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   Abstract
  Introduction
  Methods
  Results
  Discussion
  Conclusions
   References
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  Table of Contents    
ARTICLE  
Year : 2015  |  Volume : 17  |  Issue : 78  |  Page : 328-336
Does exposure to aircraft noise increase the mortality from cardiovascular disease in the population living in the vicinity of airports? Results of an ecological study in France

1 Epidemiological Research and Surveillance Unit in Transport, Occupation and Environment (UMRESTTE), Université de Lyon, Lyon, Transport, Health and Safety Department of the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR), Bron, Université Lyon 1, UMRESTTE, Lyon, France
2 IFSTTAR, Planning, Mobilities and Environment Department, Transport and Environment Laboratory (LTE), Bron, France
3 IFSTTAR, Transport, Health and Safety Department, Bron, France

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Date of Web Publication10-Sep-2015
 
  Abstract 

The impact of aircraft noise on health is of growing concern. We investigated the relationship between this exposure and mortality from cardiovascular disease, coronary heart disease, myocardial infarction, and stroke. We performed an ecological study on 161 communes (commune being the smallest administrative unit in France) close to the following three major French airports: Paris-Charles de Gaulle, Lyon Saint-Exupéry, and Toulouse-Blagnac. The mortality data were provided by the French Center on Medical Causes of Death for the period 2007-2010. Based on the data provided by the French Civil Aviation Authority, a weighted average exposure to aircraft noise (L den AEI) was computed at the commune level. A Poisson regression model with commune-specific random intercepts, adjusted for potential confounding factors including air pollution, was used to investigate the association between mortality rates and L den AEI. Positive associations were observed between L den AEI and mortality from cardiovascular disease [adjusted mortality rate ratio (MRR) per 10 dB(A) increase in L den AEI = 1.18; 95% confidence interval (CI): 1.11-1.25], coronary heart disease [MRR = 1.24 (1.12-1.36)], and myocardial infarction [MRR = 1.28 (1.11-1.46]. Stroke mortality was more weakly associated with L den AEI [MRR = 1.08 (0.97-1.21]. These significant associations were not attenuated after the adjustment for air pollution. The present ecological study supports the hypothesis of an association between aircraft noise exposure and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction. However, the potential for ecological bias and the possibility that this association could be due to residual confounding cannot be excluded.

Keywords: Aircraft noise, environment, health, mortality

How to cite this article:
Evrard AS, Bouaoun L, Champelovier P, Lambert J, Laumon B. Does exposure to aircraft noise increase the mortality from cardiovascular disease in the population living in the vicinity of airports? Results of an ecological study in France. Noise Health 2015;17:328-36

How to cite this URL:
Evrard AS, Bouaoun L, Champelovier P, Lambert J, Laumon B. Does exposure to aircraft noise increase the mortality from cardiovascular disease in the population living in the vicinity of airports? Results of an ecological study in France. Noise Health [serial online] 2015 [cited 2019 Nov 20];17:328-36. Available from: http://www.noiseandhealth.org/text.asp?2015/17/78/328/165058

  Introduction Top


The impact of exposure to aircraft noise on health is of growing concern [1] because of a steady rise in flights; people's annoyance with this kind of noise also seems to be rising. [2] While many studies address the annoyance associated with aircraft noise [3],[4],[5] or report the adverse effects on the quality of sleep, [6],[7],[8] a fewer number of studies consider other health effects of this noise exposure such as cardiovascular disease. Noise is a psychosocial stressor that activates the sympathetic and endocrine system. According to the general stress model, [9] neuroendocrine arousal is associated with adverse metabolic outcomes that are well-known and established risk factors for cardiovascular disease. Therefore, aircraft noise exposure could increase the prevalence or incidence of these diseases, ultimately increasing the risk of premature death.

Several studies have shown an association between aircraft noise exposure and hypertension. [10],[11],[12],[13] A multi-airport study in the United States found that high exposure to aircraft noise was significantly associated with hospitalization for cardiovascular disease among people above 65 years of age living near airports. [14] The evidence of an association of aircraft noise with mortality is currently limited. In 2010, Huss et al.[15] reported an association between aircraft noise and mortality from myocardial infarction in Switzerland with a dose-response relationship for the level and duration of noise exposure but no association with stroke or cardiovascular mortality. A Danish study did not find any association of aircraft noise with stroke mortality [16] and even a Canadian study did not find any association of aircraft noise with coronary heart disease mortality. [17] More recently, a small area study near Heathrow airport in London, England showed a significant association between high levels of aircraft noise and hospital admissions and mortality for stroke, coronary heart disease, and cardiovascular disease. [18]

We carried out an ecological study addressing the issue of an association between weighted average exposure to aircraft noise and mortality for some specific causes of interest such as cardiovascular disease, coronary heart disease, myocardial infarction, and stroke. Since air pollution has been found to be associated with cardiovascular disease, [19],[20],[21] concerns for disentangling the effects of noise and of air pollution on cardiovascular outcomes have been raised. [1] A secondary aim of the present study was, therefore, to examine if the association between aircraft noise and mortality was confounded by air pollution.


  Methods Top


Spatial scale

We carried out an ecological study based on 161 communes (a commune is the smallest administrative unit in France) spread over three geographical areas located in the vicinity of the following three major French airports [Figure 1]: Paris-Charles de Gaulle (108 communes), Lyon Saint-Exupéry (31 communes), and Toulouse-Blagnac (22 communes); these areas are hereafter, referred to as the areas of Paris, Lyon, and Toulouse, respectively. In 2011, Paris-Charles de Gaulle airport counted about 61 million passengers, Lyon Saint-Exupéry airport about 8.5 million passengers, and Toulouse-Blagnac airport about 7 million passengers. [22] The study population corresponded to the population of these 161 communes living in the vicinity of these three airports and this was estimated to be 1.9 million inhabitants in 2009 (that is, 3% of the total population of mainland France).
Figure 1: The three airports included in the present study

Click here to view


Aircraft noise exposure and mortality data used in the present study were obtained at the commune level.

Aircraft noise exposure assessment

The estimated exposure to aircraft noise was assessed by the French Civil Aviation Authority, which produces outdoor noise exposure maps with the "Integrated Noise Model" [23] (INM) for France's largest airports. The INM is an internationally well-established computer model that evaluates aircraft noise impact in the vicinity of airports. The INM outputs noise contours for an area. Aircraft noise contours were available for the year 2008 for Paris-Charles de Gaulle airport, 2003 for Lyon-Saint-Exupéry airport, and 2004 for Toulouse-Blagnac airport. For Lyon-Saint-Exupéry and Toulouse-Blagnac airports, we used the most recent noise exposure data available. These aircraft noise contours were considered to be representative of the years preceding the mortality assessment. The study area comprises all the communes exposed to aircraft noise, defined as being included in these noise contours and also the communes sharing a common border with them. The inclusion of these neighboring communes allowed us to increase the contrast in aircraft noise exposure.

The noise indicator used in the present study is the day-evening-night equivalent level (L den ) in A-weighted decibels [dB(A)]. It is defined as a weighted average of sound pressure levels from daytime (6 AM-18 PM), evening (18 PM-22 PM), and night (22 PM-6 AM). It is determined over the year at the most exposed faηade. In this calculation, evening and night sound pressure levels receive a penalty of 5 dB(A) and 10 dB(A), respectively, to reflect people's sensitivity to noise. [24] Noise levels were estimated with a 1-dB(A) resolution from a minimum of 50 dB(A) for the Paris area, and 45 dB(A) for both the Lyon and Toulouse areas. For each commune in the study area, the number of inhabitants of the commune living within these noise levels that were at 1-dB (A) intervals was available based on the 2009 French census.

Noise levels were aggregated to obtain an estimate of commune-level exposure to aircraft noise. A population-weighted average called average energetic index (L den AEI) [25] was estimated by weighting for a given commune, the L den by the number of inhabitants living within this noise level. For a given commune i, L den AEI was defined as follows:



where, L den,j is the noise level j (the difference between L den,j and L den,j+1 is 1 dB(A)), Pj the number of inhabitants in the commune exposed to L den,j dB(A), and is the total number of inhabitants in the commune.

L den AEI was then used as a measure of aircraft noise exposure in the statistical analyses presented in the present paper.

Air pollution exposure assessment

Information on exposure to air pollution, including both nitrogen dioxide (NO 2 ) and particulate matter of 10 μm or less (PM 10 ), was available at the commune level but only for the communes of the Paris and Lyon areas. Dispersion modeling was used to estimate annual background air pollution concentration for each commune in the study area. Briefly, modeled concentrations were provided at a 50 m × 50 m resolution by Airparif for the Paris area, and at a resolution of 1,000 m × 1,000 m by Air Rhτne-Alpes for the Lyon area. For both areas, modeled concentrations were validated by comparison with concentrations measured by a monitoring station network. The average air pollution exposure (for both NO 2 and PM 10 indicators and expressed in μg/m 3 ) for the years 2008-2010 was used in the statistical analyses. It was then categorized into three categories corresponding to the tertiles of the distribution.

Mortality data

The mortality data were provided by the French Center on Medical Causes of Death (CépiDc-Inserm) for the period 2007-2010. The tenth revision of the International Classification of Diseases (ICD-10) was used to code and classify the mortality data based on the death records. The commune of residence, which is systematically included in the death record, was used as the spatial location.

The following four underlying causes of death were investigated in the present study: 1) cardiovascular disease (I00-I52), 2) coronary heart disease (I20-I25), 3) myocardial infarction (I21-I22), and 4) stroke (I60-I64, excluding I63.6).

Statistical analysis

Correlations between aircraft noise and air pollution exposure were assessed using Spearman's rank correlation coefficients.

The effects of aircraft noise on mortality rates were first examined with Poisson generalized additive mixed models (GAMMs) [26],[27] including a smooth cubic spline function in order to account for a potential nonlinear effect. As the smoothed fit does not deviate from the linear fit for L den AEI, associations with the continuous exposure variable were then estimated and presented in the present paper. We fitted Poisson GLMMs, [28] including a commune-specific random effect term, to account for overdispersion. For each outcome, the GLMMs model could be written as follows:



where, i refers to the commune, Yi denotes the number of deaths observed in the commune i, Pop i the population number in the commune i (considered as an offset), Xi a vector of explanatory covariates for adjustment, and ui represents the corresponding random effect. βt denotes the regression coefficients corresponding to these covariates. As usual, the nonspatial random effect ui , also called heterogeneity, was assumed to be normally distributed with a zero mean and a constant variance.

Data on potential confounders

The models were adjusted for the following covariates, at the commune level, considered to be a priori confounding factors: Gender, age, log-population density, lung cancer mortality, and a deprivation index. The log-population density was introduced instead of the population density in order to take into account the fact that the density was greatly different between the different communes.

Lung cancer mortality (ICD-10 code: C34) was used at the commune level as a proxy measure for commune-level smoking because data on individual smoking or smoking prevalence at the commune level were not available in France.

As using the Townsend deprivation index [29] in France may be not suitable for different reasons, [30],[31] we preferred to introduce the deprivation index proposed by Rey et al.[32] It was constructed at the commune level based on the following four variables, each representing a dimension of the socioeconomic level:

  1. The median household income,
  2. The percentage of high school graduates in the population aged 15 years and above,
  3. The percentage of blue-collar workers in the active population, and
  4. The unemployment rate.


These socioeconomic data were provided by the French National Institute for Statistics and Economic Studies (INSEE).

Our deprivation index was defined as the first component of a principal component analysis (PCA) of the four variables. This index accounted for 67% of the total variation of the model and was strongly correlated with each of the initial variables (positively with the unemployment rate and the percentage of blue-collar workers and negatively with income and the percentage of high school graduates). Positive values of the deprivation index correspond to the deprived communes.

Adjusted mortality rate ratios (MRRs) with their 95% confidence intervals (CIs) were computed for each covariate included in the models by taking the exponential of the corresponding regression coefficient.

Additional analyses were also carried out to examine the impact of air pollution on the relationship between aircraft noise exposure and mortality by adjusting the models on air pollution (NO 2 and PM 10 concentrations).

Sensitivity analyses

The models were stratified on gender to test whether the potential associations between aircraft noise exposure and mortality from the causes of interest remained similar for both men and women.

The Townsend deprivation index was introduced in the models instead of the deprivation index obtained with the PCA.

As aircraft noise levels were assumed to be much higher in the Paris area compared to the other areas due to the larger size of the airport, the effect of the additional adjustment for the study area was explored and a sensitivity analysis using the Paris data only was carried out.

The version 10.1 of the ArcGIS software (Redlands, California) [33] was used to produce the maps. All data management was carried out using SAS software (Cary NC, USA) [34] version 9.3 and statistical analyses were carried out using R statistical software (Vienna, Austria) [35] version 3.0.2 with the gam function of the mgcv package. [36]


  Results Top


Overall, the average L den AEI was estimated as 49.6 dB(A) [range: 42.0-64.1 dB(A)], as shown in [Table 1]. Half of the communes of the study area had an L den AEI lower than 48.9 dB(A). The highest average of L den AEI was observed in the Paris area [51.6 dB(A) compared to 45.3 dB(A) for the Lyon area and to 45.7 dB(A) for the Toulouse area]. The commune with the highest L den AEI [64.1 dB(A)] was located in the Paris area. Moreover, L den AEI varied more widely in the Paris area. The NO 2 concentration was higher in the Paris area (mean: 24.0 μg.m -3 ) than in the Lyon area (mean: 16.5 μg.m -3 ) and varied more widely in the Paris area. The PM 10 concentrations were very similar in both the areas.
Table 1: Distribution of aircraft noise levels (LdenAEI) and of background air pollution concentrations (NO2 and PM10) for the 161 communes of the study area

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[Figure 2] shows the distribution of L den AEI in the communes included in the study area according to the quartiles of L den AEI. A fairly increasing pattern of L den AEI was observed on both the west and east sides of the Paris-Charles de Gaulle airport, whereas no specific geographical pattern of L den AEI was found either for the Lyon area or the Toulouse area.
Figure 2: Distribution of LdenAEI in the communes included in the present study

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Adjusted MRRs derived from the models are presented in [Table 2]. Increased MRRs were observed with increasing age for mortality from all specific causes of interest. The population density was negatively associated with mortality from all specific causes of interest except stroke. The deprivation index was associated with mortality from all specific causes of interest, showing an increase in mortality for the most deprived communes. The lung cancer mortality was not associated with any specific cause of interest.
Table 2: Adjusted MRRs estimated in models without air pollution

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Increasing L den AEI was associated with mortality from cardiovascular disease [MRR per 10 dB(A) increase in L den AEI = 1.18 (1.11-1.25)], coronary heart disease [MRR = 1.24 (1.12-1.36)], and myocardial infarction [MRR = 1.28 (1.11-1.46)]. L den AEI was more weakly associated with stroke mortality [MRR = 1.08 (0.97-1.21)].

Supplementary results with adjustment for air pollution

Aircraft noise levels (L den AEI) were moderately correlated with NO 2 concentrations (ρ = 0.45) while they were not correlated with PM 10 concentrations (ρ = 0.06). The correlation between L den AEI and NO 2 concentration was lower for the Paris area (ρ = 0.26). NO 2 and PM 10 concentrations were positively correlated (ρ = 0.64).

When NO 2 concentration was taken into account in the models including L den AEI, the results did not change [Table 3].
Table 3: Adjusted MRRs* related to LdenAEI obtained in sensitivity analyses

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Introducing PM 10 concentration in the model instead of NO 2 concentration did not change the results.

Sensitivity analyses

When stratified by gender, MRRs were higher in men than in women for mortality from cardiovascular disease, coronary heart disease, and myocardial infarction. After adjustment for NO 2 concentration, MRRs per 10 dB(A) increase in L den AEI were 1.29 (1.17-1.42) and 1.12 (1.03-1.23) for mortality from cardiovascular disease, 1.29 (1.12-1.49) and 1.15 (0.97-1.37) for mortality from coronary heart disease, and 1.37 (1.11-1.68) and 1.21 (0.94-1.55) for mortality from myocardial infarction among men and women, respectively [Table 3].

Introducing the Townsend deprivation index in the models did not change the results [Table 3]. The Townsend deprivation index was highly correlated with the deprivation index obtained with PCA (ρ = 0.85).

The additional adjustment for the study area in the models did not alter the results [Table 3]. Moreover, the associations between aircraft noise exposure and mortality from all causes of interest remained similar when only the Paris data were used [Table 3].


  Discussion Top


The present study is the first ecological study investigating the relationship between exposure to aircraft noise and the mortality of the population living in the vicinity of the airports in France. This study covers 161 communes of France with a population of 1.9 million people living close to Paris-Charles de Gaulle, Lyon-Saint-Exupéry, and Toulouse-Blagnac airports. Positive associations were reported between weighted average exposure to aircraft noise and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction. Controlling the socioeconomic status of the commune (measured by a deprivation index), demographic factors of the commune (such as age and gender of the inhabitants), and lung cancer mortality used as a proxy for smoking did not change the results. When the models were stratified on the basis of gender, the associations between exposure to aircraft noise and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction remained significant with higher risks among men than women.

As aircraft noise levels were much higher in the Paris area than in the other areas due to the larger size of the airport, the effect of the additional adjustment for the study area was explored and a sensitivity analysis using the Paris data only was conducted; however, the results remained similar.

The present study seems to confirm the findings of recent studies, suggesting that high levels of aircraft noise are associated with mortality from cardiovascular disease and coronary heart disease [18] and with mortality from myocardial infarction. [15] Moreover, we observed a weak association between aircraft noise and stroke mortality. These results are in accordance with the results of Huss et al.[15] and Sorensen et al.[16]

The present study has attempted to take into account the issue of confounding air pollution. Accounting for NO 2 or PM 10 concentration did not change the results - air pollution does not seem to be a confounding factor in the relationship between aircraft noise and mortality from all causes of interest. These results are consistent with previous studies. Huss et al.[15] found that the association between aircraft noise and mortality from myocardial infarction was not attenuated with the adjustment for air pollution. Correia et al.[14] showed that the association between high exposure to aircraft noise and hospitalization for cardiovascular disease among people above 65 years of age remained even after controlling air pollution. Hansell et al.[18] reported that the significant association between high levels of aircraft noise and mortality for stroke, coronary heart disease, and cardiovascular disease was robust to the adjustment for PM 10 concentration.

Strengths and limitations of this study

The number of communes (n = 161) included in the study area was relatively small compared to the number of geographical units included in other studies investigating the relation between aircraft noise and mortality or hospitalization. Hansell et al.[18] studied the risks for hospital admissions in 12,110 census output areas and the risks for mortality in 2,378 super output areas. Correia et al.[14] estimated the percentage increase in the zip code level hospital admission rate associated with a 10 dB(A) increase in the zip code level aircraft noise for 2,218 zip codes surrounding 89 airports in the United States.

However, the first major strength of the present study is both the accuracy and the exhaustiveness of the mortality data provided by the French Center on Medical Causes of Death (CépiDc-Inserm) and the large number of deaths from cardiovascular disease (7,450) for a 4-year period (2007-2010). The second point of interest in the present study is that the ecological approach allowed us to take advantage of large contrasts in the exposure between geographical units to give evidence of small associations between mortality and aircraft noise exposure: L den AEI varied from 42.0 dB(A) to 64.1 dB(A) at the commune level.

Depending on the airports, exposure to aircraft noise for the years 2003, 2004, and 2008 was used and the mortality data for the period 2007-2010 were examined, thus not allowing us to take into account a possible latency period between exposure and mortality, especially in the Paris area. For Lyon-Saint-Exupéry and Toulouse-Blagnac airports, we used the most recent noise exposure data available. For the Paris-Charles de Gaulle airport, we compared noise maps for the years 2008 and 2011 and we found that they were very similar. As no other information was available for Lyon and Toulouse areas and as noise contours seemed to be stable over the period 2008-2011, the noise contours used in the present study were considered to be representative of the years preceding the mortality assessment.

The use of the population-weighted average of exposure to aircraft noise (L den AEI) in the models allowed us to take into account a part of the variability of aircraft noise exposure within the communes. There was no other alternative to consider this variability because both mortality data and exposure to aircraft noise were not available on a smaller spatial scale than that of the communes.

In the present study, we were not able to distinguish night-time exposure to aircraft noise at the place of residence from daytime exposure to aircraft noise at the place of work. Therefore, it was not possible to disentangle the effect of such exposure on mortality even if it would have been relevant, given that recent studies suggest that sleep disturbances due to aircraft noise could mediate the effect of aircraft noise on health, especially cardiovascular disease. [37],[38]

It is worth wondering whether L den is the most relevant indicator to describe the relationship between aircraft noise exposure and health effects. In health studies, L den is currently recommended, along with event-related indicators such as the number of noise events or the number of events exceeding a certain LAmax level, [1] especially for the night period regarding the effects of aircraft noise on the quality of sleep. In addition to L den , it would have been interesting to consider such noise indicators in the present study to increase the impact of our results. Unfortunately, these indicators were not available in France. [39] However, such indicators will be available for 100 participants in an ongoing longitudinal study in France where acoustic measurements have been carried out for 1 week.

Living in the vicinity of an airport was not associated with socioeconomic status in the present study. The percentages of blue-collar workers and of white-collar workers in the active population were very similar for the communes under study and for the communes of the whole of France, as was the proportion of the population having a certificate higher than the French high school certificate. Moreover, the deprivation index obtained with PCA was not correlated with L den AEI (ρ = 0.12) and the interaction term between these variables was not significant in the model. Finally, the residential mobility of the study population in the Paris and Lyon areas was slightly lower than the overall French population. The residential mobility was somewhat higher in the Toulouse area but this could be explained by the fact that this area included the city of Toulouse with a high number of inhabitants, and the population of this area was also younger and more educated than in the other areas under study. Therefore, the positive associations reported between the exposure to aircraft noise and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction do not seem to be explained by a different vulnerability of the population living near airports.

The possible adverse effect of aircraft noise on cardiovascular health could have led to a lower proportion of sensitive people among those living in the vicinity of airports. We have little information to judge whether this had occurred or not. However, if it had occurred, it would have resulted in an underestimation of the association between aircraft noise exposure and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction in the present study.

The ecological association between average exposure to aircraft noise and mortality may be different from the individual relationship. [40] This issue has been particularly discussed in settings where there is a powerful individual risk factor for a disease, such as smoking or diet for cardiovascular disease. In the present study, it was not possible to collect information on confounding factors such as smoking or diet at the individual level. We used lung cancer mortality at the commune level as a smoking proxy because there does not exist another source providing information on smoking at both the individual and aggregate levels in France. However, as living in the vicinity of an airport was not associated with socioeconomic status in the present study and as it is well-established that smoking is related to low socioeconomic status, it is unlikely that the association between aircraft noise and mortality was confounded by smoking.

Results at the commune level may not be applicable to the individual level (ecological fallacy). However, in the near future, it will be possible to cross-check the results observed at the commune level in the present ecological study with those obtained at the individual level in the ongoing longitudinal study in France [39] where information on confounding factors such as smoking has been collected. In addition, the possibility that the association could be due to some unmeasured confounding factors with geographical distributions similar to that of exposure to aircraft noise cannot be excluded but we attempted to limit this bias by introducing a large set of potential confounding factors when the information was available.


  Conclusions Top


The present ecological study reported positive associations between weighted average exposure to aircraft noise and mortality from cardiovascular disease, coronary heart disease, and myocardial infarction even after controlling some confounding factors, in particular air pollution. However, the number of studies investigating the relationship between exposure to aircraft noise and mortality is clearly insufficient and their results are not entirely consistent. Therefore, further individual studies are necessary in order to better understand the association observed in the present paper.

Acknowledgement

The Airport Pollution Control Authority (Acnusa) requested the French Institute of Science and Technology for Transport, Spatial Planning, Development and Networks (Ifsttar) for carrying out the present study. We would like to thank them for their confidence in us.

The authors are grateful to Grégoire Rey and Mireille Eb (CépiDc-Inserm) for providing mortality data from the database of the French Center on Medical Causes of Death and to Aéroports de Paris and the French Civil Aviation Authority for providing noise exposure maps produced with the Integrated Noise Model. We are also grateful to Airparif and Air Rhτne-Alpes for providing background air pollution concentrations and to Grégoire Rey for his skillful revision of the manuscript.

Financial support and sponsorship

The present study was supported by funds from the French Ministry of Health, the French Ministry of Environment, and the French Civil Aviation Authority.

Conflicts of interest

There are no conflicts of interest.

 
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Correspondence Address:
Anne-Sophie Evrard
French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR), Cité des Mobilités, 25 Avenue François Mitterrand, F-69675, Bron
France
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1463-1741.165058

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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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