Long-Term Exposure to Road Traffic Noise and Air Pollution, and Incident Atrial Fibrillation in the Danish Nurse Cohort
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 8 Linguagem: Inglês
10.1289/ehp8090
ISSN1552-9924
AutoresZorana Jovanovic Andersen, Johannah Cramer, Jeanette T. Jørgensen, Christian Dehlendorff, Heresh Amini, Amar Mehta, Tom Cole‐Hunter, Laust Hvas Mortensen, Rudi G. J. Westendorp, Rina So, Shuo Li, Barbara Hoffmann, Steffen Loft, Elvira V. Bräuner, Matthias Ketzel, Ole Hertel, Jørgen Brandt, Steen Solvang Jensen, Jesper H. Christensen, Camilla Geels, Lise M. Frohn, Claus Backalarz, Mette Kildevæld Simonsen, Youn‐Hee Lim,
Tópico(s)Phonocardiography and Auscultation Techniques
ResumoVol. 129, No. 8 ResearchOpen AccessLong-Term Exposure to Road Traffic Noise and Air Pollution, and Incident Atrial Fibrillation in the Danish Nurse Cohort Zorana J. Andersen, Johannah Cramer, Jeanette T. Jørgensen, Christian Dehlendorff, Heresh Amini, Amar Mehta, Tom Cole-Hunter, Laust H. Mortensen, Rudi Westendorp, Rina So, Shuo Li, Barbara Hoffmann, Steffen Loft, Elvira V. Bräuner, Matthias Ketzel, Ole Hertel, Jørgen Brandt, Steen Solvang Jensen, Jesper H. Christensen, Camilla Geels, Lise M. Frohn, Claus Backalarz, Mette K. Simonsen, and Youn-Hee Lim Zorana J. Andersen Address correspondence to Zorana J. Andersen, Section of Environmental Health, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Opgang B, Post Box 2099, 1014 Copenhagen, Denmark. Telephone: +45 20 74 04 62. Email: E-mail Address: [email protected]. Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Johannah Cramer Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Jeanette T. Jørgensen Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Christian Dehlendorff Statistics and Data Analysis, Danish Cancer Society Research Center, Copenhagen, Denmark , Heresh Amini Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Amar Mehta Denmark Statistics, Copenhagen, Denmark Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Tom Cole-Hunter Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Centre for Air Pollution, Energy, and Health Research, University of New South Wales, Sydney, New South Wales, Australia International Laboratory for Air Quality and Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia , Laust H. Mortensen Denmark Statistics, Copenhagen, Denmark Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Rudi Westendorp Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark , Rina So Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Shuo Li Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Barbara Hoffmann Institute for Occupational, Social and Environmental Medicine; Centre for Health and Society, Medical Faculty, Heinrich-Heine-University of Düsseldorf, Düsseldorf, Germany , Steffen Loft Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark , Elvira V. Bräuner Department of Growth and Reproduction, Rigshospitalet, University of Copenhagen, Denmark , Matthias Ketzel Department of Environmental Science, Aarhus University, Roskilde, Denmark Global Centre for Clean Air Research (GCARE), University of Surrey, United Kingdom , Ole Hertel Department of Environmental Science, Aarhus University, Roskilde, Denmark , Jørgen Brandt Department of Environmental Science, Aarhus University, Roskilde, Denmark iClimate, Aarhus University, Roskilde, Denmark , Steen Solvang Jensen Department of Environmental Science, Aarhus University, Roskilde, Denmark , Jesper H. Christensen Department of Environmental Science, Aarhus University, Roskilde, Denmark , Camilla Geels Department of Environmental Science, Aarhus University, Roskilde, Denmark , Lise M. Frohn Department of Environmental Science, Aarhus University, Roskilde, Denmark , Claus Backalarz DELTA Acoustics, Hørsholm, Denmark , Mette K. Simonsen Diakonissestiftelsen, Frederiksberg, Denmark The Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen University Hospital, Copenhagen, Denmark , and Youn-Hee Lim Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Published:2 August 2021CID: 087002https://doi.org/10.1289/EHP8090AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Associations between long-term exposure to air pollution and road traffic noise have been established for ischemic heart disease, but findings have been mixed for atrial fibrillation (AF).Objectives:The goal of the study was to examine associations of long-term exposure to road traffic noise and air pollution with AF.Methods:Time-varying Cox regression models were used to estimate associations of 1-, 3-, and 23-y mean road traffic noise and air pollution exposures with AF incidence in 23,528 women enrolled in the Danish Nurse Cohort (age >44y at baseline in 1993 or 1999). AF diagnoses were ascertained via the Danish National Patient Register. Annual mean weighted 24-h average road traffic noise levels (Lden) at the nurses' residences, since 1970, were estimated using the Nord2000 model, and annual mean levels of particulate matter with a diameter 58 dB vs. 208,000 adult residents of the Greater London area who were followed for up to 7 y (Carey et al. 2016). Monrad et al. reported an association between incident AF and 5-y mean road traffic noise levels before diagnosis in a cohort of >50,000 Danish adults, with an average follow-up of 14.7 y (Monrad et al. 2016). However, the association attenuated to null after adjustment for traffic-related air pollution (NO2 or NOx), and AF was not associated with railway noise (Monrad et al. 2016). Finally, an association between long-term exposure to wind turbine noise and incident AF was recently reported for participants in the Danish Nurse Cohort (DNC) (Bräuner et al. 2019).In this study, we examined the association between long-term exposure to both road traffic noise and air pollution and the incidence of AF.MethodsThe DNCThe DNC includes nurses from the Danish Nurse Organization, which includes 95% of the Danish nursing workforce (Hundrup et al. 2012). In 1993, 23,170 female nurses (age >44y) were mailed initial questionnaires, of which 19,898 (86%) responded. In 1999, 8,833 additional nurses were recruited (489 reinvited nonresponders from 1993 and 8,344 nurses who had reached 45 years of age), giving a total of 28,731 nurses recruited in 1993 or 1999. Questionnaires completed at baseline in 1993 or 1999 gathered information on working conditions, lifestyle factors (dietary, physical activity, smoking, and alcohol consumption), self-reported health (including hypertension, diabetes, and myocardial infarction), height and weight, and reproductive health. The DNC study was approved by the Danish scientific ethics committee, and written informed consent was obtained from all the participants. The present study was approved by the Danish Data Protection Agency (Case number: 514-0518/20-3,000).AF DefinitionsAF diagnoses were ascertained through linkage to the Danish National Patient Registry, which includes information from all Danish hospitals on in-patient hospitalizations since 1977, and information from outpatient clinic contacts and emergency room visits since 1995. Incident AF was defined as the first hospital contact for AF [International Classification of Diseases (ICD) codes: ICD-8: 427.93, 427.94 and ICD-10: I48 (AF and flutter)] after baseline in 1993 or 1999. Women who had a hospital contact for AF prior to baseline were excluded. Use of the Danish National Patient Registry for AF ascertainment is supported by a review of medical records for 300 incident AF cases identified based on hospital discharge diagnoses in the Danish National Patient Registry that reported a positive predicted value (PPV) of 92.6% (Rix et al. 2012), and by a more recent medical records review of 100 incident cases of AF identified through the Danish National Patient Registry that reported a PPV of 95% (Sundbøll et al. 2016). Hospitals do not receive financial reimbursement unless services and discharge diagnoses are reported to the Registry; therefore, ascertainment of AF diagnoses should be close to100%. Apart from inclusion of outpatient visits in 1995, there were no changes in the AF incidence registration system in the Danish National Patient Registry for the rest of the study during the study period.Exposure AssignmentThe geographical coordinates of each residential address between 1970 and 31 December 2014 were identified for each participant through the Danish Address Register and were used to estimate residential road traffic noise and air pollution exposures.Assessment of Residential Road Traffic NoiseResidential road traffic noise at each residential address was calculated using the Nord2000 method (DELTA 2002; DELTA 2006), which uses following input variables: the address geocode; the height of apartments above street level; information on nearby roads, including annual average daily traffic, traffic composition and speed, road type and properties (e.g., motorway, rural highway, road wider than 6m, and other roads); building polygons for all surrounding buildings; and meteorology, including wind speed and direction, air temperature, and cloud cover. The propagation model is based on geometrical ray theory computing the one-third octave band sound attenuation along the path from the source to the receiver, which accounts for properties of the terrain (shape and ground type, including impedance and roughness) and variations in weather conditions. Annual mean road traffic noise contributed by roads within a 3-km radius were estimated for each residential address during 1970–2015 as the equivalent continuous A-weighted sound pressure level (LAeq) at the most exposed façade of the dwelling during the day (Ld; 07:00–19:00 hours), evening (Le; 19:00–22:00 hours), and night (Ln; 22:00–07:00 hours). These data were used to derive Lden, the annual weighted 24-h average noise levels during the day, evening, and night, after adding 5- and 10-dB penalties to the estimated evening and night noise levels, respectively. Validation of the Nord2000 model was done by comparing the model predictions with outdoor noise measurements at 544 different sites, which showed, on average, small differences, with higher predicted noise levels than measurements, in the order of 0.5 dB on average and 1 dB at the worst (DELTA Acoustics & Electronics 2006).The Danish air pollution modeling system DEHM/UBM/AirGIS ( http://au.dk/AirGIS) (Khan et al. 2019) was used to estimate annual average NO2 exposures for 1970–2014, and annual average PM2.5 exposures for 1990–2014. The system comprises three air pollution models: the Danish Eulerian Hemispheric Model, which is used to assess the long-range transport components; the Danish Urban Background Model, to estimate the local background on a 1-km2 resolution grid overlaying Denmark; and the Operational Street Pollution Model (OSPM), to estimate front-door concentrations at each residential address. When compared with routine measurements from the Danish monitoring program (comprising 17 stations across the country), R2 values for DEHM/UBM/AirGIS ranged from 0.7 to 0.9, depending on the site, pollutant, and averaging period; PM2.5 concentrations were underestimated by 7%–13% (Hvidtfeldt et al. 2018; Ellermann et al. 2020). In this study we estimated associations between AF and 1-, 3-, and 23-y moving average NO2 and Lden exposures (prior to the end of follow-up for each participant), and associations with 1- and 3-y moving average PM2.5 exposures. The 1-y mean exposures represented recent exposures, whereas the longer-term 3- and 23-y time windows were selected because they represented the longest possible exposure windows from the first year with annual average data (1970 for NO2 and Lden, and 1990 for PM2.5) through 1992, the year before follow-up began among women enrolled in 1993. Data were split by year, from 1970 until the end of follow-up for each person, from which we constructed three exposure periods for PM2.5, NO2, and Lden.Statistical AnalysesAssociations between Lden, PM2.5, and NO2, and incident AF were estimated using time-varying Cox regression models, with attained age as the underlying time scale. Age at cohort entry (as of 1 April 1993 or 1 April 1999) was the start of follow-up, whereas end of follow-up was the date of incident AF ascertainment (event), the date of death or emigration, or 31 July 2015, whichever occurred first. All exposure data were time-varying. Women with missing exposure data for the cohort baseline year (1993 or 1999) were excluded from analyses (n=2,163). When exposure data were missing for other years due to gaps in residential address information, the missing data were replaced with the average value for all years with available data in the 3- or 23-year time window. The proportion of missing exposure data across all years was 0.40% for PM2.5 and NO2 and 0.39% for Lden, and <2% of participants had missing exposure data.Single- and two-pollutant models were used to estimate the effects of exposure to 1-, and 3-y running means of each pollutant (Lden, PM2.5, and NO2), and 23-y running means of Lden and NO2, modeled continuously (per 10 dB for Lden and per interquartile range (IQR) increase for the air pollutants).Associations were investigated with increasing adjustment for confounders, in three steps: Model 1, adjusted for age (underlying time scale) and baseline year (1993 or 1999, to control for possible differences between the two cohort rounds); Model 2, further adjusted for AF risk factors including smoking status (never, previous, or current), fruit consumption (rarely, a few times per week, daily, or several times per day), alcohol consumption (none, moderate: 1–14 drinks/wk, or heavy: ≥15 drinks/wk), typical leisure time physical activity level (low: reading or watching TV or doing other mainly sedentary activities; medium: walking, biking, or other types of light physical activity at least four times a week, including weekend walks, light gardening, and cycling/walking to and from work; or high: exercising regularly, participation in organized sports or heavy gardening at least four times a week, high intensity training or participating in professional sports at least four times a week), marital status (married, separated, divorced, single, or widowed), and level of urbanization (rural: 5,220 persons/km2), all assessed at the cohort baseline in 1993 or 1999. In addition, models included a spline term of calendar year with 3 degrees of freedom to control for long-term temporal trends in air pollution, road traffic noise, and AF incidence. Women with missing data for any of the Model 2 covariates were excluded from the analysis.We assessed potential deviations from linearity by modeling each exposure (adjusted for Model 2 covaries) using restricted cubic splines, and we used likelihood ratio tests to compare the fit of the spline models to models with continuous (linear) exposure variables. We checked for violations of the proportional hazards assumption using Schoenfeld residuals. In two-pollutant models, we further adjusted Model 2 for mean PM2.5, NO2, or Lden exposures during the same time window.Associations with road traffic noise were further modeled using a categorical Lden variable with cut points at the 25th and 75th percentiles of the exposure distribution ( 58 dB). We also explored potential threshold effects of Lden by estimating associations with Lden in subgroups of the study population with average exposures >53 and >58 dB, which represent maximum road traffic noise levels recommended by the World Health Organization and the Danish government, respectively (Ministry of the Environment of Denmark 2007; WHO Regional Office for Europe 2018). In addition, we estimated associations with PM2.5 in subgroups exposed to <10, <20, and <25 μg/m3 to explore associations with AF at low levels of exposure.We examined effect modification of associations between AF and 3-y running average exposures to Lden and PM2.5 by factors that may increase susceptibility through effects on overall health, including obesity [body mass index (BMI)≥30 kg/m3, yes/no], physical activity (low, medium, or high), and level of urbanization (rural, provincial, or urban), and by increasing the risk of cardiovascular disease, including hormone replacement therapy (never, previous, or current), and comorbid conditions (self-reported diagnosis or medication use), including hypertension (yes/no), diabetes (yes/no), and a history of myocardial infarction (yes/no). For associations between AF and road traffic noise, we also examined potential effect modification by night shift work (night shift only vs. day, evening, or rotating shifts) which is related to sleep disturbance (similar to noise exposure at night) and may increase the risk of cardiovascular disease, and potential modification by concurrent exposures to PM2.5 ( 23.2 μg/m3) and NO2 ( 15.7 μg/m3). For associations between PM2.5 and AF we also examined potential effect modification by Lden ( 58 dB) and smoking status (never, previous, or current), because effect mechanisms may be similar for tobacco and air pollution. All potential modifiers were defined at baseline. Interactions were modeled by including interaction terms between the main exposure (Lden or PM2.5) and indicator terms for categories of the potential modifier, and interaction p-values were derived using likelihood ratio tests.We performed additional sensitivity analyses to examine the effect of adjusting Model 2 for area-level socioeconomic status (SES) using municipality mean family income in Danish crowns at the year of cohort recruitment, 1993 or 1999, available from Danish national registers on personal income, and the cluster option for municipality to account for correlations among women living in the same municipalities. Statistical analyses were performed with R (version 3.6.1; R Development Core Team), and exposure maps for each pollutant at cohort baseline were created using features of the Spatial Analyst extension to ESRI's ArcMap 10.7.1 GIS.ResultsOf the 28,731 nurses, 105 were excluded due to a preexisting AF diagnosis at baseline, and 5,098 due to missing exposure or covariate data, leaving a total of 23,528 for the final analyses. Over mean follow-up of 18.4 y (minimum–maximum; 16 d–22.4 y) and 432,384 person-years, 1,522 (6.5%) nurses developed AF. The mean age [mean ±standard deviation (SD)] at baseline was 52.6±7.7 y (Table 1). At baseline, mean (mean ±SD) residential exposure to road traffic noise was 52.7±8.2 dB, whereas mean exposure levels for PM2.5, and NO2 were 19.7±3.6 and 12.6±8.1 μg/m3, respectively (Table 1). Median levels (IQR) of Lden (9.5), PM2.5 (5.2), and NO2 (8.2) were 53.1 dB, 19.7 μg/m3, and 10.2 μg/m3, respectively (Supplementary Table S1). Nurses included in the analyses were younger; smoked less; drank more alcohol; were more physically active; had lower prevalence of hypertension, diabetes, and myocardial infarction; and had lower incidence of AF in the study period from cohort entry in 1993 or 1991 until 2015, than nurses excluded from analyses (Supplementary Table S2). There were 275 municipalities in Denmark at the cohort recruitment in 1993 or 1999, with mean number of women living in municipality 86 (median 41), with minimum of 3 and maximum of 1,554 women per municipality.Table 1 Descriptive statistics at the cohort baseline in 1993 or 1999 for 23,528 Danish Nurse Cohort study participants by AF incidence status at the end of follow-up on 31 July 2015.Table 1, in four columns, lists Categories, Total lowercase italic n equals 23528, Atrial Fibrillation lowercase italic n equals 1522, and No Atrial Fibrillation lowercase italic n equals 22006.Total n=23,528AF n=1,522No AF n=22,006Cohort year or entry [n (%)] 199315,035 (63.9)1,264 (83.0)13,771 (62.6) 19998,493 (36.1)258 (17.0)8,253 (37.4)Age, mean±SD52.6±7.757.0±8.652.3±7.5Body mass index (kg/m2), mean±SD23.7±3.524.5±4.023.6±3.5Body mass index (kg/m2) [n (%)] Underweight ( 15 drinks/wk)5,450 (23.2)330 (21.7)5,120 (23.3) Missing (n)86177784Physical activitya [n (%)] Low1,561 (6.6)118 (7.8)1,443 (6.6) Medium15,676 (66.6)1,019 (67.0)14,657 (66.6) High6,291 (26.7)385 (25.3)5,906 (26.8) Missing (n)35632324Fruit consumption [n (%)] Rarely864 (3.7)58 (3.8)806 (3.7) Few times per week6,724 (28.6)402 (26.4)6,322 (28.7) Daily or several times per day15,940 (67.7)1,062 (69.8)14,878 (67.6) Missing (n)45450404Hypertension [n (%)] No20,553 (87.5)1,197 (78.8)19,356 (88.1) Yes2,945 (12.5)323 (21.2)2,622 (11.9) Missing (n)44341Diabetesb [n (%)] No23,066 (98.8)1,486 (98.2)21,580 (98.8) Yes284 (1.2)27 (1.8)257 (1.2) Missing (n)26016244Myocardial infarctionb [n (%)] No23,254 (99.3)1,495 (98.7)21,759 (99.4) Yes156 (0.7)20 (1.3)136 (0.6) Missing (n)18913176Employment shift typec [n (%)] Day11,683 (62.4)602 (61.2)11081 (62.5) Evening1,860 (9.9)116 (11.8)1,744 (9.8) Night1,019 (5.4)71 (7.2)948 (5.3) Rotating4,163 (22.2)195 (19.8)3,968 (22.4) Missing (n)7,2908356,455Marital status [n (%)] Married16,557 (70.4)973 (63.9)15,584 (70.8) Separated421 (1.8)26 (1.7)395 (1.8) Divorced2,786 (11.8)181 (11.9)2,605 (11.8) Single2,329 (9.9)190 (12.5)2,139 (9.7) Widowed1,435 (6.1)152 (10.0)1,283 (5.8) Missing [n (%)]24718229Hormone-replacement therapy [n (%)] Never16,973 (73.3)967 (64.5)16,006 (73.9) Previous2,238 (9.7)229 (15.3)2,009 (9.3) Current3,945 (17.0)303 (20.2)3,642 (16.8) Missing (n)50530475Urbanization level [n (%)] Rural9,716 (41.3)657 (43.2)9,059 (41.2) Provincial10,262 (43.6)631 (41.5)9,631 (43.8) Urban3,550 (15.1)234 (15.4)3,316 (15.1) Missing (n)84080760Annual air pollution exposure levels at cohort baselinePM2.5 levels (μg/ m3), mean±SD19.7±3.620.7±3.319.6±3.6PM2.5 levels (μg/ m3) [n (%)] Low ( 23.2)3,423 (14.5)319 (21.0)3,104 (14.1) Missing (n)2,9173042,613NO2 levels (μg/ m3), mean±SD12.6±8.113.2±8.412.6±8.1NO2 levels (μg/m3) [n (%)] Low ( 15.7)5,826 (24.8)434 (28.5)5,392 (24.5) Missing (n)2,9173042,613Annual road traffic noise exposure levels at cohort baselineLden (dB), mean±SD52.7±8.253.2±7.852.7±8.2Lden (dB), n (%) Low ( 58)5,931 (25.2)414 (27.2)5,517 (25.1) Missing (n)2,9173042,613Note: Data are complete, unless otherwise indicated. "Missing" represents data that were excluded from analyses. AF, atrial fibrillation; dB, decibel; Lden, annual mean 24-h road traffic noise levels; NO2, nitrogen dioxide; PM2.5, particulate matter with an aerodynamic diameter of <2.5 μg/m3; SD, standard deviation.aPhysical activity defined: low (typically reading or watching television in leisure time or doing other mainly sedentary leisure time activities)/medium (walking, biking, or other types of light physical activity at least 4 times a week, including weekend walks, light gardening, and cycling/walking to and from work)/high (exercising regularly/participation in organized sports and/or heavy gardening at least 4 times a week; high-intensity training or participating in professional sports at least 4 times a week).bSelf-reported (diagnosed/taking medication for).cAmong those actively employed at cohort entry (n=18,725).At baseline, correlations between 1-y average air pollutants and Lden were low to moderate, with Spearman's rank correlation coefficients (ρ) of 0.36 and 0.61 for PM2.5 and NO2, respectively, whereas PM2.5 was moderately correlated with NO2 (p=0.65) (Supplementary Table S3). Maps depicting the spatial distribution of each pollutant at baseline illustrate differences in air pollutant sources (Figure 1). PM2.5 levels in Denmark are influenced by secondary pollution from central Europe (across southeastern Denmark) (Ellermann et al. 2020), whereas NO2 (primarily traffic-related) and Lden levels were notably higher in urban areas.Figure 1. Smoothed maps of annual residential exposure levels for Lden (A), PM2.5 (B), and NO2 (C) (breaks represent quintiles of exposure) in the Danish Nurse Cohort at year of cohort entry (1993/1999).Associations between 3-y mean Lden or PM2.5 and AF showed no significant deviations from linearity, with likelihood ratio test p-values comparing restricted cubic splines to linear models of 0.94 and 0.25, respectively (Figure S1). In addition, we found no violations of the proportional hazards assumption (data not shown).The hazard ratio (HR) and 95% confidence interval (95% CI) from Model 2 for AF in association with 3-y mean Lden was 1.03 (0.97, 1.09) per 10 dB in all women, with s
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