Traffic noise and risk for incident atrial fibrillation

Transcription

1 Traffic noise and risk for incident atrial fibrillation Mette SØRENSEN 1 ; Maria MONRAD 1, Ahmad SAJADIEH 2 ; Jeppe CHRISTENSEN 1 1 Danish Cancer Society Research Center, Denmark 2 Copenhagen University Hospital of Bispebjerg, Denmark ABSTRACT Introduction: Traffic noise exposure is associated with hypertension, ischemic heart disease and stroke. We aimed to investigate the novel hypothesis that traffic noise increases the risk for atrial fibrillation. Methods: In a population-based cohort of people aged years at enrolment in , we identified 2692 cases of first-ever hospital admission for atrial fibrillation from enrolment to follow-up (2011). Historical residential addresses were identified for all cohort members ( ). For all addresses, exposure to road traffic and railway noise was estimated using the Nordic prediction method and air pollution estimated using dispersion model. Cox proportional hazard model was used for analyses. Results: A 10 db higher 5-years time-weighted mean exposure to road traffic noise was associated with a 6 % higher risk for atrial fibrillation (incidence rate ratio: 1.06; 95% CI: ) in models adjusted for lifestyle and socioeconomic position. The association followed a monotonic exposure-response relationship. In analyses with further adjustment for air pollution, estimates were attenuated and statistically insignificant. Exposure to railway noise was not associated with atrial fibrillation. Conclusions: Road traffic noise seemed associated with higher risk for atrial fibrillation. Adjustment for air pollution attenuated the results, and more studies including both traffic exposures are needed. Keywords: Extra-auditory effects (62.2); Road traffic noise (76.1.1) 1. INTRODUCTION Exposure to traffic noise has been associated with cardiovascular disease, including hypertension, ischemic heart disease and stroke. No studies have investigated whether exposure to traffic noise is associated with atrial fibrillation (A-fib) (1-3). A-fib is the most common type of arrhythmia and associated with both increased cardiovascular morbidity and mortality. 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. Exposure to noise during night is though particular hazardous, as night-time noise at normal urban levels is associated with reduced sleep quality and duration (4). Noise also acts as a stressor, with hyperactivity of the autonomic nervous system and activation of the HPA axis, leading to a cascade of effects (5). This includes changes of atrial electrophysiology, which may be one pathway through which noise may initiate A-fib. Another potential pathway is noise-induced effects on the immune system, as systemic inflammation in various studies has been associated with increased risk A-fib (6). Lastly, release of cortisol may increase the glycogen concentration within atrial myocytes, which is suggested to be a risk factor for A-fib (7). 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 1 mettes@cancer.dk 2683

2 Copenhagen or Aarhus aged years were enrolled between 1993 and The participants had to be born in Denmark with no history of cancer at the time of enrolment. At enrolment, each participant completed self-administered, 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 (31th December 2011) were identified by linking the unique personal identification number of each cohort member to the nationwide Danish National Patient Register. Cases were identified using ICD-8 codes and 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. 2.2 Exposure Assessment Complete residential address history between 1st of July 1987 and event or end of follow-up at 31th December 2011 was available for 93 % of the cohort members using the Danish civil registration system. Exposure to road traffic noise was calculated for the years 1990, 1995, 2000, 2005 and 2010 using SoundPLAN, which implements the joint Nordic prediction method for road traffic noise; a method which has been the standard method for noise calculation in Scandinavia during many years. 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 less than 6 m and more than 3 m, and other road); and building polygons for all buildings. No information was available on noise barriers. We obtained traffic counts for all Danish roads from a national road and traffic database. 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 more than 1,000 vehicles per day; 2) Traffic data from a central database covering all major state and county roads; 3) Traffic data for 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 and building polygons for all Danish buildings, including screening from buildings (as described for road traffic noise). 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 was calculated as the equivalent continuous A-weighted sound pressure level (L Aeq) at the most exposed facade of the dwelling at each address for the day (L d; 07:00 19:00 h), evening (L e; 19:00 22:00 h) and night (L n; 22:00 07:00 h) and expressed as L den (day, evening, night). The concentration of traffic-related air pollution (nitrogen dioxide; NO 2 and nitrogen oxides; NO x) was calculated using the dispersion model AirGIS, for each year ( ) at each address at which the cohort members had lived (8). 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. 2.1 Statistical Analyses The analyses were based on a Cox proportional hazards model with age as the underlying time-scale. 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 2684

3 was used at the age of A-fib (event), death, emigration or end of follow-up (31th December 2011), which ever came first. Exposures to road traffic and railway noise as well as to air pollution (NO 2 and NO x) 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 analysed 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, > 10 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), calendar-year 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-years time-weighted exposures of road traffic noise according to quintiles among cases. 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 analysed 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 in which we a) including adjustment for air pollution and b) excluded participants with myocardial infarction (before censoring), stroke (before censoring), diabetes (before censoring, though only until 2006) or baseline hypertension. 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, 1,015 participants with a diagnosis of A-fib before enrolment, 2,677 participants with incomplete residential address history in the period from 1st of July 1987 to censoring and 2,547 participants with missing data on one or more covariates, leaving a study population of 50,242 individuals. Among these, 2,692 were diagnosed with incident A-fib, during a mean follow-up time of 14.7 years. Participants exposed to more than 55 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 with exposure to railway noise and to be exposed to higher levels of air pollution compared to participants exposed to less than 55 db (Table 1). The Spearman correlation between road traffic noise and air pollution (at enrolment) was 0.63 (P <0.0001) for NO 2 and 0.68 (P <0.0001) for NO x. Table 2 shows associations between exposure to traffic noise and risk of A-fib for two different exposure periods: 1- and 5-years time-weighted 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 (Figure 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, age or exposure to railway noise. 2685

4 Table 1 Baseline characteristics for the Diet, Cancer, and Health cohort according to exposure to road traffic noise below and above 55 db (L den) at enrolment of 50,242 cohort participants L den road < 55 db L den road 55 db Characteristic at enrollment (n= 17,122) (n= 33,120) Men (%) Age (years) 56.0 ( ) 56.4 ( ) Length of school attendance (%) 7 years years years Smoking status (%) Never Former Current Among present and former smokers Smoking duration (years) 32.0 ( ) 33.0 ( ) Smoking intensity(g/day) b 14.6 ( ) 14.8 ( ) Alcohol intake (g/day) 13.5 ( ) 13.1 ( ) Physical active (%) BMI (kg/m 2 ) 25.4 ( ) 25.6 ( ) Waist circumference (cm) 89.0 ( ) 88.0 ( ) Railway noise (%) Air pollution, NO 2 (µg/m 3 ) 14.7 ( ) 19.9 ( ) Air pollution, NO x (µg/m 3 ) 17.7 ( ) 25.8 ( ) Values are medians (5th 95th percentiles) unless otherwise stated. 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 Cases Crude a IRR (95% CI) Adjusted b IRR (95% CI) 1-year preceding diagnosis 2, ( ) 1.04 ( ) 5-year preceding diagnosis 2, ( ) 1.06 ( ) Railway noise 1-year preceding diagnosis 2, ( ) 0.94 ( ) 5-year preceding diagnosis 2, ( ) 0.97 ( ) a Adjusted for age and sex b Adjusted for age, sex, BMI, waist circumference, smoking status, smoking duration, smoking intensity, intake of alcohol, sport during leisure time, length of school attendance, area level socioeconomic position, calendar year, airport noise, and mutual adjustment for road traffic/railway noise. 2686

5 Figure 1 Association between exposure to road traffic noise at the residence 5-years preceding diagnosis and risk for atrial fibrillation in the fully adjusted model. The vertical whiskers show incidence rate ratios with 95 % confidence interval at the median of exposure categories (Q2: , Q3: , Q4: and Q5: 64.2 db) compared with the reference category (Q1: < 52.7 db). Figure 2 Incidence rate ratios of atrial fibrillation in association with 5-years 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. 4. DISCUSSION This study indicated that long-term residential exposure to road traffic noise may be associated with a higher risk of A-fib. Further adjustment for air pollution attenuated the estimates. Analyses 2687

6 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 long-term 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. These studies rather consistently find exposure to transport noise to be associated with higher risk for cardiovascular disease. 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, 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, 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, which might partly explain the null-finding for railway noise (9). In analyses with further adjustment for NO x or NO 2, there were, respectively, weak and no association between road traffic noise and A-fib. 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 (10, 11). 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. 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 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-hours road traffic noise exposure (L den) (12). Therefore, L den 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 (L n) was highly correlated with modelled daytime exposure (L d) and we could not separate the effect of the two exposures. Exposure to traffic noise has previously been associated with cardiovascular disease and diabetes. 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. Another strength of our study is the sensitivity analyses with inclusion of air pollution adjustment. The present study also has some limitations. Although we used a validated noise exposure model the estimation of noise is associated with some degree of uncertainty. One reason could be inaccurate input data, which would result in exposure misclassification. As the noise model does not distinguish between cases and the cohort, such misclassification is thought to be non-differential. We lacked information on factors that might influence personal exposure to noise, such as bedroom location, window opening habits, noise from neighbours and hearing impairment. 5. 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. ACKNOWLEDGEMENTS This work was supported by the European Research Council, EU 7th Research Framework Programme (grant: ). 2688

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