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Residential exposure to traffic noise and risk of incident atrial fibrillation: A cohort study
Environment International 92–93 (2016) 457–463
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Residential exposure to traffic noise and risk of incident atrial fibrillation: A cohort study Maria Monrad a,⁎, Ahmad Sajadieh b, Jeppe Schultz Christensen a, Matthias Ketzel c, Ole Raaschou-Nielsen a,c, Anne Tjønneland a, Kim Overvad d,e, Steffen Loft f, Mette Sørensen a a
Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark Department of Cardiology, Copenhagen University Hospital of Bispebjerg, Bispebjerg, Denmark Department of Environmental Science, Aarhus University, Roskilde, Denmark d Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark e Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark f Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark b c
a r t i c l e
i n f o
Article history: Received 2 February 2016 Received in revised form 6 April 2016 Accepted 24 April 2016 Available online xxxx Keywords: Arrhythmia Traffic noise Cohort Epidemiology
a b s t r a c t Background: Studies have found long-term exposure to traffic noise to be associated with higher risk for hypertension, ischemic heart disease and stroke. We aimed to investigate the novel hypothesis that traffic noise increases the risk of atrial fibrillation (A-fib). Methods: In a population-based cohort of 57,053 people aged 50–64 years at enrolment in 1993–1997, we identified 2692 cases of first-ever hospital admission of A-fib from enrolment to end of follow-up in 2011 using a nationwide registry. The mean follow-up time was 14.7 years. Present and historical residential addresses were identified for all cohort members from 1987 to 2011. For all addresses, exposure to road traffic and railway noise was estimated using the Nordic prediction method and exposure to air pollution was estimated using a validated dispersion model. We used Cox proportional hazard model for the analyses with adjustment for lifestyle, socioeconomic position and air pollution. Results: A 10 dB higher 5-year time-weighted mean exposure to road traffic noise was associated with a 6% higher risk of A-fib (incidence rate ratio (IRR): 1.06; 95% confidence interval (95% CI): 1.00–1.12) in models adjusted for factors related to lifestyle and socioeconomic position. The association followed a monotonic exposure–response relationship. In analyses with adjustment for air pollution, NOx or NO2, there were no statistically significant associations between exposure to road traffic noise and risk of A-fib; IRR: 1.04; (95% CI: 0.96–1.11) and IRR: 1.01; (95% CI: 0.94–1.09), respectively. Exposure to railway noise was not associated with A-fib. Conclusion: Exposure to residential road traffic noise may be associated with higher risk of A-fib, though associations were difficult to separate from exposure to air pollution. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Traffic noise has become an almost inevitable environmental exposure with rising levels following urbanization. Exposure to traffic noise has generally been associated with cardiovascular disease, including hypertension (van Kempen and Babisch, 2012), ischemic heart disease (Vienneau et al., 2015) and stroke (Sorensen et al., 2011). No studies have investigated whether exposure to traffic noise is associated with atrial fibrillation (A-fib).
Abbreviations: A-fib, atrial fibrillation; HPA, hypothalamus–pituitary–adrenal; dB, decibel; IRR, incidence rate ratio; Lden, equivalent continuous noise level day–evening– night. ⁎ Corresponding author at: Diet, Genes and Environment, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark. E-mail address: [email protected] (M. Monrad).
http://dx.doi.org/10.1016/j.envint.2016.04.039 0160-4120/© 2016 Elsevier Ltd. All rights reserved.
A-fib is the most common type of arrhythmia (Schnabel et al., 2015), and associated with both increased cardiovascular morbidity and mortality (Go et al., 2001). Although A-fib affects approximately 4% of the population over 50 years of age with rising prevalence, knowledge on the aetiology behind the development of A-fib is sparse (Andrade et al., 2014; Miyasaka et al., 2006). Exposure to noise during the night is thought to be particularly hazardous, as night-time noise at normal urban levels (45–65 dB) is associated with reduced sleep quality and duration (Miedema and Vos, 2007). Noise also acts as a stressor, with hyperactivity of the autonomic nervous system and activation of the hypothalamus–pituitary–adrenal (HPA) axis, leading to a cascade of effects. This includes changes of atrial electrophysiology, which may be one pathway through which noise may initiate A-fib (Chen et al., 2014; Shen and Zipes, 2014). Another potential pathway is noise-induced effects on the immune system, as systemic inflammation in various studies has been associated with
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increased risk A-fib (Dewland et al., 2015; Issac et al., 2007). Both stress and disturbance of sleep may affect the immune system. Studies have found exposure to acute and chronic stressors, e.g. exams and stressful life events, to be associated with an impaired immune system (Picardi et al., 2009; Segerstrom and Miller, 2004) and release of cortisol, as a result of an active HPA axis, has been found to impair the function of glucocorticoid receptors and thereby contribute to systemic inflammation (Bellavance and Rivest, 2014; Silverman and Sternberg, 2012; Wolkow et al., 2015). Sleep is known to have a strong regulatory influence on the immune system (Ali and Orr, 2014; Gomez-Gonzalez et al., 2012), and disturbance of sleep has been associated with impairment of the immune system, including reduced post-vaccination antibody titers, changed numbers of circulating white blood cells and increased production of pro-inflammatory molecules (Aho et al., 2013; Besedovsky et al., 2012; Irwin et al., 2015; Lange et al., 2011). Lastly, release of cortisol may increase the glycogen concentration within atrial myocytes, which is suggested to be a risk factor for A-fib (Embi and Scherlag, 2014; Zhang et al., 2015). The aim of the present study was to investigate the association between residential exposure to road traffic and railway noise and risk for incident A-fib in a large prospective cohort. 2. Methods 2.1. Study population The study was based on the Diet, Cancer and Health study, into which 57,053 residents of Copenhagen or Aarhus aged 50–64 years were enrolled between 1993 and 1997 (Tjonneland et al., 2007). The participants had to be born in Denmark with no history of cancer at the time of enrolment. At enrolment, each participant completed selfadministered, interviewer-checked, lifestyle questionnaires covering smoking habits, diet, alcohol consumption, physical activity and education. Height, weight, and waist circumference were measured by trained staff members according to standardized protocols. The study was conducted in accordance with the Helsinki Declaration and approved by the local Ethics Committees and written informed consent was obtained from all participants. 2.2. Identification of outcome Cases who developed A-fib between baseline and death, emigration, or end of follow-up (31st December 2011) were identified by linking the unique personal identification number of each cohort member to the nationwide Danish National Patient Register (Lynge et al., 2011). Since 1977 patients diagnosed in-hospital have been registered in The Danish National Patient Register and coded using the International Classification of Diseases 8th (until 1994) and 10th (from 1994) Revision (ICD-8 and ICD-10) coding system. Since 1995 diagnoses from emergency rooms and outpatient visits have also been registered (Lynge et al., 2011). Cases were identified using ICD-8 codes 427.93 and 427.94 and ICD-10 code I48.9. We excluded participants with a diagnosis of A-fib before enrolment, and considered only the first hospitalization of A-fib. 2.3. Exposure assessment Complete residential address history between 1st of July 1987 and event or end of follow-up at 31st December 2011 was available for 93% of the cohort members using the Danish civil registration system (Pedersen, 2011). Exposure to road traffic noise was calculated for the years 1990, 1995, 2000, 2005 and 2010 using SoundPLAN (http:// www.soundplan.dk), which implements the joint Nordic prediction method for road traffic noise; a method which has been the standard method for noise calculation in Scandinavia since the introduction in 1981 (Bendtsen, 1999). Traffic noise for the year 1990 was used as a
proxy for the period 1st of July 1987 to 30th of June 1992, the year 1995 was a proxy of the period 1st of July 1992 to 30th of June 1997, etc. Equivalent noise levels were calculated for each address on the most exposed facade of the building using the following input variables; geographical coordinates and height (corresponding to floor level) of each address; road links with information on yearly average daily traffic, vehicle distribution (of light and heavy vehicles), travel speed and road type (motorway, express road, road wider than 6 m, road b6 m and N3 m, and other road); and building polygons for all buildings including information on building height. Information on building polygons was obtained from the Danish Geodata Agency, and is based on different sources including a laser scan of Denmark. First and second order reflections from buildings (3 dimensional) have been included in the modeling and reflection loss for buildings was set to 1 dB. No information was available on noise barriers. We obtained traffic counts for all Danish roads from a national road and traffic database (Jensen et al., 2009). This database is based on a number of different traffic data sources: 1) Collection of traffic data from the 140 Danish municipalities with most residents, covering 97.5% of the addresses included in the present study. Included roads typically have N 1000 vehicles per day and are based on traffic counts as well as estimated/modeled numbers. Traffic data represents the period from 1995 to 1998; 2) Traffic data from a central database covering all the major state and county roads; 3) Traffic data for 1995–2000 for all major roads in the Greater Copenhagen Area; 4) traffic data for 1995 for all roads based on a simple method where estimated figures for distribution of traffic by road type and by urban/rural zone were applied to the road network and subsequently calibrated against known traffic data at county level. New roads were included in the calculations from the year they opened. Values below 40 dB were set to 40 dB, because we considered this as a lower limit of road traffic noise. Exposure to railway noise was calculated for all addresses using SoundPLAN, with implementation of NORD2000. The input variables for the noise model were receptor point (geographical coordinate and height), railway links with information on annual average daily train lengths, train types, travel speed (obtained from BaneDanmark, which is operating and developing the Danish state railway network), and building polygons for all Danish buildings, including screening from buildings (as described for road traffic noise). The daily train lengths are given for 1997 and 2011. All noise barriers along the railway are included in the model. In estimating noise we assumed that the terrain was flat, which is a reasonable assumption in Denmark, and that urban areas, roads, and areas with water were hard surfaces whereas all other areas were acoustically porous. Road traffic and railway noise were calculated as the equivalent continuous A-weighted sound pressure level (LAeq) at the most exposed facade of the dwelling at each address for the day (Ld; 07:00–19:00 h), evening (Le; 19:00–22:00 h) and night (Ln; 22:00–07:00 h) and these where expressed as an indicator of the overall noise level during 24 h, as Lden (day, evening, night) by applying a 5-dB penalty for the evening and a 10-dB penalty for the night. Lden was used as estimator of road traffic and railway noise in all statistical analyses. The noise impact from all Danish airports and airfields was determined from information about noise zones (5-dB categories) obtained from local authorities. Airport noise was transformed into digital maps and linked to each address by geocodes. The concentration of traffic-related air pollution (nitrogen dioxide; NO2 and nitrogen oxides; NOx) was calculated using the dispersion model AirGIS, for each year (1987–2011) at each address at which the cohort members had lived. The Danish AirGIS modelling system calculates air pollution as the sum of regional background, urban background, and local street level calculated with the Operational Street Pollution Model (OSPM) (Berkowicz et al., 2008; Jensen et al., 2001; Kakosimos et al., 2010). Data of street configurations are the physical environment around the geocoded address, and the data are describing average height of building in the street, the width of the street from facade to
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facade and the building heights in wind sectors. Input data for the AirGIS system included traffic data for individual road links (same input data as described for the noise modelling), emission factors for the Danish car fleet, street and building geometry, building height and meteorological data (Jensen et al., 2001). The AirGIS system has been successfully validated and applied in several studies (Jensen et al., 2009; Ketzel et al., 2011; Raaschou-Nielsen et al., 2011). Both NO2 and NOx are indicators of traffic emission, and thus, markers of a number of air pollutants. NOx correlated strongly with ultrafine particles (total particle number concentration (10–700 nm)): r = 0.93 and also with PM10: r = 0.70 in a street with dense traffic (Hertel et al., 2001; Ketzel et al., 2003). 2.4. Statistical analysis The analyses were based on a Cox proportional hazards model with age as the underlying time-scale (Thiebaut and Benichou, 2004). This ensured comparison of individuals of the same age. We used left truncation at the age of enrolment, so that people were considered at risk from the exact age they had at the day they were enrolled into the cohort (delayed entry). Right censoring was used at the age of A-fib (event), death, emigration or end of follow-up (31st December 2011), whichever came first. Exposures to road traffic and railway noise as well as to air pollution (NO2 and NOx) were modelled as time-weighted averages for time-windows of 1 and 5 years preceding the A-fib event (taking all present and historical addresses in that period into account). These exposure measures were entered as time-dependent variables into the statistical risk model. Incidence rate ratios (IRR) for A-fib in association with traffic noise (road traffic and railway) were analyzed in a crude model (adjusted for age (by design) and sex) and adjusted for a priori defined potential confounders: age (by design), sex, body mass index (BMI; kilograms per meter squared), waist circumference (centimetres), smoking status (never, former, current), smoking duration (years), smoking intensity (lifetime average, gram tobacco/day), alcohol consumption (yes/no), intake of alcohol (gram/day), physical activity (yes/no), sport during leisure time (hours/week), length of school attendance (≤ 7, 8–10, N10 years) and area level socioeconomic position of the participant's enrolment municipality (or district for Copenhagen; 10 districts in total) classified as low, medium or high, based on municipality/ district-level information on education, work market affiliation and income, occupational status (employed, unemployed/retired), calendaryear and airport noise (yes/no). In addition, exposure to road traffic and railway noise was mutually adjusted. We also performed analyses with five categories of exposure to 5-year time-weighted exposures of road traffic noise according to quintiles among cases (Q2: 52.7–b 55.9, Q3: 55.9≤–b 59.7, Q4:59.7≤–64.2 and Q5: ≥ 64.2 dB) compared with the reference category Q1: b52.7 dB. Potential effect modification of the association between 5 years' time-weighted averages road traffic noise and A-fib by sex and railway noise exposure were evaluated by introducing an interaction term into the model and tested by Wald test. We also analyzed whether the association between railway noise and A-fib is different for patients diagnosed before the age of 67.5 years as compared with patients diagnosed after the age of 67.5 years. Furthermore, we conducted a number of sensitivity analyses. Firstly, we conducted analyses, where adjustment for air pollution (NO2 or NOx) was included. In these analyses exposure to air pollution (NO2 or NOx) was modelled as timeweighted averages the preceding 5 years and entered as timedependent variables (similarly to the procedure for road traffic noise). Secondly, we conducted analyses where we excluded participants with myocardial infarction (before censoring), stroke (before censoring), diabetes (before censoring, though only until 2006) or baseline hypertension. Information on myocardial infarction and stroke was obtained by linkage with the Danish National Patient Registry using ICD-8 and ICD-10; myocardial infarction ICD-8: 410 and ICD-10: DI21; stroke ICD-8: 431.0, 431.9, 432.0, 432.9, 433.09, 433.99, 434.09,
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434.99, 436.0, and 436.9 and ICD-10 DI61, DI63, and DI64. Diabetes cases were identified by linkage with the Danish National Diabetes Registry (Carstensen et al., 2008), for which we had information until 2006. We defined baseline hypertension as answering “yes” to the following question in the baseline questionnaire: “Do you suffer, or have you ever suffered from high blood pressure?”. The assumption of linearity of the function between variables (traffic noise, air pollution, smoking intensity, alcohol consumption, BMI, and waist circumference) and to risk of A-fib were evaluated both visually and by formal testing with linear spline models with three knots placed at quartiles for cases. We found smoking intensity to deviate from linearity, and included this variable as a spline with cut-point at 20 g/day. Traffic noise and the other covariates did not deviate from linearity. All analyses were performed using SAS version 9.3 (SAS Institute, North Carolina, USA). 3. Results From the initial study population of 57,053 individuals, we excluded 572 participants with a diagnosis of cancer before enrolment, 1015 participants with a diagnosis of A-fib before enrolment, 2677 participants with incomplete residential address history in the period from 1st of July 1987 to censoring and 2547 participants with missing data on one or more covariates, leaving a study population of 50,242 individuals. Among these, 2692 were diagnosed with incident A-fib, during a mean follow-up time of 14.7 years. Participants exposed to N55 dB of road traffic noise seemed less well educated, having a higher socioeconomic position, to be less physically active, to have higher prevalence of hypertension, to live more often Table 1 Baseline characteristics for the Diet, Cancer, and Health cohort according to exposure to road traffic noise below and above 55 dB (Lden) at enrolment of 50,242 cohort participants. Characteristic at enrollment
Lden road b55 dB (n = 17.122)
Lden road ≥55 dB (n = 33.120)
Men (%) 49.3 45.8 Age (years) 56.0 (50.7–64.1) 56.4 (50.8–64.2) Length of school attendance (%) ≤7 years 31.2 34.1 8–10 years 46.2 46.5 ≥10 years 22.6 19.4 a Socioeconomic position (%) Low 20.7 21.2 Medium 69.4 62.4 High 9.9 16.4 Smoking status (%) Never 37.7 35.3 Former 33.6 37.8 Current 28.7 26.9 Among present and former smokers Smoking duration (years) 32.0 (6.0–46.0) 33.0 (8.0–46.0) 14.6 (3.4–34.9) 14.8 (3.9–33.9) Smoking intensity(g/day)b Alcohol consumption (%) 98.0 97.6 Alcohol intake (g/day) 13.5 (1.3–62.2) 13.1 (1.0–65.4) Physical active (%) 56.3 53.2 2.0 (0.5–7.0) 2.0 (0.5–6.5) Sport during leisure time (h/week)c 2 25.4 (20.5–32.9) 25.6 (20.4–33.5) BMI (kg/m ) Waist circumference (cm) 89.0 (69.0–109.0) 88.0 (69.0–110.0) Stroke before censuring (%) 4.5 5.1 Myocardial infarction before censuring (%) 5.7 6.0 9.7 10.7 Diabetes before censuring (%)d Hypertension at baseline (%) 11.9 12.7 Railway noise (%) 16.8 20.4 Airport noise (%) 0.97 0.37 14.7 (11.6–20.3) 19.9 (13.2–37.3) Air pollution, NO2 (μg/m3) 17.7 (13.7–25.2) 25.8 (15.9–111.5) Air pollution, NOx (μg/m3) Values are medians (5th–95th percentiles) unless otherwise stated. a Socioeconomic position of municipalities based on municipality information on education, work market affiliation and income. b The average amount of tobacco smoked per day during lifetime. c Among active. d Information on diabetes only until 2006.
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Table 2 Associations between residential exposure to traffic noise (per 10 dB) and risk for atrial fibrillation. Exposure to traffic noise (per 10 dB) Road traffic noise 1-year preceding diagnosis 5-year preceding diagnosis Railway noise 1-year preceding diagnosis 5-year preceding diagnosis
Cases
Crudea IRR (95% CI)
Adjustedb IRR (95% CI)
2692 2692
1.07 (1.01–1.13) 1.08 (1.02–1.15)
1.04 (0.98–1.10) 1.06 (1.00–1.12)
2692 2692
0.95 (0.88–1.03) 0.98 (0.91–1.05)
0.94 (0.87–1.02) 0.97 (0.90–1.05)
road traffic noise and risk of A-fib was non-significant with IRR's of 1.04 (95% CI: 0.96–1.11) and 1.01 (95% CI: 0.94–1.09), respectively. Exclusion of subjects with cardiovascular disease or diabetes had only minor impact on the risk estimates. 4. Discussion
IRR, Incidence rate ratio; CI, confidence interval. a Adjusted for age and sex. b Adjusted for age, sex plus adjustment for lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol and sport during leisure time), socioeconomic position (length of school attendance and area socioeconomic position), calendar year, airport noise, and mutual adjustment for road traffic/railway noise.
with exposure to railway noise and to be exposed to higher levels of air pollution compared to participants exposed to b55 dB. Participants exposed to b 55 dB of road traffic noise seemed to live more often with airport noise (Table 1). There was a very high correlation between Lden road traffic noise and Ln road traffic noise: Rs = 0.999. The Spearman correlation between road traffic noise and air pollution (at enrolment) was 0.63 (P b 0.0001) for NO2 and 0.68 (P b 0.0001) for NOx. Table 2 shows associations between exposure to traffic noise and risk of A-fib for two different exposure periods: 1- and 5-year timeweighted mean exposures. A 10-dB higher 5-year mean exposure to road traffic noise was associated with a statistically significant 6% (95% confidence interval (CI): 0–12%) higher risk of A-fib in adjusted analyses. The association seemed to follow a monotonic exposure–response relationship (Fig. 1). There was no association between exposure to railway noise and risk of A-fib, neither before nor after adjustment for potential confounders. There were no clear tendencies regarding effect modification by sex, or exposure to railway noise. Also, there were no significant differences in estimates according to age, although the association between noise and risk of A-fib appeared confined to people below the age of 67.5 years (Table 3). Fig. 2 shows sensitivity analyses of the associations between 5-year time-weighted exposures of road traffic noise and A-fib. In analyses with adjustment for NOx or NO2 the association between exposure to
This study indicated that long-term residential exposure to road traffic noise may be associated with a higher risk of A-fib, following a monotonic exposure–response relationship. In analyses with further adjustment for air pollution there were no statistically significant association between road traffic noise and A-fib. Analyses restricted to subjects without cardiovascular disease or diabetes only resulted in small changes in estimates. There was no association between railway noise and risk of A-fib. The present study is the first to evaluate associations between longterm exposure to traffic noise and risk of A-fib. Previous studies on transport noise have focused on other cardiovascular disease outcomes, mainly hypertension and myocardial infarction as well as a few studies on stroke (Vienneau et al., 2015). These studies rather consistently find exposure to transport noise to be associated with higher risk for cardiovascular disease (Sorensen et al., 2011; van Kempen and Babisch, 2012; Vienneau et al., 2015). Proposed mechanisms of noise include sleep disturbance, annoyance and stress, which in turn can activate the autonomic nervous system, and thereby potentially increase a number of biological risk factors (Babisch, 2002; WHO, 2009), some of which are important for development of A-fib, such as neuro-hormonal activation and impaired immune system. We found road traffic noise to be associated with higher risk of A-fib. In support of this finding, we observed that the association followed a monotonic exposure–response relationship. On the other hand, the association between road traffic noise and A-fib was only borderline statistically significant, and, furthermore, the study indicated no association between noise from railways and risk of A-fib. However, railway noise is perceived as less annoying than road traffic noise when comparing equal noise levels (Miedema and Oudshoorn, 2001), which might partly explain the null-finding for railway noise. There were no clear tendencies regarding effect modification by sex or railway noise in the association between road traffic noise and A-fib. However, the association between noise and risk of A-fib appeared confined to people below the age of 67.5 years. One potential explanation
Fig. 1. Association between exposure to road traffic noise at the residence 5 years preceding diagnosis and risk for atrial fibrillation adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, physical activity), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise. The vertical whiskers show incidence rate ratios with 95% confidence interval at the median of exposure categories (Q2: 52.7–b55.9, Q3: 55.9≤–b59.7, Q4:59.7≤–64.2 and Q5: ≥64.2 dB) compared with the reference category Q1: b52.7 dB.
M. Monrad et al. / Environment International 92–93 (2016) 457–463 Table 3 Effect modification of the associations between road traffic noise (per 10 dB) 5 years preceding diagnosis and risk of atrial fibrillation. Covariates Sex Men Women Ageb b67.5 years ≥67.5 years Exposed to railway noisec Yes No
N cases
IRR (95% CI)a
P interaction 0.96
1709 983
1.05 (0.98–1.13) 1.06 (0.96–1.16)
1354 1338
1.11 (1.03–1.20) 1.00 (0.92–1.08)
594 2098
1.12 (0.99–1.26) 1.04 (0.98–1.11)
0.07
0.33
IRR, Incidence rate ratio; CI, confidence interval. a Adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, sport during leisure time), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise. b At time of diagnosis. c Age at diagnosis of atrial fibrillation.
could be related to the fact that hearing ability declines with age (Lin et al., 2011) and, thus, noise sensitivity may be lower in the elderly. Unfortunately, we do not have information on hearing ability for the cohort used in the present study, but it would be relevant to include in future studies. In analyses with further adjustment for NOx or NO2, there were, respectively, weak and no association between road traffic noise and Afib. This suggests that the observed association between road traffic noise and A-fib may be driven by an association between air pollution and A-fib (Liao et al., 2011; Link et al., 2013; Rich et al., 2006). Road traffic noise and air pollution are correlated in the present study, reflecting that road traffic is a source of both exposures. This collinearity complicates the interpretation of the results of the multiple pollutant models. A recent study indicated that independent effects of road noise and traffic-related air pollution could be reliably determined within London (Fecht et al., 2016). However, we cannot rule out that the air pollution models predict air pollution levels more precisely than the noise model predicts road traffic noise, which could potentially explain
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why estimates were reduced after adjustment for air pollution. A study on biological mechanisms found that in mutually adjusted models both long-term exposure to air pollution and nighttime traffic noise was associated with subclinical atherosclerosis, whereas there was no association with 24-h road traffic noise exposure (Lden) (Kalsch et al., 2014). Therefore, Lden may not always be the optimal estimate for exposure to road traffic noise, especially when disturbance of sleep is thought to be an important pathway between exposure and disease. In the present study the modelled noise during the night (Ln) was highly correlated with modelled daytime exposure (Ld) and we could not separate the effect of the two exposures. Exposure to traffic noise has previously been associated with cardiovascular disease and diabetes (Sorensen et al., 2013; van Kempen and Babisch, 2012; Vienneau et al., 2015). We found that exclusion of cases with myocardial infarction, stroke, hypertension or diabetes, did not influence the association between road traffic noise and A-fib, which suggests, that the association observed in the present study is not driven by the already established association between noise and cardiovascular disease. The strengths of this study include a 15-year prospective follow-up of a large cohort, with a large number of cases and adjustment for potential lifestyle and socioeconomic confounders as well as access to residential address history, enabling us to investigate long-term exposure to noise in different exposure time-windows. Follow-up for incident A-fib was possible through a high-quality nationwide hospital register, which has been reported to have a very high positive predictive value of 93% with regard to the diagnosis of A-fib (Rix et al., 2012). Patients diagnosed with Afib at their general practitioner are not registered in the hospital registry unless they are referred to a hospital. However, most Danish patients with clinical symptoms of A-fib will be referred to a hospital for further evaluation. Another strength of our study is the sensitivity analyses with inclusion of air pollution adjustment, which is a potentially important confounder as road traffic is a main source of both air pollution and noise (Sorensen et al., 2014) and air pollution is suspected to be associated with A-fib (Liao et al., 2011; Link et al., 2013; Rich et al., 2006). The present study also has some limitations. Although we used a validated noise exposure model the estimation of noise is associated with
Fig. 2. Incidence rate ratios of atrial fibrillation in association with 5-year time-weighted exposure to road traffic noise (per 10 dB) in models with adjustment for air pollutants as well as with exclusion of persons with cardiovascular disease or diabetes. Analyses were adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, sport during leisure time), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise.
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some degree of uncertainty. One reason could be inaccurate input data, which would result in exposure misclassification. However, the data on traffic used in the present study is rather detailed with information on both traffic count, speed and composition for most roads with N1000 vehicles per day, and for smaller roads traffic an estimation based on information on road type and urban/rural zone. We lacked information on noise barriers, which is a potential important variable in the noise model. This is mainly an issue in more recent years, as before 2000 there were only very few noise barriers. Presently, only a small percentage of the Danish buildings are close to noise barriers and we therefore estimate that the misclassification from missing this information is not large. Also, as the noise model does not distinguish between cases and the cohort, such misclassification is thought to be non-differential. More importantly, our model included screening from all Danish buildings. As exposure was estimated from information on outdoor exposure at the most exposed facade of residential addresses we lacked information on factors that might influence personal exposure to noise, such as bedroom location, window opening habits, noise from neighbors and hearing impairment. The present study is based on an urban cohort of elderly people and results may, therefore, not be readily generalizable to the general population. 4.1. Conclusion Residential exposure to road traffic noise seemed associated with higher risk for developing A-fib in an exposure-dependent manner, though associations were difficult to separate from exposure to air pollution. Disclosures None. Acknowledgment This work was supported by the European Research Council, EU 7th Research Framework Programme (grant: 281760). References Aho, V., Ollila, H.M., Rantanen, V., Kronholm, E., Surakka, I., van Leeuwen, W.M.A., Lehto, M., Matikainen, S., Ripatti, S., Härmä, M., Sallinen, M., Salomaa, V., Jauhiainen, M., Alenius, H., Paunio, T., Porkka-Heiskanen, T., 2013. Partial sleep restriction activates immune response-related gene expression pathways: experimental and epidemiological studies in humans. PLoS ONE 8, e77184. Ali, T., Orr, W.C., 2014. Sleep disturbances and inflammatory bowel disease. Inflamm. Bowel Dis. 20, 1986–1995. Andrade, J., Khairy, P., Dobrev, D., Nattel, S., 2014. The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ. Res. 114, 1453–1468. Babisch, W., 2002. The noise/stress concept, risk assessment and research needs. Noise Health 4, 1–11. Bellavance, M.-A., Rivest, S., 2014. The HPA — immune axis and the immunomodulatory actions of glucocorticoids in the brain. Front. Immunol. 5, 136. Bendtsen, H., 1999. The Nordic prediction method for road traffic noise. Sci. Total Environ. 235, 331–338. Berkowicz, R., Ketzel, M., Jensen, S.S., Hvidberg, M., Raaschou-Nielsen, O., 2008. Evaluation and application of OSPM for traffic pollution assessment for a large number of street locations. Environ. Model. Softw. 23, 296–303. Besedovsky, L., Lange, T., Born, J., 2012. Sleep and immune function. Pflugers Arch. 463, 121–137. Carstensen, B., Kristensen, J.K., Ottosen, P., Borch-Johnsen, K., 2008. The Danish National Diabetes Register: trends in incidence, prevalence and mortality. Diabetologia 51, 2187–2196. Chen, P.S., Chen, L.S., Fishbein, M.C., Lin, S.F., Nattel, S., 2014. Role of the autonomic nervous system in atrial fibrillation: pathophysiology and therapy. Circ. Res. 114, 1500–1515. Dewland, T.A., Vittinghoff, E., Harris, T.B., Magnani, J.W., Liu, Y., Hsu, F.C., Satterfield, S., Wassel, C., Marcus, G.M., 2015. Inflammation as a mediator of the association between race and atrial fibrillation: results from the health, aging, and body composition study. JACC Clin. Electrophysiol. 1, 248–255.
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Contents lists available at ScienceDirect
Environment International journal homepage: www.elsevier.com/locate/envint
Residential exposure to traffic noise and risk of incident atrial fibrillation: A cohort study Maria Monrad a,⁎, Ahmad Sajadieh b, Jeppe Schultz Christensen a, Matthias Ketzel c, Ole Raaschou-Nielsen a,c, Anne Tjønneland a, Kim Overvad d,e, Steffen Loft f, Mette Sørensen a a
Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark Department of Cardiology, Copenhagen University Hospital of Bispebjerg, Bispebjerg, Denmark Department of Environmental Science, Aarhus University, Roskilde, Denmark d Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark e Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark f Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark b c
a r t i c l e
i n f o
Article history: Received 2 February 2016 Received in revised form 6 April 2016 Accepted 24 April 2016 Available online xxxx Keywords: Arrhythmia Traffic noise Cohort Epidemiology
a b s t r a c t Background: Studies have found long-term exposure to traffic noise to be associated with higher risk for hypertension, ischemic heart disease and stroke. We aimed to investigate the novel hypothesis that traffic noise increases the risk of atrial fibrillation (A-fib). Methods: In a population-based cohort of 57,053 people aged 50–64 years at enrolment in 1993–1997, we identified 2692 cases of first-ever hospital admission of A-fib from enrolment to end of follow-up in 2011 using a nationwide registry. The mean follow-up time was 14.7 years. Present and historical residential addresses were identified for all cohort members from 1987 to 2011. For all addresses, exposure to road traffic and railway noise was estimated using the Nordic prediction method and exposure to air pollution was estimated using a validated dispersion model. We used Cox proportional hazard model for the analyses with adjustment for lifestyle, socioeconomic position and air pollution. Results: A 10 dB higher 5-year time-weighted mean exposure to road traffic noise was associated with a 6% higher risk of A-fib (incidence rate ratio (IRR): 1.06; 95% confidence interval (95% CI): 1.00–1.12) in models adjusted for factors related to lifestyle and socioeconomic position. The association followed a monotonic exposure–response relationship. In analyses with adjustment for air pollution, NOx or NO2, there were no statistically significant associations between exposure to road traffic noise and risk of A-fib; IRR: 1.04; (95% CI: 0.96–1.11) and IRR: 1.01; (95% CI: 0.94–1.09), respectively. Exposure to railway noise was not associated with A-fib. Conclusion: Exposure to residential road traffic noise may be associated with higher risk of A-fib, though associations were difficult to separate from exposure to air pollution. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Traffic noise has become an almost inevitable environmental exposure with rising levels following urbanization. Exposure to traffic noise has generally been associated with cardiovascular disease, including hypertension (van Kempen and Babisch, 2012), ischemic heart disease (Vienneau et al., 2015) and stroke (Sorensen et al., 2011). No studies have investigated whether exposure to traffic noise is associated with atrial fibrillation (A-fib).
Abbreviations: A-fib, atrial fibrillation; HPA, hypothalamus–pituitary–adrenal; dB, decibel; IRR, incidence rate ratio; Lden, equivalent continuous noise level day–evening– night. ⁎ Corresponding author at: Diet, Genes and Environment, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark. E-mail address: [email protected] (M. Monrad).
http://dx.doi.org/10.1016/j.envint.2016.04.039 0160-4120/© 2016 Elsevier Ltd. All rights reserved.
A-fib is the most common type of arrhythmia (Schnabel et al., 2015), and associated with both increased cardiovascular morbidity and mortality (Go et al., 2001). Although A-fib affects approximately 4% of the population over 50 years of age with rising prevalence, knowledge on the aetiology behind the development of A-fib is sparse (Andrade et al., 2014; Miyasaka et al., 2006). Exposure to noise during the night is thought to be particularly hazardous, as night-time noise at normal urban levels (45–65 dB) is associated with reduced sleep quality and duration (Miedema and Vos, 2007). Noise also acts as a stressor, with hyperactivity of the autonomic nervous system and activation of the hypothalamus–pituitary–adrenal (HPA) axis, leading to a cascade of effects. This includes changes of atrial electrophysiology, which may be one pathway through which noise may initiate A-fib (Chen et al., 2014; Shen and Zipes, 2014). Another potential pathway is noise-induced effects on the immune system, as systemic inflammation in various studies has been associated with
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increased risk A-fib (Dewland et al., 2015; Issac et al., 2007). Both stress and disturbance of sleep may affect the immune system. Studies have found exposure to acute and chronic stressors, e.g. exams and stressful life events, to be associated with an impaired immune system (Picardi et al., 2009; Segerstrom and Miller, 2004) and release of cortisol, as a result of an active HPA axis, has been found to impair the function of glucocorticoid receptors and thereby contribute to systemic inflammation (Bellavance and Rivest, 2014; Silverman and Sternberg, 2012; Wolkow et al., 2015). Sleep is known to have a strong regulatory influence on the immune system (Ali and Orr, 2014; Gomez-Gonzalez et al., 2012), and disturbance of sleep has been associated with impairment of the immune system, including reduced post-vaccination antibody titers, changed numbers of circulating white blood cells and increased production of pro-inflammatory molecules (Aho et al., 2013; Besedovsky et al., 2012; Irwin et al., 2015; Lange et al., 2011). Lastly, release of cortisol may increase the glycogen concentration within atrial myocytes, which is suggested to be a risk factor for A-fib (Embi and Scherlag, 2014; Zhang et al., 2015). The aim of the present study was to investigate the association between residential exposure to road traffic and railway noise and risk for incident A-fib in a large prospective cohort. 2. Methods 2.1. Study population The study was based on the Diet, Cancer and Health study, into which 57,053 residents of Copenhagen or Aarhus aged 50–64 years were enrolled between 1993 and 1997 (Tjonneland et al., 2007). The participants had to be born in Denmark with no history of cancer at the time of enrolment. At enrolment, each participant completed selfadministered, interviewer-checked, lifestyle questionnaires covering smoking habits, diet, alcohol consumption, physical activity and education. Height, weight, and waist circumference were measured by trained staff members according to standardized protocols. The study was conducted in accordance with the Helsinki Declaration and approved by the local Ethics Committees and written informed consent was obtained from all participants. 2.2. Identification of outcome Cases who developed A-fib between baseline and death, emigration, or end of follow-up (31st December 2011) were identified by linking the unique personal identification number of each cohort member to the nationwide Danish National Patient Register (Lynge et al., 2011). Since 1977 patients diagnosed in-hospital have been registered in The Danish National Patient Register and coded using the International Classification of Diseases 8th (until 1994) and 10th (from 1994) Revision (ICD-8 and ICD-10) coding system. Since 1995 diagnoses from emergency rooms and outpatient visits have also been registered (Lynge et al., 2011). Cases were identified using ICD-8 codes 427.93 and 427.94 and ICD-10 code I48.9. We excluded participants with a diagnosis of A-fib before enrolment, and considered only the first hospitalization of A-fib. 2.3. Exposure assessment Complete residential address history between 1st of July 1987 and event or end of follow-up at 31st December 2011 was available for 93% of the cohort members using the Danish civil registration system (Pedersen, 2011). Exposure to road traffic noise was calculated for the years 1990, 1995, 2000, 2005 and 2010 using SoundPLAN (http:// www.soundplan.dk), which implements the joint Nordic prediction method for road traffic noise; a method which has been the standard method for noise calculation in Scandinavia since the introduction in 1981 (Bendtsen, 1999). Traffic noise for the year 1990 was used as a
proxy for the period 1st of July 1987 to 30th of June 1992, the year 1995 was a proxy of the period 1st of July 1992 to 30th of June 1997, etc. Equivalent noise levels were calculated for each address on the most exposed facade of the building using the following input variables; geographical coordinates and height (corresponding to floor level) of each address; road links with information on yearly average daily traffic, vehicle distribution (of light and heavy vehicles), travel speed and road type (motorway, express road, road wider than 6 m, road b6 m and N3 m, and other road); and building polygons for all buildings including information on building height. Information on building polygons was obtained from the Danish Geodata Agency, and is based on different sources including a laser scan of Denmark. First and second order reflections from buildings (3 dimensional) have been included in the modeling and reflection loss for buildings was set to 1 dB. No information was available on noise barriers. We obtained traffic counts for all Danish roads from a national road and traffic database (Jensen et al., 2009). This database is based on a number of different traffic data sources: 1) Collection of traffic data from the 140 Danish municipalities with most residents, covering 97.5% of the addresses included in the present study. Included roads typically have N 1000 vehicles per day and are based on traffic counts as well as estimated/modeled numbers. Traffic data represents the period from 1995 to 1998; 2) Traffic data from a central database covering all the major state and county roads; 3) Traffic data for 1995–2000 for all major roads in the Greater Copenhagen Area; 4) traffic data for 1995 for all roads based on a simple method where estimated figures for distribution of traffic by road type and by urban/rural zone were applied to the road network and subsequently calibrated against known traffic data at county level. New roads were included in the calculations from the year they opened. Values below 40 dB were set to 40 dB, because we considered this as a lower limit of road traffic noise. Exposure to railway noise was calculated for all addresses using SoundPLAN, with implementation of NORD2000. The input variables for the noise model were receptor point (geographical coordinate and height), railway links with information on annual average daily train lengths, train types, travel speed (obtained from BaneDanmark, which is operating and developing the Danish state railway network), and building polygons for all Danish buildings, including screening from buildings (as described for road traffic noise). The daily train lengths are given for 1997 and 2011. All noise barriers along the railway are included in the model. In estimating noise we assumed that the terrain was flat, which is a reasonable assumption in Denmark, and that urban areas, roads, and areas with water were hard surfaces whereas all other areas were acoustically porous. Road traffic and railway noise were calculated as the equivalent continuous A-weighted sound pressure level (LAeq) at the most exposed facade of the dwelling at each address for the day (Ld; 07:00–19:00 h), evening (Le; 19:00–22:00 h) and night (Ln; 22:00–07:00 h) and these where expressed as an indicator of the overall noise level during 24 h, as Lden (day, evening, night) by applying a 5-dB penalty for the evening and a 10-dB penalty for the night. Lden was used as estimator of road traffic and railway noise in all statistical analyses. The noise impact from all Danish airports and airfields was determined from information about noise zones (5-dB categories) obtained from local authorities. Airport noise was transformed into digital maps and linked to each address by geocodes. The concentration of traffic-related air pollution (nitrogen dioxide; NO2 and nitrogen oxides; NOx) was calculated using the dispersion model AirGIS, for each year (1987–2011) at each address at which the cohort members had lived. The Danish AirGIS modelling system calculates air pollution as the sum of regional background, urban background, and local street level calculated with the Operational Street Pollution Model (OSPM) (Berkowicz et al., 2008; Jensen et al., 2001; Kakosimos et al., 2010). Data of street configurations are the physical environment around the geocoded address, and the data are describing average height of building in the street, the width of the street from facade to
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facade and the building heights in wind sectors. Input data for the AirGIS system included traffic data for individual road links (same input data as described for the noise modelling), emission factors for the Danish car fleet, street and building geometry, building height and meteorological data (Jensen et al., 2001). The AirGIS system has been successfully validated and applied in several studies (Jensen et al., 2009; Ketzel et al., 2011; Raaschou-Nielsen et al., 2011). Both NO2 and NOx are indicators of traffic emission, and thus, markers of a number of air pollutants. NOx correlated strongly with ultrafine particles (total particle number concentration (10–700 nm)): r = 0.93 and also with PM10: r = 0.70 in a street with dense traffic (Hertel et al., 2001; Ketzel et al., 2003). 2.4. Statistical analysis The analyses were based on a Cox proportional hazards model with age as the underlying time-scale (Thiebaut and Benichou, 2004). This ensured comparison of individuals of the same age. We used left truncation at the age of enrolment, so that people were considered at risk from the exact age they had at the day they were enrolled into the cohort (delayed entry). Right censoring was used at the age of A-fib (event), death, emigration or end of follow-up (31st December 2011), whichever came first. Exposures to road traffic and railway noise as well as to air pollution (NO2 and NOx) were modelled as time-weighted averages for time-windows of 1 and 5 years preceding the A-fib event (taking all present and historical addresses in that period into account). These exposure measures were entered as time-dependent variables into the statistical risk model. Incidence rate ratios (IRR) for A-fib in association with traffic noise (road traffic and railway) were analyzed in a crude model (adjusted for age (by design) and sex) and adjusted for a priori defined potential confounders: age (by design), sex, body mass index (BMI; kilograms per meter squared), waist circumference (centimetres), smoking status (never, former, current), smoking duration (years), smoking intensity (lifetime average, gram tobacco/day), alcohol consumption (yes/no), intake of alcohol (gram/day), physical activity (yes/no), sport during leisure time (hours/week), length of school attendance (≤ 7, 8–10, N10 years) and area level socioeconomic position of the participant's enrolment municipality (or district for Copenhagen; 10 districts in total) classified as low, medium or high, based on municipality/ district-level information on education, work market affiliation and income, occupational status (employed, unemployed/retired), calendaryear and airport noise (yes/no). In addition, exposure to road traffic and railway noise was mutually adjusted. We also performed analyses with five categories of exposure to 5-year time-weighted exposures of road traffic noise according to quintiles among cases (Q2: 52.7–b 55.9, Q3: 55.9≤–b 59.7, Q4:59.7≤–64.2 and Q5: ≥ 64.2 dB) compared with the reference category Q1: b52.7 dB. Potential effect modification of the association between 5 years' time-weighted averages road traffic noise and A-fib by sex and railway noise exposure were evaluated by introducing an interaction term into the model and tested by Wald test. We also analyzed whether the association between railway noise and A-fib is different for patients diagnosed before the age of 67.5 years as compared with patients diagnosed after the age of 67.5 years. Furthermore, we conducted a number of sensitivity analyses. Firstly, we conducted analyses, where adjustment for air pollution (NO2 or NOx) was included. In these analyses exposure to air pollution (NO2 or NOx) was modelled as timeweighted averages the preceding 5 years and entered as timedependent variables (similarly to the procedure for road traffic noise). Secondly, we conducted analyses where we excluded participants with myocardial infarction (before censoring), stroke (before censoring), diabetes (before censoring, though only until 2006) or baseline hypertension. Information on myocardial infarction and stroke was obtained by linkage with the Danish National Patient Registry using ICD-8 and ICD-10; myocardial infarction ICD-8: 410 and ICD-10: DI21; stroke ICD-8: 431.0, 431.9, 432.0, 432.9, 433.09, 433.99, 434.09,
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434.99, 436.0, and 436.9 and ICD-10 DI61, DI63, and DI64. Diabetes cases were identified by linkage with the Danish National Diabetes Registry (Carstensen et al., 2008), for which we had information until 2006. We defined baseline hypertension as answering “yes” to the following question in the baseline questionnaire: “Do you suffer, or have you ever suffered from high blood pressure?”. The assumption of linearity of the function between variables (traffic noise, air pollution, smoking intensity, alcohol consumption, BMI, and waist circumference) and to risk of A-fib were evaluated both visually and by formal testing with linear spline models with three knots placed at quartiles for cases. We found smoking intensity to deviate from linearity, and included this variable as a spline with cut-point at 20 g/day. Traffic noise and the other covariates did not deviate from linearity. All analyses were performed using SAS version 9.3 (SAS Institute, North Carolina, USA). 3. Results From the initial study population of 57,053 individuals, we excluded 572 participants with a diagnosis of cancer before enrolment, 1015 participants with a diagnosis of A-fib before enrolment, 2677 participants with incomplete residential address history in the period from 1st of July 1987 to censoring and 2547 participants with missing data on one or more covariates, leaving a study population of 50,242 individuals. Among these, 2692 were diagnosed with incident A-fib, during a mean follow-up time of 14.7 years. Participants exposed to N55 dB of road traffic noise seemed less well educated, having a higher socioeconomic position, to be less physically active, to have higher prevalence of hypertension, to live more often Table 1 Baseline characteristics for the Diet, Cancer, and Health cohort according to exposure to road traffic noise below and above 55 dB (Lden) at enrolment of 50,242 cohort participants. Characteristic at enrollment
Lden road b55 dB (n = 17.122)
Lden road ≥55 dB (n = 33.120)
Men (%) 49.3 45.8 Age (years) 56.0 (50.7–64.1) 56.4 (50.8–64.2) Length of school attendance (%) ≤7 years 31.2 34.1 8–10 years 46.2 46.5 ≥10 years 22.6 19.4 a Socioeconomic position (%) Low 20.7 21.2 Medium 69.4 62.4 High 9.9 16.4 Smoking status (%) Never 37.7 35.3 Former 33.6 37.8 Current 28.7 26.9 Among present and former smokers Smoking duration (years) 32.0 (6.0–46.0) 33.0 (8.0–46.0) 14.6 (3.4–34.9) 14.8 (3.9–33.9) Smoking intensity(g/day)b Alcohol consumption (%) 98.0 97.6 Alcohol intake (g/day) 13.5 (1.3–62.2) 13.1 (1.0–65.4) Physical active (%) 56.3 53.2 2.0 (0.5–7.0) 2.0 (0.5–6.5) Sport during leisure time (h/week)c 2 25.4 (20.5–32.9) 25.6 (20.4–33.5) BMI (kg/m ) Waist circumference (cm) 89.0 (69.0–109.0) 88.0 (69.0–110.0) Stroke before censuring (%) 4.5 5.1 Myocardial infarction before censuring (%) 5.7 6.0 9.7 10.7 Diabetes before censuring (%)d Hypertension at baseline (%) 11.9 12.7 Railway noise (%) 16.8 20.4 Airport noise (%) 0.97 0.37 14.7 (11.6–20.3) 19.9 (13.2–37.3) Air pollution, NO2 (μg/m3) 17.7 (13.7–25.2) 25.8 (15.9–111.5) Air pollution, NOx (μg/m3) Values are medians (5th–95th percentiles) unless otherwise stated. a Socioeconomic position of municipalities based on municipality information on education, work market affiliation and income. b The average amount of tobacco smoked per day during lifetime. c Among active. d Information on diabetes only until 2006.
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Table 2 Associations between residential exposure to traffic noise (per 10 dB) and risk for atrial fibrillation. Exposure to traffic noise (per 10 dB) Road traffic noise 1-year preceding diagnosis 5-year preceding diagnosis Railway noise 1-year preceding diagnosis 5-year preceding diagnosis
Cases
Crudea IRR (95% CI)
Adjustedb IRR (95% CI)
2692 2692
1.07 (1.01–1.13) 1.08 (1.02–1.15)
1.04 (0.98–1.10) 1.06 (1.00–1.12)
2692 2692
0.95 (0.88–1.03) 0.98 (0.91–1.05)
0.94 (0.87–1.02) 0.97 (0.90–1.05)
road traffic noise and risk of A-fib was non-significant with IRR's of 1.04 (95% CI: 0.96–1.11) and 1.01 (95% CI: 0.94–1.09), respectively. Exclusion of subjects with cardiovascular disease or diabetes had only minor impact on the risk estimates. 4. Discussion
IRR, Incidence rate ratio; CI, confidence interval. a Adjusted for age and sex. b Adjusted for age, sex plus adjustment for lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol and sport during leisure time), socioeconomic position (length of school attendance and area socioeconomic position), calendar year, airport noise, and mutual adjustment for road traffic/railway noise.
with exposure to railway noise and to be exposed to higher levels of air pollution compared to participants exposed to b55 dB. Participants exposed to b 55 dB of road traffic noise seemed to live more often with airport noise (Table 1). There was a very high correlation between Lden road traffic noise and Ln road traffic noise: Rs = 0.999. The Spearman correlation between road traffic noise and air pollution (at enrolment) was 0.63 (P b 0.0001) for NO2 and 0.68 (P b 0.0001) for NOx. Table 2 shows associations between exposure to traffic noise and risk of A-fib for two different exposure periods: 1- and 5-year timeweighted mean exposures. A 10-dB higher 5-year mean exposure to road traffic noise was associated with a statistically significant 6% (95% confidence interval (CI): 0–12%) higher risk of A-fib in adjusted analyses. The association seemed to follow a monotonic exposure–response relationship (Fig. 1). There was no association between exposure to railway noise and risk of A-fib, neither before nor after adjustment for potential confounders. There were no clear tendencies regarding effect modification by sex, or exposure to railway noise. Also, there were no significant differences in estimates according to age, although the association between noise and risk of A-fib appeared confined to people below the age of 67.5 years (Table 3). Fig. 2 shows sensitivity analyses of the associations between 5-year time-weighted exposures of road traffic noise and A-fib. In analyses with adjustment for NOx or NO2 the association between exposure to
This study indicated that long-term residential exposure to road traffic noise may be associated with a higher risk of A-fib, following a monotonic exposure–response relationship. In analyses with further adjustment for air pollution there were no statistically significant association between road traffic noise and A-fib. Analyses restricted to subjects without cardiovascular disease or diabetes only resulted in small changes in estimates. There was no association between railway noise and risk of A-fib. The present study is the first to evaluate associations between longterm exposure to traffic noise and risk of A-fib. Previous studies on transport noise have focused on other cardiovascular disease outcomes, mainly hypertension and myocardial infarction as well as a few studies on stroke (Vienneau et al., 2015). These studies rather consistently find exposure to transport noise to be associated with higher risk for cardiovascular disease (Sorensen et al., 2011; van Kempen and Babisch, 2012; Vienneau et al., 2015). Proposed mechanisms of noise include sleep disturbance, annoyance and stress, which in turn can activate the autonomic nervous system, and thereby potentially increase a number of biological risk factors (Babisch, 2002; WHO, 2009), some of which are important for development of A-fib, such as neuro-hormonal activation and impaired immune system. We found road traffic noise to be associated with higher risk of A-fib. In support of this finding, we observed that the association followed a monotonic exposure–response relationship. On the other hand, the association between road traffic noise and A-fib was only borderline statistically significant, and, furthermore, the study indicated no association between noise from railways and risk of A-fib. However, railway noise is perceived as less annoying than road traffic noise when comparing equal noise levels (Miedema and Oudshoorn, 2001), which might partly explain the null-finding for railway noise. There were no clear tendencies regarding effect modification by sex or railway noise in the association between road traffic noise and A-fib. However, the association between noise and risk of A-fib appeared confined to people below the age of 67.5 years. One potential explanation
Fig. 1. Association between exposure to road traffic noise at the residence 5 years preceding diagnosis and risk for atrial fibrillation adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, physical activity), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise. The vertical whiskers show incidence rate ratios with 95% confidence interval at the median of exposure categories (Q2: 52.7–b55.9, Q3: 55.9≤–b59.7, Q4:59.7≤–64.2 and Q5: ≥64.2 dB) compared with the reference category Q1: b52.7 dB.
M. Monrad et al. / Environment International 92–93 (2016) 457–463 Table 3 Effect modification of the associations between road traffic noise (per 10 dB) 5 years preceding diagnosis and risk of atrial fibrillation. Covariates Sex Men Women Ageb b67.5 years ≥67.5 years Exposed to railway noisec Yes No
N cases
IRR (95% CI)a
P interaction 0.96
1709 983
1.05 (0.98–1.13) 1.06 (0.96–1.16)
1354 1338
1.11 (1.03–1.20) 1.00 (0.92–1.08)
594 2098
1.12 (0.99–1.26) 1.04 (0.98–1.11)
0.07
0.33
IRR, Incidence rate ratio; CI, confidence interval. a Adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, sport during leisure time), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise. b At time of diagnosis. c Age at diagnosis of atrial fibrillation.
could be related to the fact that hearing ability declines with age (Lin et al., 2011) and, thus, noise sensitivity may be lower in the elderly. Unfortunately, we do not have information on hearing ability for the cohort used in the present study, but it would be relevant to include in future studies. In analyses with further adjustment for NOx or NO2, there were, respectively, weak and no association between road traffic noise and Afib. This suggests that the observed association between road traffic noise and A-fib may be driven by an association between air pollution and A-fib (Liao et al., 2011; Link et al., 2013; Rich et al., 2006). Road traffic noise and air pollution are correlated in the present study, reflecting that road traffic is a source of both exposures. This collinearity complicates the interpretation of the results of the multiple pollutant models. A recent study indicated that independent effects of road noise and traffic-related air pollution could be reliably determined within London (Fecht et al., 2016). However, we cannot rule out that the air pollution models predict air pollution levels more precisely than the noise model predicts road traffic noise, which could potentially explain
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why estimates were reduced after adjustment for air pollution. A study on biological mechanisms found that in mutually adjusted models both long-term exposure to air pollution and nighttime traffic noise was associated with subclinical atherosclerosis, whereas there was no association with 24-h road traffic noise exposure (Lden) (Kalsch et al., 2014). Therefore, Lden may not always be the optimal estimate for exposure to road traffic noise, especially when disturbance of sleep is thought to be an important pathway between exposure and disease. In the present study the modelled noise during the night (Ln) was highly correlated with modelled daytime exposure (Ld) and we could not separate the effect of the two exposures. Exposure to traffic noise has previously been associated with cardiovascular disease and diabetes (Sorensen et al., 2013; van Kempen and Babisch, 2012; Vienneau et al., 2015). We found that exclusion of cases with myocardial infarction, stroke, hypertension or diabetes, did not influence the association between road traffic noise and A-fib, which suggests, that the association observed in the present study is not driven by the already established association between noise and cardiovascular disease. The strengths of this study include a 15-year prospective follow-up of a large cohort, with a large number of cases and adjustment for potential lifestyle and socioeconomic confounders as well as access to residential address history, enabling us to investigate long-term exposure to noise in different exposure time-windows. Follow-up for incident A-fib was possible through a high-quality nationwide hospital register, which has been reported to have a very high positive predictive value of 93% with regard to the diagnosis of A-fib (Rix et al., 2012). Patients diagnosed with Afib at their general practitioner are not registered in the hospital registry unless they are referred to a hospital. However, most Danish patients with clinical symptoms of A-fib will be referred to a hospital for further evaluation. Another strength of our study is the sensitivity analyses with inclusion of air pollution adjustment, which is a potentially important confounder as road traffic is a main source of both air pollution and noise (Sorensen et al., 2014) and air pollution is suspected to be associated with A-fib (Liao et al., 2011; Link et al., 2013; Rich et al., 2006). The present study also has some limitations. Although we used a validated noise exposure model the estimation of noise is associated with
Fig. 2. Incidence rate ratios of atrial fibrillation in association with 5-year time-weighted exposure to road traffic noise (per 10 dB) in models with adjustment for air pollutants as well as with exclusion of persons with cardiovascular disease or diabetes. Analyses were adjusted for age, sex, lifestyle factors (BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, sport during leisure time), socioeconomic position (length of school attendance, area socioeconomic position), calendar year, railway and airport noise.
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some degree of uncertainty. One reason could be inaccurate input data, which would result in exposure misclassification. However, the data on traffic used in the present study is rather detailed with information on both traffic count, speed and composition for most roads with N1000 vehicles per day, and for smaller roads traffic an estimation based on information on road type and urban/rural zone. We lacked information on noise barriers, which is a potential important variable in the noise model. This is mainly an issue in more recent years, as before 2000 there were only very few noise barriers. Presently, only a small percentage of the Danish buildings are close to noise barriers and we therefore estimate that the misclassification from missing this information is not large. Also, as the noise model does not distinguish between cases and the cohort, such misclassification is thought to be non-differential. More importantly, our model included screening from all Danish buildings. As exposure was estimated from information on outdoor exposure at the most exposed facade of residential addresses we lacked information on factors that might influence personal exposure to noise, such as bedroom location, window opening habits, noise from neighbors and hearing impairment. The present study is based on an urban cohort of elderly people and results may, therefore, not be readily generalizable to the general population. 4.1. Conclusion Residential exposure to road traffic noise seemed associated with higher risk for developing A-fib in an exposure-dependent manner, though associations were difficult to separate from exposure to air pollution. Disclosures None. Acknowledgment This work was supported by the European Research Council, EU 7th Research Framework Programme (grant: 281760). References Aho, V., Ollila, H.M., Rantanen, V., Kronholm, E., Surakka, I., van Leeuwen, W.M.A., Lehto, M., Matikainen, S., Ripatti, S., Härmä, M., Sallinen, M., Salomaa, V., Jauhiainen, M., Alenius, H., Paunio, T., Porkka-Heiskanen, T., 2013. Partial sleep restriction activates immune response-related gene expression pathways: experimental and epidemiological studies in humans. PLoS ONE 8, e77184. Ali, T., Orr, W.C., 2014. Sleep disturbances and inflammatory bowel disease. Inflamm. Bowel Dis. 20, 1986–1995. Andrade, J., Khairy, P., Dobrev, D., Nattel, S., 2014. The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ. Res. 114, 1453–1468. Babisch, W., 2002. The noise/stress concept, risk assessment and research needs. Noise Health 4, 1–11. Bellavance, M.-A., Rivest, S., 2014. The HPA — immune axis and the immunomodulatory actions of glucocorticoids in the brain. Front. Immunol. 5, 136. Bendtsen, H., 1999. The Nordic prediction method for road traffic noise. Sci. Total Environ. 235, 331–338. Berkowicz, R., Ketzel, M., Jensen, S.S., Hvidberg, M., Raaschou-Nielsen, O., 2008. Evaluation and application of OSPM for traffic pollution assessment for a large number of street locations. Environ. Model. Softw. 23, 296–303. Besedovsky, L., Lange, T., Born, J., 2012. Sleep and immune function. Pflugers Arch. 463, 121–137. Carstensen, B., Kristensen, J.K., Ottosen, P., Borch-Johnsen, K., 2008. The Danish National Diabetes Register: trends in incidence, prevalence and mortality. Diabetologia 51, 2187–2196. Chen, P.S., Chen, L.S., Fishbein, M.C., Lin, S.F., Nattel, S., 2014. Role of the autonomic nervous system in atrial fibrillation: pathophysiology and therapy. Circ. Res. 114, 1500–1515. Dewland, T.A., Vittinghoff, E., Harris, T.B., Magnani, J.W., Liu, Y., Hsu, F.C., Satterfield, S., Wassel, C., Marcus, G.M., 2015. Inflammation as a mediator of the association between race and atrial fibrillation: results from the health, aging, and body composition study. JACC Clin. Electrophysiol. 1, 248–255.
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