MOF, Alm.del - 2018-19 (1. samling) - Bilag 466: Orientering om offentliggørelse af de sidste to af i alt seks atikler om Kræftens Bekæmpelses undersøgelse om evt. sammenhæng mellem vindmøllestøj og helbredseffekter, fra sundhedsministeren

Miljø- og Fødevareudvalget 2018-19 (1. samling)
MOF Alm.del Bilag 466
Offentligt

Research

A Section 508–conformant HTML version of this article

is available at

https://doi.org/10.1289/EHP3340.

Long-Term Exposure to Wind Turbine Noise and Risk for Myocardial Infarction

and Stroke: A Nationwide Cohort Study

Aslak Harbo Poulsen,

Ole Raaschou-Nielsen,

1,3

Alfredo Peña,

Andrea N. Hahmann,

Rikke Baastrup Nordsborg,

Matthias Ketzel,

3,5

Jørgen Brandt,

and Mette Sørensen

1,4

Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark

DTU Wind Energy, Technical University of Denmark, Roskilde, Denmark

Department of Environmental Science, Aarhus University, Roskilde, Denmark

Department of Natural Science and Environment, Roskilde University, Roskilde, Denmark

Global Center for Clean Air Research (GCARE), University of Surrey, United Kingdom

ACKGROUND

Noise from wind turbines (WTs) is reported as more annoying than traﬃc noise at similar levels, raising concerns as to whether WT

noise (WTN) increases risk for cardiovascular disease, as observed for traﬃc noise.

BJECTIVES

We aimed to investigate whether long-term exposure to WTN increases risk of myocardial infarction (MI) and stroke.

ETHODS

We identiﬁed all Danish dwellings within a radius 20 times the height of the closest WT and 25% of the dwellings within 20–40 times the

height of the closest WT. Using data on WT type and simulated hourly wind at each WT, we estimated hourly outdoor and low frequency (LF) indoor

WTN for each dwelling and derived 1-y and 5-y running nighttime averages. We used hospital and mortality registries to identify all incident cases of

MI (n = 19,145) and stroke (n = 18,064) among all adults age 25–85 y (n = 717,453), who lived in one of these dwellings for

≥one

year over the pe-

riod 1982–2013. We used Poisson regression to estimate incidence rate ratios (IRRs) adjusted for individual- and area-level covariates.

ESULTS

IRRs for MI in association with 5-y nighttime outdoor WTN >42 (vs. <24) dB(A) and indoor LF WTN >15 (vs. <5) dB(A) were 1.21

[95% conﬁdence interval (CI): 0.91, 1.62; 47 exposed cases] and 1.29 (95% CI: 0.73, 2.28; 12 exposed cases), respectively. IRRs for intermediate cat-

egories of outdoor WTN [24–30, 30–36, and 36–42 dBðAÞ vs. <24 dBðAÞ] were slightly above the null and of similar size: 1.08 (95% CI: 1.04, 1.12),

1.07 (95% CI: 1.00, 1.12), and 1.06 (95% CI: 0.93, 1.22), respectively. For stroke, IRRs for the second and third outdoor exposure groups were similar

to those for MI, but near or below the null for higher exposures.

ONCLUSIONS

We did not

ﬁnd

convincing evidence of associations between WTN and MI or stroke.

https://doi.org/10.1289/EHP3340

Introduction

During recent decades, focus on renewable energy has increased

globally, and advancements in wind energy technologies have

resulted in an increased number of wind turbines (WTs). WT noise

(WTN) has consistently been associated with annoyance among

people living near WTs (Janssen

et al. 2011; Michaud et al. 2016a;

Schmidt and Klokker 2014),

and some studies have indicated that

WTN may also disturb sleep (Schmidt

and Klokker 2014),

although results are inconsistent (Jalali

et al. 2016; Michaud et al.

2016b).

Long-term exposure to transportation noise has been associ-

ated with higher risk for myocardial infarction (MI) and stroke

(Hansell

et al. 2013; Héritier et al. 2017; Sørensen et al. 2011;

Vienneau et al. 2015).

The pathophysiologic pathways are

believed to involve activation of a general stress response and

disturbance of sleep, in turn leading to increases in cardiovascular

risk factors, including blood pressure, endothelial dysfunction,

and oxidative stress, as well as a weakened immune system

(Münzel

et al. 2017a; Schmidt et al. 2013, 2015; van Kempen

and Babisch 2012).

This assocation has raised concerns about

whether WTN may increase risk for cardiovascular disease.

Address correspondence to Mette Sørensen, Danish Cancer Society Research

Center, Strandboulevarden 49, 2100 Copenhagen, Denmark. Telephone: +45

3525 7626. Email:

[email protected]

Supplemental Material is available online (https://doi.org/10.1289/EHP3340).

The authors declare they have no actual or potential competing

ﬁnancial

interests.

Received 9 January 2018; Revised 30 January 2019; Accepted 30 January

2019; Published 0 Month 0000.

Note to readers with disabilities:

EHP

strives to ensure that all journal

content is accessible to all readers. However, some

ﬁgures

and Supplemental

Material published in

EHP

articles may not conform to

508 standards

due to

the complexity of the information being presented. If you need assistance

accessing journal content, please contact

[email protected].

Our staﬀ

will work with you to assess and meet your accessibility needs within 3

working days.

The

ﬁndings

on traﬃc noise and health are not readily applicable

to WTN. Generally, levels of WTN are considerably lower than lev-

els of traﬃc noise found in urban settings; e.g., in Denmark app. 30%

of all dwellings are exposed to levels of road traﬃc noise that exceed

58 dBðAÞ, whereas Danish legislation does not allow WTN to

exceed 44 dBðAÞ (at 8 m=s) at dwellings (except WT owners erect-

ing WTs on their private property) (Miljø-

og Fødevareministeriet

2011; Miljøministeriet 2007).

However, at comparable noise levels,

WTN has been associated with a higher proportion of annoyed resi-

dents than traﬃc noise (Janssen

et al. 2011).

A potential explanation

for this increased annoyance is that WTN depends on wind speed

and direction, making it less predictable for the exposed population

than road traﬃc noise, which often follows a distinct pattern with

high levels during rush hours and lower levels during the night. Also,

amplitude modulation gives WTN a rhythmic quality that is diﬀerent

from that of road traﬃc noise. It has therefore been suggested that the

characteristics of WTN relevant for annoyance may be better cap-

tured by metrics focusing on amplitude modulation or low frequency

(LF) noise (Waye

et al. 2003),

rather than the full spectrum A-

weighted noise, as typically used in studies of traﬃc noise (Jeﬀery

et al. 2014).

Last, WTs are mainly located in rural areas, where the

auditory impact of WTs may be more pronounced due to lower lev-

els of background noise in general, together with an expectation of a

quieter environment among rural residents in comparison with

expectations of people living in more densely populated areas.

Only a few studies have investigated whether residential out-

door WTN is associated with cardiovascular risk factors or dis-

eases. The studies were all of cross-sectional design. A study based

on two Swedish study populations and one Dutch study population,

with a total of 1,755 participants, found no associations between

WTN and self-reported high blood pressure or cardiovascular dis-

ease, for neither A-weighted WTN nor indoor or outdoor WT

annoyance (Pedersen

2011).

Similarly, a Canadian study of 1,238

participants living within 12 km of a WT found no associations

between estimated A-weighted residential WTN and self-reported

prevalent high blood pressure, medication for high blood pressure,

or heart disease (Michaud

et al. 2016a).

Furthermore, the Canadian

00(0) 0 0000

Environmental Health Perspectives

000000-1

MOF, Alm.del - 2018-19 (1. samling) - Bilag 466: Orientering om offentliggørelse af de sidste to af i alt seks atikler om Kræftens Bekæmpelses undersøgelse om evt. sammenhæng mellem vindmøllestøj og helbredseffekter, fra sundhedsministeren

study found no associations between 1-y mean residential outdoor

WTN (modeled) and measurements of blood pressure, heart rate,

or hair cortisol (Michaud

et al. 2016c).

We aimed to prospectively investigate whether long-term res-

idential exposure to WTN is associated with risk for MI and

stroke in a nationwide register-based study. We combined data

on WT position and type, simulated meteorological conditions

and WTN, residential addresses, socioeconomic indicators, and

development of disease over the period 1982–2013.

Methods

The study was based on the Danish population, where all citizens

since 1968 can be tracked in and across all Danish health and

administrative registers by means of a personal identiﬁcation

number that is maintained by the Central Population Register

(Schmidt

et al. 2014).

Locating and Classifying WTs

We identiﬁed all WTs (7,860) in operation in Denmark at any time

between 1980 and 2013 using the administrative Master Data

Energy Agency (Energistyrelsen

2014).

In Denmark, it is manda-

tory for all WT owners to report geographical coordinates and ca-

dastral codes of their WT(s) to the registry. Furthermore, for WTs

in operation at the time of data extraction (May 2014), the register

also contained coordinates from the Danish Geodata Agency. In

case of disagreement between these coordinates and geographical

locations reported by WT owners, the WT location was validated

against aerial photographs and historical topographic maps. We

excluded 517 oﬀshore WTs and 87 WTs for which no credible

location was found. Moreover, 314 WTs wrongly recorded in the

Master Data Register were assigned new coordinates based on

maps and aerial photographs. Information on height, model, type,

and operational settings was collected through the Master Data

99 noise spectra classes, with detailed noise spectrum information

from 10 to 10,000 Hz in thirds of octaves for wind speeds from 4 to

25 m=s. These noise classes were made from existing measure-

ments of sound power for Danish WTs (Backalarz

et al. 2016;

Sondergaard and Backalarz 2015).

Wind Conditions

For each WT location, we estimated the wind speed and direction

at hub height for each hour over the period 1982–2013, using

mesoscale model simulations performed with the Weather

Research and Forecasting model (Hahmann

et al. 2015; Peña and

Hahmann 2017).

Noise Modeling

The WTN exposure modeling has been described in detail else-

where (Backalarz

et al. 2016).

In summary, we

ﬁrst

identiﬁed

buildings that might have WTN discernable above background

noise, which we deﬁned as having

≥24

dBðAÞ outdoor WTN or

≥5

dBðAÞ indoor LF WTN (10–160 Hz) under the unrealistically

extreme scenario that all WTs ever operational in Denmark were

simultaneously operating at a wind speed of 8 m/s with downwind

sound propagation in all directions (to ensure that no eligible build-

ings were excluded). Second, we performed a detailed modeling of

noise exposure for the 553,066 buildings identiﬁed as eligible in

the

ﬁrst

step, calculating noise levels in one-third octave bands

from 10 to 10,000 Hz, using the Nord2000 noise propagation

model (Kragh

et al. 2001),

and taking into account the hourly esti-

mates of wind speed and direction at hub height during the period

Environmental Health Perspectives

1982–2013. The Nord2000 model has been successfully validated

for WTs (Søndergaard

et al. 2009).

For each dwelling, we calcu-

lated the noise contribution from all WTs within a 6-km radius

hour by hour. Noise contributions from WTs beyond 6 km were

not evaluated because they could not appreciably aﬀect the noise

estimate. These modeled values were then aggregated over the

nighttime period [2200 to 0700 hours (10 P.M. to 7 A.M.)], which

we considered the most relevant time window as people are most

likely at home and asleep during these hours. We calculated out-

door A-weighted sound pressure level, which is the metric most

commonly used in noise and health studies (Michaud

et al. 2016c;

Pedersen 2011),

as well as A-weighted indoor LF (10–160 Hz)

sound pressure level. LF noise penetrates buildings well and has

been suggested as an important component of WTN in relation to

health (Jeﬀery

et al. 2014).

We determined a validity score for the indoor and the outdoor

noise estimates as follows: For estimation of WTN, WTs were

grouped into 99 noise spectra classes with similar noise proﬁles.

The noise spectra for each class was determined from existing

noise data describing the noise spectra of all WT-models within

each class, typically for 8 m=s wind speed and since 2006 also for

6 m=s wind speed. For some WTs, data were available for other

wind speeds. In WT classes with many WT models, more data

were available than in classes with few WTs. In general, fewer data

were available for old or rare WT types. For each dwelling time

point (hour by hour), the validity score reﬂects information for all

contributing WTs on

the number and quality of measurement

data used to determine the WTN spectra classes, and

how

closely the simulated meteorological conditions for that hour

resembled the conditions under which the relevant WTN spectra

were measured. These validity data were then combined for all

WTs contributing noise to a given dwelling on a given night and

subsequently aggregated over the past 1 and 5 y following the run-

ning exposure periods for each participant. The higher the number

of this score, the more reliable the noise estimate was assumed to

be. Finally, we dichotomized validity by the median among those

dwellings exposed to outdoor WTN >30 or indoor LF WTN

>10 dBðAÞ. This method was used because WTN and the likeli-

hood of a high validity score is by deﬁnition correlated, and we

wanted to focus on highly exposed subjects with comparatively

high validity for that noise level.

For the calculation of indoor LF noise, all dwellings were clas-

siﬁed into one of six sound insulation classes, based on building

attributes in the Building and Housing register (Christensen

2011):

“1�½-story

houses” (residents assumed to sleep on the second

ﬂoor

with most sound transmitted through the roof),

“light

façade” (e.g.,

wood),

“aerated

concrete” (and similar materials, including timber

framing),

“farm

houses” (remaining buildings in the registry classi-

ﬁed

as farms),

“brick

buildings,” and

“unknown”

(assigned the

mean attenuation value of the

ﬁve

previous classes). The frequency-

speciﬁc attenuation values for each of the six classes are shown in

Backalarz et al. 2016.

Study Population and Exposure Assignment

To deﬁne the study population, we

ﬁrst

identiﬁed a set of

“inclu-

sion dwellings,” which were all Danish dwellings located within

a radius of 20 times the height of a neighboring WT at any time

during the years 1982–2013, plus a random selection of 25% of

all dwellings within a radius of 20–40 WT heights from at least

one WT during the same period. These criteria were selected to

ensure that the study population would include all people living

within a radius of 20 times the height of one or more WTs, plus

people living in the same areas, but with little or no WTN expo-

sure. We excluded all hospitals and residential institutions and all

dwellings situated within 100 m of areas classiﬁed as a

“town

00(0) 0 0000

000000-2

center” (i.e., the central part of larger towns, characterized by

multistory adjoined buildings) in GIS data from the Danish

Geodata Agency (dataset: KORT10, precision ± 1 m, data from

2013), as traﬃc conditions and lifestyle in town centers may dif-

fer substantially from the main study population.

We subsequently used the Danish Civil Registration System

(Schmidt

et al. 2014)

to identify the study cohort, deﬁned as all

adults (age 25–84 y) who lived in one of these inclusion dwell-

ings any time between

ﬁve

years before the erection of the

ﬁrst

neighboring WT and the end of 2013. Including people before

and after a WT was operational ensured inclusion of subjects

living in exactly the same dwellings but with no exposure. For

this population, we then established complete migration histor-

ies (including also town centers and other addresses not counted

as inclusion dwellings) from

ﬁve

years before start of follow-up

until

ﬁve

years after moving from the inclusion dwelling. This

method allowed us to assign daily residential WTN exposure to

each member of the cohort based on all dwellings they had

lived in during that period. Subjects without complete geocod-

able address history for the period

ﬁve

years before start of

follow-up were excluded (later holes in address history resulted

in censoring).

The study was approved by the Danish Data Protection

Agency (J.nr: 2014-41-2,671). By Danish Law, ethical approval

and informed consent are not required for entirely register-based

studies.

Covariates

Selection of potential confounders was done

a priori.

From the

registries of Statistic Denmark, we obtained information on age and

sex, highest attained educational level (time-dependent, updated

yearly), personal income (time-dependent, updated yearly), marital

status (time-dependent, updated yearly), work-market aﬃliation

(time-dependent, updated yearly), and areal level (10,000 m

) mean

household income (year 2010) (Baadsgaard

and Quitzau 2011;

Jensen and Rasmussen 2011; Petersson et al. 2011).

Information on

type of dwelling was obtained from the building and housing regis-

ter (Christensen

2011).

As proxies for local road traﬃc noise and air

pollution, we identiﬁed the distance from each dwelling to the near-

est road with an average daily traﬃc count of

≥5,000

vehicles (in

2005) as well as total distance driven by vehicles within 500 m of

the residence each day as the product of street length and traﬃc

density.

Identification of Outcome

Cases with MI and stroke were identiﬁed by linking the personal

identiﬁcation number of each member of the study population to

the nationwide Danish National Patient Register, which started in

1977 (Lynge

et al. 2011),

and the Danish Register of Causes of

Death from 1970 (Helweg-Larsen

2011).

We deﬁned stroke cases

using the International Classiﬁcation of Diseases (ICD) 8 codes

431–434 and 436 or ICD10 codes I61, I63, and I64, and cases of

MI as ICD8 code 410 or ICD10 code I21. The subgroup of ische-

mic strokes was deﬁned as ICD8 432–434 or ICD10 I63. We

considered only incident events (from either register) and

excluded all persons with events prior to start of follow-up.

Statistical Methods

Log-linear Poisson regression analysis was used to calculate

incidence rate ratios (IRRs) for MI and stroke according to out-

door [<24, 24

−

< 30, 30

−

< 36, 36

−

< 42, and

≥42

dBðAÞ] or

indoor LF WTN [<5, 5

−

< 10, 10

−

< 15, and

≥15

dBðAÞ] ex-

posure, calculated as running means over the previous 1 y and

5 y. We performed categorical analyses to avoid concealment

Environmental Health Perspectives

of potential eﬀects at high exposures due to forced linearity dic-

tated by the vast majority of our population exposed to less than

the (as-yet-unknown) levels potentially associated with the

health outcomes. The categorizations were determined

a priori.

At present, there are no standards regarding categorizations of

WTN. After consulting acousticians, we chose <24 dBðAÞ out-

door and <5 dBðAÞ indoor LF WTN as the references, as the

acousticians evaluated that WTN in all likelihood will be inaud-

ible below these levels. For outdoor WTN, the upper limit of

42 dBðAÞ was chosen as this is the regulatory WTN limit in

Denmark (at wind speed 6 m=s) and therefore of interest from

an administrative point of view, and the intermediate cut points

chosen were 30 dBðAÞ and 36 dBðAÞ, which separated catego-

ries by 6 dBðAÞ. For indoor LF WTN, the Danish regulatory

limit was 20 dBðAÞ, and we chose intermediate cut points of

10 dBðAÞ and 15 dBðAÞ. However, there were very few cases

exposed to levels >20 dBðAÞ, and we therefore chose

>15 dBðAÞ as the highest exposure category.

For dwellings located so far from WTs as to never have WTN

above 24 dBðAÞ outdoors and 5 dBðAÞ indoors, or when WTs were

not operating due to wind conditions, a value of

−20

dBðAÞ was

used to represent close to no noise in calculating the average.

Follow-up was started after residents had lived one year in the

recruitment dwelling, turning 25 y or 1 January 1982, whichever

came last, and follow-up ended at time of MI (in analyses of MI) or

stroke (in analyses of stroke), death, age 85 y, disappearance, or

having no recorded address geocodes for more than seven consecu-

tive days, 31 December 2013, or

ﬁve

years after moving from the

inclusion dwelling, whichever came

ﬁrst.

In addition, we estimated

IRRs for the subgroup of strokes diagnosed as ischemic and cen-

sured all other types of stroke at time of diagnosis.

All analyses were adjusted for sex, calendar year (1982–1984,

1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009,

and 2010–2013) and age (25–84 y, in

ﬁve-year

categories).

Additionally, we adjusted for marital status [currently married/

registered partnership and other (never or formerly married)],

education (basic or high school, vocational, higher, and unknown),

work-market aﬃliation [employed, retired, and other (e.g., under

education or unemployed)], personal income (20 equal-sized annual

categories, and an unknown income category), area-level average

mean household income (20 equal-sized categories, and an

unknown income category; data from 2010), dwelling classiﬁcation

(farm, single-family detached house, and other (e.g., apartments and

terraced housing), distance to road with

≥5,000

vehicles per day

(<500 m, 500

−

< 1,000 m, 1,000

−

< 2,000 m and

≥2,000

m),

and traﬃc load within 500 m radius of dwelling (ﬁrst, and second

quartile, and above median; quartiles calculated in larger sample of

Danish addresses). Subjects were allowed to change between cate-

gories of covariates (yearly and/or at change of address) and expo-

sure variables over time.

We used Poisson models, including an interaction term, to inves-

tigate sex and age (above and below 65 y) as potential (multiplica-

tive) eﬀect modiﬁers. Also, we conducted a number of sensitivity

analyses in subpopulations for whom we hypothesized that a poten-

tial association between modeled exposure and risk could be more

pronounced. First, we investigated association among cases with a

diagnosis of MI or stroke after year 2000 (persons diagnosed with

MI or stroke before year 2000 were censored at diagnosis and not

counted as cases), reﬂecting improved diagnostic practices and

more comprehensive data on more modern WTs. Second, we looked

only at people while they were living in dwellings classiﬁed as farms

(a large proportion of the highly exposed people live on farms, and

we hypothesize that there is less variation in lifestyle and other expo-

sures among this subpopulation in comparison with the whole popu-

lation, potentially reducing susceptibility to residual confounding in

00(0) 0 0000

000000-3

Table 1.

Characteristics of the populations for study of MI and stroke, respectively, at start of follow-up according to residential A-weighted exposure to out-

door wind turbine noise calculated as mean exposure during the preceding year.

Characteristics at

start of follow-up

<24 dBðAÞ MI/stroke

(N = 587,866=589,098)

50/50%

43/42%

19/19%

15/15%

23/23%

39/39%

29/29%

8/8%

19/19%

23/23%

24/24%

23/23%

12/12%

35/35%

37/37%

15/15%

13/13%

57/57%

15/15%

28/28%

65/65%

19/19%

16/16%

24/23%

28/28%

27/27%

19/19%

3/3%

13/13%

62/62%

25/25%

36/36%

27/27%

37/37%

33/33%

25/25%

19/19%

23/23%

13/13%

63/63%

24/24%

Outdoor wind turbine noise

24–30 dBðAÞ MI/stroke

30–36 dBðAÞ MI/stroke

(N = 86,280=86,194)

(N = 29,869=29,888)

51/51%

60/60%

16/16%

11/11%

14/14%

6/6%

27/27%

50/50%

18/18%

19/19%

28/28%

21/21%

4/4%

33/33%

46/46%

18/18%

3/3%

41/41%

16/16%

43/43%

70/70%

16/16%

14/14%

22/22%

29/29%

17/17%

3/3%

14/14%

60/60%

25/26%

25/25%

30/30%

45/45%

40/40%

27/27%

21/21%

12/12%

17/17%

68/68%

15/15%

52/52%

63/63%

16/16%

10/10%

6/6%

28/28%

39/39%

16/16%

18/18%

28/28%

21/21%

5/5%

32/32%

48/48%

18/18%

2/2%

40/40%

15/15%

45/45%

73/74%

12/12%

14/14%

15/15%

29/29%

32/32%

19/19%

5/5%

23/23%

61/61%

17/17%

19/19%

28/28%

53/53%

54/54%

25/25%

15/15%

6/6%

21/21%

68/68%

11/11%

36–42 dBðAÞ MI/stroke

(N = 6,063=6,050)

53/53%

65/65%

17/17%

10/10%

8/7%

9/9%

35/35%

44/44%

12/12%

18/18%

27/27%

28/28%

21/21%

6/6%

31/31%

49/49%

18/18%

2/2%

39/39%

14/14%

47/47%

75/76%

10/9%

15/15%

12/12%

27/27%

33/33%

21/22%

7/7%

34/34%

52/52%

14/13%

18/18%

27/27%

55/55%

67/67%

13/13%

7/7%

28/28%

63/64%

8/8%

≥42

dBðAÞ MI/stroke

(N = 1,171=1,171)

54/54%

68/68%

17/17%

8/8%

6/6%

16/16%

49/49%

31/31%

4/4%

16/16%

25/25%

27/27%

26/26%

5/5%

33/33%

46/46%

19/19%

2/2%

45/45%

13/13%

42/42%

77/77%

7/8%

16/16%

15/15%

21/21%

33/33%

25/25%

6/6%

34/34%

54/54%

12/12%

20/20%

27/27%

53/53%

63/63%

16/16%

12/12%

9/9%

28/28%

63/63%

9/9%

Men

Age

<40 years

40–50 years

50–60 years

≥60

years

Year of start of follow-up

1982–1990

1990–2000

2000–2010

2010–2013

Personal income

Quartile 1 (low)

Quartile 2

Quartile 3

Quartile 4 (high)

Unknown

Highest attained education

Basic or high school

Vocational

High

Unknown

Marital status

Married

Divorced/widow(er)

Never married

Affiliation to work

market

Working

Retired

Other

Area-level income

Quartile 1 (low)

Quartile 2

Quartile 3

Quartile 4 (high)

Unknown

Type of dwelling

Farm

Single-family

detached house

Others

Distance to major road

<500 m

500–2,000 m

≥2,000

Traffic load

<2:5 million

vehicles

2.5-5.3 million

vehicles

5.3-9.7 million

vehicles

>9:7 million

vehicles

Tree coverage

<5%

5–20%

>20

Include under education and unemployed among others.

Average mean household income among all households in a 100 × 100 m grid cell.

Include apartments and terraced housing among others.

Major road defined as

≥5,000

vehicles per day.

In a 500 meters radius around the dwelling.

this group). Third, we investigated people while their nearest WT

had a total height of >35 m (the WTN may qualitatively diﬀer, for

example, in terms of frequency composition by WT size, and there

is less likelihood that the WT is owned by those exposed). Fourth,

we addressed exposure misclassiﬁcation by looking at people with

validity of the cumulated noise estimate better than the median. As

exposure misclassiﬁcation among individuals with very low expo-

sure was unlikely to be suﬃcient to alter exposure categorization,

Environmental Health Perspectives

000000-4

00(0) 0 0000

Table 2.

Characteristics of wind turbines at the dwellings of the study participants at start of follow-up, according to residential exposure to outdoor wind

turbine noise calculated as mean exposure during the preceding year.

Wind turbine characteristics at

of the study population

dwellings at start of follow-up

<24 dBðAÞ MI/stroke

(N = 587,866=589,098)

Outdoor wind turbine noise

24–30 dBðAÞ MI/stroke 30–36 dBðAÞ MI/stroke 36–42 dBðAÞ MI/stroke

≥42

dBðAÞ MI/stroke

(N = 86,280=86,194)

(N = 29,869=29,888)

(N = 6,063=6,050)

(N = 1,171=1,171)

93/92%

7/7%

0/0%

17/17%

79/79%

4/4%

19/19%

59/59%

20/20%

2/2%

64/64%

30/30%

6/6%

0/0%

52/52%

45/45%

3/3%

23/23%

60/60%

16/16%

2/2%

27/27%

45/45%

26/26%

3/3%

91/91%

7/7%

2/2%

35/35%

52/52%

12/12%

1/1%

7/7%

38/38%

43/43%

12/12%

95/95%

2/2%

3/3%

71/71%

27/27%

1/1%

0/0%

Indoor LF wind turbine noise (1-year mean)

<5 dBðAÞ

100%

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Distance to nearest wind turbine

<500 m

1/1%

500–2,000 m

37/37%

≥2,000

61/61%

Total height, nearest wind turbine

<35 m

41/41%

35–70 m

70–100 m

9/9%

≥100

1/1%

Note: LF, low frequency.

the median was calculated only among those individuals with expo-

sures

≥10

dBðAÞ LF or 30 dBðAÞ in indoor and outdoor analyses,

respectively. Fifth, we investigated eﬀects in people living in dwell-

ings located far from a major road (>2,000 m to nearest road with

>5,000 vehicles=day, as exposure to traﬃc noise may mask WTN).

Last, we investigated associations among people in dwellings with

low tree coverage [<5% of the area within 500 m of dwelling cov-

ered by forest, thicket, groves, single trees, and hedgerows, accord-

ing to GIS data (dataset KORT10, precision ± 1 m) from the Danish

Geodata Agency, as vegetation noise may mask WTN]. Data were

analyzed using SAS (version 9.3; SAS Institute Inc.).

Results

We identiﬁed 844,228 adults (25–84 y) living

≥one

year in the

inclusion dwellings between 1982–2013. We excluded persons

who had emigrated (n = 44,049) or disappeared (n = 1,570) prior

to start of follow-up, who had unknown address for eight or more

consecutive days in the

ﬁve

years prior to start of follow-up

(n = 78,830; 98% had address holes of more than 30 d and 64%

of more than a year), and who lived in hospitals or institution at

study start of follow-up (n = 2,004). Also, we excluded all per-

sons diagnosed with MI (n = 6,526 only relevant for the MI study

population) or stroke (n = 5,374, only relevant for the stroke

study population) before start of follow-up. The

ﬁnal

study popu-

lation was 711,249 persons for the MI analyses, of whom 19,145

(2.7%) developed MI during 7,440,090 person-years, and 712,401

persons for the stroke analyses, of whom 18,064 (2.5%) developed

stroke during 7,466,239 person-years.

In comparison with people exposed to 1-y mean outdoor WTN

<24 dBðAÞ, the likelihood of being male, <40 years of age, living

on a farm, living in areas with high area-level income and low tree

coverage increased with increasing levels of outdoor WTN,

whereas the likelihood of having high levels of traﬃc, major roads

nearby, or being retired decreased (Table

1).

People exposed

≥24

dBðAÞ were likely to have higher levels of education than

those exposed to <24 dB, and exposure to

≥42

dBðAÞ WTN was

associated with higher income. In general, similar tendencies as

those for outdoor WTN were seen when looking at indoor LF

WTN levels, except that no diﬀerences in gender distribution were

found, and the likelihood of high personal income decreased with

increased WTN levels (Table S1). Also, when comparing people

with outdoor or indoor WTN above the respective reference levels,

the latter tended to enter the study later, live further from major

roads, have lower tree coverage, and more likely have vocational

training as highest attained education level.

People exposed to outdoor WTN above 30 dBðAÞ were more

frequently exposed to levels of indoor LF WTN above the

Table 3.

Characteristics of wind turbines at the dwellings of the study participants at start of follow-up, according to residential exposure to indoor low

frequency wind turbine noise calculated as mean exposure during the preceding year.

Wind turbine characteristics at of

the study population dwellings at

<5 dBðAÞ MI/stroke

start of follow-up

(N = 688,489=689,608)

Outdoor wind turbine noise (1-year mean)

<24 dBðAÞ

85/85%

24–30 dBðAÞ

12/12%

30–36 dBðAÞ

3/3%

36–42 dBðAÞ

0/0%

≥42

dBðAÞ

0/0%

Distance to nearest wind turbine

<500 m

5/5%

500–2,000 m

42/42%

≥2,000

54/54%

Total height, nearest wind turbine

<35 m

38/38%

35–70 m

44/44%

70–100 m

10/10%

≥100

1/1%

Note: LF, low frequency.

Indoor LF wind turbine noise

5–10 dBðAÞ MI/stroke

10–15 dBðAÞ MI/stroke

(N = 18,465=18,497)

(N = 3,996=3,997)

0/0%

35/35%

48/48%

15/15%

2/2%

30/30%

68/68%

2/2%

11/11%

59/59%

26/26%

4/4%

0/0%

1/1%

48/48%

39/39%

13/13%

63/63%

35/35%

2/2%

11/11%

54/54%

31/31%

4/4%

≥15

dBðAÞ MI/stroke

(N = 299=299)

0/0%

2/2%

53/53%

45/45%

91/91%

7/7%

2/2%

24/25%

56/56%

18/17%

2/2%

Environmental Health Perspectives

000000-5

00(0) 0 0000

Table 4.

Associations between mean 1- and 5-year exposure to residential A-weighted outdoor wind turbine noise and risk of myocardial infarction and stroke.

Myocardial infarction

Crude IRR

N cases

(95% CI)

13,916

3,756

1,200

228

14,151

3,616

1,119

212

1 (ref)

1.10 (1.06-1.14)

1.05 (0.99-1.11)

0.99 (0.87-1.13)

1.09 (0.81-1.46)

1 (ref)

1.09 (1.05-1.13)

1.04 (0.97-1.10)

0.99 (0.87-1.14)

1.10 (0.82-1.46)

Adjusted IRR

(95% CI)

1 (ref)

1.09 (1.05-1.13)

1.08 (1.02-1.15)

1.07 (0.94-1.22)

1.21 (0.90-1.63)

1 (ref)

1.08 (1.04-1.12)

1.07 (1.00-1.12)

1.06 (0.93-1.22)

1.21 (0.91-1.62)

Stroke

Crude IRR

(95% CI)

1 (ref)

1.06 (1.02-1.10)

1.03 (0.97-1.10)

0.84 (0.72-0.97)

0.62 (0.41-0.95)

1 (ref)

1.07 (1.03-1.11 )

1.04 (0.98-1.11)

0.87 (0.75-1.01)

0.62 (0.41-0.94)

Adjusted IRR

(95% CI)

1 (ref)

1.08 (1.04-1.12)

1.10 (1.03-1.17)

0.92 (0.80-1.07)

0.71 (0.46-1.08)

1 (ref)

1.09 (1.05-1.13)

1.10 (1.03-1.17)

0.95 (0.82-1.11)

0.69 (0.46-1.05)

Outdoor wind turbine noise

1-year mean exposure

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

5-year mean exposure

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Person-years

5,543,711

1,313,384

467,029

96,282

19,685

5,644,428

1,265,628

425,855

85,193

18,986

Person-years

5,562,511

1,318,515

468,658

96,736

19,819

5,664,088

1,270,239

427,200

85,595

19,117

N cases

13,136

3,596

1,136

175

13,205

3,566

1,095

175

Note: CI, confidence interval; IRR, incidence rate ratio.

Adjusted for age, sex, and calendar year.

Adjusted for age, sex, calendar year, personal income, education, marital status, work-market affiliation, area-level socioeconomic status, type of dwelling, traffic load in 500-m

radius, and distance to major road.

reference level than were people exposed to lower levels of out-

door WTN, and a similar pattern was observed for indoor LF

WTN (Tables

and

3).

Among dwellings exposed to

≥36

dBðAÞ

outdoor WTN or

≥10

dBðAÞ indoor LF WTN, the vast majority

were located <500 m from a WT. With regard to height of the

nearest WT, 71% of the dwellings with

≥42

dBðAÞ were located

near low WTs (35 m) in comparison with percentages between

19 and 41 for the lower exposure groups, whereas for indoor LF

WTN, height of nearest WT distributed more evenly across the

exposure groups.

Exposure to 1- or 5-y mean outdoor WTN above the reference

level (<24 dBðAÞ) was positively associated with point estimates

for MI in all exposure groups in the adjusted analyses; however,

IRRs did not increase monotonically with increasing exposures

(Table

4).

Associations were signiﬁcant for the 24–30 dBðAÞ and

30–36 dBðAÞ exposure groups (e.g., for 5-y exposure with IRRs

of 1.08; 95% CI: 1.04, 1.12; 3,616 MI events and 1.07; 95% CI:

1.00, 1.12; 1,119 MI events, respectively) and similar but not sig-

niﬁcant for the 36–42 dBðAÞ group (IRR = 1:06; 95% CI: 0.93,

1.22; 212 MI events), whereas the IRR for the highest exposure

group (≥42 dBðAÞ) was 1.21 (95% CI: 0.91, 1.62) based on 47

MI events.

For indoor LF WTN, we found no associations between 1-y

mean exposure and risk of MI. For the 5-y exposure time win-

dow, we observed IRRs of 1.02 (95% CI: 0.95, 1.11), 1.08 (95%

CI: 0.91, 1.28) and 1.29 (95% CI: 0.73, 2.28) for people exposed

to 5–10 dBðAÞ, 10–15 and

≥15

dBðAÞ, respectively in compari-

son with exposure levels <5 dBðAÞ (Table

5).

Only 12 persons

with MI were exposed

≥15

dBðAÞ. In all analyses, IRRs

increased with adjustment for potential confounders (i.e., closer

to or past the null for crude IRRs <1:0, further from the null for

crude IRRs >1:0).

IRRs for stroke did not show consistent patterns of associa-

tions with 1- and 5-y outdoor WTN or indoor LF WTN (Tables

and

5).

IRRs for 5-y outdoor WTN relative to the <24 dBðAÞ ref-

erence group were 1.09 (95% CI: 1.05, 1.13; 3,566 stroke events),

1.10 (95% CI: 1.03, 1.17; 1,095 stroke events), 0.95 (95% CI:

0.82, 1.11; 175 stroke events), and 0.69 (95% CI: 0.46, 1.05;

23 stroke events) for the 24–30, >30

−

36, >36

−

42, and

≥42

dBðAÞ exposure groups, respectively (Table

4).

For indoor

LF WTN, all IRRs were null or inverse and nonsigniﬁcant (Table

5).

As for MI analyses, adjustment for potential confounders

resulted in slightly higher IRRs. IRRs for ischemic stroke were

similar, with adjusted IRRs of 0.78 (95% CI: 0.42, 1.45; 10 stroke

events) for

≥42

vs. <24 dBðAÞ 5-y outdoor WTN, and 0.94 (95%

CI: 0.35, 2.52; 4 stroke events) for

≥15

vs. <5 dBðAÞ indoor 5-y

LF WTN (Table S2).

In general, patterns of associations between 5-y exposures

and MI were similar to estimates from the main models when re-

stricted to population or outcome subgroups, albeit often based

on reduced populations of small size (Table

and

7).

For situa-

tions with high validity of the noise estimate, the IRR for MI

Table 5.

Associations between mean 1- and 5-year exposure to residential A-weighted indoor low frequency wind turbine noise and risk of myocardial infarc-

tion and stroke.

Indoor low frequency wind

turbine noise

1-year mean exposure

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ]

≥15

dBðAÞ

5-year mean exposure

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ]

≥15

dBðAÞ

Myocardial infarction

Crude IRR

N cases

(95% CI)

18,189

780

165

18,319

681

133

1 (ref)

0.97 (0.91-1.05)

0.97 (0.83-1.13)

0.80 (0.44-1.44)

1 (ref)

0.96 (0.89-1.04)

0.97 (0.82-1.15)

1.11 (0.63-1.96)

Adjusted IRR

(95% CI)

1 (ref)

1.04 (0.96-1.12)

1.09 (0.93-1.27)

0.92 (0.51-1.67)

1 (ref)

1.02 (0.95-1.11)

1.08 (0.91-1.28)

1.29 (0.73-2.28)

Stroke

Crude IRR

(95% CI)

1 (ref)

0.94 (0.87-1.01)

0.88 (0.75-1.04)

0.76 (0.41-1.40)

1 (ref)

0.92 (0.85-1.00)

0.81 (0.67-0.97)

0.87 (0.45-1.67)

Adjusted IRR

(95% CI)

1 (ref)

1.02 (0.95-1.10)

1.00 (0.85-1.18)

0.89 (0.48-1.65)

1 (ref)

0.99 (0.92-1.07)

0.91 (0.75-1.10)

1.02 (0.53-1.96)

Person-years

7,031,863

329,970

72,551

5,706

7,097,455

283,001

55,408

4,226

Person-years

7,056,494

331,166

72,847

5,732

7,122,406

283,933

55,660

4,239

N cases

17,157

749

148

17,288

656

111

Note: CI, confidence interval; IRR, incidence rate ratio.

Adjusted for age, sex and calendar-year.

Adjusted for age, sex, calendar year, personal income, education, marital status, work-market affiliation, area-level socioeconomic status, type of dwelling, traffic load in 500-m radius

and distance to major road.

Environmental Health Perspectives

000000-6

00(0) 0 0000

Table 6.

Associations between 5-year exposure to outdoor wind turbine noise and risk of myocardial infarction in different subpopulations.

Sub-populations

All

Exposure categories

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Diagnosis of MI after 2000

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Living on a farm

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Total height of nearest wind turbine

≥35

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

High validity score of noise estimate

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Dwelling

≥2,000

m from

major road

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

Less than 5 % tree coverage

<24 dBðAÞ

24–30 dBðAÞ

30–36 dBðAÞ

36–42 dBðAÞ

≥42

dBðAÞ

726,414

250,387

129,503

37,124

7,345

3,922,187

1,042,138

339,990

58,893

7,175

4,349,483

780,854

256,089

39,720

3,164

1,977,176

609,642

235,000

48,496

10,943

670,421

222,014

98,341

24,062

5,202

1,408

504

277

10,116

2,999

893

147

10,161

2,220

654

102

5,128

1,825

629

115

1,571

613

254

1 (ref)

0.98 (0.88-1.08)

1.04 (0.91-1.18)

1.07 (0.86-1.35)

1.13 (0.68-1.87)

1 (ref)

1.08 (1.03-1.12)

1.07 (1.00-1.15)

1.06 (0.90-1.25)

1.54 (1.04-2.26)

1 (ref)

1.16 (1.11-1.22)

1.11 (1.03-1.21)

1.19 (0.98-1.45)

1.43 (0.74-2.75)

1 (ref)

1.12 (1.06-1.18)

1.07 (0.98-1.16)

1.01 (0.84-1.22)

1.28 (0.89-1.84)

1 (ref)

1.12 (1.02-1.22)

1.12 (0.98-1.27)

1.25 (0.98-1.61)

0.92 (0.48-1.78)

3,288,875

948,866

312,238

55,170

9,074

8,590

2,643

814

131

1 (ref)

1.04 (0.99-1.08)

1.04 (0.97-1.12)

1.00 (0.84-1.19)

1.36 (0.93-1.98)

Outdoor wind turbine noise

Person-years

cases

5,644,428

1,265,628

425,855

85,193

18,986

14,151

3,616

1,119

212

Adjusted IRR (95% CI)

1 (ref)

1.08 (1.04-1.12)

1.07 (1.00-1.12)

1.06 (0.93-1.22)

1.21 (0.91-1.62)

Note: CI, confidence interval; IRR, incidence rate ratio.

Adjusted for age, sex, calendar-year, personal income, education, marital status, work-market affiliation, area-level socioeconomic status, type of dwelling, traffic load in 500-m

radius and distance to major road.

Corresponding to IRRs and CIs in

Table 4.

Includes only study participants with validity score better than the median among those with exposures

≥30

dBðAÞ outdoor wind turbine noise. The validity score reflects the esti-

mated uncertainty associated with all aspects of noise estimation at a specific address and day.

Major road defined as

≥5,000

vehicles per day.

In a 500-m radius around the dwelling.

among people with outdoor WTN

≥42

dBðAÞ was 1.43 (95% CI:

0.74, 2.75; 9 MI events) and for indoor LF WTN

≥15

dBðAÞ the

corresponding IRR was 1.55 (95% CI: 0.64, 3.72; 5 MI events).

When restricted to subjects living in inclusion dwellings where

the height of the closest WT was

≥35

m, the IRR for outdoor

WTN

≥42

dBðAÞ was increased relative to the main model

(IRR = 1:54; 95% CI: 1.04, 2.26; 26 MI events), whereas the IRR

for indoor LF WTN

≥15

dBðAÞ was similar to the main model

(IRR = 1:36; 95% CI: 0.75, 2.45; 11 MI events). Conversely,

when we restricted to people living in dwellings with less than

5% tree coverage within a 500-m radius of their dwelling, indoor

LF WTN

≥15

dBðAÞ was associated with an IRR for MI of 2.43

(95% CI: 1.26, 4.67; 9 MI events), whereas the corresponding

IRR for outdoor WTN

≥42

dBðAÞ was 0.92 (95% CI: 0.48, 1.78;

9 MI events).

For stroke, we observed patterns of associations between 5-y

exposures of outdoor or indoor LF WTN and risk for stroke to be

Environmental Health Perspectives

similar in comparison with estimates from the main models when

restricted to population or outcome subgroups (Tables S3 and

S4). In analyses restricted to people with high validity score of

the exposure estimate, IRRs could not be shown for the highest

exposure group for both exposures as the categories contained

three stroke events or less, which cannot be presented due to

Danish anonymity legislation.

We found no signiﬁcant eﬀect modiﬁcation by sex (all

> 0:07) or age (all

> 0:22) for neither indoor nor outdoor

noise in relation to MI or stroke (Tables S4 and S5).

Discussion

For both long-term nighttime outdoor WTN above 42 dBðAÞ and

indoor LF WTN above 15 dBðAÞ, we found slightly elevated rel-

ative risk estimates for MI in comparison with exposures below

24 dBðAÞ and 5 dBðAÞ, respectively, but the number of cases

00(0) 0 0000

000000-7

Table 7.

Associations between 5-year exposure to indoor low frequency wind turbine noise and risk of myocardial infarction in different subpopulations.

Subpopulations

All

Exposure categories

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Diagnosis of MI after 2000

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Living on a farm

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Total height of nearest wind turbine

≥35

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

High validity score of noise estimate

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Dwelling

≥2,000

m from

major road

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

Less than 5 % tree coverage

<5 dBðAÞ

5–10 dBðAÞ

10–15 dBðAÞ

≥15

dBðAÞ

1,017,885

103,730

26,980

2,177

5,063,410

254,128

49,379

3,465

4,122,053

124,062

25,344

1,547

2,684,822

158,807

34,642

2,985

933,801

67,112

17,311

1,816

2,025

205

13,427

624

119

9,636

305

7,269

364

2,304

159

1 (ref)

0.97 (0.84-1.13)

0.90 (0.68-1.21)

1.62 (0.77-3.40)

1 (ref)

1.04 (0.95-1.12)

1.07 (0.89-1.28)

1.36 (0.75-2.45)

1 (ref)

1.16 (1.04-1.31)

1.12 (0.86-1.44)

1.55 (0.64-3.72)

1 (ref)

0.97 (0.88-1.08)

1.11 (0.89-1.37)

1.33 (0.69-2.56)

1 (ref)

1.08 (0.92-1.27)

1.05 (0.76-1.44)

2.43 (1.26-4.67)

4,312,355

249,411

49,101

3,358

11,496

584

115

1 (ref)

1.00 (0.92-1.08)

1.06 (0.88-1.27)

1.33 (0.71-2.47)

Indoor low frequency wind turbine noise

Person-years

cases

7,097,455

283,001

55,408

4,226

18,319

681

133

Adjusted IRR (95% CI)

1 (ref)

1.02 (0.95-1.11)

1.08 (0.91-1.28)

1.29 (0.73-2.28)

Note: CI, confidence interval; IRR, incidence rate ratio.

Adjusted for age, sex, calendar year, personal income, education, marital status, work-market affiliation, area-level socioeconomic status, type of dwelling, traffic load in 500-m ra-

dius, and distance to major road.

Corresponding to IRRs and CIs in

Table 5.

Includes only study participants with validity score better than the median among those with exposures

≥10

dBðAÞ LF indoor wind turbine noise. The validity score reflects the esti-

mated uncertainty associated with all aspects of noise estimation at a specific address and day.

Major road defined as

≥5,000

vehicles per day.

In a 500-m radius around the dwelling.

were low in the highest exposure groups, and the associations

were not statistically signiﬁcant. There was no monotonic expo-

sure–response relationship between WTN and MI, especially for

outdoor WTN, where we found slightly increased relative risks

of similar size in the three intermediate exposure groups. The

IRRs for MI in the highest exposure group for both indoor LF

and outdoor WTN were unchanged or slightly higher across dif-

ferent subpopulations in comparison with the IRRs in the main

analysis. For stroke, all levels of indoor LF WTN were associated

with IRRs close to unity, whereas for outdoor WTN, we observed

IRRs above unity in the intermediate exposure groups and below

unity in the highest exposure groups.

A major strength of this study is the prospective nationwide

design with information on potential socioeconomic and environ-

mental confounders, the large number of incident cases identiﬁed

through a high-quality nationwide register (Helweg-Larsen

2011;

Lynge et al. 2011),

and access to complete residential address his-

tory for the entire exposure and follow-up period. Also, we esti-

mated long-term exposure to WTN using a state-of-the-art

exposure model that used detailed WTN spectra for all WT types

and allowed for time varying wind direction, wind speed, and cli-

matic conditions. The later information was modeled hour by hour

for each WT position, which allowed us to estimate noise levels

Environmental Health Perspectives

speciﬁcally for nighttime, when people are most likely to be at

home sleeping. Additionally, we estimated exposure to the poten-

tially more biologically relevant indoor noise, accounting for dif-

ferent housing sound insulation properties, although we could only

diﬀerentiate into few insulation categories, based on relatively

crude information. Further strengths were estimation of WTN for

all dwellings in Denmark that might experience WTN, the access

to a number of individual and area-level socioeconomic variables,

and that we accounted for living on a farm, which is conceivably

associated with many diﬀerences in lifestyle and environment.

Due to the register-based nature of the study, we did not have

access to detailed potential lifestyle confounders, such as dietary

habits, obesity, and physical activity, which is a study weakness.

It is, however, important to note that adjusting for lifestyle in

studies of noise is not straightforward, as studies have indicated

that traﬃc noise may be associated with factors such as obesity,

physical activity, and smoking habits (Christensen

et al. 2015;

Eriksson et al. 2014; Foraster et al. 2016; Pyko et al. 2015;

Roswall et al. 2017; Roswall et al. 2018),

suggesting that these

are intermediaries and not confounders on the pathway between

noise and disease. Another limitation is the rather crude adjust-

ment for local road traﬃc noise, using traﬃc load and distance to

major road. However, residual confounding by traﬃc noise is

00(0) 0 0000

000000-8

probably not a major issue in the present study, as adjusting for

the traﬃc-related proxies resulted in only minor changes on the

second decimal in estimates (data not shown), and we obtained

similar estimates among people living far from major roads in

comparison with the whole study population. Another limitation

is potential bias from missing data.

The Nord2000 has been successfully validated for WTs

(Sondergaard

et al. 2009).

Even so, there is inevitable exposure

misclassiﬁcation in the modeled noise-exposure metrics. This

circumstance is unlikely to depend on the case status and will

in most cases inﬂuence the estimates towards the null. Although

not covering all aspects of uncertainty pertaining to the noise

estimates, our validity score allowed us to look further into this

uncertainty. Although based on small numbers, our

ﬁnding

of a

higher IRR for MI for both outdoor and indoor LF WTN among

people with a high validity score indicates that exposure mis-

classiﬁcation may have aﬀected the results. For stroke, the num-

ber of highly exposed cases was too small (<4), precluding

meaningful interpretation. It was also a limitation that we could

not model exposure from WTs before 1982. However, only 103

cohort members were recruited due to living within a 1000 m of

one of the relatively small WT operating before 1982. Lastly,

statistical power was impaired for the highest exposure groups

by the small number of cases with high exposure to WTN.

The few previous studies that investigated associations between

WTN and cardiovascular disease found no indications of an associa-

tion (Michaud

et al. 2016a; Michaud et al. 2016c; Pedersen 2011).

However, these studies were all cross-sectional, they were based on

much smaller study populations than the present study, they had

more crude exposure models, and the diagnosis of cardiovascular

disease was self-reported, which makes direct comparison with the

present study diﬃcult.

We found that long-term high exposure to WTN was associ-

ated with slightly elevated point estimates for MI, both for expo-

sure to outdoor WTN and for exposure to the potentially more

biologically relevant indoor WTN noise. This pattern persisted

across a range of subpopulations, for whom we hypothesized that

a potential association between exposure and outcome could be

more pronounced, such as high validity of the noise estimate and

living far from major roads. However, the numbers of MI cases

in the highest exposure groups were small, and the CIs were

wide. Furthermore, we did not observe any monotonic exposure–

response trends. This observation was most evident for outdoor

WTN, where small and almost identical increases in risk were

found for the three intermediate exposure groups. Also, because

the biological mechanisms behind an eﬀect of noise on disease

are believed similar for MI and stroke (Münzel

et al. 2017b)

and

because traﬃc noise has been associated with both diseases

(Hansell

et al. 2013; Héritier et al. 2017; Sørensen et al. 2011;

Vienneau et al. 2015),

we expected similar results for MI and

stroke in the present study. However, for outdoor WTN above

42 dBðAÞ, the IRR was below unity for stroke. We are not aware

of plausible biological mechanisms to explain a protective eﬀect

of WTN. Also, numbers of highly exposed stroke cases were

small, the CI was wide, and the IRR was noninsigniﬁcant; fur-

thermore, high exposure to the potentially more biologically rele-

vant indoor WTN noise was not associated with a decreased risk

for stroke. The discrepant results for MI and stroke among the

highly exposed in the present study further underscores that the

observed IRRs for MI and stroke should be interpreted with

caution.

In conclusion, although we found the highest levels of WTN

to be associated with the highest relative risk for incident MI,

numbers of highly exposed cases were small, and the associations

were nonsigniﬁcant. Inverse or null associations between high

Environmental Health Perspectives

exposures and stroke were also based on a small number of cases.

Therefore, it is not possible to draw

ﬁrm

conclusions from our

ﬁnding.

Future studies should, if possible, include larger numbers

of highly exposed individuals.

Acknowledgments

The authors wish to thank DELTA, who showed great

expertise in all steps of the process towards estimating detailed

wind turbine noise data, as well as Geoinfo A/S, who made it

possible to extract the GIS information for all addresses.

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).

References

Baadsgaard M, Quitzau J. 2011. Danish registers on personal income and transfer

payments. Scand J Public Health 39(7 Suppl):103–105, PMID:

21775365,

https://doi.org/10.1177/1403494811405098.

Backalarz C, Sondergaard LS, Laursen JE. 2016.

“Big

Noise Data” for wind tur-

bines. In: Wolfgang Kropp, Brigitte Schulte-Fortkam, eds.

http://assets.

madebydelta.com/docs/share/Akustik/big_noise_data_for_wind_turbines_

internoise_2016.pdf.

Hamburg: INTER-NOISE 2016 [accessed 21 February 2019].

Christensen G. 2011. The Building and Housing Register. Scand J Public Health

39(7 Suppl):106–108, PMID:

21775366, https://doi.org/10.1177/1403494811399168.

Christensen JS, Raaschou-Nielsen O, Tjønneland A, Nordsborg RB, Jensen SS,

Sørensen TI, et al. 2015. Long-term exposure to residential traffic noise and

changes in body weight and waist circumference: a cohort study. Environ Res

143(Pt A):154–161, PMID:

26492400, https://doi.org/10.1016/j.envres.2015.10.007.

Energistyrelsen. (Danish Energy Agency). 2014. Stamdataregister for vindkraftværk. [in

Danish]

https://ens.dk/service/statistik-data-noegletal-og-kort/data-oversigt-over-

energisektoren

[accessed 21 February 2019].

Eriksson C, Hilding A, Pyko A, Bluhm G, Pershagen G, Östenson CG. 2014. Long-

term aircraft noise exposure and body mass index, waist circumference, and

type 2 diabetes: a prospective study. Environ Health Perspect 122(7):687–694,

PMID:

24800763, https://doi.org/10.1289/ehp.1307115.

Foraster M, Eze IC, Vienneau D, Brink M, Cajochen C, Caviezel S, et al. 2016. Long-

term transportation noise annoyance is associated with subsequent lower lev-

els of physical activity. Environ Int 91:341–349, PMID:

27030897, https://doi.org/

10.1016/j.envint.2016.03.011.

Hahmann AN, Vincent CL, Pena A, Lange J, Hasager CB. 2015. Wind climate esti-

mation using WRF model output: method and model sensitivities over the sea.

Int J Climatol 35:18,

https://doi.org/10.1002/joc.4217.

Hansell AL, Blangiardo M, Fortunato L, Floud S, de HK, Fecht D, et al. 2013. Aircraft

noise and cardiovascular disease near Heathrow airport in London: small area

study. BMJ 347:f5432, PMID:

24103537, https://doi.org/10.1136/bmj.f5432.

Helweg-Larsen K. 2011. The Danish Register of Causes of Death. Scand J

Public Health 39(7 Suppl):26–29, PMID:

21775346, https://doi.org/10.1177/

1403494811399958.

Héritier H, Vienneau D, Foraster M, Eze IC, Schaffner E, Thiesse L, et al. 2017.

Transportation noise exposure and cardiovascular mortality: a nationwide

cohort study from Switzerland. Eur J Epidemiol 32(4):307–315, PMID:

28280950,

https://doi.org/10.1007/s10654-017-0234-2.

Jalali L, Bigelow P, Nezhad-Ahmadi MR, Gohari M, Williams D, McColl S. 2016.

Before-after field study of effects of wind turbine noise on polysomnographic

sleep parameters. Noise Health 18(83):194–205, PMID:

27569407, https://doi.org/10.

4103/1463-1741.189242.

Janssen SA, Vos H, Eisses AR, Pedersen E. 2011. A comparison between

exposure-response relationships for wind turbine annoyance and annoy-

ance due to other noise sources. J Acoust Soc Am 130(6):3746–3753, PMID:

22225031, https://doi.org/10.1121/1.3653984.

Jeffery RD, Krogh CM, Horner B. 2014. Industrial wind turbines and adverse health

effects. Can J Rural Med 19(1):21–26, PMID:

24398354.

Jensen VM, Rasmussen AW. 2011. Danish Education Registers. Scand J Public Health

39(7 Suppl):91–94, PMID:

21775362, https://doi.org/10.1177/1403494810394715.

Kragh J, Plovsing B, Storeheier SÅ, Jonasson HG. 2001. Nordic Environmental

Noise Prediction Methods, Nord2000 Summary Report. General Nordic Sound

Propagation Model and Applications in Source-Related Prediction Methods.

Lyngby, Denmark: DELTA (Danish Electronics, Light & Acoustics).

http://assets.

madebydelta.com/docs/share/Akustik/Nordic_Environmental_Noise_Prediction_

Methods_Nord2000_Summary_Report_-_Prediction_Methods.pdf

[accessed 21

February 2019].

000000-9

00(0) 0 0000

Lynge E, Sandegaard JL, Rebolj M. 2011. The Danish National Patient Register.

Scand J Public Health 39(7 Suppl):30–33, PMID:

21775347, https://doi.org/10.

1177/1403494811401482.

Michaud DS, Feder K, Keith SE, Voicescu SA, Marro L, Than J, et al. 2016a.

Exposure to wind turbine noise: perceptual responses and reported health

effects. J Acoust Soc Am 139(3):1443–1454, PMID:

27794326, https://doi.org/10.

1121/1.4964754.

Michaud DS, Feder K, Keith SE, Voicescu SA, Marro L, Than J, et al. 2016b. Effects

of wind turbine noise on self-reported and objective measures of sleep. Sleep

39(1):97–109, PMID:

26518593, https://doi.org/10.5665/sleep.5326.

Michaud DS, Feder K, Keith SE, Voicescu SA, Marro L, Than J, et al. 2016c. Self-

reported and measured stress related responses associated with exposure to

wind turbine noise. J Acoust Soc Am 139(3):1467–1479, PMID:

27036285,

https://doi.org/10.1121/1.4942402.

Miljø- og Fødevareministeriet. 2011. Bekendtgørelse om støj fra vindmøller [in

Danish]. j.nr. MST-5114-00070.

Miljøministeriet. 2007. Støj fra veje [in Danish]. Vejledning fra Miljøstyrelsen Nr. 4.

Münzel T, Daiber A, Steven S, Tran LP, Ullmann E, Kossmann S, et al. 2017a.

Effects of noise on vascular function, oxidative stress, and inflammation:

mechanistic insight from studies in mice. Eur Heart J 38(37):2838–2849, PMID:

28329261, https://doi.org/10.1093/eurheartj/ehx081.

Münzel T, Sorensen M, Gori T, Schmidt FP, Rao X, Brook FR, et al. 2017b.

Environmental stressors and cardio-metabolic disease: part II-mechanistic

insights. Eur Heart J 38:557–564, PMID:

27460891, https://doi.org/10.1093/

eurheartj/ehw294.

Pedersen E. 2011. Health aspects associated with wind turbine noise - Results

from three field studies. Noise Control Eng J 59(1):47–53,

https://doi.org/10.

3397/1.3533898.

Peña A, Hahmann AN. 2017. 30-year mesoscale model simulations for the

“Noise

from Wind Turbines and Risk of Cardiovascular Disease” project. DTU Wind

Energy E: Volume 0055.

http://orbit.dtu.dk/files/130825582/DTU_WindEnergyReport_

0055_EN_.pdf%20

[accessed 21 February 2019].

Petersson F, Baadsgaard M, Thygesen LC. 2011. Danish registers on personal

labour market affiliation. Scand J Public Health 39(7 Suppl):95–98, PMID:

21775363, https://doi.org/10.1177/1403494811408483.

Pyko A, Eriksson C, Oftedal B, Hilding A, Östenson CG, Krog NH, et al. 2015.

Exposure to traffic noise and markers of obesity. Occup Environ Med

72(8):594–601, PMID:

26009579, https://doi.org/10.1136/oemed-2014-102516.

Roswall N, Ammitzbøll G, Christensen JS, Raaschou-Nielsen O, Jensen SS,

Tjønneland A, et al. 2017. Residential exposure to traffic noise and leisure-time

sports - a population-based study. Int J Hyg Environ Health 220(6):1006–1013,

PMID:

28579229, https://doi.org/10.1016/j.ijheh.2017.05.010.

Roswall N, Christensen JS, Bidstrup PE, Raaschou-Nielsen O, Jensen SS, Tjønneland

A, et al. 2018. Associations between residential traffic noise exposure and smok-

ing habits and alcohol consumption-a population-based study. Environ Pollut

236:983–991, PMID:

29122366, https://doi.org/10.1016/j.envpol.2017.10.093.

Schmidt FP, Basner M, Kröger G, Weck S, Schnorbus B, Muttray A, et al. 2013.

Effect of nighttime aircraft noise exposure on endothelial function and stress

hormone release in healthy adults. Eur Heart J 34(45):3508–3514a, PMID:

23821397, https://doi.org/10.1093/eurheartj/eht269.

Schmidt JH, Klokker M. 2014. Health effects related to wind turbine noise exposure: a

systematic review. PLoS One 9(12):e114183, PMID:

25474326, https://doi.org/10.

1371/journal.pone.0114183.

Schmidt F, Kolle K, Kreuder K, Schnorbus B, Wild P, Hechtner M, et al. 2015.

Nighttime aircraft noise impairs endothelial function and increases blood

pressure in patients with or at high risk for coronary artery disease. Clin Res

Cardiol 104(1):23–30, PMID:

25145323, https://doi.org/10.1007/s00392-014-

0751-x.

Schmidt M, Pedersen L, Sørensen HT. 2014. The Danish Civil Registration System

as a tool in epidemiology. Eur J Epidemiol 29(8):541–549, PMID:

24965263,

https://doi.org/10.1007/s10654-014-9930-3.

Søndergaard B, Plovsing B, Sørensen T. 2009. Validation of the Nord2000 propaga-

tion model for use on wind turbine noise. PSO-07 F&U project no.7389 Final

report.

http://assets.madebydelta.com/docs/share/Akustik/Noise_and_energy_

optimization_of_wind_farms.pdf

[accessed 21 February 2019].

Sondergaard LS, Backalarz C. 2015. Noise from wind turbines and health effects -

Investigation of wind turbine noise spectra. International Conference on Wind

Turbine Noise. Glasgow, UK.

Sondergaard LS, Plovsing B, Sørensen T. 2009. Noise and energy optimization of wind

farms, Validation of the Nord2000 propagation model for use on wind turbine

noise. Hørsholm, Denmark: DELTA (Danish Electronics, Light & Acoustics).

Sørensen M, Hvidberg M, Andersen ZJ, Nordsborg RB, Lillelund KG, Jakobsen J,

et al. 2011. Road traffic noise and stroke: a prospective cohort study. Eur Heart

J 32(6):737–744, PMID:

21266374, https://doi.org/10.1093/eurheartj/ehq466.

van Kempen E, Babisch W. 2012. The quantitative relationship between road traffic

noise and hypertension: a meta-analysis. J Hypertens 30(6):1075–1086, PMID:

22473017, https://doi.org/10.1097/HJH.0b013e328352ac54.

Vienneau D, Schindler C, Perez L, Probst-Hensch N, Röösli M. 2015. The relation-

ship between transportation noise exposure and ischemic heart disease: a

meta-analysis. Environ Res 138:372–380, PMID:

25769126, https://doi.org/10.

1016/j.envres.2015.02.023.

Waye KP, Clow A, Edwards S, Hucklebridge F, Rylander R. 2003. Effects of night-

time low frequency noise on the cortisol response to awakening and subjec-

tive sleep quality. Life Sci 72(8):863–875, PMID:

12493567.

Environmental Health Perspectives

000000-10

00(0) 0 0000