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

Noise and hypertension: Testing alternative acoustic indicators
Peter LERCHER1; Reto PIEREN2; Jean Marc WUNDERLI2;
1

Institute for Highway Engineering and Transport Planning, Graz University of Technology, Austria

2

Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

ABSTRACTStandard acoustic indicators are reasonable means for health impact assessment of average transportationnoise exposure situations. However, in its Annex I.3, the environmental noise directive admits problem areas(low frequency, tonal or impulsive components, quiet areas), where the application of "supplementary"
indices may be necessary. Various indices accounting better for the traffic dynamics were proposed recently,
with intermittency ratio (IR) being one of these. Hitherto, IR was mainly evaluated in the SIRENE study andthe additional contribution was small. Our aim is to test, whether IR and another indicator (Leq-L50) works
similar in an alpine study, which covered main and side valleys with a different transportation mix.
In a secondary analysis (cross-sectional survey, N=2002, 80% participation) we were able to estimate IR and
Leq-L50 for 1500 participants from the original data set. These indicators were tested against diagnosis of
hypertension in multiple logistic regression analyses, adjusted for main confounders (age, sex, BMI, healthstatus, sensitivity, education, air pollution, smoking). The relationship with hypertension varied. Models
explained 30 to 35% of the variance.
The contribution of both indicators differed between sources and source combinations. The strongest
contributions were observed in mixed noise source scenarios.
Keywords: Sound indicator, Intermittency ratio, traffic dynamics, hypertension

1. INTRODUCTION
The use of standard acoustic indicators is a reasonable first step, when a health impact assessmentof a typical transportation noise exposure is required. However, in its Annex I.3, the environmental
noise directive admits problem areas (low frequency, tonal or impulsive components, quiet areas),
where the application of "supplementary" indices may be necessary.
Specifically, the recent WHO evidence review on annoyance indicated rather large differences instandardized highly annoyance judgements not only at high but also at lower road traffic sound levels(1). Notably, the exposure effect curves of the included five alpine studies in the evidence review show
a consistently higher annoyance response. At first glance, you may judge this as a systematic responsebias related to psychological factors involved in annoyance judgements (e.g. media attention or thelike). However, the cardio-vascular evidence reviews also show consistently increased risk for more
severe illness (ischemic heart disease, hypertension) in the same alpine studies (2).
In previous analyses we found other perceived factors associated with road traffic exposure(exhaust fumes, vibration) explain some of the observed higher risk of annoyance (3). In addition, highcoping efforts, (closing windows etc.) associated with anger, and sleep disturbance contributed.
Another finding, related to main roads and railways, was the contribution of higher emergence of thesound exposure (signal to noise ratio) in the context of multiple sound sources.
In a follow-up analysis on the annoyance findings, we tested whether indices of emergence orfluctuation or Lnight would contribute to adjusted models with hypertension, an objective indicator of
disease, beyond the classical Lden (4). We could repeat the importance of emergence and Lnight alsoin participants with reported diagnoses of hypertension or on antihypertensive medication. In addition,
indicators of fluctuation played a role, while the perceived factors (exhaust fumes, vibration) or12

peter.lercher@tugraz.at
Reto.Pieren@empa.ch; Jean-Marc.Wunderli@empa.ch7963

needed coping efforts did not play the role they had when annoyance was the outcome.
A major problem of the analyses was, however, to keep both Lden and the indicators of emergenceor fluctuation in the same regression model due to its mutual high correlation.
In our current analyses, our aim was to test other sound indicators, which accou nt for the soundlevel dynamics as in the previous analyses – but at the same time are not so highly correlated with theclassic Lden.
The noise indicator Intermittency Ratio (IR) (5)has been recently applied within the SIRENE study
with annoyance as outcome (6). IR showed lower correlation with Lden as compared to other noise
metrics. The observed additional contribution was significant but small compared with the Lden.
Furthermore, no moderation was observed between these two indicators. Also, IR was tested in thenationwide Swiss National Cohort (SNC) with cardiovascular mortality (CVM) and diurnal
variability of noise exposure in focus (7). While the effects on CVM of noise exposure were confirmedwith Lden as indicator, except for stroke, the IR did not show a clear picture , and revealed even an
opposite trend with mortality due to hypertensive disease. This raises the question, whether the IR,
better reflecting the temporal features of noise, may be a helpful indicator for annoyance assessment,
but not for possible far distal effects of noise, such as mortality, where the immediate perception ofnoise is not important anymore
As hypertension is a precursor (intermediate) for more serious cardiovascular illness (IHD, stroke),and such persons are known to be vulnerable to stress reactions by noise exposure – the perception of
temporal pattern may be more important for the induction of biologic reactions.
Our common aim in this analysis is therefore to test how the IR and another indicator (Leq-L50)
performs in an alpine data set with proven, strong traffic dynamics and with hypertension as healthoutcome in addition to the Lden.

2. METHODS
2.1 Study Areas and Samples
The study took part in the Wipptal and its side valleys. The main valley is part of the most important
north-south access route for heavy goods traffic over the Brenner Pass. The area consist of villages
with a mix of small business, service and agricultural activities. In the side valleys touristic activities
dominate.
The selected study population (age 20-75 yrs) was approached by phone with a two-step sampling
procedure based on traffic source and distance. The available sample varied for the traffic sources andwas smaller for the highway and railway exposure (~800). For main road exposure the full sample(~1500) was available. The overall participation was high (80%).
2.2 Sound Exposure Assessment and Sound IndicesRoad emissions were calculated with an early version of the Harmonoise source model (8)supplemented with additional traffic counting and micro-simulations of the traffic flow with Paramics.
Railway noise emission was extracted from a typical day out of several long-term sound immission
measurements near the source (25 m). Sound propagation modeling was carried out with Bass3(9)(10), an extended version of ISO9613.
The propagation model includes up to four reflections and two sideway diffractions. The validity ofthese simulations was calibrated against measurement results from extensive sound monitoring
campaigns during summer and winter. Indicators of day, evening, night exposure and Lden were
calculated for each sound source and total exposure for all facades of the participant’s home.
To estimate the time-varying sound level at the dwelling façade of each survey participant , timeseries of levels caused by each source were simulated (11). Therefrom, percentile levels were
calculated for each source, combinations of all sources and total sound exposure.
From these percentile levels of the overall sound exposure, the IR was estimated with the following
procedure: The percentile sound levels L1, L5, L10, L50, L90, L95 and L99 were used to fit acumulative distribution function (CDF) of the level with a resolution of 0.1 dB based on ashape-preserving piecewise cubic inter- and extrapolation. The probability density function (PDF) of
the level was calculated as the derivative of the CDF by finite differences. Finally, the Leq andsubsequently the IR was derived by energy integration considering the PDF. The indicator Leq-L50
was calculated from the same acoustic data basis.

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2.3 Questionnaire Information
The questionnaire covered socio-demographic data, housing, and satisfaction with the environment,noise annoyance, and interference of activities, coping with noise, occupational exposures, life styles,
dispositions (noise, weather sensitivity), health status, selected illnesses and medications. Reported
hypertension ever and current use of anti-hypertensive medication was inquired as "doctor-related
information". A CATI-laboratory contacted participants by phone three times before replacement. The
standardized interview took about 15-20 minutes.
2.4 Statistical Analysis
The analysis was carried out with R version 3.3.2 . Exposure-effect curves are calculated withextended logistic regression methods. When appropriate, restricted cubic spline functions wereapplied to accommodate for non-linear components in the fit .
Approximate 95 % confidence intervals were estimated using smoothing spline routines with threeknots and exposure-effect plots generated with the RMS-library from R (12). Predicted probabilities ofhypertension ever or anti-hypertensive medication are derived from the estimated odds with a specificfunction in the RMS-library (plogis). The predicted probabilities in the exposure-effect plots areadjusted to the median (continuous variables) or the reference category (non-continuous variables) of
the variables adjusted for in the full model.

3. RESULTS
3.1 General Descriptive Statistics
Table 1 shows the univariate descriptive statistics for the categorical variables us ed in thehypertension models. Age, education, family history of hypertension, and health status are highly
significant predictors. Gender and area are nearly significant associated, while noise sensitivity is not.
Table 1. Statistical description of categorical model variables by hypertension diagnosis
Categorical
Reported hypertension
Total
Test statistic
variables
Total

No n(%)

Yes n(%)

Sample

1269

299

1572

Age, years<30

206 (16.2)

14 (4.7)

220 (14)30-44

521 (41.1)

52 (17.4)

573 (36.5)

45-59

369 (29.1)

93 (31.1)

462 (29.5)>=60

173 (13.6)

140 (46.8)

313 (20)

GenderMale

836 (65.9)

180 (60.2)

1016 (64.8)

Female433 (34.1)

119 (39.8)

552 (35.2)

Education
Basic

324 (25.7)

137 (46.1)

461 (29.6)

Skilled341 (27)51 (17.2)

392 (25.2)

Vocational

267 (21.2)

56 (18.9)323 (20.7)

Higher329 (26.1)

53 (17.8)

382 (24.5)

Area
Northern Wipptal

859 (67.7)

185 (61.9)

1044 (66.6)

Southern Wipptal

410 (32.3)

114 (38.1)

524 (33.4)

Family history

d.f. – χ2

P valueχ 2 (3 df) = 193.97< 0.001χ 2 (1 df) = 3.18

0.075

χ 2 (3 df) = 50.52< 0.001χ 2 (1 df) = 3.42

0.064

χ 2 (1 df) = 38.94< 0.0017965

No

891 (70.8)

154 (51.7)

1045 (67.1)

Yes

368 (29.2)

144 (48.3)

512 (32.9)

Health status
Excellent

392 (31.3)

36 (12.1)

428 (27.6)

Good

525 (41.9)

93 (31.2)

618 (39.8)

Poor

337 (26.9)

169 (56.7)

506 (32.6)

Noise sensitive
I fully agree

213 (16.9)

42 (14)255 (16.3)

I somewhat agree

321 (25.4)

76 (25.4)

397 (25.4)

rather not correct271 (21.5)

68 (22.7)

339 (21.7)

Not correct at all

457 (36.2)

113 (37.8)

570 (36.5)

χ 2 (2 df) = 104.73< 0.001χ 2 (3 df) = 1.53

0.675

In Table 2 the univariate descriptive statistics with all noise indicators and body mass index (BMI) is
broken down by hypertension diagnosis ever. Among the tested exposure variables, significant
associations are seen with main road Lden, main road and railway Lnight and the Intermittency Ratio(IR). BMI is a highly significant health variable.
Table 2. Statistical description of exposure variables used in the models by hypertension
Continous

Reported hypertension

Total sample

Ranksum test

variables

No (median, IQR) Yes (median, IQR)

median, IQR

P value

Main road, Lden
median(IQR)

0.034
48 (41.2,56.9)

49.5 (42.2,59.8)

48.1 (41.4,57.4)

Railway, Lden
median(IQR)

0.074
53.7 (42.8,61.8)

56.4 (45.2,64.8)

54.1 (43.3,62.8)

Highway, Lden
median(IQR)

0.939
52.1 (46.2,59.1)

52.4 (44.1,59.8)

52.3 (45.6,59.2)

Main road, Lnight
median(IQR)

0.037
41.4 (36.1,49.5)

44.8 (36.2,53)

41.8 (36.1,49.9)

Railway, Lnight
median(IQR)

0.013
49.9 (42.2,57.1)

52.4 (44.7,59.8)

50.4 (42.8,57.7)

Highway, Lnight
median(IQR)

0.923
45.4 (40,52.2)

45.7 (38.9,53.1)

45.4 (39.6,52.5)

Main road (Leq-L50)
median(IQR)

0.091
13.5 (6.2,26.2)

15.3 (8.3,28.7)

13.7 (6.4,26.6)

Railway (Leq-L50)
median(IQR)

0.363
42.6 (33.9,53)

44.1 (34.3,54.8)

43 (33.9,53.5)

Highway (Leq-L50)
median(IQR)

0.227
0.5 (0.3,1.1)

0.6 (0.4,1.2)

0.6 (0.3,1.1)

Intermittency Ratio
median(IQR)
Body mass index

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0.003
34 (7,61)

42 (16,64)

37 (8,62)<0.001

median(IQR)

23.1 (21.1,25.4)

25.7 (23.5,28.4)

23.5 (21.3,25.9)

3.2 Detailed descriptive Statistics of the alternative Sound Indices
Table 3 describes the correlation between the classical sound exposure indices and the new ones
(Leq-L50 and IR). The IR shows a gradual relationship in the expected direction with the highest
correlation with main road (r=0.6) and railway Lden (r=0.5). The IR and highway even shows anegative correlation. The correlations of the Leq-L50 indicators with its Lden equivalents are
generally lower, when compared with the IR.
Table 3. Correlation statistics of the sound exposure variables
Highway, Lden
Main road, Lden
Railway, Lden
Highway (Leq-L50)
Main road (Leq-L50)
Railway (Leq-L50)
Intermittency ratio

Highway, Lden Main road, Lden Railway, Lden Highway (Leq-L50) Main road (Leq-L50) Railway (Leq-L50) Intermittency ratio
1.00
-0.03
0.06
0.08
0.00
-0.01
-0.31
-0.03
1.00
0.40
0.18
0.50
0.15
0.60
0.06
0.40
1.00
0.17
0.22
0.36
0.50
0.08
0.18
0.17
1.00
0.36
0.01
0.16
0.00
0.50
0.22
0.36
1.00
-0.10
0.32
-0.01
0.15
0.36
0.01
-0.10
1.00
0.26
-0.31
0.60
0.50
0.16
0.32
0.26
1.00

Figure 1a describes the distribution of the IR across several road type. Main roads show higher
intermittency characteristics than the highway. Notably, smaller roads exhibit also significant
proportions of high intermittency characteristics. Also across the investigated areas, differences in the
distribution of the IR can be noted (Figure 1b). The Northern main valley shows higher prevalence of
relevant intermittency episodes, but also in side valleys (South) this feature is relevant.

Figure 1a (left) – Distribution of the Intermittency Ratio across the varying types of roads in the sample
Figure 1b (right) – Distribution of the Intermittency Ratio across the geographic features
3.3 Preliminary Results of the Hypertension Model
The multivariable regression models presented here refer to reported diagnosis of hypertension
only. The models are adjusted for age, gender, education, BMI, family history of hypertension,
smoking and air pollution (PM10). Due to the non-linearity of both noise indices, restricted cubic
splines are used in all models. Combined traffic sound exposure in Figure 2 means that only those,
who are exposed to all three sources (main road, railway, highway) are in this analysis. Therefore, thesample is smaller. The odds ratio for Lden becomes significant between 60 and 70 dB (OR =1.58(1.10-2.3)). The IR is not significant (OR between 10 and 40 percent = 1.65(0.88-3.07). The
pseudo R² of the model is 0.33.

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Figure 2a (left) – Intermittency ratio by increments of the combined sound level exposure as Lden
Figure 2b (right) – Combined sound level as Lden by increments of the Intermittency ratio
Total traffic sound exposure in Figures 3 means that participants are exposed to either one, two or
three sources. Therefore, the sample is larger. The odds ratio for Lden becomes significant between
around 57 dB (OR = 1.31(1.01-1.7)). The IR is not overall significant (p=0.13) but the OR between 10
and 50 percent = 1.46(1.01-2.10) is significant due to the smaller confidence intervals. The χ2
contribution is larger than for gender, education, air pollution, smoking or noise sensitivity. The
pseudo R² of the model is also 0.33.

Figure 3a (left) – Intermittency ratio by increments of the total sound level exposure as Lden
Figure 3b (right) – Total sound level as Lden by increments of the Intermittency ratio

4. DISCUSSION
In this analysis, we can confirm the earlier findings (6,7), that the IR shows lower correlations withthe main classical sound indicator Lden as compared to other indicators. Therefore, we did notexperience relevant collinearity problems in the multiple regression models. The highly variable andnot fully normal distribution of the IR indicator across the sample can pose a statistical problem andcategorization may be necessary in some situations. Notably, we tested for interaction between Lden
and IR, but no relevant effect could be observed – which replicates earlier findings (6,7).
The IR was created with the idea in mind to provide an integrated description of a mixed source
exposure situation, which could add to the Lden, where temporal features of the sources are averaged
out. Therefore, this preliminary analysis was restricted to indices capturing the overall sound exposurein this area.
For this purpose, we used two different sound exposure scenarios. In the “Combined exposure”

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scenario, we included only those, who are exposed to all three sources (main road, railway, highway),
which resulted in a much smaller sample (N=718) and consequently lower power.
In the “Total exposure” scenario, we included all participants exposed to one, two or three sources.
While we observed a significant association with Lden in both scenarios, the association with got
stronger in the larger sample with the IR and the OR for the IR between 10 and 50 percent wassignificant (OR=1.46(1.01-2.10)). Obviously, the most important contributors in the hypertension
model were age, family history of hypertension, BMI, and health status. Notably, the IR, did make amodest contribution – compared with other factors in the model like gender, education, air pollution,smoking or noise sensitivity and was close after Lden in terms of importance.
The Leq-L50 indicators exhibit even lower correlations with the Lden (Table 3). Furthermore, they
were not so critical with respect to its distributions – although the observed distributions differedstrongly by source. It may make sense, therefore, to use the Leq -L50 in situations when the effect of
single sources need to be compared. Due to the limited space, we omit to present the larger variationsin the specific association of the three traffic sources with hypertension.
In the paper at Euronoise 2018 (4), where we tested the additional contribution of indicators of
emergence or fluctuation, we found that the most pronounced effects were observed in two-source
models, namely the combination of main road and highway. In these analyses, even both indicators
contributed similar to the model in terms of importance.
In our current analysis, using the IR indicator, we could replicate the importance of this predictor
for two mixed source exposure situations. In both models, the “Total exposure” scenario and the“Combined exposure” scenario, the IR made a contribution of relative importance.
Although this study has several strengths on the exposure assessment side (micro -simulations of
the traffic flow, traffic counting on smaller roads, extended version of ISO 9613 for modeling,calibration against hundreds of spring and autumn measurements across the area), other issues,
discussed in the literature, may still limit conclusions.
The sound exposure was assigned to the most exposed façade. The modeling did not consider larger
buffers (13,14) and the A-weighting could underestimate the disturbing effect of low frequencies(15,16) from trucks against the low background sound on the valley slopes. This may be responsible
for the higher response between 40 and 50 dB Lden.
Eventually, the cross-sectional design limits causal inference – although adjustments were made forthe most important, including also high quality air pollution assignments.
Nevertheless, given the current not well-established state of the noise-hypertension relationship,
the results of this preliminary analysis add to the knowledge and show the feasibility to use further
supplementary noise indicators beyond Lden (17).

5. CONCLUSIONS
There is still place for further research implementation of acoustic indices in health related surveys.
At least, it may help to establish a more firm relationship between traffic noise with hypertension,
especially in two-source situations, where the traffic dynamics differ substantially. This requires more
researcher applying alternative sound indicators in different data sets to verify or refute the single
findings from the Swiss and BBT-studies.

ACKNOWLEDGEMENTS
This survey was conducted within the framework of a legally required EHIA and got EU support via
the BBT Company, which was responsible for the EHIA. We still thank our colleagues for their earlier
work: the original noise mapping and the percentile calculations were done by INTEC (now WAVE),
Ghent University.

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