Home Email this page Print this page Bookmark this page Decrease font size Default font size Increase font size
Noise & Health  
 CURRENT ISSUE    PAST ISSUES    AHEAD OF PRINT    SEARCH   GET E-ALERTS    
 
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Email Alert *
Add to My List *
* Registration required (free)  
 


 
   Abstract
  Introduction
  Subjects and methods
  Results
  Discussion
  Conclusions
   References
   Article Figures
   Article Tables
 

 Article Access Statistics
    Viewed131    
    Printed4    
    Emailed0    
    PDF Downloaded1    
    Comments [Add]    

Recommend this journal

 


 
  Table of Contents    
ORIGINAL ARTICLE  
Year : 2019  |  Volume : 21  |  Issue : 103  |  Page : 248-257
Pathways and contingencies linking road traffic noise to annoyance, noise sensitivity, and mental Ill-Health

1 Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
2 Medical College, Medical University of Plovdiv, Plovdiv; Department of Management, Faculty of Economics and Management, University of Agribusiness and Rural Development, Plovdiv, Bulgaria
3 Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
4 Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria

Click here for correspondence address and email
Date of Submission24-Mar-2020
Date of Acceptance07-Jul-2020
Date of Web Publication18-Sep-2020
 
  Abstract 


Context: Traffic noise may contribute to depression and anxiety through higher noise annoyance (NA). However, little is known about noise sensitivity (NS) and mental health status as contextual factors. Objective: We tested three hypotheses: (1) Traffic noise is associated with mental ill-health through higher NA; (2) Mental ill-health and NS moderate the association between traffic noise and NA; and (3) NS moderates the indirect effect of traffic noise on mental ill-health. Subjects and Methods: We used a convenience sample of 437 undergraduate students from the Medical University in Plovdiv, Bulgaria (mean age 21 years; 35% male). Residential road traffic noise (LAeq; day equivalent noise level) was calculated using a land use regression model. Depression and anxiety symptoms were measured with the Patient Health Questionnaire 9-item (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) scale, respectively. NA was measured using a 5-point verbal scale. The Noise Sensitivity Scale Short Form (NSS-SF) was used to measure NS. To investigate how these variables intertwine, we conducted mediation, moderation and moderated mediation analyses. Results: LAeq was indirectly associated with higher PHQ-9/GAD-7 scores through higher NA, but only in the low NS group. The relationship between LAeq and NA was stronger in students reporting depression/anxiety. While high NS was associated with high NA even at low noise levels, LAeq contributed to NA only in students low on NS. Conclusions: We found complex conditional relationships between traffic noise, annoyance and mental ill-health. Understanding respective vulnerability profiles within the community could aid noise policy and increase efficacy of interventions.

Keywords: Anxiety, depression, noise exposure, noise perception, perceived control psychoacoustics

How to cite this article:
Dzhambov AM, Tilov B, Makakova-Tilova D, Dimitrova DD. Pathways and contingencies linking road traffic noise to annoyance, noise sensitivity, and mental Ill-Health. Noise Health 2019;21:248-57

How to cite this URL:
Dzhambov AM, Tilov B, Makakova-Tilova D, Dimitrova DD. Pathways and contingencies linking road traffic noise to annoyance, noise sensitivity, and mental Ill-Health. Noise Health [serial online] 2019 [cited 2020 Nov 30];21:248-57. Available from: https://www.noiseandhealth.org/text.asp?2019/21/103/248/295355



  Introduction Top


Depression and anxiety are common mental disorders associated with a considerable social and economic burden.[1],[2] While they can partially be explained by genetic factors and heritability, evidence suggests that contextual factors can modify brain plasticity and function.[3],[4] For example, adverse psycho-physiological reactions can be enhanced by urban living.[5],[6] Traffic noise is a ubiquitous stressor among urban dwellers,[7] which triggers neuroendocrine stress and stimulates cortical structures involved in processing of auditory information.[8],[9],[10] Such chronic stress can take a toll on mental health via functional and structural alterations in the brain.[11] Noise annoyance (NA), a multifaceted construct representing negative evaluation of living conditions with respect to noise,[12],[13] has been hypothesized to mediate mental health effects of traffic noise.[14] Traffic noise may also have a negative effect through NA as a constraint on restorative experiences in the residential environment.[15]

As a complex psychological construct, NA is contingent not only on noise exposure but also on various non-acoustic factors (cognitive, attitudinal, and behavioural).[12] Noise sensitivity (NS), in particular, has been conceptualized as a psycho-physiological internal state of an individual that increases the degree of reactivity to noise in general.[16],[17] Although evidence of its correlations with personality characteristics is mixed,[17],[18] some authors have linked it to neuroticism and negative affect.[16],[18] Noise sensitive individuals have difficulty adapting to noise because they pay more attention to sounds perceived as threatening and beyond their control.[19] Therefore, NS may act as a moderator of the relationship between noise and NA, with highly sensitive individuals being more susceptible.[20],[21],[22] Evidence suggests that NS may also be a vulnerability factor for psychological ill-health of itself.[22],[23] Likewise, people experiencing depression and/or anxiety may be particularly vulnerable to noise because of over-vigilance and perceived helplessness against aversive environmental stimuli.[24],[25],[26] Fyhri and Klaeboe[27] speculated that individual vulnerability to noise is reflected both in ill-health and in being sensitive to noise.

It is conceivable that highly noise sensitive individuals would already have high baseline level of NA, therefore, noise exposure may play a lesser role compared with individuals with low NS.[28] There is also growing recognition that mental health may be a context in which the effects of noise unfold, rather than simply an outcome of noise exposure.[29] This motivates reification of mental health as another moderator of the traffic noise − NA relationship. However, few studies have investigated these hypothesised contingencies.

Here we aim to investigate the conditional processes linking traffic noise exposure, NA, NS, and ill-mental. We test three complementary hypotheses: first, that traffic noise is associated with poor mental health through higher NA; second, that mental health and NS moderate the association between traffic noise and NA; and third, that NS moderates the indirect effect of traffic noise on mental health. To that end, we use a sample of undergraduate students whose occupation is characterized by high levels of attentional demands and stressors such as time pressure and deadlines that make them susceptible to impaired mental health.[30]


  Subjects and methods Top


Study design and sampling

Data were collected in October 2018 in Plovdiv, the second largest city in Bulgaria. We used a convenience sample of undergraduate students from all faculties of the Medical University in the city. They were recruited during classes and invited to participate in a survey on residential surroundings and quality of life. We invited students with different ethnic and cultural background and program enrolment to ensure sufficient variation in the data. To be included, they had to be aged from 18 to 35 years and, to ensure that they were familiar with their neighbourhood environment, had to be resident in their current home for at least one year prior to the study. Students who had lived in their home for less than a year, who did not report their address or were unable to clearly understand the questionnaire (e.g., foreign students whose English was not good) were excluded.[31]

Out of the 620 invited students, 581 agreed to take part in the survey (94%). After excluding 52 students who did not finish the questionnaire or provided insufficient residential data, and 92 students for whom traffic noise exposure could not be calculated because they did not live in the city of Plovdiv, 437 were retained for the main analyses (70%).

The survey included questions about sociodemographic factors, residential environment, noise perception, mental health, and the student’s current living address, which was needed for subsequent assignment of traffic noise level and other geographic variables. Questions about psychological states and evaluative judgements referred to the last month. The survey was administered in two languages − an English version for foreign students and a Bulgarian version for domestic students. Members of the research group were present so that participants had the opportunity to give feedback and receive clarifications about each question.

The design and conduct of the study accorded with the general principles outlined in the Declaration of Helsinki. Participants signed informed consent forms agreeing that their personal information would be processed and stored according to the General Data Protection Regulation in the European Union. There were no incentives provided to students who consented to participate and no penalties for those who chose not to do so.

Traffic noise assessment

Residential noise (LAeq; day equivalent noise level) was calculated by applying a land use regression (LUR) model. The LUR was developed for an earlier study within the same student population and was based on the 2016 noise measurement campaign in Plovdiv. Measurements were conducted over the 12-hour period from 07.00 to 19.00 hours, according to the ISO 1996-2:1987. Predictor variables were derived from the Geographical Information System. The final LUR has an adjusted R2 of 0.72 and leave-one-out cross validation R2 of 0.65. Further details are reported elsewhere.[32]

Assessment of mental ill-health

Severity of depression and anxiety were measured with two screening instruments. The Patient Health Questionnaire 9-item (PHQ-9) taps symptoms of depression like anhedonia, hopelessness, sleep problems, fatigue, appetite changes, and thoughts of death. The items are based on the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders − IV for major depressive disorder. Response options ranged from 0 (not at all) to 3 (nearly every day) during the past two weeks.[33] Scores (sum of the item responses) could range from 0 to 27. Cronbach’s alpha in our sample was 0.77.

The Generalized Anxiety Disorder 7-item (GAD-7) scale was designed to assess how often during the past two weeks the person was bothered by common symptoms of anxiety, such as feeling nervous, worrying too much, having trouble relaxing, becoming easily annoyed, and feeling afraid that something bad might happen.[34] Response options ranged from 0 (not at all) to 3 (nearly every day). Scores (sum of the item responses) could range from 0 to 21. Cronbach’s alpha in our sample was 0.87.

The PHQ-9 and GAD-7 scales were modelled as continuous variables for a more nuanced assessment of symptoms severity, and as dichotomized scores (cut-off ≥ 10) to define moderate depression[35] and generalized anxiety disorder.[36]

Noise annoyance

NA was measured using a single item mimicking the phrasing and response options of the 5-point verbal International Commission on Biological Effects of Noise (ICBEN) NA scale.[37] The question was formulated thus: “How much does road traffic noise in your neighbourhood bother, disturb, or annoy you?”. Possible responses were: “0 = Not at all”, “1 = Slightly”, “2 = Moderately”, “3 = Very”, and to “4 = Extremely”. Our approach differed from the standard ICBEN instructions in that we considered NA in the living environment, not just at home. This formulation was expected to better represent the potential of noise to decrease neighbourhood restorative quality.[15] NA was modelled as a continuous variable with higher scores indicating higher NA.

Noise sensitivity

The Noise Sensitivity Scale Short Form (NSS-SF) was used to measure NS.[38] The NSS-SF was developed as a more practical form of the classical Weinstein noise sensitivity scale,[39] and later translated to Bulgarian (BNSS-SF).[40] It has five items expressing attitudes toward noise in general and emotional reactions to environmental sounds encountered in the everyday life: “I find it hard to relax in a place that’s noisy”; “I get mad at people who make noise that keeps me from falling asleep or getting work done”; “I get annoyed when my neighbours are noisy”; “I get used to most noises without much difficulty” (reverse coded); and “I am sensitive to noise”. These items are measured on 6-point bipolar Likert scales (from “strongly disagree” to “strongly agree”), where higher mean scores indicate higher NS. Internal consistency of the BNSS-SF in our study was acceptable (Cronbach’s alpha = 0.80).

Other covariates

We collected information on factors that may relate to traffic noise and mental ill-health or modify the nature of their relationship. Questionnaire-elicited variables were age, gender, nationality (Bulgarian vs other), perceived income adequacy (“Having in mind your monthly income, how easy is it for you to ‘make ends meet’ and meet your expenses without depriving yourself?”; 0, very difficult to 5, very easy), and average time spent at home/day. To account for differences in students’ timetable and academic demands, we considered at which university faculty they were enrolled.

Geographic data included population density in the 500-m buffer around the residence and nitrogen dioxide (NO2), calculated as a proxy for residential air pollution. For population density, we used a map based on the 2011 Bulgarian Census (1 × 1 km grid).[32] For NO2, we employed a global LUR model constructed using data from air quality monitoring stations, which were predicted from satellite-based NO2 and other commonly used geographic variables related to air pollution.[41]

Statistical analyses

Missing values (<10% on any given variable) were missing at random, therefore, they were imputed using the expectation-maximization algorithm.[42] All variables included in the multivariate analysis models were also included in the imputation procedure.

T-test, chi-square test, Mood’s median test, and correlations (Pearson, point-biserial, and phi) were calculated to identify general patterns of association in the data. We employed the product-of-coefficients approach to mediation analysis[43] implemented in the PROCESS v 3.4. macro (model template 4).[44] We calculated the total effect of LAeq on GAD-7 and PHQ-9, as well as the direct and indirect effects through NA. Linear regression was used for the models with continuous GAD-7 and PHQ-9 scores, and logistic regression for the dichotomized GAD-7 and PHQ-9 scores. Models were adjusted for gender, age, nationality, income adequacy, population density, and university faculty. These models did not suffer from multicollinearity according to tolerance (>0.2) and Variance Inflation Factor (<5) values. Indirect effects were calculated as the product of the association of LAeq with NA and the association of NA with GAD-7/PHQ-9 controlling for LAeq. The indirect effect was then tested using the percentile bootstrap 95% CI (based on 5000 resamples), where an indirect effect that significantly exceeded zero was taken as evidence of mediation.[44] Continuous variables deviating from normal distribution were still analysed with parametric methods because parametric tests[45],[46] and bootstrapping[47],[48] are fairly robust against moderate violations of the normal distribution assumption.

Then, we conducted moderation analysis (PROCESS, model template 1) to test the hypothesis that the strength of the relationship between LAeq and NA was conditioned on NS and dichotomized GAD-7/PHQ-9. The BNSS-SF score was split at the median (<4 vs. ≥4) to define high and low NS. Because of the small sample size, the criterion for statistical consideration of interactions (LAeq×BNSS-SF and LAeq×GAD-7/PHQ-9) was relaxed to P < 0.1 (i.e., Type I error rate of 10%) to report relevant effect modification that might otherwise remain undetected.[49],[50],[51]

Next, we rendered a moderated mediation model (PROCESS, model template 7), in which the indirect effect of LAeq on GAD-7/PHQ-9 through NA was conditional on participant’s BNSS-SF score. We used the index of moderated mediation to test the difference between the conditional indirect effects across the low and high NS groups. That index is based on an interval estimate of the parameter of a function linking the indirect effect of LAeq to NS.[52]

In further analyses, we constructed multiplicative interaction terms to investigate possible moderation of the relationships of LAeq with NA and GAD-7/PHQ-9 by gender, nationality, time spent at home/day (<8 vs. ≥8 hours/day), and NO2.

Data were processed with SPSS (IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.). Associations were considered statistically significant at the P < 0.05 level, except for interactions in the moderation models.


  Results Top


Sample characteristics and univariate associations

[Table 1] and [Table 2] shows descriptive characteristics of the sample. Evidence of at least moderate depression and anxiety was found for 19.7% and 14.2% of the students, respectively. Univariate tests indicated that depressive symptoms were more common in foreign students. Those with anxiety reported lower income adequacy. Presence of depression and anxiety was associated with markedly higher NS and NA.
Table 1: Participant characteristics according to mental ill-health status (N = 437)

Click here to view
Table 2: Bivariate associations between the main variables in the study

Click here to view


The directions of correlations between GAD-7/PHQ-9 and other core variables in the model were in line with theory. Higher GAD-7/PHQ-9 scores related to higher NS and NA, while NA was positively correlated with LAeq and NS. NS and LAeq were not correlated.

Main analyses

[Table 3] shows the total, direct and indirect effects of LAeq on PHQ-9 and GAD-7. Tests of the mediation models indicated that LAeq related to depression and anxiety only indirectly through higher NA. This effect was observed for both continuous and dichotomized PHQ-9/GAD-7.
Table 3: Mediation models of the relationship between road traffic noise (LAeq) and mental ill-health through noise annoyance

Click here to view


As can be seen in [Figure 1] and [Figure 2], an increase in LAeq was associated with higher NA, but this association was stronger in participants with depression compared with their counterparts. The interaction term for LAeq and anxiety was borderline significant and in the same direction.
Figure 1: Conditional effect of residential road traffic noise (LAeq) on noise annoyance depending on presence of depression symptoms. Note. Abbreviations: β − unstandardized linear regression coefficient for trend per 1 dB(A), p int. – p-value from test of interaction between LAeq and depression. Statistically significant associations between LAeq and noise annoyance (β, P-value<0.05) are denoted by an asterisk (*). The model is adjusted for gender, age, nationality, income adequacy, population density, and university faculty.

Click here to view
Figure 2: Conditional effect of residential road traffic noise (LAeq) on noise annoyance depending on presence of anxiety symptoms. Note. Abbreviations: β − unstandardized linear regression coefficient for trend per 1 dB(A), p int. – p-value from test of interaction between LAeq and anxiety. Statistically significant associations between LAeq and noise annoyance (β, P-value<0.05) are denoted by an asterisk (*). The model is adjusted for gender, age, nationality, income adequacy, population density, and university faculty.

Click here to view


As can be seen in Figure 3, NS moderated the LAeq – NA relationship. While the average level of NA was higher in noise sensitive individuals, increase in LAeq was positively associated with NA only in those low on NS. In line with this finding, in the moderated mediation model LAeq only had an indirect effect on PHQ-9/GAD-7 in the low NS individuals [Table 4].
Figure 3: Conditional effect of residential road traffic noise (LAeq) on noise annoyance depending on participant’s noise sensitivity. Note. Abbreviations: β − unstandardized linear regression coefficient for trend per 1 dB(A), p int. – p-value from test of interaction between LAeq and noise sensitivity. Statistically significant associations between LAeq and noise annoyance (β, P-value<0.05) are denoted by an asterisk (*). The model is adjusted for gender, age, nationality, income adequacy, population density, and university faculty.

Click here to view
Table 4: Moderated mediation models of the relationship between residential noise (LAeq) and mental ill-health through noise annoyance, depending on participant’s noise sensitivity

Click here to view


Further analyses

No interaction between LAeq and NO2 was observed. However, gender moderated the association between LAeq and PHQ-9 (p int. = 0.046), with a positive association observed in women (β per 5dB(A) = 1.15; 95% CI: −0.51, 2.81) and a negative association in men (β per 5dB(A) = −1.54; 95% CI: −3.65, 0.56). The association with NA was moderated by time spent at home (p int. = 0.006) and nationality (pint. = 0.033). It was more pronounced in foreign (β per 5dB(A) = 1.38; 95% CI: 0.63, 2.14) than in domestic students (β per 5dB(A) = 0.47; 95% CI: 0.10, 0.83). An increase in LAeq was associated with higher NA only in students spending > 8 hours/day at home (β per 5dB(A) = 1.09; 95% CI: 0.63, 1.54) but not in their counterparts (β per 5dB(A) = 0.19; 95% CI: −0.27, 0.64).


  Discussion Top


Key findings

This is one of the few studies investigating effects of road traffic noise on depression and anxiety.[53] No direct association was found between exposure to road traffic noise and mental ill-health. However, noise exposure was indirectly associated with symptoms of depression and anxiety through higher NA. Although NA is widely accepted as an intervening variable,[14] few other studies have formally tested mediation by NA of the noise − mental ill-health relationship.[27],[32],[54] Findings of recent systematic reviews on the topic yielded evidence of “very low quality”.[53],[55] In a meta-analysis including ten studies on depression and five on anxiety, Dzhambov and Lercher observed 4% (95% CI: −3%, 11%) higher odds of depression and 12% (95% CI: −4%, 30%) of anxiety associated with a 10 dB(A) increase in day-evening-night noise level.[53] However, the authors noted that none of the reviewed studies had investigated underlying mechanisms.[53] As demonstrated here and in earlier research,[32] decomposition of the total effect of noise when multiple indirect pathways operate in opposite directions reveals a broader picture than focusing only on the total effect. Thus far, such path analysis has rarely been employed in the field[27],[32],[54] which could have resulted in inconsistent findings.

In line with previous research,[22] we observed moderation by NS of the indirect association between traffic noise exposure and mental ill-health. However, our findings expand the conventional view that individuals high on NS experience stronger adverse effects of noise.[20],[21] To be sure, NA was considerably higher even at low noise levels in the high NS group − an aspect of this interaction that is usually of interest.[16],[22] However, we believe that interpreting the difference in regression slopes across NS groups has greater applied value for predicting effectiveness of noise abatement interventions. In our case, increase in traffic noise contributed modestly to NA that was already high in the high NS group. On the other hand, in the low NS group, traffic noise had a pronounced positive association with NA. One angle to this could be that in highly noise sensitive people non-acoustic and contextual factors [56],[57],[58] outweigh the influence of noise exposure and act as risk factors in themselves. For example, noise sensitive people may tend to avoid noise exposure more and this avoidance behaviour may dilute the associations linking noise to mental ill-health.[22] They would either leave high noise areas or not move into these areas in the first place.[59] Hence, for them psychological and social interventions may confer greater health benefits than noise abatement through engineering approaches. There have been accounts of high NA persisting even after objective reduction in road traffic noise − a natural experiment found no reduction in NA and no change in mental health following reduction in noise exposure.[60] Conversely, physical reduction of noise level may effectively lower NA in individuals low on NS, for whom non-acoustic factors play a lesser role. Understanding these vulnerability profiles within the community could aid noise policy and increase efficacy of interventions.

In light of our findings about NS, the latter cannot explain why the relationship between traffic noise and NA was considerably stronger in participants with symptoms severity consistent with depression/anxiety. Individuals with poor mental health have limited resources to cope with noise [61] beyond their high NS. Mental disorders are characterised by dysfunctional cognitive schemas, such as feelings of loss of control over one’s environment and ensuing perceived helplessness,[62],[63] which could make the individual more susceptible to noise. We could not explore those factors here, but empirical evidence lends support to this conjecture. Riedel et al. [64] found that the value residents ascribed to being able to control noise exposure moderated the potential indirect effect of road traffic noise on NA through perceived noise control. Relatedly, over-vigilance to danger signals may also precipitate vulnerability to environmental noise.[65],[66] In specific phobia, for instance, auditory cues can trigger arousal and negative emotional response.[67] Depression may also be characterized by genetic hypersensitivity to the biological effect of stress, which may extent to environmental noise.[19] Thus, our findings support an earlier proposition that the effect of noise is contingent on individual’s mental health status.[29]

Finally, we found stronger associations with depression in women and with NA in foreign students and those spending more time at home. Evidence about gender differences in the noise and health literature is mixed and inconclusive.[18] As for being a foreign student in Plovdiv, that entails daily mobility largely limited to the residential neighbourhood, which could have reduced exposure misclassification. The same applies to spending more leisure time at home.

Limitations

This study has a number of limitations. First, a cross-sectional design can only capture a snapshot of a truly longitudinal effect; therefore, causal inferences are hindered. The stronger temporal predictor in the potentially reciprocal association between NA and mental ill-health could not be established.[61]

Second, due to limited data availability, we only considered the daytime LAeq indicator. Yet, alternative noise indicators for exposure to multiple sound sources may perform better and yield stronger exposure-response relationships in noise and health research.[68],[69] Further, the quality of LUR models is lower than sophisticated propagation models,[70],[71] therefore exposure misclassification could have attenuated the association with depression/anxiety. Unfortunately, the EU strategic noise map of Plovdiv only reports noise levels in 5-dB isophones and lacks precision. Therefore, we preferred to use the LUR noise estimates.

Third, our sample was from a very specific setting (i.e., medical university), so it was not representative of all young adults in Plovdiv. The internal validity of our study should still be high though, because we controlled for sociodemographic and residential factors.

Fourth, we relied on self-reported measures of depression/anxiety symptoms. While objective indicators like psychiatric diagnoses and information on psychotropic mediation use eliminate self-report bias and facilitate standardization, self-reports still provide relevant information about subthreshold conditions that often remain undiagnosed.

Fifth, we only considered a narrow set out of a multitude of intertwined processes and contextual influences.[58],[72] Effects of other co-exposures and protective resources, which shape the cycles of stress and restoration in the residential environment,[73] should be explored further. In order to inform stakeholders and develop viable public health strategies for mitigating non-auditory traffic noise effects, research on potential mechanisms and their contingencies should continue. Through the lens of life-course epidemiology, critical developmental periods for counteracting these processes can be identified.[74]


  Conclusions Top


Road traffic noise exposure was indirectly associated with depression and anxiety through higher noise annoyance, but only among those low on noise sensitivity. While high noise sensitivity was associated with high noise annoyance even at low noise levels, increase in traffic noise contributed to noise annoyance only in those low on noise sensitivity. The relationship between noise and annoyance was stronger in those reporting mental ill-health. Understanding these vulnerability profiles within the community could aid noise policy and increase efficacy of interventions.

Acknowledgement

We are grateful to the participating students for making this study possible. Angel Dzhambov’s work on this publication was partially supported by the National program “Young Scientists and Postdoctoral Candidates” of the Ministry of Education and Science, Bulgaria.

Financial support and sponsorship

National program “Young Scientists and Postdoctoral Candidates” of the Ministry of Education and Science, Bulgaria.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Stansfeld S, Clark C, Bebbington P, King M, Jenkins R, Hinchliffe S. Common mental disorders. Adult Psychiatric Morbidity Survey 2014.  Back to cited text no. 1
    
2.
WHO. Depression and Other Common Mental Disorders: Global Health Estimates. Geneva:World Health Organization 2017.  Back to cited text no. 2
    
3.
Rosenzweig MR, Krech D, Bennett EL, Diamond MC. Effects of environmental complexity and training on brain chemistry and anatomy: a replication and extension. J Comp Physiol Psychol 1962;55:429-37.  Back to cited text no. 3
    
4.
Lovden M, Wenger E, Martensson J, Lindenberger U, Backman L. Structural brain plasticity in adult learning and development. Neurosci Biobehav Rev 2013;37:2296-310.  Back to cited text no. 4
    
5.
Uher R. Gene-environment interactions in severe mental illness. Front Psychiatry 2014;5:48.  Back to cited text no. 5
    
6.
van Os J, Kenis G, Rutten BP. The environment and schizophrenia. Nature 2010;468:203-12.  Back to cited text no. 6
    
7.
WHO. Environmental Noise Guidelines for the European Region. WHO Regional Office for Europe; 2018.  Back to cited text no. 7
    
8.
Westman JC, Walters JR. Noise and stress: a comprehensive approach. Environ Health Perspect 1981;41:291-309.  Back to cited text no. 8
    
9.
Ising H, Kruppa B. Health effects caused by noise: evidence in the literature from the past 25 years. Noise Health 2004;22:5-13.  Back to cited text no. 9
    
10.
Hahad O, Prochaska JH, Daiber A, Münzel T. Environmental noise-induced effects on stress hormones, oxidative stress, and vascular dysfunction: key factors in the relationship between cerebrocardiovascular and psychological disorders. Oxidative Medicine and Cellular Longevity 2019;4623109.  Back to cited text no. 10
    
11.
Lupien SJ, Juster RP, Raymond C, Marin MF. The effects of chronic stress on the human brain: From neurotoxicity, to vulnerability, to opportunity. Front Neuroendocrinol 2018;49:91-105.  Back to cited text no. 11
    
12.
Guski R. Personal and social variables as co-determinants of noise annoyance. Noise Health 1999;1:45-56.  Back to cited text no. 12
[PUBMED]  [Full text]  
13.
Guski R, Felscher-Suhr U, Schuemer R. The concept of noise annoyance: how international experts see it. J Sound Vib 1999;223:513-27.  Back to cited text no. 13
    
14.
van Kamp I, Davies H. Environmental noise and mental health: Five year review and future directions. Proceedings of 9th International Congress on Noise as a Public Health Problem (ICBEN), Foxwoods, CT; 2008.  Back to cited text no. 14
    
15.
von Lindern E, Hartig T, Lercher P. Traffic-related exposures, constrained restoration, and health in the residential context. Health Place 2016;39:92-100.  Back to cited text no. 15
    
16.
Heinonen-Guzejev M. Noise sensitivity − medical, psychological and genetic aspects [PhD thesis]. University of Helsinki; 2008.  Back to cited text no. 16
    
17.
Shepherd D, Heinonen-Guzejev M, Hautus MJ, Heikkilä K. Elucidating the relationship between noise sensitivity and personality. Noise Health 2015;17:165-71.  Back to cited text no. 17
[PUBMED]  [Full text]  
18.
Hill EM. Noise sensitivity and diminished health: the role of stress-related factors [Ph.D. thesis]. Auckland University of Technology; 2012.  Back to cited text no. 18
    
19.
Stansfeld SA. Noise, noise sensitivity and psychiatric disorder: epidemiological and psychophysiological studies. Psychol Med 1992;22:1-44.  Back to cited text no. 19
    
20.
Van Kamp I, Job RFS, Hatfield J, Haines M, Stellato RK, Stansfeld SA. The role of noise sensitivity in the noise-response relation: a comparison of three international airport studies. J Acoust Soc Am 2004;116:3471-9.  Back to cited text no. 20
    
21.
Miedema HM, Vos H. Noise sensitivity and reactions to noise and other environmental conditions. J Acoust Soc Am 2003;113:1492-504.  Back to cited text no. 21
    
22.
Stansfeld S, Clark C, Smuk M, Gallacher J, Babisch W. Noise sensitivity, health and mortality − a review and new analyses. Proceedings of 12th ICBEN Congress on Noise as a Public Health Problem, 18-22 June 2017, Zurich; 2017.  Back to cited text no. 22
    
23.
Park J, Chung S, Lee J, Sung JH, Cho SW, Sim CS. Noise sensitivity, rather than noise level, predicts the non-auditory effects of noise in community samples: a population-based survey. BMC Public Health 2017;17:315.  Back to cited text no. 23
    
24.
Mogg K, Bradley BP. A cognitive-motivational analysis of anxiety. Behav Res Ther 1998;36:809-48.  Back to cited text no. 24
    
25.
Clark DA, Beck AT. Cognitive Therapy of Anxiety Disorders. Science and Practice. New York:The Guilford Press 2010.  Back to cited text no. 25
    
26.
Stecker R. Motivational consequences of environmental stress. J Environ Psychol 2004;24:143-65.  Back to cited text no. 26
    
27.
Fyhri A, Klaeboe R. Road traffic noise, sensitivity, annoyance and self-reported health − a structural equation model exercise. Environ Int 2009;35:91-97.  Back to cited text no. 27
    
28.
Tarnopolsky A, Watkins G, Hand DJ. Aircraft noise and mental health: I. Prevalence of individual symptoms. Psychol Med 1980;10:683-98.  Back to cited text no. 28
    
29.
van Kamp I, van Kempen E, Baliatsas C, Houthuijs D. Mental health as context rather than health outcome of noise: competing hypotheses regarding the role of sensitivity, perceived soundscapes and restoration. In: INTER-NOISE Conference Proceedings, Innsbruck. Institute of Noise Control Engineering; 2013.  Back to cited text no. 29
    
30.
Ribeiro ÍJS, Pereira R, Freire IV, de Oliveira BG, Casotti CA, Boery EN. Stress and quality of life among university students: A systematic literature review. Health Profes Educ 2018;4:70-77.  Back to cited text no. 30
    
31.
Dzhambov AM, Hartig T, Tilov B, Atanasova V, Makakova DR, Dimitrova DD. Residential greenspace is associated with mental health via intertwined capacity-building and capacity-restoring pathways. Environ Res. 2019;178:108708.  Back to cited text no. 31
    
32.
Dzhambov AM, Markevych I, Tilov B, Arabadzhiev Z, Stoyanov D, Gatseva P et al. Pathways linking residential noise and air pollution to mental ill-health in young adults. Environ Res 2018;166:458-65.  Back to cited text no. 32
    
33.
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606-13.  Back to cited text no. 33
    
34.
Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006;166:1092-7.  Back to cited text no. 34
    
35.
Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ 2012;184:E191-E196.  Back to cited text no. 35
    
36.
Plummer F, Manea L, Trepel D, McMillan D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry 2016;39:24-31.  Back to cited text no. 36
    
37.
Fields JM, de Jong RG, Gjestland T, Flindell IH, Job RFS, Kurra S et al. Standardized General-Purpose Noise Reaction Questions for Community Noise Surveys: Research and a Recommendation. J Sound Vib 2001;242:641-79.  Back to cited text no. 37
    
38.
Benfield JA, Nurse AG, Jakubowski R, Gibson AW, Taff BD, Newman P et al. Testing noise in the field: A brief measure of individual noise sensitivity. Environ Behav 2012;20:1-20.  Back to cited text no. 38
    
39.
Weinstein ND. Individual differences in reactions to noise: a longitudinal study in a college dormitory. J Appl Psychol 1978;63:458-66.  Back to cited text no. 39
    
40.
Dzhambov AM, Dimitrova DD. Psychometric properties of the Bulgarian translation of noise sensitivity scale short form (NSS-SF): implementation in the field of noise control. Noise Health 2014;16:361-7.  Back to cited text no. 40
[PUBMED]  [Full text]  
41.
Larkin A, Geddes JA, Martin RV, Xiao Q, Liu Y, Marshall JD et al. Global land use regression model for nitrogen dioxide air pollution. Environ Sci Technol 2017;51:6957-64.  Back to cited text no. 41
    
42.
Dempster AP, Laird NM, Rubin DB. Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). J Royal Stat Assoc 1977;B39:1-38.  Back to cited text no. 42
    
43.
Alwin DF, Hauser RM. The decomposition of effects in path analysis. Am Sociol Rev 1975;40:37-47.  Back to cited text no. 43
    
44.
Hayes A. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. 2nd Edition. Guilford Press, New York 2017.  Back to cited text no. 44
    
45.
Schmider E, Ziegler M, Danay E, Beyer L, Bühner M. Is it really robust? Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption. Meth Eur J Res Meth Behav Soc Sci 2010;6:147-51.  Back to cited text no. 45
    
46.
Blanca MJ, Alarcón R, Arnau J, Bono R, Bendayan R. Non-normal data: is ANOVA still a valid option? Psicothema 2017;29:552-7.  Back to cited text no. 46
    
47.
Haukoos JS, Lewis RJ. Advanced statistics: bootstrapping confidence intervals for statistics with “difficult” distributions. Acad Emerg Med 2005;12:360-5.  Back to cited text no. 47
    
48.
Kelley K. The effects of nonnormal distributions on confidence intervals around the standardized mean difference: bootstrap and parametric confidence intervals. Educational and Psychological Measurement 2005;65:51-69.  Back to cited text no. 48
    
49.
Selvin S. Statistical Analysis of Epidemiologic Data New York: NY: Oxford University Press 1996, pp. 213-214.  Back to cited text no. 49
    
50.
Greenland S, Rothman KJ. Chapter 18: Concepts of Interaction. In: Rothman KJ, Greenland S, editors. Modern Epidemiology. 2nd ed. New York:Lippincott-Raven 1998. pp. 329-42  Back to cited text no. 50
    
51.
Marshall SW. Power for tests of interaction: effect of raising the Type I error rate. Epidemiologic Perspectives and Innovations 2007.  Back to cited text no. 51
    
52.
Hayes AF. An Index and Test of Linear Moderated Mediation. Multivar Behav Res 2015;50:1-22.  Back to cited text no. 52
    
53.
Dzhambov AM, Lercher P. Road traffic noise exposure and depression/anxiety: an updated systematic review and meta-analysis. Int J Environ Res Public Health 2019;16:4134.  Back to cited text no. 53
    
54.
Dzhambov A, Tilov B, Markevych I, Dimitrova D. Residential road traffic noise and general mental health in youth: The role of noise annoyance, neighborhood restorative quality, physical activity, and social cohesion as potential mediators. Environ Int 2017;109:1-9.  Back to cited text no. 54
    
55.
Clark C, Paunovic K. WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Quality of Life, Wellbeing and Mental Health. Int J Environ Res Public Health 2018;15:2400.  Back to cited text no. 55
    
56.
Lercher P. Environmental noise and health: An integrated research perspective. Environ Int 1996;22:117-29.  Back to cited text no. 56
    
57.
Lercher P. Environmental noise: A contextual public health perspective. In: Luxon D, Prasher LM, editors. Noise and its effects. London:Wiley 2007, pp. 345-377.  Back to cited text no. 57
    
58.
Lercher P, De Coensel B, Dekonink L, Botteldooren D. Community response to multiple sound sources: Integrating acoustic and contextual approaches in the analysis. Int J Environ Res Public Health 2017;14:663.  Back to cited text no. 58
    
59.
Nijland HA, Hartemink S, van Kamp I, van Wee B. The influence of sensitivity for road traffic noise on residential location: does it trigger a process of spatial selection? J Acoust Soc Am 2007;122:1595.  Back to cited text no. 59
    
60.
Stansfeld SA, Haines MM, Berry B, Burr M. Reduction of road traffic noise and mental health: an intervention study. Noise Health 2009; 11:169–75.  Back to cited text no. 60
    
61.
Schreckenberg D, Meis M, Kahl C, Peschel C, Eikmann T. Aircraft noise and quality of life around Frankfurt Airport. Int J Environ Res Public Health 2010;7:3382-405.  Back to cited text no. 61
    
62.
Evans GW. The built environment and mental health. J Urban Health 2003;80:536-55.  Back to cited text no. 62
    
63.
Hatfield J, Job RS, Hede AJ, Carter NL, Peploe P, Taylor R et al. Human response to environmental noise: The role of perceived control. Int J Behav Med 2002;9:341-59.  Back to cited text no. 63
    
64.
Riedel N, Köckler H, Scheiner J, van Kamp I, Erbel R, Loerbroks A et al. Urban road traffic noise and noise annoyance-a study on perceived noise control and its value among the elderly. Eur J Public Health 2019;29:377-9.  Back to cited text no. 64
    
65.
Somerville LH, Wagner DD, Wig GS, Moran JM, Whalen PJ, Kelley WM. Interactions between transient and sustained neural signals support the generation and regulation of anxious emotion. Cereb Cortex 2013;23:49-60.  Back to cited text no. 65
    
66.
Shepherd D, Hautus MJ, Lee SY, Mulgrew J. Electrophysiological approaches to noise sensitivity. J Clin Exp Neuropsychol. 2016;38:900-12.  Back to cited text no. 66
    
67.
Schröder A, Vulink N, Denys D. Misophonia: diagnostic criteria for a new psychiatric disorder. PLoS One 2013;8:e54706.  Back to cited text no. 67
    
68.
Lercher P, Boeckstael A, De Coensel B, Dekoninck L, Botteldooren D. The application of a notice-event model to improve classical exposure-annoyance estimation. J Acoust Soc Am 2012; 131:3223.  Back to cited text no. 68
    
69.
Lercher P, De Coensel B, Dekoninck L, Botteldooren D. Alternative traffic noise indicators and its association with hypertension. In: Proceedings of EuroNoise. Hersonissos, Crete, Greece, 27-31 May 2018, pp. 457-464  Back to cited text no. 69
    
70.
Aguilera I, Foraster M, Basagaña X, Corradi E, Deltell A, Morelli X et al. Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities. J Expo Sci Environ Epidemiol 2015;25:97-105.  Back to cited text no. 70
    
71.
Ragettli MS, Goudreau S, Plante C, Fournier M, Hatzopoulou M, Perron S et al. Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics. J Expo Sci Environ Epidemiol 2016;26:597-605.  Back to cited text no. 71
    
72.
Lovasi GS, Mooney SJ, Muennig P, DiMaggio C. Cause and context: place-based approaches to investigate how environments affect mental health. Soc Psychiatry Psychiatr Epidemiol 2016;51:1571-9.  Back to cited text no. 72
    
73.
Riedel N, Köckler H, Scheiner J, Berger K. Objective exposure to road traffic noise, noise annoyance and self-rated poor health − framing the relationship between noise and health as a matter of multiple stressors and resources in urban neighbourhoods. J Environ Plan Manage 2015;58:336-56.  Back to cited text no. 73
    
74.
Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology J Epidemiol Community Health 2003;57:778-83.  Back to cited text no. 74
    

Top
Correspondence Address:
Angel M Dzhambov
15A Vassil Aprilov Blvd., 4002 Plovdiv
Bulgaria
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/nah.NAH_15_20

Rights and Permissions


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top