PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Examination of the causal relationship between aircraft noise
exposure, noise annoyance and diagnoses of depression using
structural equation modelling
Sarah BENZ1,2; Dirk SCHRECKENBERG1
1
2

ZEUS GmbH, Germany

Heinrich-Heine University Dusseldorf, Germany

ABSTRACT
Environmental noise is ubiquitous in daily life and a major environmental burden, though the effects oftransportation noise on mental health are sparsely investigated to date. The recently published WHO review
on the impact of transportation noise on quality of life and mental health shows inconsistent findings. Noiseannoyance, as an approved psychological stress response to noise, is identified as one of the health impacts ofenvironmental noise. There is some evidence that the impact of noise on health is at least partly mediated bynoise annoyance. A re-analysis of data is conducted using data from the socio-acoustical longitudinal survey
NORAH (Noise-Related Annoyance, Cognition, and Health), which has been performed mainly at Frankfurt
Airport, Germany, from 2011 to 2013. Residents older than 18 and living in the 40 dB-noise contour aroundFrankfurt Airport were randomly selected to participate in the study. A telephone and online survey assessing,among others, diagnosed depressions and aircraft noise annoyance were conducted once a year/annually.
Aircraft sound levels were calculated for the exact address of every participant. The present work is anapproach to examine the causal relationship between noise exposure, noise annoyance and diagnoses ofdepression using structural equation modelling.
Keywords: aircraft noise annoyance, aircraft noise exposure, depression

1. INTRODUCTION
Environmental noise has been shown to negatively affect various aspects of human health. Besides
negative effects of aircraft noise exposure on sleep or cardiovascular diseases (1, 2), there areinconsistent findings on the effect of aircraft noise exposure on mental health and well-being (3, 4).
However, there is evidence for a negative association of noise annoyance due to aircraft traffic andhealth-related mental quality of life (5-6) as well as noise annoyance and depression (7-8). Noiseannoyance is considered as a three dimensional psychological concept covering cognitive, affective
and behavioural reactions on noise (9). Further, noise annoyance as a stress reaction is discussed to(partly) mediate the influence of aircraft noise on mental health (6). Most of these findings are basedon cross-sectional studies, not allowing for a causal interpretation.
The objective of this study is to investigate the causal relationship between aircraft noise,diagnosed depression and aircraft noise annoyance. Three research questions have been addressed:
1) Is there a direct impact of aircraft noise exposure on the prevalence of diagnosed depression?
2) Is there a mediation effect of aircraft noise annoyance on future diagnosed depression?
3) In case, aircraft noise exposure affects the prevalence of diagnosed depression, can thepresence of a depression mediate the impact of aircraft noise exposure on the magnitude of
future aircraft noise annoyance?

2. PROCEDURE, METHODS
2.1 Study design, sampling
Data from the socio-acoustical longitudinal survey NORAH (Noise-Related Annoyance, Cognition,
1

benz@zeusgmbh.de; schreckenberg@zeusgmbh.de3033

and Health), which was conducted in the vicinity of Frankfurt Airport, was re-analyzed. The study
sample was randomly selected from the full-aged population living in the Rhine Main area,
particularly from people living within the envelope of the 40-dB L pAeq contours for day and night-timearound Frankfurt Airport, following a stratification plan with two strata. The first stratum was thecurrent aircraft noise exposure. The second stratum was the predicted change of aircraft noise
exposure after the opening of the new runway and the implementation of the night flight ban for 2020
in relation to the aircraft noise exposure in 2007 (increase in LpAeq,24h > 2 dB, decrease in LpAeq,24h > 2
dB, change within the range of ± 2 dB). From 2011 to 2013 annually measurements were carried out:
one before and two after the opening of a new runway and the implementation of a night flight ban.
Telephone interviews were performed (with the alternative option to participate via an online survey)
to assess, among others, diagnosed depression and aircraft noise annoyance (for further details see(10)). As a new runway was opened and a night-flight ban was implemented at Frankfurt Airport in
October 2011, the subsequent two years are investigated (2012 and 2013) to study the impact of thechange in noise exposure.
2.2 Assessment of aircraft noise exposure
The aircraft noise exposure was calculated for each participant’s exact address at the most exposed
façade of the residential building/residency. In this study, aircraft noise was operationalised with the
L den, the weighted and averaged day–evening–night noise level for 24 hours. It is composed of the day
level, which in the present case refers to the time between 6am to 6pm, the evening level which refers
to 6pm to 10pm and the night level from 10pm to 6am. For the evening and night levels a penalty isapplied; with 5 dB for the evening and 10 dB for the night level. For this study, the aircraft noise
exposure was modelled for 12 months from October 2011 to September 2012 for t1 (2012) and from
October 2012 to September 2013 for the survey wave in 2013 (t2), respectively.
2.3 Assessment of noise annoyance and diagnosis of depression
Noise annoyance was measured using the standardised noise annoyance question as recommended
by the International Commission on the Biological Effects of Noise (11), “Thinking about the last 12
months, when you are here at home, how much does noise from aircraft bother, disturb, or annoy you?”with a 5-point semantic answer scale ranging from (1) ‘not at all’ to (5) ‘extremely’.
Two items were used to assess life time prevalence and 12-months prevalence of diagnosed depression.
The first item assessed whether a physician or therapist ever diagnosed a depressive disorder(dichotomous item with “yes” and “no”). If yes, it was asked if a depressive disorder was diagnosed in
the last 12-months.
2.4 Statistical analysis
The descriptive analysis of the data includes the calculation of frequency, means, standard
deviations, and correlations. The percentage of annoyed people (%A) as well as the prevalence ofdiagnosed depressions have been calculated for 2.5-dB classes of Lden. Further, for each level of noise
annoyance the share of people diagnosed with depression has been examined. Differences between
groups (people with a diagnosis of depression vs. no depression) were tested using inference statisticaltests (Mann-Whitney-U and Chi-Square test).
Logistic regression models were used to evaluate the effects of aircraft noise exposure and noise
annoyance on the prevalence of depression for last 12 months. In augmented models the potential
influence of additional determinants of depression was analysed, including age, sex, migration
background, period of residence, hours spent out of home, home ownership, socio -economic status,
noise sensitivity, body mass index (BMI), sports, railway noise exposure (Lden), road noise exposure(Lden), alcohol consumption and tobacco consumption.
Structural equation modelling was carried out to assess the causal relationship between aircraft
noise exposure, noise annoyance and diagnoses of depression with the statistics software MPLUS
V.6.11.

3. RESULTS
3.1 Descriptive statistics
A total of 3,508 subjects participated in all three waves of the longitudinal survey from 2011 to
2013. Cases with missing depression data were excluded from the analysis, thus, the investigated3034

sample included 3,319 residents. The mean age was 53.7 years (SD±14.6; range 19-97 years) with 53%
being female and 47% male. The average period of residence at the registered address was 26.5 years(SD± 18.2).
Table 1 shows the degree of noise annoyance for each 2.5 dB class of L den of aircraft noise exposurein t1 (2012) and t2 (2013). At both measurement time points, there is an increase in aircraft noise
annoyance with increasing aircraft noise exposure, while mean noise annoyance in t1 (2012, M = 3.38,
SD = ±1.30) is higher than in t2 (2013, M = 3.22, SD = ±1.32).
Table 1 - Aircraft noise annoyance for 2.5 dB classes of Ldenaircraft noise annoyancet1 (2012)

t2 (2013)

2,5 dB classes of
Lden

n

M

SD

n

M

SD

≤ 40 dB

100

2,13

1,05

126

2,13

0,95

40,1 - 42,5 dB

255

2,36

1,12

309

2,23

1,06

42,6 - 45,0 dB

407

2,64

1,22

425

2,51

1,18

45,1 - 47,5 dB

357

2,88

1,17

338

2,84

1,17

47,6 - 50,0 dB

309

3,30

1,23

338

3,21

1,28

50,1 - 52,5 dB

369

3,49

1,19

439

3,41

1,24

52,6 - 55,0 dB

404

3,75

1,17

377

3,63

1,19

55,1 - 57,5 dB

461

3,86

1,16

442

3,75

1,22

57,6 - 60,0 dB

416

4,02

1,09

296

3,97

1,13

> 60 dB

241

4,22

0,93

229

4,05

1,05

Total
3319 3,38 1,30
3319 3,22 1,32
Note. n = number of participants; M= mean; SD= standard deviation.
Table 2 shows the life time prevalence of diagnosed depression in t1 (2012) and t2 (2013) for each 2.5
dB class of aircraft noise exposure. Naturally, absolute numbers of prevalence rise from t1 (2012) to t2(2013). Within each study year, there is no continuous rise of prevalence with increasing levels ofaircraft noise exposure.
Table 2 - Prevalence of depression for aircraft noise exposure in 2.5 dB classes of Ldenlife time prevalence ofdepression
t1 (2012)

t2 (2013)

of LDEN

%

n

%

n

≤ 40 dB

1,5

7

2,2

11

40,1 - 42,5 dB

8,4

38

10,5

52

42,6 - 45,0 dB

12,8

58

12,9

64

45,1 - 47,5 dB

11,9

54

11,3

56

47,6 - 50,0 dB

9,5

43

8,7

43

50,1 - 52,5 dB

10,6

48

13,7

68

52,6 - 55,0 dB

10,8

49

11,3

56

2,5 dB classes

3035

55,1 - 57,5 dB

14,6

66

14,1

70

57,6 - 60,0 dB

12,1

55

8,7

43

> 60 dB

7,7

35

6,8

34

Total
100 453
100 497
Note. n = sample size; dB = decibel.
Life time prevalence for depressive disorders diagnosed by a physician or therapist was 13.6% in t1(2012), 12 months prevalence was 7.8%. This is comparable to the prevalence rates in the German
population with 11.6% of life time prevalence and 8.1% for the 12 months prevalence of diagnosed
depressive disorders (12).
There was no significant difference in noise exposure from aircraft, road and railway noise between
the two groups of people with and without diagnosed depression (p > .100). Further, there was no
significant difference between the two groups for the change of noise exposure from t1 to t2 after the
opening of the new runway and the implementation of the night flight ban (p > .100). However, the twogroups differed in the degree of aircraft noise annoyance (p < 0.01), with higher means of aircraft noise
annoyance for the three survey years in the depression group.
Table 3 shows the share of people for levels of aircraft noise annoyance. The share of people risewith the intensity/degree of noise annoyance, from (1) not at all to (5) extremely in both study groups(people with depression vs. without depression).
Table 3 - Distribution of people with and without life-time depression for levels of aircraft noise annoyancet1 (2012)

t2 (2013)

depression

healthydepression

healthyannoyance

n

%

n

%

n

%

n

%

not at all (1)

42

9,3

293

10,3

54

10,9

359

12,8

slightly (2)

60

13,2

520

18,3

83

16,7

574

20,5

moderately (3)

92

20,3

609

21,5

99

19,9

610

21,8

very (4)

114 25,2

751

26,5

129 26,0

694

24,8

extremely (5)145 32,0

662

23,4

132 26,6

557

19,9

levels of noise

Total

453 100 2.835 100
497 100 2.794 100
Note. n =sample size.

3.2 Logistic regression models for the prediction of diagnoses of depression
Logistic regression models were used to evaluate the effects of aircraft noise exposure and noise
annoyance in t1 (2012) on prevalence of depression for the last 12 months in t2 (2013). In augmented
models we tested the potential improvement of models and the prediction of depression by adding
possible predictors. Results of logistic regression models are shown in excerpts in table 4.
In a basic model (model 1), aircraft noise exposure in t1 (2012) was shown to have a significant
effect on the prevalence of diagnosed depression in t2 (2013; B = -0.02, SE = 0.01, p < .05). In an
augmented model, potential determinants of depression were included, resulting in a smaller andnon-significant effect of aircraft noise exposure (B = 0.01, SE = 0.01, p = .26). In model 2, noise
annoyance was included and a significant predictor of depression (B = -0.20; SE = 0.08, p <.01),
whereas the noise level remained a non-significant predictor. . Hence, we tested two additionalmodels: model 3 as the basic model with noise annoyance as a further predictor and model 4 including
aircraft noise annoyance and excluding aircraft noise levels. Model 3turned out to better fit than theother models (AIC = 1131.7), but not better than the basic model. The best model fit is found in model
4 with noise annoyance instead of aircraft noise levels (AIC =59.0), with the same effect size of3036

aircraft noise annoyance. This indicates that aircraft noise annoyance is a better predictor for adiagnosis of depression than the acoustic variable aircraft noise level.
Table 4 - Results of logistic regression models for the evaluation of effects of aircraft noise exposure andnoise annoyance in t1 on the period prevalence of depression in t2 (excerpt)
model 1parameter

B

SE

p

model 2
Cl-

Cl+

B

SE

p

model 3
Cl-

Cl+

B

SE

p

model 4
Cl-

Cl+

B

SE

p Cl- Cl+
(constant term)
3.68 0.56 *** 2.59 4.77 3.42 0.71 *** 2.02 4.82 3.44 0.57 *** 2.33 4.55 3.37 0.22 *** 2.93 3.81
aircraft noise level
LDEN-0.02 0.01 * -0.04 -0.00 0.00 0.02 0.76 -0.03 0.03 -0.00 0.01 0.89 -0.02 0.02
mode
-0.15 0.06 * -0.27 -0.03 -0.21 0.08 * -0.37 -0.05 -0.16 0.06 ** -0.27 -0.04 -0.16 0.06 ** -0.27 -0.04
aircraft noise
annoyance-0.20 0.08 ** -0.34 -0.05 -0.21 0.06 *** -0.38 -0.09 -0.21 0.06 *** -0.33 -0.10
AIC value
718.3
1252.71131.759.0

Note. B = regression coefficient; SE = standard error; p = probability value; CI -/+ = lower/upper bound of 95 %wald-confidence interval, AIC = Akaike information criterion; * p < 0,05; ** p < 0,01; *** p < 0,001, mode = survey
mode (telephone vs. online); additional predictors included in the augmented model (model 2): age, sex, migration
background, period of residence, hours spent out of home, home ownership, socio -economic status, noise sensitivity,
body mass index (BMI), sports, railway noise exposure (LDEN), road noise exposure (LDEN), alcohol consumption
and tobacco consumption.

3.3

Structural equation modelling to test the causal relationship of aircraft noise

exposure, aircraft noise annoyance and diagnosis of depression
To test the causal association between aircraft noise exposure, aircraft noise annoyance and theprevalence of depression for 12 months, structural equation modelling was conducted.
Co-determinants are not included in the model due to a better fit of the basic models without
determinants.
Figure 1a) specifies the model to test whether the impact of aircraft noise exposure in t1 (2012) on
diagnoses of depression in t2 (2013) is mediated by aircraft noise annoyance in t1 (2012). Figure 1b)
shows the model that examines a potential mediation effect of diagnosed depression in t1 (2012) on theeffect of aircraft noise exposure in t1 (2012) on the magnitude/intensity of aircraft noise annoyance int2 (2013). To control for the influence of depression and noise annoyance in the previous year, theireffect on the following year was tested.

Figure 1 – the effect of aircraft noise exposure in t1 (2012) on a) prevalence of depression in t2, potentiallymediated by aircraft noise annoyance in t1, b) on aircraft noise annoyance in t2, potentially mediated bydepression in t1.
Results show no significant direct effect of aircraft noise exposure in t1 (2012) on prevalence ofdepression in t2(2013) (β* = -0.032, p =.372), whereas there are significant effects via the indirect pathfrom aircraft noise exposure on aircraft noise annoyance (β* = 0.464, p <.001) and aircraft noise3037

annoyance in t1 on the prevalence of depression in t2 (2013) (β* = 0.127, p <.01). In the present study
the effect of aircraft noise exposure in t1 on depression in t2 is largely mediated by aircraft noise
annoyance in t1, the mediation effect of annoyance (t1) is of moderate size (β* = 0.059, p <.01).
Results do not confirm the reversed hypothesis that prevalence of depression in t1 mediates theeffect of aircraft noise exposure on the intensity of aircraft noise annoyance in t2. There is a direct
effect of aircraft noise exposure in t1 on aircraft noise annoyance in t2 (β* = 0.110, p <.001), but nosignificant effect of aircraft noise exposure on depression (β* = 0.041, p =.205). However, prevalenceof depression in t1 appears to affect aircraft noise annoyance in t2 (β* = 0.103, p <.001).

4. DISCUSSION AND CONCLUSIONS
Findings indicate no direct effect of noise exposure on the 12 months prevalence of depression,confirming results of previous studies with no effect of aircraft noise exposure on depression or mental
health (12-14). Thus, aircraft noise annoyance in t1 has been found to significantly mediate the effectof aircraft noise exposure in t1 on the diagnosed 12 months prevalence of depression in the following
year (t2). Further, a significant reciprocal effect of noise annoyance and depression was observed,
showing that depression also accounts for the degree of noise annoyance in the following year. These
results support evidence for aircraft noise annoyance as a mediator of the relationship between aircraft
noise exposure and mental health outcomes (6), indicating further that effects cannot only be found for
quality of life but also for manifest mental disorders, emphasizing the adverse effects of annoyancefrom aircraft noise.
Noise annoyance as a cognitive, emotional and behavioural stress response to noise (9, 16)
combined with physiological stress reactions (17, 18) might enhance the impact of noise and take its
full effect in contributing to the development of depression.
Depression cannot be confirmed as a mediator of the relationship between aircraft noise exposureand aircraft noise annoyance in the following year. However, depression can be confirmed as apotential predictor (either moderator or co-determinant) of aircraft noise annoyance, which is in linewith evidence for an association of noise annoyance and mental health quality of life (6, 19).
Depression could result in lower perceived coping capacity and the feeling of lack of control of thenoise situation that might lead to more annoyance. This is in line with Stallen, emphasizing the role ofcoping capacity and perceived control on noise annoyance (16). Underlying processes for the
mediation effect of noise annoyance on depression should be addressed in future research. Overall,these results support the relevance of aircraft noise annoyance as an indicator of the health impacts ofnoise.

ACKNOWLEDGEMENTS
This study is part of a master’s thesis within the frame of the conceptual and methodological workof the WPs 2 and 3 of the EU project ANIMA (Aviation Noise Impact Management through Novel
Approaches). ANIMA has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 769627. For this study, data of the research project
NORAH (Noise-Related Annoyance, Cognition, and Health) was used. NORAH was commissioned by
the Environment & Community Center / Forum Airport & Region, Kelsterbach, Germany.

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