Marcela Rivera

Local determinants of road traffic noise levels versus determinants of air pollution levels in a Mediterranean city.

BACKGROUND Both traffic-related noise and air pollution have been associated with cardiovascular disease (CVD). Spatial correlations between these environmental stressors may entail mutual confounding in epidemiological studies investigating their long-term effects. Few studies have investigated their correlation - none in Spain - and results differ among cities. OBJECTIVES We assessed the contribution of urban land-use and traffic variables to the noise-air pollution correlation in Girona town, where an investigation of the chronic effects of air pollution and noise on CVD takes place (REGICOR-AIR). METHODOLOGY Outdoor annual mean concentrations of nitrogen dioxide (NO(2)) derived from monthly passive sampler measurements were obtained at 83 residential locations. Long-term average traffic-related noise levels from a validated model were assigned to each residence. Linear regression models were fitted both for NO(2) and noise. RESULTS The correlation between NO(2) and noise (L(24h)) was 0.62. However, the correlation differed across the urban space, with lower correlations at sites with higher traffic density and in the modern downtown. Traffic density, distance from the location to the sidewalk and building density nearby explained 35.6% and 73.2% of the variability of NO(2) and noise levels, respectively. The correlation between the residuals of the two models suggested the presence of other unmeasured common variables. CONCLUSIONS The substantial correlation between traffic-related noise and NO(2), endorsed by common determinants, and the dependence of this correlation on complex local characteristics call for careful evaluations of both factors to ultimately assess their cardiovascular effects.

Association between Long-Term Exposure to Traffic-Related Air Pollution and Subclinical Atherosclerosis: The REGICOR Study

Background: Epidemiological evidence of the effects of long-term exposure to air pollution on the chronic processes of atherogenesis is limited. Objective: We investigated the association of long-term exposure to traffic-related air pollution with subclinical atherosclerosis, measured by carotid intima media thickness (IMT) and ankle–brachial index (ABI). Methods: We performed a cross-sectional analysis using data collected during the reexamination (2007–2010) of 2,780 participants in the REGICOR (Registre Gironí del Cor: the Gerona Heart Register) study, a population-based prospective cohort in Girona, Spain. Long-term exposure across residences was calculated as the last 10 years’ time-weighted average of residential nitrogen dioxide (NO2) estimates (based on a local-scale land-use regression model), traffic intensity in the nearest street, and traffic intensity in a 100 m buffer. Associations with IMT and ABI were estimated using linear regression and multinomial logistic regression, respectively, controlling for sex, age, smoking status, education, marital status, and several other potential confounders or intermediates. Results: Exposure contrasts between the 5th and 95th percentiles for NO2 (25 µg/m3), traffic intensity in the nearest street (15,000 vehicles/day), and traffic load within 100 m (7,200,000 vehicle-m/day) were associated with differences of 0.56% (95% CI: –1.5, 2.6%), 2.32% (95% CI: 0.48, 4.17%), and 1.91% (95% CI: –0.24, 4.06) percent difference in IMT, respectively. Exposures were positively associated with an ABI of > 1.3, but not an ABI of < 0.9. Stronger associations were observed among those with a high level of education and in men ≥ 60 years of age. Conclusions: Long-term traffic-related exposures were associated with subclinical markers of atherosclerosis. Prospective studies are needed to confirm associations and further examine differences among population subgroups.

High blood pressure and long-term exposure to indoor noise and air pollution from road traffic

Background: Traffic noise has been associated with prevalence of hypertension, but reports are inconsistent for blood pressure (BP). To ascertain noise effects and to disentangle them from those suspected to be from traffic-related air pollution, it may be essential to estimate people’s noise exposure indoors in bedrooms. Objectives: We analyzed associations between long-term exposure to indoor traffic noise in bedrooms and prevalent hypertension and systolic (SBP) and diastolic (DBP) BP, considering long-term exposure to outdoor nitrogen dioxide (NO2). Methods: We evaluated 1,926 cohort participants at baseline (years 2003–2006; Girona, Spain). Outdoor annual average levels of nighttime traffic noise (Lnight) and NO2 were estimated at postal addresses with a detailed traffic noise model and a land-use regression model, respectively. Individual indoor traffic Lnight levels were derived from outdoor Lnight with application of insulations provided by reported noise-reducing factors. We assessed associations for hypertension and BP with multi-exposure logistic and linear regression models, respectively. Results: Median levels were 27.1 dB(A) (indoor Lnight), 56.7 dB(A) (outdoor Lnight), and 26.8 μg/m3 (NO2). Spearman correlations between outdoor and indoor Lnight with NO2 were 0.75 and 0.23, respectively. Indoor Lnight was associated both with hypertension (OR = 1.06; 95% CI: 0.99, 1.13) and SBP (β = 0.72; 95% CI: 0.29, 1.15) per 5 dB(A); and NO2 was associated with hypertension (OR = 1.16; 95% CI: 0.99, 1.36), SBP (β = 1.23; 95% CI: 0.21, 2.25), and DBP (β⊇= 0.56; 95% CI: –0.03, 1.14) per 10 μg/m3. In the outdoor noise model, Lnight was associated only with hypertension and NO2 with BP only. The indoor noise–SBP association was stronger and statistically significant with a threshold at 30 dB(A). Conclusion: Long-term exposure to indoor traffic noise was associated with prevalent hypertension and SBP, independently of NO2. Associations were less consistent for outdoor traffic Lnight and likely affected by collinearity. Citation: Foraster M, Künzli N, Aguilera I, Rivera M, Agis D, Vila J, Bouso L, Deltell A, Marrugat J, Ramos R, Sunyer J, Elosua R, Basagaña X. 2014. High blood pressure and long-term exposure to indoor noise and air pollution from road traffic. Environ Health Perspect 122:1193–1200; http://dx.doi.org/10.1289/ehp.1307156

Association of long-term exposure to traffic-related air pollution with blood pressure and hypertension in an adult population-based cohort in Spain (the REGICOR study).

Background: Long-term exposure to traffic-related air pollution may increase blood pressure (BP) and induce hypertension. However, evidence supporting these associations is limited, and they may be confounded by exposure to traffic noise and biased due to inappropriate control for use of BP-lowering medications. Objectives: We evaluated the associations of long-term traffic-related air pollution with BP and prevalent hypertension, adjusting for transportation noise and assessing different methodologies to control for BP-lowering medications. Methods: We measured systolic (SBP) and diastolic BP (DBP) at baseline (years 2003–2005) in 3,700 participants, 35–83 years of age, from a population-based cohort in Spain. We estimated home outdoor annual average concentrations of nitrogen dioxide (NO2) with a land-use regression model. We used multivariate linear and logistic regression. Results: A 10-μg/m3 increase in NO2 levels was associated with 1.34 mmHg (95% CI: 0.14, 2.55) higher SBP in nonmedicated individuals, after adjusting for transportation noise. Results were similar in the entire population after adjusting for medication, as commonly done, but weaker when other methods were used to account for medication use. For example, when 10 mmHg were added to the measured SBP levels of medicated participants, the association was β = 0.78 (95% CI: –0.43, 2.00). NO2 was not associated with hypertension. Associations of NO2 with SBP and DBP were stronger in participants with cardiovascular disease, and the association with SBP was stronger in those exposed to high traffic density and traffic noise levels ≥ 55 dB(A). Conclusions: We observed a positive association between long-term exposure to NO2 and SBP, after adjustment for transportation noise, which was sensitive to the methodology used to account for medication. Citation: Foraster M, Basagaña X, Aguilera I, Rivera M, Agis D, Bouso L, Deltell A, Marrugat J, Ramos R, Sunyer J, Vila J, Elosua R, Künzli N. 2014. Association of long-term exposure to traffic-related air pollution with blood pressure and hypertension in an adult population–based cohort in Spain (the REGICOR study). Environ Health Perspect 122:404–411; http://dx.doi.org/10.1289/ehp.1306497

Measurement Error in Epidemiologic Studies of Air Pollution Based on Land-Use Regression Models

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Effect of fireworks events on urban background trace metal aerosol concentrations: is the cocktail worth the show?

We report on the effect of a major firework event on urban background atmospheric PM(2.5) chemistry, using 24-h data collected over 8 weeks at two sites in Girona, Spain. The firework pollution episode (Sant Joan fiesta on 23rd June 2008) measured in city centre parkland increased local background PM(2.5) concentrations as follows: Sr (x86), K (x26), Ba (x11), Co (x9), Pb (x7), Cu (x5), Zn (x4), Bi (x4), Mg (x4), Rb (x4), Sb (x3), P (x3), Ga (x2), Mn (x2), As (x2), Ti (x2) and SO(4)(2-) (x2). Marked increases in these elements were also measured outside the park as the pollution cloud drifted over the city centre, and levels of some metals remained elevated above background for days after the event as a reservoir of metalliferous dust persisted within the urban area. Transient high-PM pollution episodes are a proven health hazard, made worse in the case of firework combustion because many of the elements released are both toxic and finely respirable, and because displays commonly take place in an already polluted urban atmosphere.

Air pollution and atherosclerosis: a cross-sectional analysis of four European cohort studies in the ESCAPE study.

Background: In four European cohorts, we investigated the cross-sectional association between long-term exposure to air pollution and intima-media thickness of the common carotid artery (CIMT), a preclinical marker of atherosclerosis. Methods: Individually assigned levels of nitrogen dioxide, nitrogen oxides, particulate matter ≤ 2.5 μm (PM2.5), absorbance of PM2.5 (PM2.5abs), PM10, PMcoarse, and two indicators of residential proximity to highly trafficked roads were obtained under a standard exposure protocol (European Study of Cohorts for Air Pollution Effects—ESCAPE study) in the Stockholm area (Sweden), the Ausburg and Ruhr area (Germany), and the Girona area (Spain). We used linear regression and meta-analyses to examine the association between long-term exposure to air pollution and CIMT. Results: The meta-analysis with 9,183 individuals resulted in an estimated increase in CIMT (geometric mean) of 0.72% (95% CI: –0.65%, 2.10%) per 5-μg/m3 increase in PM2.5 and 0.42% (95% CI: –0.46%, 1.30%) per 10–5/m increase in PM2.5abs. Living in proximity to high traffic was also positively but not significantly associated with CIMT. Meta-analytic estimates for other pollutants were inconsistent. Results were similar across different adjustment sets and sensitivity analyses. In an extended meta-analysis for PM2.5 with three other previously published studies, a 0.78% (95% CI: –0.18%, 1.75%) increase in CIMT was estimated for a 5-μg/m3 contrast in PM2.5. Conclusions: Using a standardized exposure and analytical protocol in four European cohorts, we found that cross-sectional associations between CIMT and the eight ESCAPE markers of long-term residential air pollution exposure did not reach statistical significance. The additional meta-analysis of CIMT and PM2.5 across all published studies also was positive but not significant. Citation: Perez L, Wolf K, Hennig F, Penell J, Basagaña X, Foraster M, Aguilera I, Agis D, Beelen R, Brunekreef B, Cyrys J, Fuks KB, Adam M, Baldassarre D, Cirach M, Elosua R, Dratva J, Hampel R, Koenig W, Marrugat J, de Faire U, Pershagen G, Probst-Hensch NM, de Nazelle A, Nieuwenhuijsen MJ, Rathmann W, Rivera M, Seissler J, Schindler C, Thiery J, Hoffmann B, Peters A, Künzli N. 2015. Air pollution and atherosclerosis: a cross-sectional analysis of four European cohort studies in the ESCAPE Study. Environ Health Perspect 123:597–605; http://dx.doi.org/10.1289/ehp.1307711

Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a “GIS-only” model, which considered only predictor variables derived with Geographic Information Systems; and (b) a “Best” model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R2) was 0.66–0.87 for “GIS-only” models, and 0.70–0.89 for “Best” models. Short-term noise measurements showed a high correlation (r=0.62–0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.

0238 Lung cancer and exposure to benzene, toluene and xylene: results from two case-control studies in Montreal

Objectives We aimed to evaluate the risks of lung cancer associated with exposure to benzene, to toluene and to xylene. Method Two population-based case-control studies conducted in Montreal included 1896 lung cancer cases and 1908 controls. Study I was conducted in 1980–1986, and study II in 1995–2001. Occupational exposures were assessed using a combination of subject-reported job history and expert assessment. Participants provided information on sociodemographic characteristics and smoking history. Using logistic regression, we evaluated the risk of lung cancer due to the exposure to each agent. Results Lifetime exposure prevalence ranged from 12% for xylene to 20% for benzene in study I, and 11% for xylene to 15% for benzene in study II. In both studies, 25% of the participants were exposed to benzene, toluene or xylene. Pooling studies, the odds ratios and 95% confidence interval (OR) for ever-exposure to any of the evaluated agents was 1.2 (1.0–1.4). In analyses including all subjects but only one agent at a time in the models, ORs were around 1.2–1.3 for each agent. When we excluded subjects ever exposed to two or three of these agents, none of the agents showed excess risk. Being ever exposed to all three agents was associated with lung cancer (OR: 1.3; 1.0–1.6). Attempts to estimate ORs for each agent while controlling for the two others resulted in co-linearity. Conclusions We found no clear indications of an association between lung cancer and exposure to toluene or xylene, but there was some evidence for an association with benzene.

The association between air pollution and subclinical atherosclerosis: Rivera et al. respond.

We appreciate Kawada’s interest in our study (Rivera et al. 2013). His letter focuses on some limitations of our study, which were already discussed in our article. Kawada notes that the small number of participants with low ankle–brachial index (ABI) provided low power to detect an association; this is a limitation that we acknowledged in our article (Rivera et al. 2013). Kawada also points out that the number of covariates in the model exceeds the common rule of thumb of having at least 10 events per variable. We recognize that, with a small number of cases, one is inevitably faced with the trade-off between including all relevant confounders and keeping the number of covariates to a minimum. However, in our article we provided results from a minimally adjusted model including only five confounders and an interaction term. In this model, the number of events per variable was > 10, and the results were not significantly different from those of the fully adjusted model. Kawada suggests selecting a “higher cut-off value of ABI, such as 1.0” for low ABI given that “an ABI of 0.9–1.0 is also associated with cardiovascular risk (Ono et al. 2003).” The findings of Ono et al. were for patients on hemodialysis due to end-stage renal disease and thus cannot be extrapolated to healthy population samples such as the one considered in our study (i.e., with no history or current signs of cardiovascular disease). We selected a cut-off value of 0.9 for low ABI because of the strong evidence of increased risks of incident cardiovascular disease, morbidity, and mortality in individuals with ABI < 0.9 (Allison et al. 2008; Ankle Brachial Index Collaboration 2008; Lee et al. 2004; McDermott et al. 2005). A cut-off value of 0.9 is also more specific (Lee et al. 2004) and much more common in the literature, which allows comparison with other studies. Kawada’s second argument involves the consideration of multicollinearity in the fully adjusted model (model 2), in which systolic and diastolic blood pressure were included. Systolic and diastolic blood pressure were only moderately correlated (correlation coefficient, 0.62), and according to the variance inflation factor (VIF), there were no multicollinearity problems (VIF was 2.45 for systolic blood pressure and 1.98 for diastolic blood pressure). Kawada points at “contradictory results” in the association between air pollution and carotid intima media thickness (IMT), mainly based on the null and weak associations found by Lenters et al. (2010) between several markers of air pollution [nitrogen dioxide, sulfur dioxide, PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), black smoke, and traffic intensity] and three indicators of vascular damage (IMT, pulse wave velocity, and augmentation index). We consider, however, that these do not constitute results contradictory to the positive association between air pollution and subclinical markers of atherosclerosis found in the six studies thoroughly discussed in our article (Bauer et al. 2010; Diez Roux et al. 2008; Hoffmann et al. 2007, 2009; Kunzli et al. 2005, 2010) as well as by Wilker et al. (2013). The study by Lenters et al. (2010) involved a cohort of young adults, on average 28 years of age. Exposure to air pollution was estimated at the current address only. As the authors acknowledged, the young age of participants and the exposure misclassification, which resulted from exposure estimated at the current address only, are likely explanations for their mixed results. Finally, we agree with Kawada’s closing remark on the need for longitudinal studies, as we concluded in our article.