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/EHP3909.

Impact of Long-Term Exposure to Wind Turbine Noise on Redemption of Sleep

Medication and Antidepressants: A Nationwide Cohort Study

Aslak Harbo Poulsen,

Ole Raaschou-Nielsen,

1,2

Alfredo Peña,

Andrea N. Hahmann,

Rikke Baastrup Nordsborg,

Matthias Ketzel,

2,5

Jørgen Brandt,

and Mette Sørensen

1,4

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

Department of Environmental Science, Aarhus University, Roskilde, Denmark

DTU Wind Energy, Department of Wind Energy, Technical University of Denmark, 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 associated with annoyance and, potentially, sleep disturbances.

BJECTIVES

Our objective was to investigate whether long-term WT noise (WTN) exposure is associated with the redemption of prescriptions for

sleep medication and antidepressants.

ETHODS

For all Danish dwellings within a radius of 20-WT heights and for 25% of randomly selected dwellings within a radius of 20-to 40-WT

heights, we estimated nighttime outdoor and low-frequency (LF) indoor WTN, using information on WT type and simulated hourly wind. During

follow-up from 1996 to 2013, 68,696 adults redeemed sleep medication and 82,373 redeemed antidepressants, from eligible populations of 583,968

and 584,891, respectively. We used Poisson regression with adjustment for individual and area-level covariates.

ESULTS

Five-year mean outdoor nighttime WTN of

≥42

dB was associated with a hazard ratio (HR) = 1.14 [95% conﬁdence interval (CI]: 0.98,

1.33) for sleep medication and HR = 1.17 (95% CI: 1.01, 1.35) for antidepressants (compared with exposure to WTN of <24 dB). We found no over-

all association with indoor nighttime LF WTN. In age-stratiﬁed analyses, the association with outdoor nighttime WTN was strongest among persons

≥65

y of age, with HRs (95% CIs) for the highest exposure group (≥42 dB) of 1.68 (1.27, 2.21) for sleep medication and 1.23 (0.90, 1.69) for antide-

pressants. For indoor nighttime LF WTN, the HRs (95% CIs) among persons

≥65

y of age exposed to

≥15

dB were 1.37 (0.81, 2.31) for sleep medi-

cation and 1.34 (0.80, 2.22) for antidepressants.

ONCLUSIONS

We observed high levels of outdoor WTN to be associated with redemption of sleep medication and antidepressants among the el-

derly, suggesting that WTN may potentially be associated with sleep and mental health.

https://doi.org/10.1289/EHP3909

Introduction

Over the last several decades, wind power deployment has

increased markedly worldwide, with a rise in the global cumulative

wind capacity from 23 GW in 2001 to 487 GW in 2016 (GWEC

2017).

In Denmark, wind power provides more than 40% of the

national electricity consumption, which is the highest proportion

worldwide. This has led to a growing number of persons being

exposed to noise from wind turbines (WTs), followed by a rise in

the number of persons complaining that WT noise (WTN) impacts

their lives negatively through noise annoyance, disturbance of

sleep, and other adverse health eﬀects (Schmidt

and Klokker

2014).

Epidemiological studies have consistently found that emission

of noise from WTs is associated with annoyance (Guski

et al.

2017; Hongisto et al. 2017; Michaud et al. 2016d).

Exposure–

response curves show that WTN is associated with a higher pro-

portion of highly annoyed persons than traﬃc noise at compara-

ble levels (Janssen

et al. 2011; Michaud et al. 2016d).

Potential

explanations include that WTN, which depends on wind speed

and direction, is less predictable for those exposed than other

noise sources such as road traﬃc noise. In addition, onshore WTs

Address correspondence to Aslak Harbo Poulsen, Danish Cancer Society

Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.

Telephone: 45 3525 7614. E-mail:

[email protected]

Supplemental Material is available online (https://doi.org/10.1289/EHP3909).

The authors declare they have no actual or potential competing

ﬁnancial

interests.

Received 14 May 2018; Revised 14 December 2018; Accepted 7 February

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.

are typically erected in rural areas, where people often expect

silent surroundings and where the sound from WTs may be more

noticeable than in urbanized areas. Furthermore, amplitude mod-

ulation gives WTN a rhythmic quality diﬀerent from traﬃc noise,

and it has been suggested that the characteristics of WTN rele-

vant for annoyance may be better captured by metrics focusing

on amplitude modulation or low-frequency (LF) noise, rather

than the full spectrum A-weighted noise (Jeﬀery

et al. 2014;

Schäﬀer et al. 2016).

Studies have indicated that exposure to WTN is associated

with the disturbance of sleep, and the potential mechanisms

include a direct association with nighttime noise, disturbance of

sleep through annoyance, or a combination of the two (Bakker

et al.

2012).

A meta-analysis from 2015 based on 1,039 persons from

six cross-sectional studies using questionnaires to assess informa-

tion on sleep disturbance, found that exposure to WTN increased

the odds for self-reported sleeping problems (Onakpoya

et al.

2015).

The investigators, however, wrote that the results should be

interpreted with caution due to large variations in the estimations

of noise and self-reported sleep disturbance across the included

studies. Since the meta-analysis in 2015, a Japanese study of 1,079

persons found that outdoor WTN levels >40 dB were associated

with self-reported insomnia (Kageyama

et al. 2016).

Interestingly,

a cross-sectional Canadian study of 1,238 persons found no associa-

tions between 1-y mean outdoor WTN and various measures of

sleep, including both subjective self-reported information of sleep

quality and use of sleep medication as well as objective measures of

sleep (Michaud

et al. 2016a, 2016b).

Thus, it remains uncertain

from which exposure levels and to what extent WTN disturbs sleep.

A few studies have investigated whether WTN is associated

with mental health, which was mainly assessed as self-reported

quality of life (Feder

et al. 2015; Jalali et al. 2016; Onakpoya et al.

2015).

While a systematic review from 2015 based on four cross-

sectional studies concluded that living in areas with WTs might

be associated with decreased quality of life (Onakpoya

et al.

2015),

a recent large Canadian study found no association (Feder

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

et al. 2015).

In addition, a study based on 31 participants with

self-reported information on quality of life before and after instal-

lation of WTs, found a worsening in diﬀerent components of

quality of life such as the mental component score (Jalali

et al.

2016).

Last, the large Canadian study also investigated whether

outdoor 1-y WTN noise was associated with self-reported anxiety

or depression medication but found no association (Michaud

et al.

2016b).

The existing studies on WTN and sleep and mental health are

generally of cross-sectional design and rely on active participa-

tion and self-reported data. We aimed to investigate whether

long-term residential exposure to WTN was associated with the

redemption of prescriptions for sleep medication and antidepres-

sants in a prospective, nationwide register-based cohort.

Methods

Study Base and Modeling of Noise

In Denmark, all WT owners are required to report the cadastral

code and geographical coordinate of their WT(s) to the national

Master Data Register of Wind Turbines. For WTs in operation at

the time of data extraction, this register also included WT coordi-

nates from the Danish Geodata Agency. In this register, we iden-

tiﬁed 7,860 WTs in operation at any time during the period 1980–

2013. We then excluded 517 oﬀshore WTs. In case of disagree-

ment between the geographical information recorded in the regis-

ter, the WT location was validated against historical topographic

maps and aerial photographs. New coordinates were assigned to

the 314 WTs that were incorrectly recorded in the register, and

87 WTs were excluded because no credible location could be

established, leaving 7,256 WTs for noise modeling. For these

WTs, we collected information on model, type, height, and

operational settings (where relevant). Subsequently, each WT

was classiﬁed into one of 99 noise spectra classes, with detailed

information on the noise spectrum from 10–10,000 Hz in thirds

of octaves for wind speeds from 4–25 m=s. The noise classes

were determined from existing measurements of noise spectra for

Danish WTs (Backalarz

et al. 2016; Sondergaard and Backalarz

2015).

We estimated the hourly wind speed and direction at hub

height for each WT location for the period 1982–2013. This was

done using mesoscale model simulations performed with the

Weather Research and Forecasting model (Hahmann

et al. 2015;

Peña and Hahmann 2017).

For each WT location, the simulations

also provided data on relative humidity and temperature at a

height of 2 m and data on atmospheric stability, which were all

used for noise modeling.

The modeling of WTN has been described in detail elsewhere

(Backalarz

et al. 2016).

Brieﬂy, we initially identiﬁed buildings

eligible for detailed noise modeling, deﬁned as all dwellings that

could experience at least 24 dB outdoor noise or 5 dB indoor LF

noise (10–160 Hz) under the (unrealistic) scenario that all WTs

ever operational in Denmark were operating at the same time at

8 m=s wind speed, with downwind sound propagation in all direc-

tions. Subsequently, 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

10–10,000 Hz with the Nord2000 noise propagation model (Kragh

et al. 2001)

and using the simulated hourly weather conditions as

input variables. The Nord2000 model has been successfully vali-

dated for WTs (Sondergaard

et al. 2009).

For each dwelling, we

modeled hourly noise contributions from all WTs within a 6-km

radius. These modeled values were averaged over the nighttime

period (2200–0700 hours), which we considered the most relevant

time window because people are likely to be in their homes and

Environmental Health Perspectives

asleep at that time. We calculated outdoor A-weighted sound pres-

sure level (10–10,000 Hz)—a metric commonly used in health

studies (Michaud

et al. 2016c; Pedersen 2011)—and

A-weighted

indoor LF (10–160 Hz) sound pressure level because LF noise is

less attenuated by distance and passage through typical building

materials and has been proposed to be an important component of

WTN in relation to health (Jeﬀery

et al. 2014).

We did not model

WTN in detail for situations where the 24-dB outdoor noise and

5-dB indoor LF noise limit would not be exceeded even under the

unrealistic scenario that all WTs ever operational in Denmark were

operating at the same time at 8 m=s wind speed, with downwind

sound propagation in all directions given that people living in these

buildings would, regardless of exposure level, be categorized in

the reference category.

The quality of the noise spectra available for diﬀerent WT

models diﬀered, and these spectra were typically only described

at certain wind speeds. We therefore determined a validity score

that for each night and dwelling summed up information for all

contributing WTs on the number of measurements used to deter-

mine the WTN spectra class and how closely the simulated mete-

orological conditions of each night resembled the conditions

under which the relevant WTN spectra were measured.

In the calculation of indoor LF noise, we classiﬁed all dwell-

ings into one of six sound insulation classes based on building

characteristics listed in the Building and Housing register

(Christensen

2011):

“1�½-story

houses” (inhabitants presumed to

sleep on second

ﬂoor),

“light

façade” (e.g., wood),

“aerated

con-

crete” (as well as similar materials such as timber framing),

“farm

houses” (remaining buildings classiﬁed as farms in the registry),

“brick

buildings,” and

“unknown”

(which were assigned the mean

attenuation value of the

ﬁve

other classes). The frequency-speciﬁc

attenuation values for these insulation classes have been presented

previously by Backalarz et al. (2016).

Study Population

We found all Danish dwellings ever situated within a radius of

20-WT heights of a WT as well as a random selection of 25% of

all dwellings situated 20-to 40-WT heights away. We excluded

residential institutions, hospitals, and dwellings situated within

100 m of areas classiﬁed as a

“town

center” because the type of

dwellings, traﬃc, and lifestyle in town centers may diﬀer sub-

stantially from town center–type areas of the main study popula-

tion. All inhabitants between 25 and 85 y of age and living at

least 1 y in one of these dwellings determining eligibility for the

study (“eligibility dwellings”), from 5 y before WT erection

(from start of follow-up in 1996) until 2013, were subsequently

found in the Danish Civil Registration System (Schmidt

et al.

2014).

This extended time frame ensured the inclusion of people

living in exactly the same dwellings before erection (or after

decommissioning) of a WT. Persons were included in the study

population after living 1 y in an eligibility dwelling. Afterward,

we obtained complete address histories from 5 y before study

entry until 5 y after moving from the eligibility dwelling for all

persons living at least 1 y in an eligibility dwelling. Persons with

an incomplete address history for the 5 y preceding entry were

excluded.

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 studies based entirely

on registries.

Covariates

We selected potential confounders

a priori.

From Statistics

Denmark, we obtained data on age and sex, personal income

00(0) 0 0000

000000-2

(time-dependent), highest attained educational level (time-

dependent), work-market aﬃliation (time-dependent), marital status

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

) mean household

income. The type of dwelling was extracted from the Building

and Housing Register (Christensen

2011).

As proxies for local

road traﬃc noise and air pollution, we identiﬁed for each dwell-

ing the total daily distance driven by vehicles within a 500-m ra-

dius as well as the distance to the nearest road with an average

daily traﬃc count of

≥5,000

vehicles (in 2005).

Redemption of Sleep Medication and Antidepressants

We collected information on redeemed prescriptions for sleep

medication and antidepressants from the Danish National

Prescription Registry, which contains data on all prescription

drugs sold in Denmark since 1995 (Kildemoes

et al. 2011).

The

the name and type of drug prescribed according to the Anatomic

Therapeutic Chemical (ATC) system (WHO

Collaborating Centre

for Drug Statistics Methodology 2012).

The indication for pre-

scribing was not available. We used these data to identify persons

who redeemed prescriptions for orally administered sleep medica-

tion (ATC: N05CC-CF, N05CH except N05CD08or antidepres-

sants [ATC: N06AA, AB, AF, AG, AX except N06AX12 and

Yntreve

(from ATC group N06AX21)].

Because cases redeeming prescriptions upon start of the regis-

ter in 1995 could have included prevalent cases from before the

start of the register, we excluded all persons with a redeemed rel-

evant prescription before 1996 or the start of the follow-up

period.

disposable income (20 categories of equal size and unknown),

type of dwelling (farm, single-family detached house, and other),

traﬃc load within a 500-m radius of the dwelling (ﬁrst and sec-

ond quartile and above median) and distance to the nearest road

with >5,000 vehicles per day (<500 m, 500 to <1,000 m, 1,000 to

<2,000 m and

≥2,000

m). Subjects were allowed to change

between categories of covariates and exposure variables over

time.

We investigated sex and age (above and below 65 y of age) as

potential eﬀect modiﬁers in the Poisson model by stratiﬁed analy-

sis and by including an interaction term. Furthermore, we investi-

gated associations between 5-y mean exposures and redemption

of sleep medication and antidepressants in subpopulations for

whom we hypothesized that a potential association between ex-

posure and risk could be more conspicuous: living on a farm

(potentially less variation in lifestyle and other exposures in this

subpopulation, which may reduce the potential for residual con-

founding in this group, although it is important to note that this

subpopulation may diﬀer substantially from the study popula-

tion); nearest WT with a total height of >35 m; high validity of

noise estimate; dwelling far from major road (>2 km to the near-

est road with >5,000 vehicles per day); and low tree coverage

deﬁned as less than 5% covered by forest, thicket, groves, single

trees, or hedgerows within 500 m of the dwelling (because we

assumed that vegetation beyond this distance would be nearly

indiscernible from background noise). Data were analyzed using

SAS (version 9.3; SAS Institute Inc.).

Results

We identiﬁed 758,736 adults (25–84 y of age) living

≥1

y in one

of the dwellings determining eligibility. We excluded persons

who had emigrated (n = 43,794) or did not have a registered

address in the address registry (n = 1,573) prior to entry, who had

an unknown address for

≥8

consecutive days in the 5 y prior to

entry (n = 59,318), or who lived in hospitals or institutions at

study start of follow-up (n = 1,586). In addition, we excluded

26,700 persons who, before the start of the follow-up period, had

redeemed both sleep medication and antidepressants. After exclu-

sion of 41,797 people redeeming sleep medication before the start

of follow-up, the

ﬁnal

study population for the sleep medication

analyses was 583,968 people of whom 68,696 had redeemed sleep

medication during 4,974,043 person-years. The

ﬁnal

study popula-

tion for antidepressants analyses was 584,891 people (after exclu-

sion of 40,874 people who had redeemed antidepressants before

the start of follow-up) of whom 82,373 redeemed antidepressants

during 4,986,327 person-years. The median age at

ﬁrst

redemption

was 56.9 y (5th–95th percentiles: 31.8–80.3) for sleep medication

and 54.2 y (5th–95th percentiles: 29.9–81.3) for antidepressants.

The two populations for the study of redemption of sleep

medication and antidepressants, respectively, were very similar

with regard to characteristics at entry (Table

1).

For both of these

study populations, persons exposed to

≥36-dB

outdoor nighttime

WTN were at entry younger, more often working, more often liv-

ing on farms and in areas with higher income, more often living

far from a major road and with low traﬃc density, and more often

living in a dwelling with low tree coverage as compared with per-

sons exposed to <36 dB (Table

1).

Furthermore, persons exposed

≥42-dB

outdoor nighttime WTN entered the study earlier, had

slightly higher education levels, had higher personal incomes,

and were more often married as compared with persons exposed

to <42 dB. We found similar tendencies for indoor nighttime LF

WTN as for outdoor nighttime WTN, although for this exposure,

participants in both of the two high-exposure categories (10–15

and

≥15

dB) had higher educations and more often were never

married (see Table S1). Furthermore, both of the two high-

00(0) 0 0000

Statistical Analyses

Log-linear Poisson regression analysis was used to calculate haz-

ard ratios (HRs) for redemption of sleep medication or depression

(as two separate outcomes) according to outdoor nighttime WTN

(<24, 24 to <30, 30 to <36, 36 to <42, and

≥42

dB) or indoor

nighttime LF WTN (<5, 5 to <10, 10 to <15, and

≥15

dB) expo-

sure, calculated as running means over the preceding 1 and 5 y.

The categorizations were determined

a priori.

At present, there

are no standards regarding categorizations of WTN. After con-

sulting acoustical experts we chose <24 dB outdoor and <5 dB

indoor LF WTN as references because the acousticians evaluated

that WTN in all likelihood would be inaudible below these levels.

For outdoor WTN, the upper limit of 42 dB was chosen because

this is the regulatory WTN limit in Denmark (at a wind speed of

6 m=s) and, therefore, of interest from an administrative point of

view, and the intermediate cut points chosen were 30 and 36 dB,

which separated categories by 6 dB.

When calculating running means, we applied a value of

−20

dB for situations in which noise had not been estimated

(when wind conditions or the distance to WTs made WTN above

24 dB outdoor or 5 dB indoor impossible). We started follow-up

after participants had been living 1 y in the recruitment dwelling,

turned 25 y of age or 1 January 1996, whichever came last, and

stopped at 31 December 2013, 85 y of age, disappearance, death,

5 y after moving from the eligibility dwelling, having no recorded

address in Denmark for

≥8

d, or at date of fulﬁlling our case cri-

teria, whichever came

ﬁrst.

We adjusted all analyses for sex, calendar year (1996–1999,

2000–2004, 2005–2009, and 2010–2013) and age (25–85 y of

age, in 5-y categories). Furthermore, we adjusted for education

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

income (20 annual categories of equal size and unknown), marital

status (married or registered partnership and other), work-market

aﬃliation (employed, retired, and other), area-level average

Environmental Health Perspectives

000000-3

Table 1.

Characteristics of the populations for study of redemption of sleep medication and antidepressants, respectively, at start of follow-up according to resi-

dential A-weighted exposure to outdoor wind turbine noise calculated as mean exposure during the preceding year.

<36 dB Sleep/antidepressants

(n = 575,899=576,857) (%)

52/52

45/43

19/19

16/16

21/22

55/57

14/14

20/20

10/9

20/20

24/23

26/26

25/25

6/6

35/35

43/42

16/16

6/7

55/56

14/14

31/30

69/69

18/19

13/13

23/23

28/28

19/19

2/2

13/13

62/62

24/24

35/35

27/27

37/37

34/34

25/25

19/19

22/23

13/13

63/63

24/24

Outdoor wind turbine noise

36–42 dB Sleep/antidepressants

(n = 6,704=6,637) (%)

54/54

51/50

20/20

15/15

56/57

19/20

16/15

9/8

21/21

25/24

26/25

22/23

6/6

36/36

45/45

15/15

4/4

52/53

12/12

36/36

75/75

13/13

12/12

11/11

28/28

34/34

20/20

7/7

40/40

51/51

9/9

17/17

26/26

57/57

69/69

13/13

12/13

6/6

30/29

63/63

7/7

≥42

dB Sleep/antidepressants

(n = 1,365=1,397) (%)

53/54

46/44

23/22

18/18

13/15

73/74

17/17

7/7

3/3

20/21

22/22

24/23

28/28

7/6

37/37

39/38

21/21

3/4

62/63

11/11

27/26

80/78

9/11

11/11

14/14

21/21

35/36

24/23

6/6

40/41

51/50

9/9

18/18

25/25

58/57

66/67

16/15

9/10

8/8

29/28

62/63

9/9

Characteristics at entry

Men

Age (y)

<40

40–50

50–60

≥60

Year of entry

1996–2000

2001–2005

2006–2010

2011–2013

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

Attachment to labor 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 (m)

<500

500–2,000

≥2,000

Traffic load within 500 m (10

vehicles km=d)

<2:5

2.5–5.3

5.3–9.7

>9:7

Tree coverage (%)

5–20

>20

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

Major road defined as

≥5,000

vehicles per day.

In a 500-m radius around the dwelling.

exposure categories (10–15 and

≥15

dB) entered the study later

than persons exposed to <10 dB.

We found that 78% of the sleep medication–study population

and 79% of the antidepressant-study population at entry lived in

dwellings with <24-dB outdoor nighttime WTN and that, for

both study populations, 97% lived in dwellings with indoor night-

time LF WTN <5 dB (see Table S2). Of those exposed to WTN

above 42 dB or 15 dB LF, the majority lived within 500 m of a

WT, whereas in the reference population less than 10% lived

<500 m from a WT. In addition, we found that people with

outdoor nighttime WTN exposure of

≥42

dB more often had a

shorter WT (<35 m) as the nearest WT, whereas people with

indoor nighttime LF WTN of

≥10

dB more often had a higher

WT (>70 m) as their nearest WT (see Table S2). We found high

correlations for both outdoor and indoor WTN between 1- and

5-y mean exposures, whereas the correlations between indoor

and outdoor WTN were lower (see Table S3).

In adjusted analyses, we found that persons exposed to 5-y

mean outdoor nighttime WTN levels >42 dB had a 14% higher

risk of redeeming sleep medication [HR = 1:14 (95% CI: 0.98,

Environmental Health Perspectives

000000-4

00(0) 0 0000

Table 2.

Associations between mean 1- and 5-y exposure to residential A-weighted outdoor wind turbine noise and redemption of prescriptions for sleep medi-

cation and antidepressants.

Outdoor wind turbine noise

1-y mean exposure (dB)

<24

24–30

30–36

36–42

≥42

5-y mean exposure (dB)

<24

24–30

30–36

36–42

≥42

Cases (n)

50,262

13,032

4,415

842

145

50,559

13,021

4,133

814

169

Sleep medication

Crude HR (95% CI)

Adjusted HR (95% CI)

1 (Ref)

0.98 (0.96, 0.99)

0.96 (0.93, 0.99)

0.95 (0.89, 1.02)

0.99 (0.84, 1.17)

1 (Ref)

1.00 (0.98, 1.02)

0.97 (0.93, 1.00)

0.98 (0.92, 1.05)

1.06 (0.91, 1.23)

1 (Ref)

1.01 (0.99, 1.03)

1.04 (1.00, 1.07)

1.05 (0.98, 1.13)

1.08 (0.92, 1.28)

1 (Ref)

1.03 (1.01, 1.05)

1.03 (1.00, 1.06)

1.08 (1.00, 1.15)

1.14 (0.98, 1.33)

Cases (n)

60,205

15,782

5,295

930

161

60,315

15,958

5,016

899

185

Antidepressants

Crude HR (95% CI)

Adjusted HR (95% CI)

1 (Ref)

0.98 (0.96, 1.00)

0.95 (0.93, 0.98)

0.89 (0.84, 0.95)

0.99 (0.85, 1.15)

1 (Ref)

1.01 (0.99, 1.02)

0.96 (0.94, 0.99)

0.92 (0.86, 0.98)

1.05 (0.90, 1.21)

1 (Ref)

1.00 (0.98, 1.02)

1.01 (0.99, 1.04)

0.99 (0.93, 1.05)

1.12 (0.96, 1.31)

1 (Ref)

1.02 (1.00, 1.04)

1.02 (0.99, 1.05)

1.01 (0.95, 1.08)

1.17 (1.01, 1.35)

Note: CI, confidence interval; HR, hazard ratio; Ref, reference.

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 a 500-m ra-

dius, and distance to nearest major road.

1.33)] and a 17% higher risk of redeeming antidepressants

[HR = 1:17 (95% CI: 1.01, 1.35)] when compared to persons

exposed to <24 dB (Table

2).

For antidepressants, similar,

although weaker, tendencies were seen for 1-y mean exposures to

outdoor nighttime WTN, with HRs for the

≥42-dB

exposure

group of 1.12 (0.96, 1.31). For sleep medication, risk estimates

remained close to the null even at high exposure. In general, the

unadjusted risk estimates were lower than the adjusted risk esti-

mates, with no clear suggestions of increased risk. The most in-

ﬂuential

confounder was dwelling type. For indoor nighttime LF

WTN, we found no association between 1- or 5-y exposure and

risk of redeeming sleep medication or antidepressants (Table

3).

In analyses stratiﬁed by age, we found that outdoor nighttime

WTN exposure among persons >65 y of age was associated with

a higher risk of redeeming sleep medication, whereas for persons

<65 y of age there was no association (Table

4).

Furthermore,

among persons >65 y of age, the association with outdoor night-

time WTN seemed to follow an exposure–response relationship,

with HR = 1.22 (95% CI: 1.08, 1.38) in the 36–42 dB exposure

group and HR = 1.68 (95% CI: 1.27, 2.21) in the

≥42

dB expo-

sure group. Similar tendencies were seen for people redeeming

antidepressants, with HR = 1.27 (95% CI: 1.13, 1.43) in the

36–42 dB exposure group and HR = 1.23 (95% CI: 1.09, 1.69) in

the

≥42

dB exposure group among persons >65 y of age. There

were also indications of a higher risk of redeeming antidepres-

sants among persons <65 y of age in the highest outdoor WTN

exposure group. When stratifying the indoor nighttime LF WTN

analyses by age, we found similar tendencies as for outdoor

nighttime WTN for both outcomes, with HRs among persons

>65 y of age of 1.13 (95% CI: 0.97, 1.32) for 10–15 dB and 1.37

(95% CI: 0.81, 2.31) for

≥15

dB for redemption of sleep medica-

tion and of 1.09 (95% CI: 0.94, 1.26) for 10–15 dB and 1.34 (95%

CI: 0.80, 2.22) for

≥15

dB for redemption of antidepressants

(Table

5).

We found no associations between indoor nighttime

LF WTN and any of the two outcomes among persons <65 y of

age.

In outdoor nighttime WTN analyses stratiﬁed by sex, we

found for sleep medication that although the

p-value

for interac-

tion was below 0.05, the HRs in the two highest exposure catego-

ries were almost identical, whereas for antidepressants, the

association seemed to be conﬁned to men (Table

4).

For indoor

nighttime LF WTN, we found no marked diﬀerences in risks

between men and women for redeeming either sleep medication

or antidepressants (Table

5).

However, for indoor exposure, the

number of cases exposed to

≥15

dB was small.

When investigating eﬀects of outdoor nighttime WTN in dif-

ferent subpopulations, we found that among people living on

farms or with low tree coverage, the increase in risk in the highest

exposure group disappeared for both sleep medication and antide-

pressants (see Table S4). For the other subpopulations investi-

gated, we found no consistent patterns when comparing results

for sleep medication and antidepressants. For example, for highly

exposed people living far from major roads, the HR for an-

tidepressants = 1.25 (95% CI: 1.00, 1.55), whereas for sleep

Table 3.

Associations between mean 1- and 5-y exposure to residential indoor low-frequency wind turbine noise and redemption of prescriptions for sleep

medication and antidepressants.

Sleep medication

Antidepressants

Indoor low-frequency wind turbine noise Cases (n) Crude HR (95% CI) Adjusted HR (95% CI) Cases (n) Crude HR (95% CI)

Adjusted HR (95% CI)

1-year mean exposure (dB)

5–10

10–15

≥15

5-y mean exposure (dB)

5–10

10–15

≥15

64,617

3,299

726

65,202

2,911

542

1 (Ref)

0.94 (0.91, 0.98)

0.96 (0.89, 1.03)

0.93 (0.71, 1.22)

1 (Ref)

0.97 (0.93, 1.01)

0.93 (0.86, 1.02)

0.92 (0.68, 1.25)

1 (Ref)

1.03 (0.99, 1.06)

1.08 (1.00, 1.16)

1.05 (0.81, 1.38)

1 (Ref)

1.05 (1.01, 1.09)

1.04 (0.96, 1.14)

1.03 (0.76, 1.40)

77,360

4,073

882

77,995

3,663

672

1 (Ref)

0.93 (0.91, 0.96)

0.92 (0.86, 0.98)

0.82 (0.63, 1.06)

1 (Ref)

0.96 (0.93, 1.00)

0.90 (0.84, 0.97)

0.80 (0.59, 1.07)

1 (Ref)

1.01 (0.98, 1.04)

1.03 (0.97, 1.10)

0.96 (0.74, 1.24)

1 (Ref)

1.04 (1.00, 1.07)

1.01 (0.94, 1.10)

0.94 (0.70, 1.27)

Note: CI, confidence interval; HR, hazard ratio; Ref, reference.

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 a 500-m ra-

dius, and distance to nearest major road.

Environmental Health Perspectives

000000-5

00(0) 0 0000

Table 4.

Associations between 5-y exposure to outdoor wind turbine noise and redemption of sleep medication and antidepressants according to age and sex.

Subpopulations

Age (y)

<65

Exposure categories (dB)

<24

24–30

30–36

36–42

≥42

<24

24–30

30–36

36–42

≥42

<24

24–30

30–36

36–42

≥42

<24

24–30

30–36

36–42

≥42

Sleep medication

Cases (n)

Adjusted HR (95% CI)

33,895

8,691

2,833

550

118

16,664

4,330

1,300

264

22,204

6,067

1,950

381

28,355

6,954

2,183

433

1 (Ref)

1.03 (1.01, 1.05)

1.02 (0.98, 1.06)

1.02 (0.94, 1.11)

1.00 (0.84, 1.20)

1 (Ref)

1.03 (0.99, 1.06)

1.06 (1.00, 1.12)

1.22 (1.08, 1.38)

1.68 (1.27, 2.21)

0.03

1 (Ref)

1.06 (1.03, 1.10)

1.05 (1.00, 1.10)

1.08 (0.97, 1.19)

1.15 (0.92, 1.44)

1 (Ref)

1.00 (0.97, 1.03)

1.01 (0.97, 1.06)

1.08 (0.98, 1.19)

1.14 (0.92, 1.40)

25,379

7,047

2,274

423

34,936

8,911

2,742

476

1 (Ref)

1.04 (1.01, 1.06)

1.03 (0.99, 1.08)

1.06 (0.96, 1.16)

1.39 (1.14, 1.69)

1 (Ref)

1.01 (0.98, 1.03)

1.01 (0.97, 1.05)

0.98 (0.89, 1.07)

1.00 (0.81, 1.23)

p-Value

0.003

Antidepressants

Cases (n)

Adjusted HR (95% CI)

41,630

10,979

3,532

610

146

18,685

4,979

1,484

289

1 (Ref)

1.02 (1.00, 1.04)

1.00 (0.96, 1.03)

0.92 (0.85, 1.00)

1.15 (0.98, 1.36)

1 (Ref)

1.02 (0.98, 1.05)

1.07 (1.01, 1.13)

1.27 (1.13, 1.43)

1.23 (0.90, 1.69)

p-Value

0.0001

≥65

Sex

Men

0.08

Women

Note: CI, confidence interval; HR, hazard ratio; Ref, reference.

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

dius and distance to nearest major road.

for interaction.

medication, it remained unchanged. Among persons with high

validity of the outdoor noise estimate, we found that the risk for

redeeming antidepressants was slightly higher than in the overall

analysis [HR = 1.25 (95% CI: 0.89, 1.74)], and for sleep medica-

tion, the risk estimate among persons exposed to 36–42 dB

increased, whereas the risk in the highest exposure group disap-

peared [HR = 0.81 (95% CI: 0.52, 1.27); 19 cases; see Table S4].

With regard to indoor LF WTN in the same subpopulations, we

found the lack of association for both outcomes to be consistent

among people living on farms, whose nearest WT was

≥35

living far from a major road and with low tree coverage,

whereas among people redeeming sleep medication/antidepres-

sants after 2005, the estimate in the highest exposure group

(≥15 dB) was increased [HR = 1.12 (95% CI: 0.79, 1.57); see

Table S5]. There was a tendency toward a slight increase in

risk for redeeming sleep medication in the highest exposure

group among people with a high validity of the noise estimate

[HR = 1.20 (95% CI: 0.75, 1.90)], whereas for redeeming anti-

depressants, the lack of an association remained [HR = 0.92

(95% CI: 0.57, 1.49)].

Discussion

We found that high levels of long-term nighttime exposure to

outdoor WTN seemed associated with redemption of sleep medi-

cation and antidepressants in a large prospective study, whereas

Table 5.

Associations between 5-y exposure to indoor low-frequency wind turbine noise and redemption of sleep medication and antidepressants according to

age and sex.

Subpopulations

Age (y)

<65

Exposure categories (dB)

5–10

10–15

≥15

5–10

10–15

≥15

5–10

10–15

≥15

5–10

10–15

≥15

Sleep medication

Cases (n)

Adjusted HR (95% CI)

43,617

2,062

381

21,585

849

161

29,017

1,393

248

36,185

1,518

294

1 (Ref)

1.05 (1.00, 1.09)

1.01 (0.91, 1.12)

0.91 (0.63, 1.33)

1 (Ref)

1.06 (0.99, 1.13)

1.13 (0.97, 1.32)

1.37 (0.81, 2.31)

0.18

1 (Ref)

1.08 (1.02, 1.14)

1.00 (0.88, 1.13)

1.27 (0.84, 1.91)

1 (Ref)

1.02 (0.97, 1.08)

1.09 (0.97, 1.22)

0.83 (0.52, 1.32)

33,206

1,687

306

44,789

1,976

366

1 (Ref)

1.06 (1.01, 1.11)

1.00 (0.89, 1.12)

1.04 (0.68, 1.60)

1 (Ref)

1.02 (0.98, 1.07)

1.03 (0.93, 1.14)

0.86 (0.57, 1.30)

p-Value

0.40

53,739

2,640

490

24,256

1,023

182

1 (Ref)

1.02 (0.98, 1.06)

0.99 (0.90, 1.08)

0.81 (0.56, 1.17)

1 (Ref)

1.10 (1.03, 1.17)

1.09 (0.94, 1.26)

1.34 (0.80, 2.22)

0.70

Antidepressants

Cases (n)

Adjusted HR (95% CI)

p-Value

0.06

≥65

Sex

Men

Women

Note: CI, confidence interval; HR, hazard ratio; Ref, reference.

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

dius, and distance to nearest major road.

for interaction.

Environmental Health Perspectives

000000-6

00(0) 0 0000

for long-term indoor nighttime LF WTN, no associations were

found. We found the strongest associations between outdoor

nighttime WTN and redemption of sleep medication and antide-

pressants among persons >65 y of age compared with those

<65 y of age. In addition, for persons >65 y of age, high levels

of indoor nighttime LF WTN seemed to be associated with

redemption of sleep medication.

Our

ﬁnding

of an association between high exposure to outdoor

nighttime WTN and redemption of sleep medication is in accord-

ance with most (Kageyama

et al. 2016; Onakpoya et al. 2015)

but

not all (Michaud

et al. 2016a)

studies on WTN and sleep problems

(high exposure was generally deﬁned as >40–41 dB in these stud-

ies). In support of our results on high outdoor nighttime WTN and

depression, most (Jalali

et al. 2016; Onakpoya et al. 2015)

but not

all (Feder

et al. 2015)

of the few studies investigating WTN and

self-reported mental health indicated that living in areas with

WTs could decrease the quality of life. Overall, this suggests

that high levels of outdoor nighttime WTN is associated with

sleep disturbance and depression, although it is important to

note that most previous studies were cross-sectional (which

hampers conclusions on causality), relied on active participa-

tion and self-reported data, and were based on much smaller

study populations than the current study. That we see similar

associations for both sleep and antidepressant medication for

outdoor WTN strengthens the plausibility of both because it is

well established that disturbed sleep and mental health prob-

lems, including depression, interact through a complex bidirec-

tional relationship (Anderson

and Bradley 2013; Lopresti et al.

2013).

It is, however, noteworthy that we found no association

between indoor nighttime LF WTN and redemption of sleep

medication or antidepressants even though this exposure esti-

mate likely better reﬂects exposure during sleep. In a recent

study based on the current study population, we found indica-

tions that high levels of indoor LF WTN during the night may

trigger cardiovascular events, whereas for outdoor nighttime

WTN we found no association (Poulsen

et al. 2018).

A potential

explanation is that outdoor WTN may be associated with a

higher overall annoyance than indoor LF WTN given that peo-

ple are disturbed during their outdoor activities during the day.

However, further research is needed to elucidate this possibil-

ity, particularly because studies on both traﬃc and WT noise

have indicated that the eﬀect of annoyance on the association

between noise exposure, sleep disturbance, and mental health is

complex and as yet not fully understood (Bakker

et al. 2012;

Frei et al. 2014; Fyhri and Aasvang 2010; Héritier et al. 2014;

WHO 2009).

We found stronger associations with WTN among the el-

derly, especially with regard to sleep medication, where the

association seemed conﬁned to persons >65 y of age, with a

positive exposure–response relationship starting at relatively low

WTN levels. Furthermore, for this age group, high levels of indoor

nighttime LF WTN also seemed to be associated with the redemp-

tion of sleep medication. A potential explanation is that the elderly

may be particularly susceptible to health eﬀects from WTN given

that a number of changes in sleep structure occur with age (Cooke

and Ancoli-Israel 2011; Wolkove et al. 2007).

Older people gener-

ally spend more time in the lighter stages of sleep (stage 1 and 2)

and less time in deep sleep and REM sleep, which could lead to

higher risk for awakenings due to nighttime WTN. Furthermore,

the nocturnal sleep time of elderly is reduced and more frag-

mented, with an increased number of arousals and awakenings,

and thus they are potentially more easily disturbed by noise, worry,

and annoyance. In addition, one might speculate that persons

>65 y of age are more likely to be retired from work and therefore

at home during the daytime, which could potentially increase

Environmental Health Perspectives

annoyance due to WTN and help explain the increased HRs

observed for outdoor exposure.

For redemption of antidepressants, we observed similar trends

as for sleep medication: The association with outdoor WTN dur-

ing the night was stronger and started at lower levels among el-

derly compared with their younger counterparts, and there was a

suggestion of an association with high levels of indoor nighttime

LF WTN. As described above, there is a strong association

between sleep and depression (Anderson

and Bradley 2013;

Lopresti et al. 2013),

and the observed association between WTN

and depression mainly among the elderly could be explained by a

WTN-induced disturbance of sleep as well as a higher WTN-

annoyance due to spending more time at home. In addition,

depression in late life may present diﬀerently from depression in

younger adults, with higher prevalence of, for example, sleep dis-

turbance, loss of interest, and fatigue (Christensen

et al. 1999;

Fiske et al. 2009),

and the incidence of diagnosed depression in

later life is generally found to be lower than at younger ages

(Büchtemann

et al. 2012; Fiske et al. 2009).

Strengths of our study include the prospective nationwide

design with access to residential moving history for the study pe-

riod and the identiﬁcation of a large number of cases through

high-quality nationwide registers with high coverage and data

quality (Kildemoes

et al. 2011; Pottegård et al. 2017; Schmidt

et al. 2014).

We also had access to information on individual and

area-level confounders though national registries with high cov-

erage and validity (Baadsgaard

and Quitzau 2011; Jensen and

Rasmussen 2011; Petersson et al. 2011)

as well as information on

environmental confounders. Furthermore, we applied state-of-the

art exposure models to estimate exposures to WTN using input

data of high quality on hourly wind speed and direction at all

WTs and detailed WTN spectra for all types of WTs, which

allowed us to model noise during nighttime, which we found to

be the most relevant time period. First, during the daytime, many

people will be away from home, whereas during the nighttime,

we expect the majority of the population to be at home, and sec-

ond, for the sleeping medication outcome, this is the relevant

time window, but for depression, nighttime exposure is also very

relevant because we expect disturbance of sleep to be on the

mechanistic pathway. In addition, by taking sound insulation

characteristics of the types of dwelling into account, we estimated

the potentially more biologically relevant indoor LF WTN,

although we were only able to diﬀerentiate this into a few insula-

tion categories. Other strengths include the modeling of WTN for

all Danish dwellings potentially exposed to WTN and the inclu-

sion of persons from the same geographical areas but with little

or no WTN exposure.

The drugs used to deﬁne the outcomes in the current study are

only available by prescription in Denmark and the redemption of

these prescriptions is registered in an almost complete national

et al. 2011).

Furthermore, all Danes have

access to free universal healthcare and subsidized drug costs. We

therefore had an excellent sensitivity and speciﬁcity toward

redemption of sleep and antidepressant medication. There are,

however, some challenges associated with interpreting them as

proxies for sleep or depressive disorders. A 2013 survey of

160,000 randomly selected Danes found the prevalence of sleep

problems and

“feeling

depressed/unhappy” to be 41% and 29%,

respectively. In the current study, 12% of the study population

redeemed sleep medication and 14% redeemed antidepressants.

This reﬂects that only people with more severe problems are

likely to both contact a physician and to qualify for these drugs.

Although we expect the lack of information on people with

undiagnosed sleep problems and depression to be nondiﬀerential

with regard to exposure, it impairs sensitivity towards sleep

00(0) 0 0000

000000-7

disturbances or depressive conditions in general and our results,

therefore, pertain most directly to more severe sleep or depressive

conditions. Furthermore, our reliance on prescription data reduced

speciﬁcity towards sleep and depressive conditions because some

of the included drugs, particularly the antidepressants, also have

other indications, primarily for anxiety-related conditions. Any

bias resulting from this will depend on both the prevalence of these

conditions among our cases and their association with WTN.

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

have access to potential lifestyle confounders, such as physical

activity and alcohol consumption, and other factors that might

aﬀect the studied associations, such as orientation of the bed-

room and hearing loss. This is a weakness of our study. We

found that adjustment for individual and area-level socioeco-

nomic variables generally tended to increase estimates in the

highest exposure group. It is conspicuous that we found no

association for either outcome when restricting analyses to peo-

ple living on farms given that lifestyle and other exposures are

expected to be more similar within this subpopulation as com-

pared with the whole population. However, attitudes towards

WTN and health behavior may also diﬀer, which might contrib-

ute to the lack of association in this group. Another potential

explanation is a healthy-worker bias, and in exploratory analy-

ses restricted to farm dwellers >65 y of age, we found that ex-

posure to

≥42

dB was associated with an increased risk for the

use of sleep medication, whereas no association was observed

for antidepressants.

Other limitations include the rather crude adjustment for local

road traﬃc noise, using traﬃc load and distance to the nearest

major road. However, residual confounding by traﬃc noise is

unlikely to be a major problem in the current study because we

obtained similar estimates among people living far from major

roads as compared with the whole study population. In addition,

there is inevitable uncertainty in the modeled noise exposure, par-

ticularly in indoor LF, where we had to rely on relatively crude

data on building sound insulation. This uncertainty is likely to be

nondiﬀerential, inﬂuencing the estimates towards unity. To inves-

tigate this further, we used a validity score, which captured some

of the features of uncertainty of the noise modeling. For outdoor

WTN, we observed that for situations with high validity WTN,

the risk estimates for antidepressants were largely unaﬀected,

whereas for sleep medication, the estimate for 36–42 dB was ele-

vated and for

≥42

dB, decreased. However, for sleeping medica-

tion, only 19 of the 169 cases exposed to

≥42

dB had a high

validity score, resulting in high uncertainty for this subanalysis.

and co-funded by the Danish Ministry of Food and Environment

and the Danish Ministry of Energy, Utilities, and Climate.

References

Anderson KN, Bradley AJ. 2013. Sleep disturbance in mental health problems

and neurodegenerative disease. Nat Sci Sleep 5:61–75, PMID:

23761983,

https://doi.org/10.2147/NSS.S34842.

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

fer 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 turbines.

In: 45th International Congress and Exposition on Noise Control Engineering

(Internoise 2016): Towards a Quieter Future, vol. 1. Kropp W, von Estorff O,

Schulte-Fortkam B, eds., 21–24 August 2016. Hamburg, Germany:Deutsche

Gesellschft fur Akustik, e.V. (DEGA), 7862–7872.

Bakker RH, Pedersen E, van den Berg GP, Stewart RE, Lok W, Bouma J. 2012.

Impact of wind turbine sound on annoyance, self-reported sleep disturbance

and psychological distress. Sci Total Environ 425:42–51, PMID:

22481052,

https://doi.org/10.1016/j.scitotenv.2012.03.005.

Büchtemann D, Luppa M, Bramesfeld A, Riedel-Heller S. 2012. Incidence of late-

life depression: a systematic review. J Affect Disord 142(1–3):172–179, PMID:

22940498, https://doi.org/10.1016/j.jad.2012.05.010.

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 H, Jorm AF, Mackinnon AJ, Korten AE, Jacomb PA, Henderson AS,

et al. 1999. Age differences in depression and anxiety symptoms: a structural

equation modelling analysis of data from a general population sample. Psychol

Med 29(2):325–339, PMID:

10218924, https://doi.org/10.1017/S0033291798008150.

Cooke JR, Ancoli-Israel S. 2011. Normal and abnormal sleep in the elderly. Handb

Clin Neurol 98:653–665, PMID:

21056216, https://doi.org/10.1016/B978-0-444-

52006-7.00041-1.

Feder K, Michaud DS, Keith SE, Voicescu SA, Marro L, Than J, et al. 2015. An

assessment of quality of life using the WHOQOL-BREF among participants liv-

ing in the vicinity of wind turbines. Environ Res 142:227–238, PMID:

26176420,

https://doi.org/10.1016/j.envres.2015.06.043.

Fiske A, Wetherell JL, Gatz M. 2009. Depression in older adults. Annu Rev Clin

Psychol 5:363–389, PMID:

19327033, https://doi.org/10.1146/annurev.clinpsy.

032408.153621.

Frei P, Mohler E, Röösli M. 2014. Effect of nocturnal road traffic noise exposure

and annoyance on objective and subjective sleep quality. Int J Hyg Environ

Health 217(2–3):188–195, PMID:

23684342, https://doi.org/10.1016/j.ijheh.2013.

04.003.

Fyhri A, Aasvang GM. 2010. Noise, sleep and poor health: modeling the rel-

ationship between road traffic noise and cardiovascular problems. Sci

Total Environ 408(21):4935–4942, PMID:

20708214, https://doi.org/10.1016/j.

scitotenv.2010.06.057.

Guski R, Schreckenberg D, Schuemer R. 2017. WHO environmental noise guide-

lines for the European Region: a systematic review on environmental noise

and annoyance. Int J Environ Res Public Health 14: PMID:

29292769,

https://doi.org/10.3390/ijerph14121539.

GWEC (Global Wind Energy Council). 2017.

Global Wind Report, Annual Market

Update 2016.

Brussels, Belgium:GWEC.

Hahmann AN, Vincent CL, Peña 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(12):3422–3439,

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

Héritier H, Vienneau D, Frei P, Eze IC, Brink M, Probst-Hensch N, et al. 2014. The

association between road traffic noise exposure, annoyance and health-

related quality of life (HRQOL). Int J Environ Res Public Health 11(12):12652–

12667, PMID:

25489999, https://doi.org/10.3390/ijerph111212652.

Hongisto V, Oliva D, Keränen J. 2017. Indoor noise annoyance due to 3-5 megawatt

wind turbines—an exposure–response relationship. J Acoust Soc Am

142(4):2185, PMID:

29092540, https://doi.org/10.1121/1.5006903.

Jalali L, Bigelow P, McColl S, Majowicz S, Gohari M, Waterhouse R. 2016.

Changes in quality of life and perceptions of general health before and after

operation of wind turbines. Environ Pollut 216:608–615,

https://doi.org/10.1016/j.

envpol.2016.06.020.

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

exposure-response relationships for wind turbine annoyance and annoyance

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 Pub-

lic Health 39(7 Suppl):91–94, PMID:

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

1403494810394715.

Conclusions

In conclusion, in a large nationwide population, we found sug-

gestions of an association between exposure to high levels of out-

door nighttime WTN and increased risk of

ﬁrst-time

redemption

of sleep medication and antidepressants. This association was

strongest among the elderly. We found no consistent associations

for indoor nighttime LF WTN. Given that this was the

ﬁrst

pro-

spective study on this topic and that we had only a few cases for

many of the groups, independent replication is desirable.

Acknowledgments

We thank DELTA (Hørsholm, Denmark), who showed great

expertise in all steps of the process of estimating detailed wind

turbine noise data, as well as Geoinfo A/S (Glostrup, Denmark)

who made it possible to extract the geographic information

systems information for all addresses. This study was supported

by a grant (J.nr. 1401329) from the Danish Ministry of Health

Environmental Health Perspectives

000000-8

00(0) 0 0000

Kageyama T, Yano T, Kuwano S, Sueoka S, Tachibana H. 2016. Exposure-response

relationship of wind turbine noise with self-reported symptoms of sleep and

health problems: a nationwide socioacoustic survey in Japan. Noise Health

18(81):53–61, PMID:

26960782, https://doi.org/10.4103/1463-1741.178478.

Kildemoes HW, Sørensen HT, Hallas J. 2011. The Danish National Prescription

Registry. Scand J Public Health 39(7 Suppl):38–41, PMID:

21775349, https://doi.org/

10.1177/1403494810394717.

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.

Technical report no. 45. Lyngby, Denmark:Danish Electronics, Light &

Acoustics (DELTA).

Lopresti AL, Hood SD, Drummond PD. 2013. A review of lifestyle factors that con-

tribute to important pathways associated with major depression: diet, sleep

and exercise. J Affect Disord 148(1):12–27, PMID:

23415826, https://doi.org/10.

1016/j.jad.2013.01.014.

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

Exposure to wind turbine noise: perceptual responses and reported health

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

27036283, https://doi.org/10.

1121/1.4942391.

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.

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

Personal and situational variables associated with wind turbine noise annoyance.

J Acoust Soc Am 139(3):1455–1466, PMID:

27036284, https://doi.org/10.1121/1.

4942390.

Onakpoya IJ, O’Sullivan J, Thompson MJ, Heneghan CJ. 2015. The effect of wind

turbine noise on sleep and quality of life: a systematic review and meta-

analysis of observational studies. Environ Int 82:1–9, PMID:

25982992,

https://doi.org/10.1016/j.envint.2015.04.014.

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.

Report-E-0055.

Roskilde, Denmark:DTU Wind Energy, Risø Campus.

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.

Pottegård A, Schmidt SAJ, Wallach-Kildemoes H, Sørensen HT, Hallas J, Schmidt

M. 2017. Data resource profile: the Danish National Prescription Registry. Int J

Epidemiol 46(3):798–798, PMID:

27789670, https://doi.org/10.1093/ije/dyw213.

Poulsen AH, Raaschou-Nielsen O, Peña A, Hahmann AN, Nordsborg RB, Ketzel M,

et al. 2018. Short-term nighttime wind turbine noise and cardiovascular events:

a nationwide case-crossover study from Denmark. Environ Int 114:160–166,

PMID:

29505969, https://doi.org/10.1016/j.envint.2018.02.030.

Schäffer B, Schlittmeier SJ, Pieren R, Heutschi K, Brink M, Graf R, et al. 2016.

Short-term annoyance reactions to stationary and time-varying wind turbine

and road traffic noise: a laboratory study. J Acoust Soc Am 139(5):2949, PMID:

27250186, https://doi.org/10.1121/1.4949566.

Schmidt JH, Klokker M. 2014. Health effects related to wind turbine noise expo-

sure: a systematic review. PloS One 9(12):e114183, PMID:

25474326,

https://doi.org/10.1371/journal.pone.0114183.

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.

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

investigation of wind turbine noise spectra. In: 6th International Conference on

Wind Turbine Noise 2015. Glasgow, UK:INCE/Europe.

Sondergaard LS, Plovsing B, Sorensen T. 2009.

Noise and Energy Optimization of

Wind Farms: Validation of the Nord2000 Propagation Model for Use on Wind

Turbine Noise.

Report no. 53. Hørsholm, Denmark:Danish Electronics, Light &

Acoustics (DELTA).

WHO (World Health Organization Europe). 2009.

Night Noise Guidelines for Europe.

Copenhagen, Denmark:WHO Regional Office for Europe.

WHO Collaborating Centre for Drug Statistics Methodology. 2012.

Guidelines for ATC

Classification and DDD Assignment 2013.

Oslo, Norway:WHO Collaborating

Centre for Drug Statistics Methodology.

Wolkove N, Elkholy O, Baltzan M, Palayew M. 2007. Sleep and aging: 1. Sleep dis-

orders commonly found in older people. CMAJ 176(9):1299–1304, PMID:

17452665, https://doi.org/10.1503/cmaj.060792.

Environmental Health Perspectives

000000-9

00(0) 0 0000