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Environment International 114 (2018) 160–166
Contents lists available at
ScienceDirect
Environment International
journal homepage:
www.elsevier.com/locate/envint
Short-term nighttime wind turbine noise and cardiovascular events: A
nationwide case-crossover study from Denmark
Aslak Harbo Poulsen
a,
, Ole Raaschou-Nielsen
a,c
, Alfredo Peña
b
, Andrea N. Hahmann
b
,
Rikke Baastrup Nordsborg
a
, Matthias Ketzel
c
, Jørgen Brandt
c
, Mette Sørensen
a
a
T
⁎
Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
DTU Wind Energy, Technical University of Denmark, Roskilde, Denmark
c
Department of Environmental Science, Aarhus University, Roskilde, Denmark
b
A R T I C L E I N F O
Handling Editor: Martí Nadal
Keywords:
Stroke
Myocardial infarction
Wind turbines
Noise
Epidemiology
A B S T R A C T
Aims:
The number of people exposed to wind turbine noise (WTN) is increasing. WTN is reported as more
annoying than traffic noise at similar levels. Long-term exposure to traffic noise has consistently been associated
with cardiovascular disease, whereas effects of short-term exposure are much less investigated due to little day-
to-day variation of e.g. road traffic noise. WTN varies considerably due to changing weather conditions allowing
investigation of short-term effects of WTN on cardiovascular events.
Methods and results:
We identified all hospitalisations and deaths from stroke (16,913 cases) and myocardial
infarction (MI) (17,559 cases) among Danes exposed to WTN between 1982 and 2013. We applied a time-
stratified, case-crossover design. Using detailed data on wind turbine type and hourly wind data at each wind
turbine, we simulated mean nighttime outdoor (10–10,000 Hz) and nighttime low frequency (LF) indoor WTN
(10–160 Hz) over the 4 days preceding diagnosis and reference days. For indoor LF WTN between 10 and 15 dB
(A) and above 15 dB(A), odds ratios (ORs) for MI were 1.27 (95% confidence interval (CI): 0.97–1.67;
cases = 198) and 1.62 (95% CI: 0.76–3.45; cases = 21), respectively, when compared to indoor LF WTN below
5 dB(A). For stroke, corresponding ORs were 1.17 (95% CI: 0.95–1.69; cases = 166) and 2.30 (95% CI:
0.96–5.50; cases = 15). The elevated ORs above 15 dB(A) persisted across sensitivity analyses. When looking at
specific lag times, noise exposure one day before MI events and three days before stroke events were associated
with the highest ORs. For outdoor WTN at night, we observed both increased and decreased risk estimates.
Conclusion:
This study did not provide conclusive evidence of an association between WTN and MI or stroke. It
does however suggest that indoor LF WTN at night may trigger cardiovascular events, whereas these events
seemed largely unaffected by nighttime outdoor WTN. These
findings
need reproduction, as they were based on
few cases and may be due to chance.
1. Introduction
As the number of wind turbines (WT) has increased so has concern
about potential health effects, particularly since WT noise (WTN) has
been reported to be more annoying than noise from other sources at
similar levels (Janssen
et al., 2011).
Also, some (Schmidt
and Klokker,
2014)
but not all (Jalali
et al., 2016; Michaud et al., 2016c)
studies have
found an association with sleep disturbances.
Noise can act as a stressor and provoke a typical stress response,
including hyperactivity of the sympathetic autonomic nervous system
and activation of the hypothalamus-pituitary-adrenal axis. Nighttime
noise exposure is considered particularly hazardous (Babisch
et al.,
2005; WHO, 2009)
and has been associated with disturbance of sleep,
from full awakenings to unconscious autonomic perturbations, such as
sleep stage changes and body movements (Griefahn
et al., 2008;
Miedema and Vos, 2007);
the latter from outdoor noise levels of down
to 30 dB (WHO,
2009).
Nighttime noise exposure has been associated
with reduced cardiac parasympathetic tone, high blood pressure, en-
dothelial dysfunction, oxidative stress and increased levels of stress
hormones shortly after noise exposure or on the morning after (Graham
et al., 2009; Schmidt et al., 2013).
Evidence from cardiac arousals does
not suggest pronounced habituation to nighttime noise (Basner
et al.,
2011; Muzet, 2007).
Long-term residential exposure to transportation
noise has consistently been associated with increased risk of cardio-
vascular diseases (Halonen
et al., 2015; Sorensen et al., 2011; Vienneau
et al., 2015),
whereas it is unknown whether short-term exposure to
⁎
Corresponding author at: Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.
E-mail address:
[email protected]
(A.H. Poulsen).
https://doi.org/10.1016/j.envint.2018.02.030
Received 10 October 2017; Received in revised form 1 February 2018; Accepted 17 February 2018
0160-4120/ © 2018 Elsevier Ltd. All rights reserved.
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A.H. Poulsen et al.
Environment International 114 (2018) 160–166
noise can trigger a cardiovascular event due to lack of studies (Recio
et al., 2016).
These results are, however, not readily applicable to WTN:
WTN levels are typically lower than those reported in relation to health
effects of traffic noise. and WTN is reported as more annoying than
traffic noise at similar sound levels (Janssen
et al., 2011).
Also, WTs are
typically erected in rural areas and amplitude modulation gives WTN a
rhythmic quality different from that generated by car tires. Further-
more, levels of WTN depend on wind speed and direction and hence
vary more unpredictably than road traffic noise, permitting investiga-
tion of acute effects of noise exposure. Such effects are virtually un-
explored, even though factors affected by noise exposure, including
increased blood pressure and oxidative stress, are believed to be im-
portant triggers of stroke and myocardial infarction (MI) (Biasucci
et al., 2008; McColl et al., 2009).
Studies from Canada (1238 participants) and Sweden and the
Netherlands (1755 participants) investigated associations between
long-term outdoor WTN and self-reported cardiovascular diseases (high
blood pressure and heart disease) (Michaud
et al., 2016b; E Pedersen
2011).
The study from Canada additionally investigated hair cortisol
levels, resting heart rate and blood pressure collected at the time of
interview (Michaud
et al., 2016a).
Neither study found any association.
However, as most scientific literature on WTN (Schmidt
and Klokker,
2014),
the studies were cross-sectional, relied on self-reported data and
had few participants potentially exposed to WTN levels above
40–45 dB. Also, their exposure metrics did not reflect day-to-day var-
iations in WTN, making the results relevant mainly for long-term health
effects.
Denmark is a densely-populated country with a high number of
residents living close to WTs. This provides a unique opportunity to
investigate acute effects of WTN on stroke and MI.
2. Methods
2.1. Study base and noise exposure assessment
The study was based on the Danish population, where all citizens
since 1968 have been assigned a personal identification number by the
Central Population Register, allowing residents to be tracked in and
across all Danish health and administrative registers (CB
Pedersen
2011).
We identified all WTs (7860) in operation in Denmark any time
between 1980 and 2013, from the administrative Master Data Register
of Wind Turbines maintained by the Danish Energy Agency. The reg-
ister, to which reporting is mandatory for all WT owners, contained
cadastral codes and geographical coordinates for each WT from the WT
owner. For WTs in operation at the time of data extraction, the register
also contained coordinates from the Danish Geodata Agency. In case of
disagreement between the recorded geographical locations, the WT
location was validated against aerial photographs and historical topo-
graphic maps of Denmark. We excluded 517 offshore WTs and 87 WTs
for which a credible location could not be established. Moreover, 314
WTs wrongly recorded in the Master Data Register were assigned co-
ordinates based on maps and aerial photographs. Information on
height, model, type and operational settings (when relevant) was
gathered for all WTs, based on which each WT was classified into one of
99 noise spectra classes detailing the noise spectrum from 10 Hz to
10,000 Hz in thirds of octaves for wind speeds from 4 to 25 m/s. These
noise classes were formed from existing measurements of sound power
for Danish WTs (details in (Backalarz
et al., 2016; Søndergaard and
Backalarz, 2015)).
For each WT location, we estimated the hourly wind speed and
direction at hub height for the period 1982–2013, using mesoscale
model simulations performed with the Weather Research and
Forecasting model (Hahmann
et al., 2015; Peña and Hahmann, 2017).
From these simulations, we also extracted the temperature and the re-
lative humidity at 2 m height as well as the atmospheric stability at
161
each WT location.
The applied noise exposure modelling has been described in details
elsewhere (Backalarz
et al., 2016).
In summary, we used a two-step
approach. First, we identified buildings eligible for detailed noise
modelling, corresponding to all dwellings in Denmark that could ex-
perience at least 24 dB(A) outdoor noise or 5 dB(A) indoor low fre-
quency (LF, 10–160 Hz) noise under the unrealistically extreme sce-
nario that all WTs ever standing in Denmark were simultaneously
operating at a wind speed of 8 m/s with downwind sound propagation
in all directions. In the second step, we performed a detailed modelling
of noise exposure for the 553,066 buildings identified in step one: We
calculated noise levels in 1/3 octave bands from 10 to 10,000 Hz using
the Nord2000 noise propagation model (Kragh
et al., 2001),
taking into
account time varying wind speed and direction, temperature, relative
humidity and atmospheric stability. The model has been successfully
validated for WTs (Søndergaard
et al., 2009).
For each dwelling, the
noise contribution from all WTs within a 6000 meters radius was cal-
culated hour by hour. For each night these modelled values were then
aggregated over the period 10 pm to 7 am (nighttime), which is con-
sidered the most relevant time-window, because people are most likely
to be at home as well as sleep during these hours. We calculated out-
door A-weighted sound pressure level at the front door of all buildings.
We also calculated A-weighted indoor LF (10–160 Hz) sound pressure
level for each dwelling using existing data on sound attenuation in this
frequency range. All dwellings were classified into one of six sound
insulation classes based on building attributes in the Building and
Housing register (Christensen,
2011):
“1�½-story
houses” (residents as-
sumed to sleep on the second
floor),
“light
façade” (e.g. wood),
“aerated
concrete” (and similar materials including timber framing),
“farm
houses” (remaining buildings in the registry classified as farms),
“brick
buildings” and
“unknown”
(assigned the mean attenuation value of the
five
previous classes). For each of the six classes, the frequency-specific
attenuation values subtracted from the outdoor noise can be found
elsewhere (Backalarz
et al., 2016).
For each dwelling, we determined a validity score for the noise
estimate for each night. This score summed up information for all
contributing WTs on the number of measurements used to determine
the WTN spectra class, and how closely the simulated meteorological
conditions of each night resembled the conditions under which the
relevant WTN spectra were measured.
2.2. Study population and identification of outcomes
From the Danish Civil Registration System (CB
Pedersen 2011),
we
identified our study population defined as all adults (≥18 years) living
in a dwelling that had on two separate days over the period 1982–2013
experienced at least 1 h with outdoor WTN above 30 dB(A). The last
criteria reduced the population while retaining all potentially high
exposed. In this study population, we identified all diagnoses of stroke
(International classification of disease (ICD) 10: I61, I63, I64 and ICD 8:
431–434 and 436) or MI (ICD 10: I21 and ICD 8: 410) from the Danish
National Patient Register (Lynge
et al., 2011)
and the Danish Register of
Cause of Death (Helweg-Larsen,
2011).
We excluded outpatients and
patients found dead, because an exact date of event could not be re-
liably established. Admissions separated by at least 28 days were
counted as separate events. We additionally required no hospitalisation
for any reason in the 28 days preceding diagnosis. Also, we excluded
cases who, at the time of diagnosis, had lived < 18 months at their
present address or if the closest WT had not been the same for the past
18 months (to ensure that any observed effect was unrelated to en-
vironmental changes from moving address or changes in nearby WTs).
The study was approved by the Danish Data Protection Agency
(J.nr: 2014-41-2671). By Danish Law, ethical approval and informed
consent are not required for entirely register-based studies not invol-
ving contact with study participants.
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A.H. Poulsen et al.
Environment International 114 (2018) 160–166
Table 1
Characteristics of each of the study populations for all case events and for those associated with high levels of nighttime wind turbine noise exposure.
Myocardial infarction
All case events
(N = 15,092)
> 36 dB(A) outdoor
noise
(N = 374)
28%
83%
41%
42%
17%
32%
39%
30%
11%
12%
77%
34%
55%
11%
48%
1%
3%
13%
29%
6%
95%
5%
–
–
14%
64%
21%
2%
0%
> 10 dB(A) LF indoor
noise
(N = 219)
30%
80%
38%
41%
21%
9%
27%
64%
10%
11%
79%
38%
54%
7%
65%
3%
14%
10%
4%
5%
67%
29%
4%
0%
5%
21%
59%
12%
2%
Stroke
All case events
(N = 14,623)
> 36 dB(A) outdoor
noise
(N = 302)
35%
81%
33%
46%
21%
18%
46%
36%
11%
15%
74%
34%
56%
11%
41%
1%
4%
16%
33%
5%
93%
7%
0%
–
18%
54%
25%
3%
0%
> 10 dB(A) LF indoor
noise
(N = 181)
40%
89%
33%
44%
23%
7%
31%
62%
7%
10%
82%
40%
57%
2%
66%
1%
18%
10%
4%
1%
55%
43%
3%
–
4%
19%
65%
12%
1%
Women
First hospital admission
a
Age at diagnosis
< 65 years
65–80 years
≥80
years
Year of diagnosis
1982–1992
1993–2003
2004–2013
Living duration at same residence
1.5–5 years
5–10 years
≥10
years
Type of dwelling
Farm
Single–family detached homes
Others
Sound insulation class
b
1 1/2 story building
Light façade
Aerated concrete
Farmhouse
Brick façade
Unknown
Distance to closest wind turbine
< 500 m
500–1000 m
1000–2000 m
≥2000
m
Total height, closest wind turbine
< 25 m
25–50 m
50–75 m
75–100 m
≥100
m
Tree coverage, 500 meters radius from
dwelling
< 1%
1–10%
10–25%
≥25%
Distance to major road
c
< 2000 m
≥2000
m
a
b
c
32%
81%
36%
41%
22%
22%
38%
40%
15%
15%
70%
14%
64%
22%
32%
1%
4%
6%
47%
10%
17%
47%
35%
1%
16%
53%
25%
5%
1%
45%
83%
27%
42%
31%
13%
41%
46%
18%
15%
67%
13%
58%
28%
28%
2%
4%
6%
19%
12%
16%
46%
37%
1%
14%
52%
26%
7%
1%
13%
40%
39%
8%
59%
41%
13%
56%
29%
2%
52%
48%
14%
57%
24%
5%
53%
47%
14%
39%
40%
8%
60%
40%
16%
55%
27%
2%
55%
45%
15%
52%
29%
3%
49%
51%
First time diagnosed with myocardial infarction or stroke respectively.
All dwellings where classified into one of six sound insulation classes based on building attributes.
Major road defined as
≥5000
vehicles per day.
2.3. Potential confounders and effect modifiers
Based on review of existing literature and biological plausibility we
a priori selected the following potential confounders: temperature,
humidity and air pollution, which have been found or suggested to be
associated with cardiovascular events (Claeys
et al., 2017; Zeng et al.,
2017),
as well as associated with WTN through associations with sound
propagation or wind speed. As potential effect modifiers, we in-
vestigated tree coverage near each dwelling and road traffic. Ad-
ditionally, we included wind speed that could conceivable mask WTN
and affect cardiovascular risk.
We divided Denmark into 25 km
∗
25 km cells, providing cells that
contained at least one WT. From the simulated data for all WT locations
within each cell (Hahmann
et al., 2015),
the daily mean temperature
and relative humidity at 2 m height as well as wind speed at 10 m
height were calculated. Furthermore, for each dwelling we calculated
the percentage of land covered by forest, thicket, groves, single trees
162
and hedgerows within a 500 m radius using GIS data from the Danish
Geodata Agency. For each dwelling, we assigned daily, regional back-
ground concentration of air pollution (NO
x
) from the nearest of seven
Danish locations for which background air pollution was modelled as
hourly time series for the complete investigation period, using the va-
lidated Danish THOR air pollution modelling system. Lastly, as a proxy
for local air pollution and road traffic noise exposure, we identified the
distance from each dwelling to the nearest road with an average daily
traffic count of
≥5000
vehicles in 2005.
2.4. Statistical analysis
We calculated odds ratios (ORs) for stroke and MI in a time-strati-
fied
case-crossover design using conditional logistic regression. The
case-crossover study is well-suited to investigate effects of an inter-
mittent exposure on the onset of acute disease outcomes, and is unique
in that the case serves as his/her own control (Janes
et al., 2005;
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A.H. Poulsen et al.
Environment International 114 (2018) 160–166
Table 2
Associations between short-term residential exposure to outdoor and indoor wind turbine noise during nighttime and myocardial infarction (MI) and stroke.
Exposure
Outcome
Mean lag 1–4 days
Case events
Outdoor wind turbine noise
< 24 dB(A)
24–30 dB(A)
30–36 dB(A)
36–42 dB(A)
≥42
dB(A)
Outdoor wind turbine noise
< 24 dB(A)
24–30 dB(A)
30–36 dB(A)
36–42 dB(A)
≥42
dB(A)
Indoor LF
c
wind turbine noise
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
Indoor LF
c
wind turbine noise
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
a
b
c
Lag 1
95% CI
b
OR
a
95% CI
b
Lag 2
OR
a
95% CI
b
Lag 3
OR
a
95% CI
b
Lag 4
OR
a
95% CI
b
OR
a
MI
8862
4418
1438
310
64
Stroke
8525
4431
1364
267
35
MI
14,042
831
198
21
Stroke
13,682
759
166
15
1
1.02
1.27
2.30
(0.89–1.17)
(0.95–1.69)
(0.96–5.50)
1
0.95
1.03
0.96
(0.85–1.07)
(0.81–1.31)
(0.45–2.04)
1
0.99
1.39
1.40
(0.88–1.11)
(1.10–1.75)
(0.72–2.73)
1
0.95
1.24
1.85
(0.85–1.07)
(0.98–1.56)
(0.97–3.54)
1
1.07
1.09
1.35
(0.95–1.20)
(0.86–1.38)
(0.69–2.66)
1
1.04
1.27
1.62
(0.91–1.18)
(0.97–1.67)
(0.76–3.45)
1
1.04
1.20
1.54
(0.94–1.17)
(0.96–1.51)
(0.87–2.72)
1
0.96
1.17
1.28
(0.86–1.08)
(0.93–1.47)
(0.71–2.34)
1
0.93
0.90
0.95
(0.83–1.04)
(0.72–1.14)
(0.54–1.67)
1
1.05
1.23
1.03
(0.94–1.18)
(0.98–1.53)
(0.58–1.82)
1
1.01
0.95
1.19
1.32
(0.95–1.07)
(0.84–1.08)
(0.92–1.54)
(0.65–2.67)
1
0.97
1.08
1.08
1.17
(0.92–1.03)
(0.98–1.18)
(0.88–1.32)
(0.66–2.08)
1
0.99
0.96
1.03
1.32
(0.94–1.05)
(0.88–1.06)
(0.84–1.25)
(0.77–2.27)
1
1.03
0.97
1.09
1.71
(0.98–1.08)
(0.88–1.07)
(0.90–1.33)
(0.97–3.01)
1
1.01
0.97
0.97
1.25
(0.95–1.06)
(0.88–1.07)
(0.79–1.18)
(0.70–2.26)
1
0.96
0.94
0.95
0.54
(0.90–1.02)
(0.84–1.06)
(0.74–1.20)
(0.30–0.95)
1
1.00
1.02
0.98
1.13
(0.95–1.05)
(0.93–1.11)
(0.81–1.19)
(0.75–1.72)
1
0.93
0.95
0.95
0.94
(0.88–0.98)
(0.87–1.04)
(0.79–1.15)
(0.61–1.42)
1
1.01
0.96
0.96
0.87
(0.96–1.06)
(0.88–1.06)
(0.79–1.16)
(0.57–1.33)
1
0.97
1.03
1.07
0.79
(0.92–1.03)
(0.94–1.13)
(0.89–1.30)
(0.52–1.18)
OR: odds ratio; adjusted for ambient temperature (°C), relative humidity (%) and air pollution (NO
X
); all included linearly.
CI: confidence interval.
Low frequency: 10–160 Hz.
Maclure, 1991).
For each case event day, reference days were defined as
all days within the month of diagnosis that were the same weekday. Our
a priori defined main exposure metric was mean nighttime WTN (L
pA
and L
pALF
) over the period 1–4 days before the event; a time-span pre-
viously investigated for noise exposure (Recio
et al., 2016)
and air
pollution (Andersen
et al., 2010).
We also investigated exposure on
each of the four preceding days separately. Noise exposure was in-
cluded categorically in the models: outdoor (< 24, 24– < 30,
30– < 36, 36– < 42 and
≥42
dB(A)) and indoor LF (< 5, 5– < 10,
10– < 15, and
≥15
dB(A)). We calculated ORs crude and adjusted for
NO
x
, temperature and relative humidity (averaged over same time-
period as WTN in each model). In two additional analyses, we included
wind speed and the second-degree polynomial of covariates.
We performed sensitivity analyses restricting the analyses to: 1)
cases with no previous records of stroke or MI, 2) cases diagnosed in
year 2000 or later, 3) cases living with < 1% tree coverage within
500 m of the dwelling (to avoid masking of the WTN noise from nearby
vegetation; we applied a 500 m buffer as we assumed that vegetation
further apart would be near indiscernible from background noise) and
4) cases living > 2000 m from a road with
≥5000
vehicles per day.
Finally, we conducted analyses restricted to WTN estimates with high
validity scores. Data were analysed using SAS 9.3 (SAS Institute Inc.,
Cary, NC, USA).
3. Results
We identified 17,559 events of MI and 16,913 events of stroke in the
study population and excluded case events where the address (816 MI
and 857 strokes) or nearest WT (1651 MI and 1433 strokes) had
changed in the 18 months preceding diagnosis, yielding for analysis
15,092 MI events and 14,623 stroke events, corresponding to 13,343
and 13,026 persons, respectively.
Compared to all events, persons with high levels of nighttime WTN
prior to their event were more likely to be male, younger, live in a
building classified as a farm, have lived at the same address for > 10
years and have lived further from roads with dense traffic (Table
1).
In
addition, those with high indoor LF WTN levels had taller WTs near
163
their home and were more likely to live in 1�½-story houses. We found
high correlations between all investigated WTN exposures (Supplement
Table 1).
Table 2
shows associations between short-term exposure to night-
time outdoor WTN and hospitalisation or death from MI and stroke. The
highest level (≥42 dB(A)) over the past 4 days was negatively asso-
ciated with risk for MI (OR: 0.54, 95% confidence interval (CI):
0.30–0.95) and positively with risk for stroke (1.32, 95% CI:
0.65–2.67). Most of the study population was exposed to outdoor
nighttime WTN below 24 dB(A), and only 64 MI events and 35 stroke
events were associated with exposures exceeding 42 dB(A).
For nighttime indoor LF WTN above 10 dB(A), the risk tended to
increase with increasing noise exposure over the past four days, with
ORs for MI of 1.27 (95% CI: 0.97–1.67) for 10–15 dB(A) and 1.62 (95%
CI: 0.76–3.45) for > 15 dB(A), and similarly for stroke the corre-
sponding ORs were 1.27 (95% CI: 0.95–1.69) and 2.30 (95% CI:
0.96–5.50). In trend tests where we included mean exposure over past
4 days as a linear variable, we found no statistically significant trends
(results not shown). Most of the study population was exposed to indoor
LF WTN below 5 dB(A), and only few cases where exposed to > 15 dB
(A). Analyses without adjustment for covariates (Supplementary
Table 2), or further adjusted for their second-degree polynomial or for
wind speed, did not substantially change risk estimates (results not
shown).
When looking at the lag times for both outdoor and indoor night-
time WTN, the highest ORs were associated with noise exposure one
day before MI events and three days before stroke events (Table
2).
Results of sensitivity analyses according to diagnosis after year
2000,
first
hospitalisation, tree coverage, and proximity to major roads
did not deviate markedly from the main analysis results for indoor LF
WTN (Table
3
and Supplement Table 3). Analyses of indoor LF WTN
with high validity score reduced the ORs in the 10–15 dB(A) category
but increased the ORs associated with exposures above 15 dB(A).
4. Discussion
This study found high levels of indoor nighttime LF WTN over the
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A.H. Poulsen et al.
Environment International 114 (2018) 160–166
Table 3
Sensitivity analyses of the associations between short-term residential exposure to indoor wind turbine noise exposure during nighttime (mean from day 1 to 4) and myocardial infarction
(MI) and stroke.
Sensitivity analysis
Indoor LF
a
wind turbine noise (mean lag 1–4)
Myocardial infarction
Case events
Main analysis (all)
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
Diagnosed after year 2000
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
First hospital admission for stroke/MI
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
< 1% tree coverage within 500 meters radius
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
> 2000 m to major road
e
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
Only exposure estimates with high validity score
f
< 5 dB(A)
5–10 dB(A)
10–15 dB(A)
≥15
dB(A)
a
Stroke
95% CI
c
Case events
OR
b
95% CI
c
OR
b
14,042
831
198
21
7264
638
153
16
11,379
694
158
18
1891
104
30
–
6698
492
124
7
4398
447
92
8
1
1.04
1.27
1.62
1
1.13
1.37
1.62
1
1.10
1.21
1.59
1
0.80
1.57
–
d
1
1.11
1.29
1.11
1
1.08
0.92
2.34
(0.91–1.18)
(0.97–1.67)
(0.76–3.45)
13,682
759
166
15
8286
605
132
12
11,395
646
150
11
1886
93
24
4
6360
431
100
12
4275
390
88
9
1
1.02
1.27
2.30
1
0.99
1.17
2.48
1
1.02
1.29
2.01
1
1.07
1.18
2.79
1
0.98
1.34
2.68
1
0.91
1.10
2.72
(0.89–1.17)
(0.95–1.69)
(0.96–5.50)
(0.98–1.31)
(1.01–1.87)
(0.68–3.88)
(0.85–1.15)
(0.86–1.59)
(0.97–6.31)
(0.95–1.27)
(0.89–1.65)
(0.70–3.58)
(0.89–1.18)
(0.95–1.74)
(0.75–5.41)
(0.57–1.13)
(0.74–3.35)
(0.75–1.53)
(0.55–2.54)
(0.47–16.61)
(0.94–1.32)
(0.91–1.82)
(0.40–3.04)
(0.82–1.17)
(0.94–1.93)
(0.99–7.24)
(0.91–1.28)
(0.64–1.33)
(0.67–8.24)
(0.76–1.09)
(0.75–1.60)
(0.62–11.89)
Low frequency: 10–160 Hz.
OR: odds ratio; adjusted for ambient temperature (°C), relative humidity (%) and air pollution (NOX); all included linearly.
c
CI: confidence interval.
d
This category contained less than three cases and was, therefore, pooled with the category above (10–15 dB(A)), because of Danish anonymity legislation.
e
Major road: road with
≥5000
vehicles/day.
f
Includes only case and reference periods with validity score better than the median among those with exposures above 10 dB(A). The validity score reflects the estimated uncertainty
associated with all aspects of noise estimation at a specific address and day.
b
preceding days to be associated with increased risk estimates for both
MI and stroke, whereas for outdoor nighttime WTN we observed higher
risk estimates for stroke and lower risk estimates for MI. The number of
cases exposed to > 15 dB(A) indoor LF WTN was, however, small, and
the CIs generally spanned one.
While two studies have investigated long-term exposure to WTN and
cardiovascular disease (Michaud
et al., 2016b; E Pedersen 2011),
no
study has investigated short-term associations. For transportation noise,
a recent case-crossover study from Madrid, using a citywide noise
measure, found death from MI and cerebrovascular events (in people
above 65 years of age) to be associated with noise exposure 0–1 days
before an event (Recio
et al., 2016).
The study, however, investigated a
range of exposure/endpoint combinations, and direct comparison to the
present study is difficult, as they investigated only mortality and used
ecological exposure data. Also, noise exceeded 55 dB on all nights,
which is substantially higher than the noise levels in the present study.
We found no short-term associations between nighttime exposure to
outdoor WTN below 36 dB(A) or indoor LF WTN below 10 dB(A) and
risk of hospitalisation or death from MI or stroke. For outdoor WTN
above 42 dB(A) the OR was decreased for MI but increased for stroke.
We are not aware of plausible biological mechanisms to explain a
protective effect of WTN; particularly one that would affect the risk of
MI and stroke in opposite direction, and we consider that the associa-
tion between MI and high levels of nighttime WTN is likely to be a
chance
finding.
Among cases exposed to indoor nighttime WTN above
10 dB(A), we found all ORs to be above unity, with the highest esti-
mates in the highest exposure categories. This tendency was seen for
both MI and stroke, and while the elevation in 10–15 dB(A) category
disappeared in a sensitivity analysis of only the most valid WTN data,
the elevated risk associated with the highest exposure was consistent
across a range of sensitivity analyses. However, we cannot rule out
chance as an explanation for the observed results, as the number of case
events preceded by the highest exposure levels was small and the wide
CIs spanned one.
An important strength of our study is the nationwide design, in-
cluding all cases of MI and stroke in Denmark with relevant WTN ex-
posure since 1982. The identification of cases and addresses from high
quality registers (Christensen,
2011; Lynge et al., 2011; CB Pedersen
2011)
and modelled noise and weather parameters minimized the po-
tential for participation or information biases. Additionally, we esti-
mated noise levels specifically for nighttime, when people are most
likely to be at home sleeping. A further strength is the detailed mod-
elling of day-to-day residential nighttime WTN, based on hourly wind
speed and direction at each WT position, combined with detailed WTN
spectra. Furthermore, we estimated the potentially more biologically
relevant indoor noise exposure, accounting for different housing sound
insulation properties. We were, however, only able to differentiate into
few insulation categories and had to classify each dwelling based on
164
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A.H. Poulsen et al.
Environment International 114 (2018) 160–166
relatively crude information. There is inevitably uncertainty in the
modelled indoor and outdoor exposure metrics. As this is unrelated to
case status, it is unlikely to bias the highest exposure category away
from the null, while it may do so for intermediate categories. Accord-
ingly, sensitivity analyses restricted to the most valid noise exposure
measures did not decrease the ORs pertaining to LF WTN above 15 dB
(A).
The applied case-crossover design, with reference days temporally
close to the date of diagnosis, controls perfectly for constant and slow
varying characteristics related to individuals (such as gender, education
and medical conditions) or dwellings. We adjusted for potential en-
vironmental confounders, including wind speed, and although residual
confounding due to the spatial resolution of these cannot be ruled out
entirely, it is unlikely to have had a major effect on the risk estimates, as
these were virtually unaffected by adjustment for our environmental
confounders. Finally, the main limitation of the study is that despite
including all relevant cases in Denmark, statistical power was impaired
by having relatively few cases with high WT noise exposure. As the
number of highly exposed cases is likely to be low in all populations,
more studies should be conducted to facilitate meta-analysis. In addi-
tion, laboratory or
field
studies with direct monitoring of cardiovas-
cular parameters and noise exposure might be informative.
4.1. Conclusions
The results did not show conclusive evidence of an association be-
tween nighttime WTN and MI or stroke. However, for the relatively few
situations with high indoor LF WTN, higher risk estimates were con-
sistently observed. A similar association was not consistently seen for
outdoor WTN. The results indicate that WTN penetrating residences at
night may act as a trigger of MI and stroke. The results may be due to
chance and justify no
firm
conclusion before reproduced in other po-
pulations.
Acknowledgements
We wish to express our gratitude to DELTA, who even in the face of
enormous datasets, has shown great expertise, diligence and ingenuity
in all steps of the process towards estimating detailed wind turbine
noise data useable for the epidemiological analyses. We are also in-
debted to Geoinfo A/S who made it possible to extract the GIS in-
formation relating to all address point covered in our study.
Funding
This study was supported by a grant [J.nr. 1401329] from the
Danish Ministry of Health (the grant was co-funded by the Danish
Ministry of Food and Environment and the Danish Ministry of Energy,
Utilities and Climate). The funding source had no role in the study
design, in the collection, analysis or interpretation of data, in the
writing the paper or in the decision to submit the paper for publication.
Conflict of interests
All authors received a grant from Danish Ministry of Health for
conducting the present study (J.nr. 1401329). All authors report no
conflicts of interest.
Authors' contributions
MS conceived the study. AHP and ORN contributed to study con-
ception and design. AHP analysed the data and drafted the manuscript.
AP and AH provided wind and climate data. MK and JB provided road
traffic and air pollution data. RBN provided GIS data. All authors par-
ticipated in interpreting results, revising the manuscript and approved
the
final
submitted version of the paper.
165
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://
doi.org/10.1016/j.envint.2018.02.030.
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