annoyance or the sensitivity. Nonetheless, these findings give support for some fundamental difference between the groups, and the obvious candidate is the traffic noise. One might therefore conclude that the noise was moderating the association between noise sensitivity and noise annoyance, and through this, the differences in HRQOL which exist between the two groups.

A potential weakness in our reasoning is that the group dwelling further from the motorway may have had better HRQOL due to influences other than the traffic noise and fumes. Our careful matching of socio-economic status between the areas mitigates this, but it remains a possibility that some other factor which we have not recognized was in play. We used a three-level self-rated measure of noise sensitivity: More information may be able to be gleaned by using a more detailed measure and/or one which incorporated some external stimulus or biological marker of arousal in response to noise. A strength of the research was the way in which we identified Motorway areas from satellite images to provide certainty about exposures. The overall response rate of 40% was moderately good for a postal questionnaire. The response rates of 34% and 49% from the non-motorway and motorway dwellers, respectively, might give rise to response bias, however, the areas were socio-economically matched and there was no difference in noise sensitivity between the samples.


  Conclusions Top


Our data suggest that noise sensitivity is related to poor health outcomes only when the noise-sensitive person is exposed to noise, as opposed to being a trait marker of susceptibility to health problems in general. There is growing evidence in the literature that exposure to environmental noise can degrade HRQOL, and that annoyance potentially mediates the relationship between sensitivity to noise and degraded HRQOL. Given that noise pollution is an ever increasing problem, and that even small changes in HRQOL can have a major impact at the population level, research in this area is becoming increasingly important. Although, research is required to ascertain causation, a growing body of evidence indicates that environmental noise is an important population health issue that demands wider public consideration.

 
  References Top

1.World Health Organization. Night noise guidelines for Europe. Copenhagen: WHO; 2009.  Back to cited text no. 1
    
2.World Health Organization. Constitution of the World Health Organization, Available from: http://apps.who.int/gb/bd/PDF/bd47/EN/constitution-en.pdf. [Last accessed 2012 Oct 10].  Back to cited text no. 2
    
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Correspondence Address:
David Welch
School of Population Health, University of Auckland Private Bag, 92019 Auckland, Auckland
NewZealand
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DOI: 10.4103/1463-1741.113513

PMID: 23771420

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    Figures

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    Tables

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