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American Journal of Epidemiology
ªThe Author 2012. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Vol. 175, No. 9
DOI: 10.1093/aje/kwr424
Advance Access publication:
April 5, 2012
Original Contribution
Association of Long-term Exposure to Community Noise and Traffic-related Air
Pollution With Coronary Heart Disease Mortality
Wen Qi Gan, Hugh W. Davies, Mieke Koehoorn, and Michael Brauer*
*Correspondence to Dr. Michael Brauer, School of Population and Public Health, The University of British Columbia, 366A-2206
East Mall, Vancouver, British Columbia, Canada, V6T 1Z3 (e-mail: michael.brauer@ubc.ca).
Initially submitted July 31, 2011; accepted for publication October 27, 2011.
In metropolitan areas,road traffic is a majorcontributor to ambient air pollution and the dominant sourceof community
noise. The authors investigated the independent and joint influences of community noise and traffic-related airpollution
on risk of coronary heart disease (CHD) mortality in a population-based cohort study with a 5-year exposure period
(January 1994–December1998) and a 4-year follow-up period (January 1999–December 2002).Individuals who were
45–85 years of age and resided in metropolitan Vancouver, Canada, during the exposure period and did not have
known CHD at baseline were included (n¼445,868). Individual exposures to community noise and traffic-related air
pollutants, including black carbon, particulate matter less than or equal to 2.5 lm in aerodynamic diameter, nitrogen
dioxide, and nitric oxide, were estimated at each person’s residence using a noise prediction model and land-use
regression models, respectively. CHD deaths were identified from the provincial death registration database. After
adjustment for potential confounders, including traffic-related air pollutants or noise, elevations in noise and black
carbon equal to the interquartile ranges were associated with 6% (95% confidence interval: 1, 11) and 4% (95%
confidence interval: 1, 8) increases, respectively, in CHD mortality. Subjects in the highest noise decile had a 22%
(95% confidence interval:4, 43) increase in CHD mortality compared withpersons in the lowest decile. These findings
suggest that there are independent effects of traffic-related noise and air pollution on CHD mortality.
air pollution; cohort studies; coronary heart disease; environmental exposure; mortality; transportation noise;
vehicle emissions
Abbreviations: CHD, coronary heart disease; CI, confidence interval; L
den
dB(A), annual day-evening-night A-weighted equivalent
continuous noise level; MI, myocardial infarction; PM
2.5
, particulate matter less than or equal to 2.5 lm in aerodynamic diameter;
SES, socioeconomic status.
Epidemiologic evidence has demonstrated that air pollution
is associated with increased cardiovascular disease (espe-
cially coronary heart disease (CHD) morbidity and mortal-
ity) (1). Meanwhile, accumulating evidence has suggested that
community noise from road and air traffic is associated with an
increased risk of CHD, especially myocardial infarction (MI)
(2–5). In metropolitan areas, road traffic is a major contributor
to ambient air pollution and the dominant source of community
noise (6–9). Persons exposed to higher levels of air pollution
might also be exposed to excessive traffic noise (6, 7, 10).
Therefore, it is possible that the observed associations be-
tween air pollution and adverse cardiovascular outcomes
could be confounded by community noise and vice versa
(6, 7). Furthermore, these coexistent environmental pollutants
might interact with each other in association with coronary
mortality (6).
In a previous study in metropolitan Vancouver, Canada (11),
we found that living close to a major road was associated with
a 29% (95% confidence interval (CI): 18, 41) increase in the risk
of death from CHD. We further examined the associations be-
tween death from coronary disease and 4 major traffic-related
air pollutants, including black carbon, particulate matter less
than or equal to 2.5 lm in aerodynamic diameter (PM
2.5
),
nitrogen dioxide, and nitric oxide, and found that black
carbon, an indicator of traffic-related fine particulate air
pollution, was associated with a 6% (95% CI: 3, 9) increase
in the risk of death from CHD. No robust associations were
found with PM
2.5
, nitrogen dioxide, or nitric oxide (12).
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These findings suggested that exposure to traffic-related air
pollutants cannot fully explain the higher risk of death from
CHD associated with residential proximity to road traffic; traffic
noise might also play a role in the observed association. We
therefore investigated the relations between long-term exposure
to community noise and CHD mortality, as well as the joint
influences of community noise and traffic-related air pollution
(black carbon) on the risk of CHD mortality.
MATERIALS AND METHODS
Study design
In British Columbia, Canada, the mandatory health insurance
program provides health care coverage for nearly all residents
(13). We used linked administrative health insurance databases
to assemble this population-based cohort (11, 12, 14). The
present study included a 5-year exposure period (January
1994–December 1998) and a 4-year follow-up period (January
1999–December 2002). All metropolitan Vancouver residents
who had registered with the provincial health insurance plan,
had resided in the study region during the 5-year exposure
period, were 45–85 years of age, and had no previous diagnosis
of CHD at baseline (January 1999) were included in the cohort.
During the 5-year exposure period, individual exposures to
community noise and traffic-related air pollutants were esti-
mated at each person’s residence (residential postal code) using
noise prediction and land-use regression models, respectively.
During the 4-year follow-up period, instances of CHD death
were identified from the provincial death registration database.
The associations of CHD mortality with noise and black carbon
were examined using the Cox proportional hazards regression
model. This study was approved by the institutional review
board of The University of British Columbia (Behavioural
Research Ethics Board certificate #H08-00185).
Noise exposure assessment
We used the noise prediction software CadnaA (Datakus-
tik, Greifenberg, Germany) to estimate annual average
community noise levels at each person’s residence (resi-
dential postal code) in 2003. The method has been de-
scribed in detail elsewhere (15). Briefly, noise exposure
was based on transportation-related information, includ-
ing road traffic data (e.g., speed limits, traffic volume,
fleet composition, and road width), railway data (e.g., type
of train, velocity, and frequency), and building heights and
footprints. Aircraft noise data were obtained from aircraft
noise exposure forecasts produced byVancouver International
Airport Authority. On the basis of these data, the annual day-
evening-night A-weighted equivalent continuous noise level
was calculated for each area covered by a 6-digit postal code.
This metric (L
den
dB(A), hereafter referred to as dB(A))
integrated noise levels during the day, evening, and night,
with a 5-dB(A) weighting added to evening noise and
a 10-dB(A) weighting added to night noise to reflect increased
sensitivity of residents to noise during these periods (16, 17).
Railways were a minor contributor to overall community noise
in this region, and thus we did not separately assess railway
noise exposure.
Air pollution exposure assessment
We used high-spatial-resolution land-use regression models
to estimate participants’ residential exposures to traffic-related
air pollutants, including black carbon, PM
2.5
, nitrogen dioxide,
and nitric oxide, in 2003 (18–20). Furthermore, these esti-
mates were combined with air quality-monitoring data to cal-
culate monthly concentrations and average concentrations
during the 5-year exposure period for each pollutant in each
postal code area (11, 12).
Assignment of exposure data
Because our exposure assessment did not cover the whole
study region, a small proportion of study subjects for whom we
were missing data were excluded. Some subjects had partially
missing data because of changes in residences (moving from
or to areas outside the exposure assessment domain); persons
missing data for more than a total of 15 months or in more than
3 consecutive months during the exposure period were also
excluded. For subjects who changed their residences, we cal-
culated equivalent noise levels and average air pollution levels
during the exposure period. The noise and air pollution data
were assigned to study subjects based on their 6-digit residen-
tial postal codes. In urban areas, a residential postal code typ-
ically represents a high-rise building or one sideof a city block;
in rural areas, it may represent a larger area. Metropolitan
Vancouver is highly urbanized; the vast majority of the postal
codes represent a small geographic area. On average, a resi-
dential postal code includes about 35 persons.
Case definitions
The study outcome was death from CHD during the 4-year
follow-up period, defined as having International Classification
of Diseases codes 410–414 and 429.2 (Ninth Revision) and
I20–I25 (Tenth Revision) listed as the cause of death in the
death registration database (see Web Table 1 (available at
http://aje.oxfordjournals.org/) for the codes for other car-
diovascular diseases). Subjects who were hospitalized with
CHD as the principal diagnosis (the diagnosis most respon-
sible for a hospital admission) or primary diagnosis (the
diagnosis that had a substantial influence on hospital length
of stay) before baseline (based on data from 1991 to 1998)
were regarded as having previously diagnosed CHD and
were excluded from the analyses.
Preexisting comorbid conditions
Diabetes (21), chronic obstructive pulmonary disease
(22, 23), and hypertensive heart disease (21) are independent
risk factors for CHD. Additionally, these chronic diseases and
CHD share common behavioral risk factors. As in our previous
analyses (11, 12), we used these preexisting comorbid condi-
tions as proxy variables for common behavioral cardiovascular
risk factors (24). To sufficiently identify subjects with preexist-
ing comorbid conditions, we used all diagnoses in a hospitaliza-
tionrecord(upto16diagnoses);one hospitalization record with
the diagnosis of any of these chronic diseases during 1991–1998
was defined as the presence of preexisting comorbid conditions.
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Neighborhood socioeconomic status
Because individual socioeconomic status (SES) data were
not available, we used neighborhood SES to estimate individ-
ual SES (25, 26). Study subjects were assigned neighborhood-
income quintiles from the 2001 Statistics Canada Census
based on their residential postal codes. The method for calcu-
lation of neighborhood income quintiles has been described
previously (11).
Statistical analysis
The baseline characteristics of the study subjects across dec-
iles of noise levels were compared using a chi-squared test for
categorical variables, 1-way analysis of variance for continuous
variables, and Tukey’s post hoc analysis for pair-wise compar-
isons of continuous variables. Correlations between pollutants
were examined using Spearman’s rank correlation analysis.
The Cox proportional hazards regression model was used to
determine the associations between noise or air pollution and
CHD mortality. Age, sex, preexisting comorbid conditions, and
neighborhood SES were included as covariates, and air pollu-
tion and noise variables were addedtothefinalmodels.Person-
years of observation were calculated from baseline to the date of
CHD death or the end of follow-up. For persons who died from
other diseases or moved out of the province, person-years were
calculated from baseline to the date of death or the last known
date in the province.
We first treated noise levels as a continuous variable to
calculate the relative risks of CHD mortality associated with
a 10-dB(A) elevation in noise levels. We then treated noise
levels as a categorical variable to examine exposure-response
relations bydividing study subjects into deciles based on noise
levels; relative risks of death from coronary disease were cal-
culated for each decile, with decile 1 (lowest) being the refer-
ence category. Because there was no substantial difference in
effect estimates across deciles 2–9 (Web Figure 1), the results
were presented in 4 exposure groups: decile 1, deciles 2–5,
deciles 6–9,and decile 10. All statistical tests were 2-sided and
were performed using SAS, version 9.2 (SAS Institute, Inc.,
Cary, North Carolina).
RESULTS
A total of 466,727 subjects who met the inclusion criteria
were included at baseline. Of these subjects, we excluded
20,859 (4.5%) subjects for whom data on air pollution or noise
were missing, which left 445,868 subjects for the analyses.
During the follow-up period, 33,448 (7.5%) subjects were lost
to follow-up; the reasons included moving out of the province
(3.9%) and dying from other diseases (3.6%).
Overall, the annual average noise level was 63.4 dB(A) (in-
terquartile range: 59.8–66.4). Noise levels were not strongly
correlated with traffic-related air pollutant concentrations;
traffic-related air pollutants were weakly correlated with
each other, with the exception of nitrogen dioxide and
nitric oxide (Table 1). Compared with persons exposed
to lower noise levels (decile 1), subjects in decile 10 were
more likely to have preexisting comorbid conditions and
lower neighborhood SES (Table 2).
During the follow-up period, 3,095 subjects died of
CHD (mortality rate ¼1.83 per 1,000 person-years). Res-
idential noise exposure was associated with CHD mortality:
A 10-dB(A) elevation in noise levels was associated with
a 26% (95% CI: 17, 35) increase in the risk of CHD mor-
tality. Adjustment for age, sex, preexisting comorbid con-
ditions, and neighborhood SES halved the effect estimate,
whereas further adjusting for PM
2.5
and nitrogen dioxide
concentrations had little influence; additional adjustment for
back carbon levels had a greater influence on the effect estimate,
but a 10-dB(A) elevation in noise levels was still associated with
a 9% (95% CI: 1, 18) increase in the risk of death from CHD
(Table 3). Web Table 2 presents results based on a 5-dB(A)
increase in noise exposure. For other cardiovascular diseases,
there were nonsignificant increases in mortality rates associated
with noise exposure (Web Table 3). The associations between
black carbon and cardiovascular disease mortality are presented
in Web Table 4.
When study subjects were categorized into noise deciles,
subjects in deciles 2–5 and deciles 6–9 had little increase in the
risk of death from CHD compared with persons in decile 1,
whereas persons in decile 10 had a 22% (95% CI: 4, 43) in-
crease in coronary mortality risk after adjustment for all co-
variates, including traffic-related air pollutants. These results
suggested that there was no linear exposure-response relation
between noise and coronary mortality (P¼0.174 for test of
linear trend across decile groups in the fully adjusted model)
(Table 3).
Both noise and black carbon exposure were independently
associated with death fromCHD (Figure 1); elevations equal to
the interquartile range in noise (6.6 dB(A)) and black carbon
(0.97 310
5
/m) were associated with 6% (95% CI: 1, 11) and
4% (95% CI: 1, 8) increases in coronary mortality, respectively.
We did not find any positive interaction between noise and black
carbon on risk of coronary mortality when they were assessed
on either additive (Table 4) or multiplicative (P¼0.980 for the
interaction term in the fully adjusted model) scales.
Stratified analysis showed that in the fully adjusted models,
the risk of death from CHD associated with noise exposure
was greater for women, persons 65 years of age or older, and
persons with higher neighborhood SES. However, there was
considerable overlap in the 95% confidence intervals of these
subgroups (Table 5).
For persons exposed to aircraft noise (n¼294,783), the
annual average noise level was 32 dB(A). Aircraft noise was
less correlated with traffic-related air pollutants than was
road traffic noise (Web Table 5). There was no significant
increase in the risk of death from CHD associated with
exposure to aircraft noise (Web Table 6).
DISCUSSION
In the present large population-based cohort study, we
found that a 10-dB(A) elevation in residential noise levels
was associated with a 9% increase in CHD mortality after
adjustment for various covariates, including traffic-related air
pollutants. There was no discernible linear exposure-response
relation; persons in the highest noise decile (>70 dB(A)) had
a 22% increase in the risk of death from CHD compared with
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persons in the lowest noise decile (58 dB(A)). Also in the
fully adjusted model, an increase in the concentration of black
carbon equal to the interquartile range (0.97 310
5
/m;
equivalent to approximately 0.8 lg/m
3
elemental carbon)
was associated with a 4% increase in the risk of death from
CHD. We did not detect any interaction between noise and
black carbon with relation to coronary mortality. In persons
exposed to aircraft noise, we did not find a significant increase
in the risk of death from CHD compared with unexposed
subjects.
Findings from previous studies have been inconsistent, but
most studies have shown positive associations between com-
munity noise and coronary events. In a 9-year Dutch cohort
study, Beelen et al. (27) reported that cardiovascular mortality
increased 17% (95% CI: 6, 45) for persons exposed to higher
levels of road traffic noise (>65 dB(A) vs. 50 dB(A)) and 11%
(95% CI: 5, 28) in response to a 10-lg/m
3
increase in black
smoke concentrations (about 1.1 lg/m
3
elemental carbon) (28);
however, there was no discernible increase in CHD mortality
rates (27). In a case-control study in Stockholm County, Se-
lander et al. (3) found that exposure to higher levels of road
traffic noise (50 dB(A) vs. <50 dB(A)) was associated with
a 12% (95% CI: 5, 33) increase in MI risk after adjustment for
nitrogen dioxide and other cardiovascular risk factors. After
further excluding persons with hearing loss or other sources
of noise exposure, the risk of MI increased by 38% (95% CI:
11, 71) (3). In a 5-year Swiss cohort study of 4.6 million subjects
and 65 airports, Huss et al. (4) found that people exposed to
higher levels of aircraft noise (60 dB(A) vs. <45 dB(A)) had
a 30% (95% CI: 4, 76) increase in MI mortality after adjusting
Table 1. Annual Average Noise Levels, 5-Year Average Traffic-related Air Pollutant Concentrations, and Correlation Coefficients, Metropolitan
Vancouver, Canada, 1994–2002
Pollutant Mean (SD) Median (Interquartile
Range) Range
Correlation Coefficient*
Noise Black
Carbon PM
2.5
Nitrogen
Dioxide
Nitric
Oxide
Noise, L
den
dB(A) 63.4 (5.0) 62.4 (59.8–66.4) 33.0–90.0 1.00
Black carbon, 10
5
/m 1.50 (1.10)
a
1.02 (0.83–1.80) 0.0–4.98 0.44 1.00
PM
2.5
,lg/m
3
4.10 (1.64) 4.04 (3.22–4.81) 0.0–10.24 0.14 0.13 1.00
Nitrogen dioxide, lg/m
3
32.3 (8.1) 30.8 (26.7–35.2) 15.3–57.5 0.33 0.39 0.47 1.00
Nitric oxide, lg/m
3
32.2 (12.0) 29.5 (24.3–37.6) 8.8–126.0 0.39 0.43 0.43 0.66 1.00
Abbreviations: L
den
dB(A), annual day-evening-night A-weighted equivalent continuous noise level; PM
2.5
, particulate matter less than or equal
to 2.5 lm in aerodynamic diameter; SD, standard deviation.
*P<0.001 for each correlation coefficient.
a
Equivalent to 1.20 (0.88) lg/m
3
elemental carbon (10
5
/m black carbon 0.8 lg/m
3
elemental carbon).
Table 2. Baseline Characteristics of Study Subjects by Decile of Noise Level, Metropolitan Vancouver, Canada, 1994–2002*
Characteristic
Decile of Noise Levels, L
den
dB(A)
1(£58) 2–5 (59–62) 6–9 (63–70) 10 (>70)
% Mean (SD) % Mean (SD) % Mean (SD) % Mean (SD)
Male sex 46.0 46.6 45.9 45.8
Age, years 59.3 (10.8) 59.0 (10.6) 59.4 (10.7) 60.0 (10.9)
Comorbid condition
Diabetes 2.1 2.1 2.4 2.9
Chronic obstructive
pulmonary disease
1.5 1.3 1.5 1.8
Hypertensive heart
disease
4.3 4.0 4.3 4.8
Any of the above 6.6 6.3 6.8 7.9
Income quintile
a
1 11.8 14.8 21.4 28.3
2 13.4 18.5 20.0 23.2
3 17.3 20.3 19.4 17.4
4 25.3 21.9 18.7 15.3
5 32.3 24.5 20.4 15.9
Abbreviations: L
den
dB(A), annual day-evening-night A-weighted equivalent continuous noise level; SD, standard deviation.
*P0.001 for all comparisons between groups.
a
Quintile 1 represents the lowest neighborhood income quintile and quintile 5 represents the highest.
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for particulate matter less than or equal to 10 lm in aerody-
namic diameter, residential proximity to major roads, and other
covariates; when the analysis was restricted to persons who had
lived in their residences for at least 15 years, the risk of death
from MI increased by 48% (95% CI: 1, 118) (4). In some pre-
vious studies, investigators did not adjust for coexistent
traffic-related air pollution but reported positive associations
between noise exposure and CHD risk (2, 29). As for other
cardiovascular outcomes, our effect estimates for hemorrhagic
stroke and congestive heart failure were largely comparable to
those described in prior studies (27, 30). As discussed previ-
ously (12), some studies have also reported associations bet-
weenblackcarbonandriskofdeathfromCHD,butmostof
these studies did not consider coexistent community noise (12).
Potential biologic mechanisms for the observed associa-
tions have been proposed. Psychological stress has been dem-
onstrated to be an independent risk factor for cardiovascular
disease (31–33). Chronic exposure to community noise might
cause annoyance, speech interference, sleep disturbance, and
psychological stress (15, 16, 34); therefore, chronic noise ex-
posure may serve as a potent environmental stressor that might
activate the sympathetic nervous and endocrine systems to
release stress hormones, such as noradrenaline and cortisol
(5, 35–38). Stress hormones bind with beta-adrenergic recep-
tors in the heart and blood vessels (39), leading to increased
myocardial contractility, flow velocity, coronary artery con-
striction, vulnerable plaque rupture, thrombosis, and subse-
quent myocardial ischemia or MI (39–42). As discussed
previously (12), there is also convincing pathophysiological
evidence, such as pulmonary and systemic oxidative stress and
inflammation, to support the associations between black car-
bon and CHD mortality (1).
In the present study, correlations between modeled commu-
nity noiseand air pollutionranged from 0.14 (forPM
2.5
)to0.44
(for black carbon), which is within the range of correlations
reported in previous studies (6, 7, 10). In practice, some road
traffic factors, such as speed, volume, a nd operating conditions,
might differentially affect the emission levels of noise and
combustion-derived traffic-related air pollution (43–45). For
example, for vehicle speeds less than 30 km/hour, noise levels
are lower, but air pollution emissions are relatively higher;
however, for vehicle speeds over 40 km/hour, noise levels,
mainly from tire-road interaction, rapidly increase, whereas
air pollution emissions are relatively lower (43, 44). Further-
more, traffic volume is more strongly related to air pollution
than to noise levels. For example, when traffic volume doubles,
traffic noise levels increase 3 dB(A) (43). In addition, some
environmental factors, such as road pavement materials, noise
barriers, and surrounding buildings, have little influence on air
pollution emissions but may strongly affect road traffic noise
levels, especially when vehicle speeds are over 40 km/hour
(43). Finally, meteorological factors, such as wind direction
and speed, may strongly affect traffic-related air pollution but
have a smaller influence on traffic noise levels (7). Rain and wet
road surfaces may increase road traffic noise but may de-
crease ambient air pollution. All of these factors might
partly explain the low-to-moderate correlations between
noise and traffic-related air pollution in this study region.
Previous findings about sex differences in the associations
between community noise and coronary mortality were
0.00
0.05
0.10
0.15
0.20
0.25
Noise or
BC only
Plus Age
and Sex
Plus SES
and CMC
Plus
PM
2.5
Plus Nitrogen
Dioxide
Plus BC
or Noise
Log Relative Risk for CHD Mortality
Noise
Black Carbon
Model
Figure 1. Log relative risks for coronary heart disease (CHD) mor-
tality associated with an elevation equal to the interquartile range
in noise levels (A-weighted equivalent continuous noise level of
6.6 decibels) or black carbon (BC) concentrations (0.97 310
5
/m),
metropolitan Vancouver, 1994–2002. Models were successively ad-
justed for age and sex; neighborhood socioeconomic status (SES)
and comorbid conditions (CMC); particulate matter less than or equal
to 2.5 lm in aerodynamic diameter (PM
2.5
) concentrations; and nitro-
gen dioxide concentrations. The final model for black carbon was
further adjusted for noise, and the final model for noise was further
adjusted for black carbon. Bars, 95% confidence interval.
Table 3. Relative Risk of Death From Coronary Heart Disease Associated With an Elevation in A-Weighted Equivalent Continuous Noise Level
of 10 Decibels or by Decile of Noise Level, Metropolitan Vancouver, Canada, 1994–2002
Model
10-dB(A) Elevation Decile of Noise Levels, L
den
dB(A)
1(£58) 2–5 (59–62) 6–9 (63–70) 10 (>70)
RR 95% CI Referent RR 95% CI RR 95% CI RR 95% CI
1: Unadjusted 1.26 1.17, 1.35 1.00 1.01 0.89, 1.15 1.09 0.96, 1.24 1.49 1.28, 1.73
2: Model 1 þsex and age 1.18 1.10, 1.26 1.00 1.06 0.93, 1.20 1.09 0.96, 1.24 1.39 1.20, 1.61
3: Model 2 þcomorbidity and SES 1.13 1.06, 1.21 1.00 1.05 0.92, 1.19 1.06 0.93, 1.20 1.30 1.12, 1.51
4: Model 3 þPM
2.5
1.13 1.06, 1.21 1.00 1.04 0.91, 1.19 1.05 0.92, 1.20 1.29 1.11, 1.50
5: Model 4 þnitrogen dioxide 1.12 1.05, 1.21 1.00 1.05 0.92, 1.20 1.05 0.92, 1.20 1.28 1.10, 1.50
6: Model 5 þblack carbon 1.09 1.01, 1.18 1.00 1.04 0.91, 1.19 1.02 0.89, 1.17 1.22 1.04, 1.43
Abbreviations: CI, confidence interval; L
den
dB(A), annual day-evening-night A-weighted equivalent continuous noise level; PM
2.5
, particulate
matter less than or equal to 2.5 lm in aerodynamic diameter; RR, relative risk; SES, neighborhood socioeconomic status.
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inconsistent. In some studies, men were found to be more
vulnerable to noise exposure than were women (2), whereas
in other studies, there were no sex differences (3, 27). We found
that men and women had similar risks; however, after adjust-
ment for traffic-related air pollutants, including PM
2.5
, nitrogen
dioxide, and black carbon, women had a 7% nonsignificant
excess risk of coronary mortality compared with men. This
observation is supported by findings that women had greater
levels of salivary cortisol than did men in response to noise
exposure (36, 46).
The present study had some limitations that should be con-
sidered. First, exposure assessment was conducted using the
residential postal codes of the study subjects to estimate
exposure at their residences. This method cannot precisely re-
flect actual individual exposure (47, 48) because many environ-
mental factors, such as street canyons (49), wind direction (7),
noise barriers, and specific housing characteristics (50), as well
as individual factors like noise sensitivity (51), time spent at
home (47, 52), living room/bedroom orientation (5, 51), and
occupational noise exposure (52, 53), might substantially affect
actual individual exposure levels. Nevertheless, it is likely that
these factors might affect study subjects equally and thus cause
nondifferential exposure misclassification, leading to underes-
timations of the true risk of coronary mortality (48).
Second, this cohort was constructed using administrative
health insurance databases. Some individual cardiovascular
risk factors were not available, and thus we could not control
for them in the data analysis. We adjusted for the following
preexisting comorbid conditions: diabetes, chronic obstructive
pulmonary disease, and hypertensive heart disease. Because
these comorbid conditions and CHD shar e common behavioral
risk factors, the adjustment was able to reduce the influences of
unmeasured risk factors and these conditions themselves on the
effect estimates to some extent (24); on the other hand, because
these conditions might serve as intermediate variables for the
associations of coronary mortality with noise and air pollution,
the adjustment might cause underestimation of thetrue adverse
effects (5, 54). Furthermore, as discussed previously (11, 12),
Table 5. Relative Risk of Coronary Heart Disease Mortality Associated With an Elevation in A-Weighted Equivalent Continuous Noise Level of
10 Decibels, Stratified by Each Covariate, Metropolitan Vancouver, Canada, 1994–2002
Characteristic %
Model
a
123
RR 95% CI RR 95% CI RR 95% CI
Sex
Male 46.2 1.28 1.17, 1.39 1.15 1.05, 1.26 1.07 0.97, 1.18
Female 53.8 1.24 1.11, 1.39 1.11 0.99, 1.23 1.12 0.99, 1.27
Age, years
<65 68.0 1.21 1.03, 1.43 1.07 0.91, 1.27 1.03 0.85, 1.25
65 32.0 1.18 1.10, 1.28 1.14 1.06, 1.23 1.09 1.00, 1.19
Comorbid conditions
No 93.3 1.22 1.13, 1.33 1.11 1.02, 1.20 1.07 0.98, 1.18
Yes 6.7 1.20 1.06, 1.35 1.18 1.05, 1.33 1.10 0.96, 1.26
Income quintiles
High (4–5) 43.1 1.32 1.17, 1.49 1.19 1.05, 1.34 1.12 0.97, 1.29
Low (1–3) 56.9 1.14 1.05, 1.24 1.12 1.03, 1.22 1.06 0.97, 1.17
a
Model 1: bivariable analysis; model 2: adjusted for age, sex, preexisting comorbid conditions, and neighborhood socioeconomic status; model
3: model 2 further adjusted for concentrations of traffic-related air pollutants, including particulate matter lessthan or equal to 2.5 lm in aerodynamic
diameter, nitrogen dioxide, and black carbon.
Table 4. Relative Risk of Death From Coronary Heart Disease by Decile of Noise Level and Quartile of Black Carbon Concentration,
Metropolitan Vancouver, Canada, 1994–2002
a
Quartile of Black Carbon Concentration
Decile of Noise Levels, L
den
dB(A)
1(£58) 2–5 (59–62) 6–9 (63–70) 10 (>70)
RR 95% CI RR 95% CI RR 95% CI RR 95% CI
1: 0–0.83 310
5
/m 1.00 Referent 1.11 0.88, 1.40 1.12 0.86, 1.45 1.29 0.79, 2.12
2: 0.84–1.02 310
5
/m 1.05 0.78, 1.41 1.09 0.87, 1.37 1.06 0.84, 1.34 1.24 0.88, 1.74
3: 1.03–1.80 310
5
/m 1.09 0.79, 1.49 1.23 0.97, 1.55 1.18 0.94, 1.49 1.48 1.09, 2.02
4: 1.81–4.98 310
5
/m 1.50 1.04, 2.17 1.21 0.93, 1.56 1.23 0.98, 1.54 1.45 1.14, 1.85
Abbreviation: CI, confidence interval; L
den
dB(A), annual day-evening-night A-weighted equivalent continuous noise level; RR, relative risk.
a
Adjusted for age, sex, preexisting comorbid conditions, neighborhood income quintiles, and concentrations of copollutants, including nitrogen
dioxide and particulate matter less than or equal to 2.5 lm in aerodynamic diameter.
Noise, Air Pollution, and Coronary Heart Disease 903
Am J Epidemiol. 2012;175(9):898–906
at The University of British Colombia Library on May 22, 2012http://aje.oxfordjournals.org/Downloaded from
although cigarette smoking is the single most important risk
factor for coronary mortality (55), it does not substantially affect
the associations between fine particulate air pollution and CHD
(56, 57). Similarly, recent studies have also shown that cigarette
smoking does not substantially affect the associations between
community noise and coronary events (3, 27).
Third, low individual SES is a risk factor for CHD (58), and
persons with low SES are more likely to be exposed to com-
munity noise and traffic-related air pollution (59). Individual
SES is thus a possible confounder for the observed associations.
As individual SES were not available in this study (11, 12), we
used neighborhood income quintiles as a surrogate for individ-
ual SES. There is some evidence that this approach is valid for
control of individual SES (25, 26). In addition, in a subgroup
analysis of the subjects (n¼1,194) who participated in the
Canadian Community Health Survey (2000–2001), neighbor-
hood income quintiles were strongly associated with individual
annual household income, educational level, marital status, and
daily fruit and vegetable intakes (all P<0.001) (Web Table 7).
Finally, A-weighted equivalent sound pressure level based
on the equal energy principle over a specific time period has
been widely used in community noise exposure assessment
(2–4, 16, 27, 29). This method may be appropriate for contin-
uous noise, such as road traffic noise, but cannot reflect actual
disturbance caused by aircraft noise, which is composed of
a small number of high-level discrete noise events (16). This
may partly explain the null association between aircraft noise
and coronary mortality in our study.
In conclusion, in the present population-based cohort study,
a 10-dB(A) elevation in residential noise levels was associated
with a 9% increase in the risk of death from CHD. There was no
discernible linear exposure-response relation; subjects in the
highest noise decile (>70 dB(A)) had a 22% increase in
CHD mortality compared with persons in the lowest decile
(58 dB(A)). An elevation in black carbon concentrations
equal to the interquartile range (0.97310
5
/m) was associated
with a 4% increase in the risk of CHD mortality. We did not find
any interactions between noise and black carbon levels with
respect to coronary mortality. These findings suggest that both
community noise and traffic-related fine particulate air pollu-
tion indicated by black carbon concentration may be partly
responsible for the observed associations between exposure
to road traffic and adverse cardiovascular outcomes.
ACKNOWLEDGMENTS
Author affiliation: School of Population and Public
Health, The University of British Columbia, Vancouver,
British Columbia.
This work was supported in part by Health Canada via an
agreement with the British Columbia Centre for Disease
Control to the Border Air Quality Study. Additional support
was provided by the Centre for Health and Environment
Research at The University of British Columbia, funded
by the Michael Smith Foundation for Health Research,
and the Canadian Institutes of Health Research. Wenqi
Gan was supported by the Canadian Institutes of Health
Research Frederick Banting and Charles Best Canada Graduate
Scholarship and by the Michael Smith Foundation for Health
Research Senior Graduate Studentship. Mieke Koehoorn was
supported in part by the Michael Smith Foundation for Health
Research Senior Scholar Award.
Selected data from this article were presented at the 10th
International Congress on Noise as a Public Health Problem,
London, United Kingdom, July 24–28, 2011, and the 23rd An-
nual Conference of the International Society for Environmental
Epidemiology, Barcelona, Spain, September 13–16, 2011.
Conflict of interest: none declared.
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