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The short-term association of road traffic noise with cardiovascular, respiratory, and diabetes-related mortality
Environmental Research 150 (2016) 383–390
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The short-term association of road traffic noise with cardiovascular, respiratory, and diabetes-related mortality Alberto Recio a,n, Cristina Linares b, José R. Banegas a, Julio Díaz b a Department of Preventive Medicine and Public Health, Universidad Autónoma de Madrid, ⁄IdiPAZ – CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain b National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
art ic l e i nf o
a b s t r a c t
Article history: Received 17 April 2016 Received in revised form 6 June 2016 Accepted 7 June 2016
Background: Road traffic noise has well-documented effects on cardiovascular, respiratory, and metabolic health. Numerous studies have reported long-term associations of urban noise with some diseases and outcomes, including death. However, to date there are no studies on the short-term association between this pollutant and a set of various specific causes of death. Objectives: To investigate the short-term association of road traffic noise with daily cause-specific mortality. Methods: We used a time-stratified case-crossover design with Poisson regression. Predictor variables were daytime, nighttime, and 24-h equivalent noise levels, and maximum daytime and nighttime noise levels. Outcome variables were daily death counts for various specific causes, stratifying by age. We adjusted for primary air pollutants (PM2.5 and NO2) and weather conditions (mean temperature and relative humidity). Results: In the Z65 age group, increased mortality rates per 1 dBA increase in maximum nocturnal noise levels at lag 0 or 1 day were 2.9% (95% CI 1.0, 4.8%), 3.5% (95% CI 1.1, 6.1%), 2.4% (95% CI 0.1, 4.8%), 3.0% (95% CI 0.2, 5.8%), and 4.0% (95% CI 1.0, 7.0%), for ischemic heart disease, myocardial infarction, cerebrovascular disease, pneumonia, and COPD, respectively. For diabetes, 1 dBA increase in equivalent nocturnal noise levels at lag 1 was associated with an increased mortality rate of 11% (95% CI 4.0, 19%). In the o 65 age group, increased mortality rates per 1 dBA increase in equivalent nocturnal noise levels at lag 0 were 11% (95% CI 4.2, 18%) and 11% (95% CI 4.2, 19%) for ischemic heart disease and myocardial infarction, respectively. Conclusion: Road traffic noise increases the short-term risk of death from specific diseases of the cardiovascular, respiratory, and metabolic systems. & 2016 Elsevier Inc. All rights reserved.
Keywords: Traffic noise Cardiovascular disease Respiratory disease Diabetes
1. Introduction An issue of increasing concern in urban environments is the health impact of road traffic noise, given the large exposed population and the long exposure time-periods (Tobías et al., 2015b). Some 20% of the EU population is exposed to noise levels higher than 65 dBA in the daytime, and 30% to levels higher than 55 dBA in the night-time, which are considered health protection values (WHO, 2011). For such noise levels, a number of studies have reported significant associations with cardiovascular diseases Abbreviations: COPD, Chronic obstructive pulmonary disease; Leqd, Diurnal equivalent noise level; Leqn, Nocturnal equivalent noise level; Leq24, 24-h equivalent noise level; Ldmax, Maximum diurnal noise level; Lnmax, Maximum nocturnal noise level n Correspondence author. E-mail address: [email protected] (A. Recio). http://dx.doi.org/10.1016/j.envres.2016.06.014 0013-9351/& 2016 Elsevier Inc. All rights reserved.
(Banerjee et al., 2014, Argalášová-Sobotová et al. 2013, Sørensen et al., 2014), respiratory diseases (Niemann et al., 2006; Ising et al., 2003, 2004), type 2 diabetes mellitus (Sørensen et al., 2013), and more recently, severe depressive symptoms (Orban et al., 2016) and adverse birth outcomes (Díaz and Linares, 2016). According to a recent meta-analysis (Hänninen et al., 2014), road traffic noise ranks among the four environmental risk factors with highest health impact in European countries, which means a loss of 400 – 1500 healthy life years due to ischemic heart disease per million people. The impact of urban noise on public health (8% of the environmental burden of disease) was rated medium-high, comparable to that of secondhand smoke and radon, and only behind fine particles (PM2.5). Prospective studies (Selander et al., 2013; Babisch et al., 2005, Sørensen et al., 2012) have reinforced the hypothesis of the longterm association between road traffic noise and the incidence of myocardial infarction in large cities, especially in the over-65 age
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group. In two cohorts (Gan et al., 2012, Sørensen et al., 2011), increased daily noise levels were associated with increased mortality from ischemic heart disease and stroke, respectively, after adjustment for air pollutants. Unlike the case of cardiovascular disease, there are few studies dealing with respiratory outcomes, even fewer those considering objective noise as a predictor variable. Two pioneering studies focusing on children and nocturnal road traffic (Ising et al., 2003, 2004) found a dose-response relationship between traffic load and the prevalence of asthma and chronic bronchitis, suggesting an important combined effect of noise and air pollution. Yet the correlation between noise levels and NO2 concentration was very high and thus confounding could not be ruled out. Afterwards the LARES study (Niemann et al., 2006) reported significant risks of bronchitis in children highly annoyed with road traffic noise, but no association was found for asthma. Owing to their cross-sectional design, the above studies could not demonstrate one-way causality. The association of road traffic noise with the incidence of type 2 diabetes has recently been investigated in a cohort (Sørensen et al., 2013); the results were significant only for the over-65 age group. There are few ecological, aggregated-data studies in current environmental epidemiology. Nonetheless, when data are geographically disaggregated such studies provide support for longterm associations between urban pollutants and health outcomes (Halonen et al., 2015). On the other hand, when data are timedisaggregated, as in the case of time series, short-term associations can be investigated. Two studies of this type assessed the short-term association between road traffic noise and cardiovascular and respiratory morbidity; among the respiratory outcomes, a significant association was found for pneumonia, but not for bronchitis (Tobías et al., 2001; Linares et al., 2006). As regards cardiovascular, respiratory and diabetes-related mortality, to date only three time-series studies have investigated their short-term association with road traffic noise (Tobías et al. 2015c, 2014, 2015a), yielding significant results only for the over-65 age group. Biological plausibility for the association of noise with cardiovascular, respiratory, and metabolic health outcomes has recently been documented in a review and summarized in an integrative model (Recio et al., 2016). Stress caused by noise may give rise to a variety of physiological reactions intended to preserve the homeostasis. When stress is considerably high and maintained, allostatic overload may lead to inefficient body responses due to overactivation of the sympathetic-adrenal-medullar and hypothalamic-pituitary-adrenocortical axes, affecting blood pressure, heart rate variability, the immune system, and the connective tissue, and promoting fat accumulation in the arteries, blood clotting, endothelial dysfunction, systemic inflammation, destabilization of atherosclerotic plaques, and insulin resistance. Some mechanisms may operate in the long- as well as the short-term, resulting in chronic or acute health outcomes, or even a concurrence of the two. Long ago, Maclure (1991) posed the possibility of acute health outcomes as a result of point exposure to an environmental stressor, considering induction time-periods of a few minutes up to a few days from the acute exposure until the adverse outcome. Modern environmental epidemiology has long used time-series analysis as a suitable strategy for the study of short-term effects of urban pollutants, allowing for different induction time-periods or lags (Maté et al. 2010). Madrid is one of the few cities in the world provided with a monitoring network that stores real-time daily sound levels, which enables accurate examination of the relation of noise to morbidity and mortality in the short term. Conceived as an extension of the studies already published on the association between daily road-traffic noise levels in Madrid and the risk of
death from cardiovascular, respiratory, and diabetes-related outcomes (Tobías et al., 2014, 2015a, 2015c), this study extends the period of follow-up – seven years instead of three –, the number of predictor variables – with maximum noise levels added –, and the number of dependent variables, in order to assess the differences in the short-term effects of road traffic noise on the following specific causes of death: ischemic heart disease, myocardial infarction, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), pneumonia, asthma, and diabetes. Age stratification at the cutoff point of 65 years is especially relevant in this study, since many cardiovascular, respiratory, and metabolic endpoints may occur as a consequence of interaction between longterm health decline and short-term exposure to a variety of risk factors such as environmental noise.
2. Methods 2.1. Setting Madrid is a dense metropolitan area with a mean population of 3,154,033 in the period 2003–2009, 19% over 65 years of age. The average daily traffic intensity was 2.4 million motor vehicles, reaching the maximum in May (2.5 million) and the minimum in August (1.7 million), with a mean speed of nearly 24 km/h. The main outdoor noise source is road traffic (80% of the overall noise exposure); other sources are industry (10%), rail traffic (6%), and leisure activities (4%) (Díaz et al., 2003). 2.2. Mortality data Daily mortality records for the period 1 January 2003 – 31 December 2009 were obtained from the Madrid Regional Inland Revenue Department. Then time-series for the following causes of death were constructed: cardiovascular disease (International Classification of Diseases 10: I00-I99), respiratory disease (ICD 10: J00-J99), ischemic heart disease (ICD 10: I20-I25), myocardial infarction (ICD 10: I21), cerebrovascular disease (ICD 10: I60-I69), pneumonia (ICD 10: J12-J18), COPD (ICD 10: J40-44, J47), asthma (ICD 10: J45-J46), and diabetes (ICD 10: E10-E14). In order to conduct age-stratified analysis, we constructed two time-series for every cause of death: one for the under-65 age group and another one for the over-65 age group. This choice was motivated by the fact that those over 65 years are especially vulnerable to the diseases and adverse outcomes studied (Nichols et al., 2014); also, setting the cut point at 65 years enables comparability with other studies. 2.3. Noise exposure Hourly equivalent noise levels were obtained from the Madrid Noise Pollution Monitoring Grid. This network consisted of 26 urban background stations in 2003–2009, 4 m above ground level in compliance with the Directive 2002/49/EC (2002) of the European Parliament and strategically allocated to be representative of the noise levels across the city (Supplemental Fig.1). Technically, the measuring process involves the following steps: (a) an outdoor antibird omnidirectional microphone, provided with wind screen, captures the data; (b) the captured signal connects with a statistical noise analyser which allows audio recording and frequency analysis (1/1- and 1/3-octaves); (c) the information stored in the analyser is transferred to a central station via a high-speed telephony modem (ISDN); and (d) the central station is equipped with a distributor adapted to ISDN that communicates with all stations at set intervals to send the data. In order for the hourly noise levels to be representative of the
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entire city, an average of the validated data from all stations were used. Then three daily equivalent-noise-level variables were created: Leqd (diurnal: 8–20 h), Leqn (nocturnal: 0–8 h), and Leq24 (0–24 h); also, we generated two maximum hourly equivalentnoise-level variables: Ldmax (diurnal: 8–20 h) and Lnmax (nocturnal: 0–8 h). By defining the nocturnal variables within the interval 0–8 h instead of, say, 20–8 h, each data point unmistakably refers to one only calendar day, which is relevant since we are dealing with very short lags between exposure and outcome (0–4 days). No equivalent noise level was defined for the interval 20– 24 h, yet its contribution is included in Leq24. 2.4. Covariates As primary air pollutants, we included mean daily concentrations of particulate matter o 2.5 mm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), obtained from the Madrid Municipal Air Quality Monitoring Grid. We considered PM2.5, and not PM10, because its short-term association with mortality has been documented formerly (Maté et al., 2010); indeed, the WHO recommends to use PM2.5 rather than PM10 as air quality indicator (WHO, 2006). Owing to the increasing use of low-sulphur fuels, nowadays NO2 is the main gaseous pollutant from road traffic in Madrid, also having the greatest effects on health (Querol et al., 2012). Road traffic emissions of NO2 and PM2.5 take place at the same time as sound levels, so they are potential confounders. On the other hand, ozone (O3), as a secondary air pollutant, cannot confound the association of noise levels with mortality. The effects of O3 on daily mortality have been shown to be significant at lags longer than those for primary pollutants (Díaz et al., 2004). Mean temperature and relative humidity were obtained from the State Meteorology Agency (AEMET). An earlier study using time series reported associations of mortality with (a) the maximum temperature of the day before and (b) the minimum temperature of several days before (Maté et al., 2010). In this sense we created two variables out of the daily mean temperature: (1) average of the mean temperature up to two days before the exposure (lags 0–2), to control for the immediate effects dominated by heat, and (2) average of the mean temperature of the third through sixth day before the exposure (lags 3–6), to control for the effects of lower temperatures at longer lags; then, for smoothing purposes, both series were modeled as natural cubic splines with 3 degrees of freedom. Though maximum and minimum temperatures relate to heat and cold waves, respectively, we used mean temperatures for simplicity, which contain information from both thermal extremes. We included humidity because in winter it is associated with (1) mortality due to flu epidemics and (2) increased road-traffic noise levels due to wet floor.
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are dealing with short-term associations and (b) the outcome of interest is of very short duration (Jaakkola, 2003). If the causal hypothesis is true, noise exposure levels in the case period should on average be higher than exposure levels in the control periods. Likewise, provided that there can be deaths in all periods, excess deaths are needed during the case period relative to the control periods, so that such an excess be attributable to noise. Only on the above conditions may positive associations be found between noise levels and mortality. Thanks to the case-crossover design, invariable personal factors such as age, sex, lifestyle, and overall health status cannot confound the associations, since every case subject serves as his or her own control. In order to avoid confounding from time-varying personal and environmental factors, control days must be close enough to case days. To this end we carried out a time-stratified analysis by including in the models a three-way interaction between year, month, and day of week (year month dow), so that the study period was divided into monthly strata and control days were all days falling on the same day of the week within the same stratum as that of the case day. The key assumption is that within each stratum the time-varying risk factors remain constant, which ensures control of both seasonality and time trends. This is equivalent to time-series modelling, on the condition that exposure levels be equal for all subjects (Lu and Zeger, 2007). We generated lagged variables (lags 0–4) for all predictors and covariates – except mean temperature – and conducted prior analysis using univariate Poisson models in order to identify the most significant lag for the association between each of these variables and every death cause studied. Two-way interactions between predictors and covariates were also tested in univariate models, being the candidates (those with po 0.05) subsequently included in the full models. We derived final Poisson models for every death cause and predictor, including all covariates at the selected lags, the predictor-covariate interactions detected in the univariate models, the mean temperature splines and the threeway time interaction term. We assumed that, owing to the inclusion of the three-way time interaction term in the models, much of the correlation present in the mortality series would be under control, with a considerable reduction in Poisson overdispersion; residual overdispersion was handled using the quasi-likelihood estimation. Last we checked the temporal stability of the models by the jack-knife method with yearly clusters (Efron and Stein, 1981), in which new models are derived after removing one different year each time, and we can see whether on average the results hold. All analyses were conducted using Stata statistical software version 12 (Stata-Corp, College Station, TX, USA).
2.5. Design and statistical analysis
3. Results
We investigated the association between road traffic noise levels (predictors) and daily death counts (outcomes) using a timestratified case-crossover design. All analyses were conducted with Poisson regression, the standard method for count data. The case-crossover design with time series has been widely used in morbidity as well as mortality studies to investigate the short-term effects of air pollutants in urban environments (Brook et al., 2010). As usual in environmental epidemiology with aggregated data (Carracedo-Martínez et al., 2010), we consider groups of the general population who have suffered a specific health outcome, and compare the level of exposure to the environmental risk factor a short time before the outcome (case period) with the exposure level in one or more periods when such an event did not occur (control periods). The time-series based case-crossover design is appropriate in this case because (a) we
Table 1 shows the summary statistics for all variables. The mean value of the 24-h equivalent noise level over the 7-year study period was 63.4 dBA; diurnal noise levels (mean 64.6 dBA) contribute to the 24-h level to a larger degree than nocturnal noise levels (mean 60.2 dBA). Indeed, Leq24 and Leqd distributions are similar (Fig. 1). The WHO guidelines on urban noise – 65 dBA for daytime noise and 55 dBA for nighttime noise – were exceeded on 50% of the days and 100% of the nights in the period 2003–2009. Highly correlated equivalent noise levels were Leqd and Leq24 (r¼ 0.96), while correlation between Leqd and Leqn was low (r¼ 0.30). As regards maximum levels, Ldmax and Leqd were highly correlated (r ¼0.96), while correlation between Leqn and Ldmax was much lower (r ¼ 0.49). Noise levels were weakly correlated with air pollutant concentrations (e.g. r ¼0.15 for Leq24PM2.5 and r ¼0.35 for Leq24-NO2); however, the NO2-PM2.5
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5–40 0–8 5–36 0–32 0–5 0–31
5.7 0.7 5 3.5 0.5 3.0 4.1 0.3 3.8 2.5 0.2 2.3 2.0 0.1 1.9 0.1 0.0 0.1 0.8 0.0 0.8
(2.6) (0.9) (2.4) (2.0) (0.8) (1.8) (2.1) (0.6) (2.0) (1.8) (4.3) (1.7) (1.5) (0.3) (1.5) (0.3) (0.1) (0.2) (0.9) (0.2) (0.9)
0–18 0–6 0–16 0–14 0–4 0–11 0–14 0–3 0–13 0–12 0–3 0–12 0–10 0–2 0–10 0–2 0–1 0–2 0–5 0–2 0–5
64.6 60.2 63.4 65.5 63.9
(1.4) (1.0) (1.1) (1.4) (1.7)
59.3–68.1 56.2–69.9 59.1–66.6 60.3–69.7 58.7–76.3
17.3 (7.8) 58.6 (18.0)
3–71 18–134
16.8 (7.6) 54.0 (16.9)
0.2–33.4 19–94.8
a
ICD 10: I00-I99. ICD 10: J00-J99. ICD 10: I20-I25. d ICD 10: I21. e ICD 10: I60-I69. f ICD 10: J12-J18. g ICD 10: J40-44, J47. h ICD 10: J45-J46. i ICD 10: E10-E14. b c
correlation was rather high (r¼ 0.71), which indicated collinearity. In order to avoid the unstable effects of collinearity, we included these covariates in separate Poisson models: (a) without air pollutants, (b) adjusted for PM2.5, and (c) adjusted for NO2. Table 2 shows the results from the best-fitted models according to the Akaike criterion. Results from all the models considered for the Z65 age group are provided in Supplemental Table 1–3. The selected lags are those which had previously yielded the most significant associations (lowest p-values) in univariate models. Scaling of standard errors by the quasi-likelihood estimation scarcely changed the confidence intervals (less than 70.001 units of 95% CI). The best-fitted models for all outcome variables were those adjust ed for PM2.5 involving nocturnal noise levels; in the case of cardiovascular mortality, such noise levels were those of the night of the calendar day when the deaths occurred (lag 0), i.e. the night that precedes daytime, while for respiratory and diabetes-related mortality such noise levels were those of the day before (lag 1).
Density
(5.2) (1.3) (4.9) (4.0) (0.8) (3.8)
0 .1 .2 .3 .4 .5
18.0 1.5 16.5 9.6 0.6 9.0
55
60
dBA
65
70
65
70
65
70
Leqn Density
Range
0 .1 .2 .3 .4 .5
Group-specific mortality Cardiovascular mortalitya o 65 years Z 65 years Respiratory mortalityb o 65 years Z 65 years Cause-specific mortality Ischemic heart diseasec o 65 years Z 65 years Myocardial infarctiond o 65 years Z 65 years Cerebrovascular diseasee o 65 years Z 65 years Pneumoniaf o 65 years Z 65 years COPDg o 65 years Z 65 years Asthmah o 65 years Z 65 years Diabetesi o 65 years Z 65 years Noise levels (dBA) Leqd Leqn Leq24 Ldmax Lnmax Air pollutants (μg/m3) PM2.5 NO2 Weather Mean temperature (°C) Relative humidity (%)
Mean (SD)
Leqd
55
60
dBA
Leq24 Density
Table 1 Summary statistics for daily mortality, noise levels, air pollutants, and weather variables in Madrid 2003–2009.
0 .1 .2 .3 .4 .5
386
55
60
dBA
Fig. 1. Distributions of diurnal (Leqd: 8–20 h), nocturnal (Leqn: 0–8 h), and 24-h (Leq24) noise levels in Madrid for the period 2003–2009.
Table 2 Relative risks of death for a 1 dBA increase in the specified noise levels, adjusted for PM2.5, by age groups. A 1 dBA increase in noise levels corresponds to an increased traffic intensity of roughly 26%.
Z 65 years of age Cardiovascular disease Ischemic heart disease Myocardial infarction Cerebrovascular disease Respiratory disease Pneumonia COPD Asthma Diabetes o 65 years of age Cardiovascular disease Ischemic heart disease Myocardial infarction Cerebrovascular disease Respiratory disease Pneumonia COPD Asthma Diabetes a
Best predictor (lag)
RR (95% CI)
Leqn (0) Lnmax (0) Lnmax (0) Lnmax (0) Leqn (1) Lnmax (1) Lnmax (1) Lnmax (1) Leqn (1)
1.033 1.029 1.035 1.024 1.022a 1.030a 1.040 1.015 1.110
(1.017, 1.049) (1.010, 1.048) (1.011, 1.061) (1.001, 1.048) (1.002, 1.043) (1.002, 1.058) (1.010, 1.070) (0.860, 1.020) (1.040, 1.192)
Leqn Leqn Leqn Leqn Leqn Leqn Leqn Leqn Leqn
1.050 1.108 1.114 1.001 1.004a 0.960a 1.111 0.493 1.082
(1.004, 1.098) (1.042, 1.177) (1.042, 1.192) (0.897, 1.118) (0.934, 1.079) (0.832, 1.108) (0.946, 1.306) (0.127, 1.914) (0.850, 1.379)
(0) (0) (0) (0) (1) (1) (1) (1) (1)
Only when NO2 430 μg/m3 (noise-NO2 interaction).
Additionally, in the models for total respiratory mortality and mortality from pneumonia, nocturnal noise was found to interact with NO2. We handled this interaction stratifying by NO2, and discovered that noise levels at lag 1 were significantly associated with mortality only when mean NO2 concentration at the same lag was higher than 30 μg/m3. With regard to the temporal stability of the models, the jackknife estimation showed that cardiovascular and diabetes-related mortality models were robust, whereas those for respiratory mortality were not, i.e. they would yield non-significant associations depending on the year removed from the analysis. 3.1. Cardiovascular mortality In the Z65 age group, the relative risk for the overall cardiovascular mortality was significant for Leqn at lag 0 (1.033 (95% CI
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Fig. 2. Relative risks of death from myocardial infarction and COPD per 1 dBA increase in maximum nocturnal noise levels (Lnmax) for every lag, in the over-65 age group.
1.017, 1.049)), Lnmax at lag 0 (1.020 (95% CI 1.009, 1.031)), and Leq24 at lag 1 (1.033 (95% CI 1.012, 1.056)) (Supplemental Table 1). According to the best-fitted model (Table 2), the risk of death increased 3.3% (95% CI 1.7, 4.9%) per 1 dBA increase in Leqn at lag 0, with no change after adjustment for either PM2.5 or NO2. In the o65 age group, increased risk was 5.0% (95% CI 0.4, 9.8%) for Leqn at lag 0. As regards cause-specific mortality, in the Z65 age group the best-fitted models involved Lnmax at lag 0, with increased risk of 2.9% (95% CI 1.0, 4.8%), 3.5% (95% CI 1.1, 6.1%), and 2.4% (95% CI 0.1, 4.8%) for ischemic heart disease, myocardial infarction (Fig. 2), and cerebrovascular disease, respectively. In the o65 age group, only nocturnal noise provided models with significant results; the bestfitted models involved Leqn at lag 0, with increased risks of 11% (95% CI 4.2, 18%) and 11% (95% CI 4.2, 19%) for ischemic heart disease and myocardial infarction, respectively. Relative risks of death from cerebrovascular disease were not significant in this age group. All of the above remained unaltered after adjustment for air pollutants. 3.2. Respiratory mortality For both general and cause-specific respiratory mortality, the only noise variables providing significant relative risks were Leqn (1.022 (95% CI 1.002, 1.043)) and Lnmax (1.017 (95% CI 1.003, 1.032)) at lag 1 (Supplemental Table 2). The risk of death from an unspecific respiratory disease in those aged Z65 years increased 2.2% (95% CI 0.2, 4.3%) per 1 dBA increase in Leqn at lag 1, after adjustment for PM2.5 and only when NO2 4 30 μg/m3 (Table 2). As regards cause-specific mortality, in those aged Z 65 years we obtained significant relative risks for pneumonia and COPD, but not for asthma. A 1 dBA increase in Lnmax at lag 1 was associated with an increase of 3.0% (95% CI 0.2, 5.8%) in mortality from pneumonia when NO2 430 μg/m3. In the best-fitted model for COPD, a 1 dBA increase in Lnmax at lag 1 was associated with an increase of 4.0% (95% CI 1.0, 7.0%) in mortality (Fig. 2), regardless of the value of NO2. There were no changes after adjustment for PM2.5. In the case of asthma, only NO2 concentration – but not noise – was significantly associated with increased mortality at lag 1 (1.4% (95% CI 0.2, 2.7%)). No significant associations were found for those aged o65 years.
3.3. Diabetes-related mortality In the Z 65 age group all noise variables were significantly associated with diabetes-related mortality (Supplemental Table 3). Relative risks for Leqd and Leqn were 1.094 (95% CI 1.010, 1.180) at lag 2 and 1.110 (95% CI 1.040, 1.190) at lag 1, respectively. The bestfitted model (Table 2) yielded an increased risk of death from diabetes of 11% (95% CI 4.0, 19%) per 1 dBA increase in Leqn at lag 1, adjusting for air pollutants. No significant associations were found in the o65 age group.
4. Discussion Noise exposure effects on mortality were examined for the same day and up to four days after the exposure. In the Z65 age group, we found significant short-term associations between the five noise exposure variables considered and eight specific causes of death (all except asthma), though the best-fitted models involved only nocturnal noise levels at lags 0 and 1. In the o65 age group, we only found significant associations between nocturnal noise levels at lag 0 and death from cardiovascular disease, ischemic heart disease and myocardial infarction. The various biological mechanisms whereby noise as a stressor may cause physiological alterations affecting the cardiovascular, respiratory, and metabolic systems provide plausibility for shortterm noise effects on health (Recio et al., 2016). Diabetes-related events that could lead to death may also be triggered by noise exposure. The most serious acute metabolic complications of diabetes are hyperglycemic crises and ketoacidosis. Noise levels may provoke rises in catecholamine and cortisol levels, mobilizing the energy resources and therefore raising blood glucose levels – which leads to hyperglycemic crises – and cell metabolism – which may cause ketoacidosis –. To compensate for this, hyperosmolar syndrome takes place, and further dehydration might end in diabetic coma and death if not resolved in time (Kitabchi et al., 2009). Maximum nighttime noise levels were significantly associated with mortality from pneumonia and COPD. Autonomic awakening caused by noise peaks at night is likely to disturb the slow wave sleep, affecting the recovery of the immune system, which may lead to the aggravation of respiratory infections (Pirrera et al.,
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2010). Airway inflammation, associated with decline of lung function, is present in exacerbations of COPD; therefore, increased systemic inflammation due to noise exposure might lead to increased morbidity and mortality from this cause (George and Brightling, 2016). For general respiratory mortality and mortality from pneumonia, the models included a positive interaction term, Leqn x NO2, meaning that the adverse effects of nocturnal noise were greater with concurrent high NO2 concentrations. In contrast, with regard to cardiovascular or diabetes-related mortality, noise effects were independent of NO2. This finding allows to speculate on the role of noise as an enhancer of the documented effects of NO2 on respiratory symptoms in the presence of viral and bacterial infections. For example, NO2 has been suggested to impair immune processes such as phagocyte function, making the lungs more prone to severe disease due to aggravated bacterial infection (Chauhan et al., 1998). The different lags of the associations found in the models – the night before (lag 0) for cardiovascular outcomes vs. two nights before (lag 1) for respiratory and diabetes-related outcomes – may be due to the different biological mechanisms involved; on the one hand, immediate physiological changes caused by stress, such as increased blood pressure, blood clotting, and heart rate, might have cardiovascular effects in a few hours, since only the neuroendocrine system mediates; on the other hand, somewhat longer lags are expected for the occurrence of respiratory events, since here also decline of the immune system mediates. Babisch et al. (2005) found a long-term odds ratio of 1.3 for the prevalence of myocardial infarction in adults exposed to a 10-dBA higher category of daytime noise levels; in a prospective study (Sørensen et al., 2012) a relative risk of 1.2 was found for the incidence of this disease for a 10-dBA increment in daily noise levels in the Z65 age group. These results are consistent with our estimate of the short-term relative risk for mortality from the same outcome in those Z 65 years exposed to a theoretical excess of 10 dBA in nighttime maximum levels, calculated as 1.03510 E1.4. Also for the Z65 age group, another prospective study (Gan et al., 2012) reported an increased risk of 9% for long-term mortality due to ischemic heart disease, while our models yielded a short-term increased risk of death of about 33% (calculated as (1.02910 1) 100%). Other recent studies (Selander et al., 2013; Banerjee et al., 2014) have reported significant relative risks for the incidence of myocardial infarction and ischemic heart disease in the aging population. Since we found similar results for the short-term mortality, all this suggests an interaction between chronic and acute stress: when chronic effects of exposure to urban pollutants in the vulnerable aging population add to acute effects, the risk of severe adverse outcome appears to increase notably. Furthermore, there is no need for the long latency time-periods for noise effects as suggested by some researchers (Lercher et al., 2011); if additional risk factors are present such as hypertension, COPD, or diabetes, noise exposure may cause severe health outcomes also in the short term, as shown in this study. In earlier research with almost identical study design but covering a shorter period (Tobías et al., 2014, 2015a), it is diurnal noise that was more strongly associated with daily cardiovascular and respiratory mortality. The present results, however, attribute larger health effects to nighttime noise when the study period is lengthened. One reason for this is the fact that average noise levels have decreased over the decade 2000–2010, being the reduction in mean diurnal levels (65.1 dBA in 2003–2005 vs. 64.6 dBA in 2003– 2009) slightly larger than that in mean nocturnal levels (60.5 dBA in 2003–2005 vs. 60.2 dBA in 2003–2009). Indeed, our result for the association between Leqn and cardiovascular mortality in those Z 65 years for the period 2003–2009 is very similar to that found in Tobías et al., (2015a) for 2003–2005. Therefore while
short-term diurnal noise effects have weakened over the past years in Madrid, nocturnal noise effects remain unchanged and the WHO health protection value of 55 dBA for nighttime noise is still far below the unusually high levels found in Madrid. In addition, the fact that in this study all of the best-fitted models involved only nocturnal noise might be partly explained by misclassification of exposure in the daytime. Indeed, over the short period of a month individuals are more likely to spend the night indoors and in the same place – their bedroom –, so misclassification bias is largely reduced in the nighttime – but some bias regarding house insulation, window-opening habits, etc. remains –. The relative risks did not change after adjustment for PM2.5 or – when not interacting – NO2, so that possible confounding due to air pollution was controlled. Although noise and air pollutants in cities largely arise from the same source – road traffic – our results lend evidence to independent effects of noise levels on cardiovascular, respiratory, and diabetes-related mortality. This has also been demonstrated in most long-term studies, where little change is reported after adjustment for other urban pollutants (Sørensen et al., 2012; Davies and Kamp, 2012). On the other hand, in the cohort of Gan et al. (2012) the increased risk of death from ischemic heart disease for a 10-dBA increase in noise levels was 26%, which notably changed to 13% after adjusting for PM2.5, and to 12% after adjusting for both PM2.5 and NO2. Davies et al. (2009) found a correlation of 0.53 between 5-minute equivalent noise levels and NO2, and therefore recommended simultaneous estimation of both pollutants in studies dealing with road traffic and cardiovascular disease. However, a systematic review on confounding with regard to environmental pollutants and cardiovascular outcomes (Tétreault et al., 2013) reported little variation – less than 10% – in risk estimates for noise levels after adjustment for other road traffic-related pollutants. Though latest research supports the view of air pollution-independent noise effects on health, some researchers are reluctant to consider the question settled (Foraster, 2013). Two LUR models yielded correlations between NO2 and 24-hour noise levels of 0.62 and 0.44, respectively (Foraster et al., 2011; Eriksson et al., 2012). Yet in our study based on real-time measurements we found much lower correlations: 0.28 for NO2-Leqd and 0.19 for NO2-Leqn. Such low correlations reveal that, on a statistical basis, noise levels and air pollutant concentrations behave differently. Noise levels on a given day do not influence those of the next day, i.e. there is no “recall” effect. In contrast, air pollutant concentrations on a day do not vanish the next day, but they accumulate in the atmosphere to a degree that depends on the weather conditions. Air pollutant concentrations are little affected by weekly seasonal factors, yet the weather is of greater influence. As regards noise levels, however, factors with marked weekly seasonality such as urban economic activity and automobile use habits are of great influence, whereas weather conditions either do not have a direct influence or the influence is of opposite sign relative to that of air pollutants – e.g. precipitation may both decrease air pollutant concentrations and increase noise levels owing to wet roads. An important strength of this study lies in the use of time series of daily road-traffic noise data as dynamic predictors, which enables accurate detection of short-term associations. This was possible thanks to the Noise Pollution Monitoring Grid, which captures real-time noise levels all over the city of Madrid. Most prospective studies do not allow short-term risk estimation, since the predictors considered are static sound levels, namely, estimated from factors supposed to be constant over time (traffic density, type of vehicles, urban activity, etc.). Therefore such studies only provide risks from chronic exposure to certain sound levels considered to be mean values over a given time-period. In contrast, sound levels in time series are not estimates, but real values which summarize the behaviour of noise over periods short
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enough to hypothesize immediate health effects – notwithstanding the limitations of spatial aggregation –. Another strength is the case-crossover design, which makes it unnecessary to adjust for a number of individual-related risk factors considered in many longitudinal studies: pre-existing diseases, lifestyles, noise sensitivity, noise in workplace, duration of residence, family history, living conditions, income, etc. (cf. van Kempen et al., 2002). The main limitation of the study is the fact that it is ecological, since spatially aggregated data are considered and thus exposure to a specific noise level in a specific site cannot be associated with a specific individual at that very site who dies within one or two days from a cardiovascular, respiratory or diabetes-related endpoint. Nonetheless, this limitation is partially overcome because data are time-disaggregated: we used daily data for noise levels as well as death counts. Anyway, bias resulting from non-differential misclassification in the exposure might give rise to underestimation of risks. Another limitation is the low daily mortality for all causes in those o65 years of age, and for diabetes and asthma in both age groups, which reduces the statistical power and thus enlarges the confidence intervals to the point that in some cases no significant associations could be detected. Additionally, all models for respiratory mortality were temporally unstable; further time-series studies with new time-periods are needed to confirm or refute the associations found here. Last, our results relate to considerably high nocturnal noise levels, ranging 56.2–69.9 dBA, and thus cannot be extrapolated to populations of quieter cities; note, though, that for such cities long-term associations between urban noise and health outcomes are well documented, so that short-term noise effects might also be expected.
5. Conclusion This study provides further evidence for short-term associations of road-traffic noise levels with cardiovascular, respiratory, and diabetes-related mortality, and also with mortality from these frequent specific causes: ischemic heart disease, myocardial infarction, cerebrovascular disease, pneumonia, and COPD. In the best-fitted Poisson models, increased nighttime noise levels were associated with daily mortality at very short lags (one or two nights before the outcome). In view of these findings as well as increasing evidence across longitudinal studies over the past decades, road-traffic noise should be seriously considered a major pollutant in large cities. Prevention strategies are a pressing need and should be put forward: on the one hand, policies targeted at noise abatement – if 12% of current motor vehicles in Madrid were electric, the estimated reduction in noise levels would be of 0.5 dBA (Warburg et al., 2014) –; on the other hand, guidelines on limiting road-traffic noise exposure in residential and hospital environments – especially in the elderly.
Disclosure of potential conflicts of interest No potential conflicts of interest were disclosed.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2016.06. 014.
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The short-term association of road traffic noise with cardiovascular, respiratory, and diabetes-related mortality Alberto Recio a,n, Cristina Linares b, José R. Banegas a, Julio Díaz b a Department of Preventive Medicine and Public Health, Universidad Autónoma de Madrid, ⁄IdiPAZ – CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain b National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
art ic l e i nf o
a b s t r a c t
Article history: Received 17 April 2016 Received in revised form 6 June 2016 Accepted 7 June 2016
Background: Road traffic noise has well-documented effects on cardiovascular, respiratory, and metabolic health. Numerous studies have reported long-term associations of urban noise with some diseases and outcomes, including death. However, to date there are no studies on the short-term association between this pollutant and a set of various specific causes of death. Objectives: To investigate the short-term association of road traffic noise with daily cause-specific mortality. Methods: We used a time-stratified case-crossover design with Poisson regression. Predictor variables were daytime, nighttime, and 24-h equivalent noise levels, and maximum daytime and nighttime noise levels. Outcome variables were daily death counts for various specific causes, stratifying by age. We adjusted for primary air pollutants (PM2.5 and NO2) and weather conditions (mean temperature and relative humidity). Results: In the Z65 age group, increased mortality rates per 1 dBA increase in maximum nocturnal noise levels at lag 0 or 1 day were 2.9% (95% CI 1.0, 4.8%), 3.5% (95% CI 1.1, 6.1%), 2.4% (95% CI 0.1, 4.8%), 3.0% (95% CI 0.2, 5.8%), and 4.0% (95% CI 1.0, 7.0%), for ischemic heart disease, myocardial infarction, cerebrovascular disease, pneumonia, and COPD, respectively. For diabetes, 1 dBA increase in equivalent nocturnal noise levels at lag 1 was associated with an increased mortality rate of 11% (95% CI 4.0, 19%). In the o 65 age group, increased mortality rates per 1 dBA increase in equivalent nocturnal noise levels at lag 0 were 11% (95% CI 4.2, 18%) and 11% (95% CI 4.2, 19%) for ischemic heart disease and myocardial infarction, respectively. Conclusion: Road traffic noise increases the short-term risk of death from specific diseases of the cardiovascular, respiratory, and metabolic systems. & 2016 Elsevier Inc. All rights reserved.
Keywords: Traffic noise Cardiovascular disease Respiratory disease Diabetes
1. Introduction An issue of increasing concern in urban environments is the health impact of road traffic noise, given the large exposed population and the long exposure time-periods (Tobías et al., 2015b). Some 20% of the EU population is exposed to noise levels higher than 65 dBA in the daytime, and 30% to levels higher than 55 dBA in the night-time, which are considered health protection values (WHO, 2011). For such noise levels, a number of studies have reported significant associations with cardiovascular diseases Abbreviations: COPD, Chronic obstructive pulmonary disease; Leqd, Diurnal equivalent noise level; Leqn, Nocturnal equivalent noise level; Leq24, 24-h equivalent noise level; Ldmax, Maximum diurnal noise level; Lnmax, Maximum nocturnal noise level n Correspondence author. E-mail address: [email protected] (A. Recio). http://dx.doi.org/10.1016/j.envres.2016.06.014 0013-9351/& 2016 Elsevier Inc. All rights reserved.
(Banerjee et al., 2014, Argalášová-Sobotová et al. 2013, Sørensen et al., 2014), respiratory diseases (Niemann et al., 2006; Ising et al., 2003, 2004), type 2 diabetes mellitus (Sørensen et al., 2013), and more recently, severe depressive symptoms (Orban et al., 2016) and adverse birth outcomes (Díaz and Linares, 2016). According to a recent meta-analysis (Hänninen et al., 2014), road traffic noise ranks among the four environmental risk factors with highest health impact in European countries, which means a loss of 400 – 1500 healthy life years due to ischemic heart disease per million people. The impact of urban noise on public health (8% of the environmental burden of disease) was rated medium-high, comparable to that of secondhand smoke and radon, and only behind fine particles (PM2.5). Prospective studies (Selander et al., 2013; Babisch et al., 2005, Sørensen et al., 2012) have reinforced the hypothesis of the longterm association between road traffic noise and the incidence of myocardial infarction in large cities, especially in the over-65 age
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group. In two cohorts (Gan et al., 2012, Sørensen et al., 2011), increased daily noise levels were associated with increased mortality from ischemic heart disease and stroke, respectively, after adjustment for air pollutants. Unlike the case of cardiovascular disease, there are few studies dealing with respiratory outcomes, even fewer those considering objective noise as a predictor variable. Two pioneering studies focusing on children and nocturnal road traffic (Ising et al., 2003, 2004) found a dose-response relationship between traffic load and the prevalence of asthma and chronic bronchitis, suggesting an important combined effect of noise and air pollution. Yet the correlation between noise levels and NO2 concentration was very high and thus confounding could not be ruled out. Afterwards the LARES study (Niemann et al., 2006) reported significant risks of bronchitis in children highly annoyed with road traffic noise, but no association was found for asthma. Owing to their cross-sectional design, the above studies could not demonstrate one-way causality. The association of road traffic noise with the incidence of type 2 diabetes has recently been investigated in a cohort (Sørensen et al., 2013); the results were significant only for the over-65 age group. There are few ecological, aggregated-data studies in current environmental epidemiology. Nonetheless, when data are geographically disaggregated such studies provide support for longterm associations between urban pollutants and health outcomes (Halonen et al., 2015). On the other hand, when data are timedisaggregated, as in the case of time series, short-term associations can be investigated. Two studies of this type assessed the short-term association between road traffic noise and cardiovascular and respiratory morbidity; among the respiratory outcomes, a significant association was found for pneumonia, but not for bronchitis (Tobías et al., 2001; Linares et al., 2006). As regards cardiovascular, respiratory and diabetes-related mortality, to date only three time-series studies have investigated their short-term association with road traffic noise (Tobías et al. 2015c, 2014, 2015a), yielding significant results only for the over-65 age group. Biological plausibility for the association of noise with cardiovascular, respiratory, and metabolic health outcomes has recently been documented in a review and summarized in an integrative model (Recio et al., 2016). Stress caused by noise may give rise to a variety of physiological reactions intended to preserve the homeostasis. When stress is considerably high and maintained, allostatic overload may lead to inefficient body responses due to overactivation of the sympathetic-adrenal-medullar and hypothalamic-pituitary-adrenocortical axes, affecting blood pressure, heart rate variability, the immune system, and the connective tissue, and promoting fat accumulation in the arteries, blood clotting, endothelial dysfunction, systemic inflammation, destabilization of atherosclerotic plaques, and insulin resistance. Some mechanisms may operate in the long- as well as the short-term, resulting in chronic or acute health outcomes, or even a concurrence of the two. Long ago, Maclure (1991) posed the possibility of acute health outcomes as a result of point exposure to an environmental stressor, considering induction time-periods of a few minutes up to a few days from the acute exposure until the adverse outcome. Modern environmental epidemiology has long used time-series analysis as a suitable strategy for the study of short-term effects of urban pollutants, allowing for different induction time-periods or lags (Maté et al. 2010). Madrid is one of the few cities in the world provided with a monitoring network that stores real-time daily sound levels, which enables accurate examination of the relation of noise to morbidity and mortality in the short term. Conceived as an extension of the studies already published on the association between daily road-traffic noise levels in Madrid and the risk of
death from cardiovascular, respiratory, and diabetes-related outcomes (Tobías et al., 2014, 2015a, 2015c), this study extends the period of follow-up – seven years instead of three –, the number of predictor variables – with maximum noise levels added –, and the number of dependent variables, in order to assess the differences in the short-term effects of road traffic noise on the following specific causes of death: ischemic heart disease, myocardial infarction, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), pneumonia, asthma, and diabetes. Age stratification at the cutoff point of 65 years is especially relevant in this study, since many cardiovascular, respiratory, and metabolic endpoints may occur as a consequence of interaction between longterm health decline and short-term exposure to a variety of risk factors such as environmental noise.
2. Methods 2.1. Setting Madrid is a dense metropolitan area with a mean population of 3,154,033 in the period 2003–2009, 19% over 65 years of age. The average daily traffic intensity was 2.4 million motor vehicles, reaching the maximum in May (2.5 million) and the minimum in August (1.7 million), with a mean speed of nearly 24 km/h. The main outdoor noise source is road traffic (80% of the overall noise exposure); other sources are industry (10%), rail traffic (6%), and leisure activities (4%) (Díaz et al., 2003). 2.2. Mortality data Daily mortality records for the period 1 January 2003 – 31 December 2009 were obtained from the Madrid Regional Inland Revenue Department. Then time-series for the following causes of death were constructed: cardiovascular disease (International Classification of Diseases 10: I00-I99), respiratory disease (ICD 10: J00-J99), ischemic heart disease (ICD 10: I20-I25), myocardial infarction (ICD 10: I21), cerebrovascular disease (ICD 10: I60-I69), pneumonia (ICD 10: J12-J18), COPD (ICD 10: J40-44, J47), asthma (ICD 10: J45-J46), and diabetes (ICD 10: E10-E14). In order to conduct age-stratified analysis, we constructed two time-series for every cause of death: one for the under-65 age group and another one for the over-65 age group. This choice was motivated by the fact that those over 65 years are especially vulnerable to the diseases and adverse outcomes studied (Nichols et al., 2014); also, setting the cut point at 65 years enables comparability with other studies. 2.3. Noise exposure Hourly equivalent noise levels were obtained from the Madrid Noise Pollution Monitoring Grid. This network consisted of 26 urban background stations in 2003–2009, 4 m above ground level in compliance with the Directive 2002/49/EC (2002) of the European Parliament and strategically allocated to be representative of the noise levels across the city (Supplemental Fig.1). Technically, the measuring process involves the following steps: (a) an outdoor antibird omnidirectional microphone, provided with wind screen, captures the data; (b) the captured signal connects with a statistical noise analyser which allows audio recording and frequency analysis (1/1- and 1/3-octaves); (c) the information stored in the analyser is transferred to a central station via a high-speed telephony modem (ISDN); and (d) the central station is equipped with a distributor adapted to ISDN that communicates with all stations at set intervals to send the data. In order for the hourly noise levels to be representative of the
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entire city, an average of the validated data from all stations were used. Then three daily equivalent-noise-level variables were created: Leqd (diurnal: 8–20 h), Leqn (nocturnal: 0–8 h), and Leq24 (0–24 h); also, we generated two maximum hourly equivalentnoise-level variables: Ldmax (diurnal: 8–20 h) and Lnmax (nocturnal: 0–8 h). By defining the nocturnal variables within the interval 0–8 h instead of, say, 20–8 h, each data point unmistakably refers to one only calendar day, which is relevant since we are dealing with very short lags between exposure and outcome (0–4 days). No equivalent noise level was defined for the interval 20– 24 h, yet its contribution is included in Leq24. 2.4. Covariates As primary air pollutants, we included mean daily concentrations of particulate matter o 2.5 mm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), obtained from the Madrid Municipal Air Quality Monitoring Grid. We considered PM2.5, and not PM10, because its short-term association with mortality has been documented formerly (Maté et al., 2010); indeed, the WHO recommends to use PM2.5 rather than PM10 as air quality indicator (WHO, 2006). Owing to the increasing use of low-sulphur fuels, nowadays NO2 is the main gaseous pollutant from road traffic in Madrid, also having the greatest effects on health (Querol et al., 2012). Road traffic emissions of NO2 and PM2.5 take place at the same time as sound levels, so they are potential confounders. On the other hand, ozone (O3), as a secondary air pollutant, cannot confound the association of noise levels with mortality. The effects of O3 on daily mortality have been shown to be significant at lags longer than those for primary pollutants (Díaz et al., 2004). Mean temperature and relative humidity were obtained from the State Meteorology Agency (AEMET). An earlier study using time series reported associations of mortality with (a) the maximum temperature of the day before and (b) the minimum temperature of several days before (Maté et al., 2010). In this sense we created two variables out of the daily mean temperature: (1) average of the mean temperature up to two days before the exposure (lags 0–2), to control for the immediate effects dominated by heat, and (2) average of the mean temperature of the third through sixth day before the exposure (lags 3–6), to control for the effects of lower temperatures at longer lags; then, for smoothing purposes, both series were modeled as natural cubic splines with 3 degrees of freedom. Though maximum and minimum temperatures relate to heat and cold waves, respectively, we used mean temperatures for simplicity, which contain information from both thermal extremes. We included humidity because in winter it is associated with (1) mortality due to flu epidemics and (2) increased road-traffic noise levels due to wet floor.
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are dealing with short-term associations and (b) the outcome of interest is of very short duration (Jaakkola, 2003). If the causal hypothesis is true, noise exposure levels in the case period should on average be higher than exposure levels in the control periods. Likewise, provided that there can be deaths in all periods, excess deaths are needed during the case period relative to the control periods, so that such an excess be attributable to noise. Only on the above conditions may positive associations be found between noise levels and mortality. Thanks to the case-crossover design, invariable personal factors such as age, sex, lifestyle, and overall health status cannot confound the associations, since every case subject serves as his or her own control. In order to avoid confounding from time-varying personal and environmental factors, control days must be close enough to case days. To this end we carried out a time-stratified analysis by including in the models a three-way interaction between year, month, and day of week (year month dow), so that the study period was divided into monthly strata and control days were all days falling on the same day of the week within the same stratum as that of the case day. The key assumption is that within each stratum the time-varying risk factors remain constant, which ensures control of both seasonality and time trends. This is equivalent to time-series modelling, on the condition that exposure levels be equal for all subjects (Lu and Zeger, 2007). We generated lagged variables (lags 0–4) for all predictors and covariates – except mean temperature – and conducted prior analysis using univariate Poisson models in order to identify the most significant lag for the association between each of these variables and every death cause studied. Two-way interactions between predictors and covariates were also tested in univariate models, being the candidates (those with po 0.05) subsequently included in the full models. We derived final Poisson models for every death cause and predictor, including all covariates at the selected lags, the predictor-covariate interactions detected in the univariate models, the mean temperature splines and the threeway time interaction term. We assumed that, owing to the inclusion of the three-way time interaction term in the models, much of the correlation present in the mortality series would be under control, with a considerable reduction in Poisson overdispersion; residual overdispersion was handled using the quasi-likelihood estimation. Last we checked the temporal stability of the models by the jack-knife method with yearly clusters (Efron and Stein, 1981), in which new models are derived after removing one different year each time, and we can see whether on average the results hold. All analyses were conducted using Stata statistical software version 12 (Stata-Corp, College Station, TX, USA).
2.5. Design and statistical analysis
3. Results
We investigated the association between road traffic noise levels (predictors) and daily death counts (outcomes) using a timestratified case-crossover design. All analyses were conducted with Poisson regression, the standard method for count data. The case-crossover design with time series has been widely used in morbidity as well as mortality studies to investigate the short-term effects of air pollutants in urban environments (Brook et al., 2010). As usual in environmental epidemiology with aggregated data (Carracedo-Martínez et al., 2010), we consider groups of the general population who have suffered a specific health outcome, and compare the level of exposure to the environmental risk factor a short time before the outcome (case period) with the exposure level in one or more periods when such an event did not occur (control periods). The time-series based case-crossover design is appropriate in this case because (a) we
Table 1 shows the summary statistics for all variables. The mean value of the 24-h equivalent noise level over the 7-year study period was 63.4 dBA; diurnal noise levels (mean 64.6 dBA) contribute to the 24-h level to a larger degree than nocturnal noise levels (mean 60.2 dBA). Indeed, Leq24 and Leqd distributions are similar (Fig. 1). The WHO guidelines on urban noise – 65 dBA for daytime noise and 55 dBA for nighttime noise – were exceeded on 50% of the days and 100% of the nights in the period 2003–2009. Highly correlated equivalent noise levels were Leqd and Leq24 (r¼ 0.96), while correlation between Leqd and Leqn was low (r¼ 0.30). As regards maximum levels, Ldmax and Leqd were highly correlated (r ¼0.96), while correlation between Leqn and Ldmax was much lower (r ¼ 0.49). Noise levels were weakly correlated with air pollutant concentrations (e.g. r ¼0.15 for Leq24PM2.5 and r ¼0.35 for Leq24-NO2); however, the NO2-PM2.5
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5–40 0–8 5–36 0–32 0–5 0–31
5.7 0.7 5 3.5 0.5 3.0 4.1 0.3 3.8 2.5 0.2 2.3 2.0 0.1 1.9 0.1 0.0 0.1 0.8 0.0 0.8
(2.6) (0.9) (2.4) (2.0) (0.8) (1.8) (2.1) (0.6) (2.0) (1.8) (4.3) (1.7) (1.5) (0.3) (1.5) (0.3) (0.1) (0.2) (0.9) (0.2) (0.9)
0–18 0–6 0–16 0–14 0–4 0–11 0–14 0–3 0–13 0–12 0–3 0–12 0–10 0–2 0–10 0–2 0–1 0–2 0–5 0–2 0–5
64.6 60.2 63.4 65.5 63.9
(1.4) (1.0) (1.1) (1.4) (1.7)
59.3–68.1 56.2–69.9 59.1–66.6 60.3–69.7 58.7–76.3
17.3 (7.8) 58.6 (18.0)
3–71 18–134
16.8 (7.6) 54.0 (16.9)
0.2–33.4 19–94.8
a
ICD 10: I00-I99. ICD 10: J00-J99. ICD 10: I20-I25. d ICD 10: I21. e ICD 10: I60-I69. f ICD 10: J12-J18. g ICD 10: J40-44, J47. h ICD 10: J45-J46. i ICD 10: E10-E14. b c
correlation was rather high (r¼ 0.71), which indicated collinearity. In order to avoid the unstable effects of collinearity, we included these covariates in separate Poisson models: (a) without air pollutants, (b) adjusted for PM2.5, and (c) adjusted for NO2. Table 2 shows the results from the best-fitted models according to the Akaike criterion. Results from all the models considered for the Z65 age group are provided in Supplemental Table 1–3. The selected lags are those which had previously yielded the most significant associations (lowest p-values) in univariate models. Scaling of standard errors by the quasi-likelihood estimation scarcely changed the confidence intervals (less than 70.001 units of 95% CI). The best-fitted models for all outcome variables were those adjust ed for PM2.5 involving nocturnal noise levels; in the case of cardiovascular mortality, such noise levels were those of the night of the calendar day when the deaths occurred (lag 0), i.e. the night that precedes daytime, while for respiratory and diabetes-related mortality such noise levels were those of the day before (lag 1).
Density
(5.2) (1.3) (4.9) (4.0) (0.8) (3.8)
0 .1 .2 .3 .4 .5
18.0 1.5 16.5 9.6 0.6 9.0
55
60
dBA
65
70
65
70
65
70
Leqn Density
Range
0 .1 .2 .3 .4 .5
Group-specific mortality Cardiovascular mortalitya o 65 years Z 65 years Respiratory mortalityb o 65 years Z 65 years Cause-specific mortality Ischemic heart diseasec o 65 years Z 65 years Myocardial infarctiond o 65 years Z 65 years Cerebrovascular diseasee o 65 years Z 65 years Pneumoniaf o 65 years Z 65 years COPDg o 65 years Z 65 years Asthmah o 65 years Z 65 years Diabetesi o 65 years Z 65 years Noise levels (dBA) Leqd Leqn Leq24 Ldmax Lnmax Air pollutants (μg/m3) PM2.5 NO2 Weather Mean temperature (°C) Relative humidity (%)
Mean (SD)
Leqd
55
60
dBA
Leq24 Density
Table 1 Summary statistics for daily mortality, noise levels, air pollutants, and weather variables in Madrid 2003–2009.
0 .1 .2 .3 .4 .5
386
55
60
dBA
Fig. 1. Distributions of diurnal (Leqd: 8–20 h), nocturnal (Leqn: 0–8 h), and 24-h (Leq24) noise levels in Madrid for the period 2003–2009.
Table 2 Relative risks of death for a 1 dBA increase in the specified noise levels, adjusted for PM2.5, by age groups. A 1 dBA increase in noise levels corresponds to an increased traffic intensity of roughly 26%.
Z 65 years of age Cardiovascular disease Ischemic heart disease Myocardial infarction Cerebrovascular disease Respiratory disease Pneumonia COPD Asthma Diabetes o 65 years of age Cardiovascular disease Ischemic heart disease Myocardial infarction Cerebrovascular disease Respiratory disease Pneumonia COPD Asthma Diabetes a
Best predictor (lag)
RR (95% CI)
Leqn (0) Lnmax (0) Lnmax (0) Lnmax (0) Leqn (1) Lnmax (1) Lnmax (1) Lnmax (1) Leqn (1)
1.033 1.029 1.035 1.024 1.022a 1.030a 1.040 1.015 1.110
(1.017, 1.049) (1.010, 1.048) (1.011, 1.061) (1.001, 1.048) (1.002, 1.043) (1.002, 1.058) (1.010, 1.070) (0.860, 1.020) (1.040, 1.192)
Leqn Leqn Leqn Leqn Leqn Leqn Leqn Leqn Leqn
1.050 1.108 1.114 1.001 1.004a 0.960a 1.111 0.493 1.082
(1.004, 1.098) (1.042, 1.177) (1.042, 1.192) (0.897, 1.118) (0.934, 1.079) (0.832, 1.108) (0.946, 1.306) (0.127, 1.914) (0.850, 1.379)
(0) (0) (0) (0) (1) (1) (1) (1) (1)
Only when NO2 430 μg/m3 (noise-NO2 interaction).
Additionally, in the models for total respiratory mortality and mortality from pneumonia, nocturnal noise was found to interact with NO2. We handled this interaction stratifying by NO2, and discovered that noise levels at lag 1 were significantly associated with mortality only when mean NO2 concentration at the same lag was higher than 30 μg/m3. With regard to the temporal stability of the models, the jackknife estimation showed that cardiovascular and diabetes-related mortality models were robust, whereas those for respiratory mortality were not, i.e. they would yield non-significant associations depending on the year removed from the analysis. 3.1. Cardiovascular mortality In the Z65 age group, the relative risk for the overall cardiovascular mortality was significant for Leqn at lag 0 (1.033 (95% CI
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Fig. 2. Relative risks of death from myocardial infarction and COPD per 1 dBA increase in maximum nocturnal noise levels (Lnmax) for every lag, in the over-65 age group.
1.017, 1.049)), Lnmax at lag 0 (1.020 (95% CI 1.009, 1.031)), and Leq24 at lag 1 (1.033 (95% CI 1.012, 1.056)) (Supplemental Table 1). According to the best-fitted model (Table 2), the risk of death increased 3.3% (95% CI 1.7, 4.9%) per 1 dBA increase in Leqn at lag 0, with no change after adjustment for either PM2.5 or NO2. In the o65 age group, increased risk was 5.0% (95% CI 0.4, 9.8%) for Leqn at lag 0. As regards cause-specific mortality, in the Z65 age group the best-fitted models involved Lnmax at lag 0, with increased risk of 2.9% (95% CI 1.0, 4.8%), 3.5% (95% CI 1.1, 6.1%), and 2.4% (95% CI 0.1, 4.8%) for ischemic heart disease, myocardial infarction (Fig. 2), and cerebrovascular disease, respectively. In the o65 age group, only nocturnal noise provided models with significant results; the bestfitted models involved Leqn at lag 0, with increased risks of 11% (95% CI 4.2, 18%) and 11% (95% CI 4.2, 19%) for ischemic heart disease and myocardial infarction, respectively. Relative risks of death from cerebrovascular disease were not significant in this age group. All of the above remained unaltered after adjustment for air pollutants. 3.2. Respiratory mortality For both general and cause-specific respiratory mortality, the only noise variables providing significant relative risks were Leqn (1.022 (95% CI 1.002, 1.043)) and Lnmax (1.017 (95% CI 1.003, 1.032)) at lag 1 (Supplemental Table 2). The risk of death from an unspecific respiratory disease in those aged Z65 years increased 2.2% (95% CI 0.2, 4.3%) per 1 dBA increase in Leqn at lag 1, after adjustment for PM2.5 and only when NO2 4 30 μg/m3 (Table 2). As regards cause-specific mortality, in those aged Z 65 years we obtained significant relative risks for pneumonia and COPD, but not for asthma. A 1 dBA increase in Lnmax at lag 1 was associated with an increase of 3.0% (95% CI 0.2, 5.8%) in mortality from pneumonia when NO2 430 μg/m3. In the best-fitted model for COPD, a 1 dBA increase in Lnmax at lag 1 was associated with an increase of 4.0% (95% CI 1.0, 7.0%) in mortality (Fig. 2), regardless of the value of NO2. There were no changes after adjustment for PM2.5. In the case of asthma, only NO2 concentration – but not noise – was significantly associated with increased mortality at lag 1 (1.4% (95% CI 0.2, 2.7%)). No significant associations were found for those aged o65 years.
3.3. Diabetes-related mortality In the Z 65 age group all noise variables were significantly associated with diabetes-related mortality (Supplemental Table 3). Relative risks for Leqd and Leqn were 1.094 (95% CI 1.010, 1.180) at lag 2 and 1.110 (95% CI 1.040, 1.190) at lag 1, respectively. The bestfitted model (Table 2) yielded an increased risk of death from diabetes of 11% (95% CI 4.0, 19%) per 1 dBA increase in Leqn at lag 1, adjusting for air pollutants. No significant associations were found in the o65 age group.
4. Discussion Noise exposure effects on mortality were examined for the same day and up to four days after the exposure. In the Z65 age group, we found significant short-term associations between the five noise exposure variables considered and eight specific causes of death (all except asthma), though the best-fitted models involved only nocturnal noise levels at lags 0 and 1. In the o65 age group, we only found significant associations between nocturnal noise levels at lag 0 and death from cardiovascular disease, ischemic heart disease and myocardial infarction. The various biological mechanisms whereby noise as a stressor may cause physiological alterations affecting the cardiovascular, respiratory, and metabolic systems provide plausibility for shortterm noise effects on health (Recio et al., 2016). Diabetes-related events that could lead to death may also be triggered by noise exposure. The most serious acute metabolic complications of diabetes are hyperglycemic crises and ketoacidosis. Noise levels may provoke rises in catecholamine and cortisol levels, mobilizing the energy resources and therefore raising blood glucose levels – which leads to hyperglycemic crises – and cell metabolism – which may cause ketoacidosis –. To compensate for this, hyperosmolar syndrome takes place, and further dehydration might end in diabetic coma and death if not resolved in time (Kitabchi et al., 2009). Maximum nighttime noise levels were significantly associated with mortality from pneumonia and COPD. Autonomic awakening caused by noise peaks at night is likely to disturb the slow wave sleep, affecting the recovery of the immune system, which may lead to the aggravation of respiratory infections (Pirrera et al.,
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2010). Airway inflammation, associated with decline of lung function, is present in exacerbations of COPD; therefore, increased systemic inflammation due to noise exposure might lead to increased morbidity and mortality from this cause (George and Brightling, 2016). For general respiratory mortality and mortality from pneumonia, the models included a positive interaction term, Leqn x NO2, meaning that the adverse effects of nocturnal noise were greater with concurrent high NO2 concentrations. In contrast, with regard to cardiovascular or diabetes-related mortality, noise effects were independent of NO2. This finding allows to speculate on the role of noise as an enhancer of the documented effects of NO2 on respiratory symptoms in the presence of viral and bacterial infections. For example, NO2 has been suggested to impair immune processes such as phagocyte function, making the lungs more prone to severe disease due to aggravated bacterial infection (Chauhan et al., 1998). The different lags of the associations found in the models – the night before (lag 0) for cardiovascular outcomes vs. two nights before (lag 1) for respiratory and diabetes-related outcomes – may be due to the different biological mechanisms involved; on the one hand, immediate physiological changes caused by stress, such as increased blood pressure, blood clotting, and heart rate, might have cardiovascular effects in a few hours, since only the neuroendocrine system mediates; on the other hand, somewhat longer lags are expected for the occurrence of respiratory events, since here also decline of the immune system mediates. Babisch et al. (2005) found a long-term odds ratio of 1.3 for the prevalence of myocardial infarction in adults exposed to a 10-dBA higher category of daytime noise levels; in a prospective study (Sørensen et al., 2012) a relative risk of 1.2 was found for the incidence of this disease for a 10-dBA increment in daily noise levels in the Z65 age group. These results are consistent with our estimate of the short-term relative risk for mortality from the same outcome in those Z 65 years exposed to a theoretical excess of 10 dBA in nighttime maximum levels, calculated as 1.03510 E1.4. Also for the Z65 age group, another prospective study (Gan et al., 2012) reported an increased risk of 9% for long-term mortality due to ischemic heart disease, while our models yielded a short-term increased risk of death of about 33% (calculated as (1.02910 1) 100%). Other recent studies (Selander et al., 2013; Banerjee et al., 2014) have reported significant relative risks for the incidence of myocardial infarction and ischemic heart disease in the aging population. Since we found similar results for the short-term mortality, all this suggests an interaction between chronic and acute stress: when chronic effects of exposure to urban pollutants in the vulnerable aging population add to acute effects, the risk of severe adverse outcome appears to increase notably. Furthermore, there is no need for the long latency time-periods for noise effects as suggested by some researchers (Lercher et al., 2011); if additional risk factors are present such as hypertension, COPD, or diabetes, noise exposure may cause severe health outcomes also in the short term, as shown in this study. In earlier research with almost identical study design but covering a shorter period (Tobías et al., 2014, 2015a), it is diurnal noise that was more strongly associated with daily cardiovascular and respiratory mortality. The present results, however, attribute larger health effects to nighttime noise when the study period is lengthened. One reason for this is the fact that average noise levels have decreased over the decade 2000–2010, being the reduction in mean diurnal levels (65.1 dBA in 2003–2005 vs. 64.6 dBA in 2003– 2009) slightly larger than that in mean nocturnal levels (60.5 dBA in 2003–2005 vs. 60.2 dBA in 2003–2009). Indeed, our result for the association between Leqn and cardiovascular mortality in those Z 65 years for the period 2003–2009 is very similar to that found in Tobías et al., (2015a) for 2003–2005. Therefore while
short-term diurnal noise effects have weakened over the past years in Madrid, nocturnal noise effects remain unchanged and the WHO health protection value of 55 dBA for nighttime noise is still far below the unusually high levels found in Madrid. In addition, the fact that in this study all of the best-fitted models involved only nocturnal noise might be partly explained by misclassification of exposure in the daytime. Indeed, over the short period of a month individuals are more likely to spend the night indoors and in the same place – their bedroom –, so misclassification bias is largely reduced in the nighttime – but some bias regarding house insulation, window-opening habits, etc. remains –. The relative risks did not change after adjustment for PM2.5 or – when not interacting – NO2, so that possible confounding due to air pollution was controlled. Although noise and air pollutants in cities largely arise from the same source – road traffic – our results lend evidence to independent effects of noise levels on cardiovascular, respiratory, and diabetes-related mortality. This has also been demonstrated in most long-term studies, where little change is reported after adjustment for other urban pollutants (Sørensen et al., 2012; Davies and Kamp, 2012). On the other hand, in the cohort of Gan et al. (2012) the increased risk of death from ischemic heart disease for a 10-dBA increase in noise levels was 26%, which notably changed to 13% after adjusting for PM2.5, and to 12% after adjusting for both PM2.5 and NO2. Davies et al. (2009) found a correlation of 0.53 between 5-minute equivalent noise levels and NO2, and therefore recommended simultaneous estimation of both pollutants in studies dealing with road traffic and cardiovascular disease. However, a systematic review on confounding with regard to environmental pollutants and cardiovascular outcomes (Tétreault et al., 2013) reported little variation – less than 10% – in risk estimates for noise levels after adjustment for other road traffic-related pollutants. Though latest research supports the view of air pollution-independent noise effects on health, some researchers are reluctant to consider the question settled (Foraster, 2013). Two LUR models yielded correlations between NO2 and 24-hour noise levels of 0.62 and 0.44, respectively (Foraster et al., 2011; Eriksson et al., 2012). Yet in our study based on real-time measurements we found much lower correlations: 0.28 for NO2-Leqd and 0.19 for NO2-Leqn. Such low correlations reveal that, on a statistical basis, noise levels and air pollutant concentrations behave differently. Noise levels on a given day do not influence those of the next day, i.e. there is no “recall” effect. In contrast, air pollutant concentrations on a day do not vanish the next day, but they accumulate in the atmosphere to a degree that depends on the weather conditions. Air pollutant concentrations are little affected by weekly seasonal factors, yet the weather is of greater influence. As regards noise levels, however, factors with marked weekly seasonality such as urban economic activity and automobile use habits are of great influence, whereas weather conditions either do not have a direct influence or the influence is of opposite sign relative to that of air pollutants – e.g. precipitation may both decrease air pollutant concentrations and increase noise levels owing to wet roads. An important strength of this study lies in the use of time series of daily road-traffic noise data as dynamic predictors, which enables accurate detection of short-term associations. This was possible thanks to the Noise Pollution Monitoring Grid, which captures real-time noise levels all over the city of Madrid. Most prospective studies do not allow short-term risk estimation, since the predictors considered are static sound levels, namely, estimated from factors supposed to be constant over time (traffic density, type of vehicles, urban activity, etc.). Therefore such studies only provide risks from chronic exposure to certain sound levels considered to be mean values over a given time-period. In contrast, sound levels in time series are not estimates, but real values which summarize the behaviour of noise over periods short
A. Recio et al. / Environmental Research 150 (2016) 383–390
enough to hypothesize immediate health effects – notwithstanding the limitations of spatial aggregation –. Another strength is the case-crossover design, which makes it unnecessary to adjust for a number of individual-related risk factors considered in many longitudinal studies: pre-existing diseases, lifestyles, noise sensitivity, noise in workplace, duration of residence, family history, living conditions, income, etc. (cf. van Kempen et al., 2002). The main limitation of the study is the fact that it is ecological, since spatially aggregated data are considered and thus exposure to a specific noise level in a specific site cannot be associated with a specific individual at that very site who dies within one or two days from a cardiovascular, respiratory or diabetes-related endpoint. Nonetheless, this limitation is partially overcome because data are time-disaggregated: we used daily data for noise levels as well as death counts. Anyway, bias resulting from non-differential misclassification in the exposure might give rise to underestimation of risks. Another limitation is the low daily mortality for all causes in those o65 years of age, and for diabetes and asthma in both age groups, which reduces the statistical power and thus enlarges the confidence intervals to the point that in some cases no significant associations could be detected. Additionally, all models for respiratory mortality were temporally unstable; further time-series studies with new time-periods are needed to confirm or refute the associations found here. Last, our results relate to considerably high nocturnal noise levels, ranging 56.2–69.9 dBA, and thus cannot be extrapolated to populations of quieter cities; note, though, that for such cities long-term associations between urban noise and health outcomes are well documented, so that short-term noise effects might also be expected.
5. Conclusion This study provides further evidence for short-term associations of road-traffic noise levels with cardiovascular, respiratory, and diabetes-related mortality, and also with mortality from these frequent specific causes: ischemic heart disease, myocardial infarction, cerebrovascular disease, pneumonia, and COPD. In the best-fitted Poisson models, increased nighttime noise levels were associated with daily mortality at very short lags (one or two nights before the outcome). In view of these findings as well as increasing evidence across longitudinal studies over the past decades, road-traffic noise should be seriously considered a major pollutant in large cities. Prevention strategies are a pressing need and should be put forward: on the one hand, policies targeted at noise abatement – if 12% of current motor vehicles in Madrid were electric, the estimated reduction in noise levels would be of 0.5 dBA (Warburg et al., 2014) –; on the other hand, guidelines on limiting road-traffic noise exposure in residential and hospital environments – especially in the elderly.
Disclosure of potential conflicts of interest No potential conflicts of interest were disclosed.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2016.06. 014.
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