Alexandros Gryparis

Confounding and Effect Modification in the Short-term Effects of Ambient Particles on Total Mortality: Results from 29 European Cities within the Aphea2 Project

We present the results of the Air Pollution and Health: A European Approach 2 (APHEA2) project on short-term effects of ambient particles on mortality with emphasis on effect modification. We used daily measurements for particulate matter less than 10 &mgr;m in aerodynamic diameter (PM10) and/or black smoke from 29 European cities. We considered confounding from other pollutants as well as meteorologic and chronologic variables. We investigated several variables describing the cities’ pollution, climate, population, and geography as potential effect modifiers. For the individual city analysis, generalized additive models extending Poisson regression, using a smoother to control for seasonal patterns, were applied. To provide quantitative summaries of the results and explain remaining heterogeneity, we applied second-stage regression models. The estimated increase in the daily number of deaths for all ages for a 10 &mgr;g/m3 increase in daily PM10 or black smoke concentrations was 0.6% [95% confidence interval (CI) = 0.4–0.8%], whereas for the elderly it was slightly higher. We found important effect modification for several of the variables studied. Thus, in a city with low average NO2, the estimated increase in daily mortality for an increase of 10 &mgr;g/m3 in PM10 was 0.19 (95% CI = 0.00–0.41), whereas in a city with high average NO2 it was 0.80% (95% CI = 0.67–0.93%); in a relatively cold climate the corresponding effect was 0.29% (95% CI = 0.16–0.42), whereas in a warm climate it was 0.82% (95% CI = 0.69–0.96); in a city with low standardized mortality rate it was 0.80% (95% CI = 0.65–0.95%), and in one with a high rate it was 0.43% (95% CI = 0.24–0.62). Our results confirm those previously reported on the effects of ambient particles on mortality. Furthermore, they show that the heterogeneity found in the effect parameters among cities reflects real effect modification, which is explained by specific city characteristics.

The temporal pattern of mortality responses to air pollution: a multicity assessment of mortality displacement.

Although the association between particulate matter and mortality or morbidity is generally accepted, controversy remains about the importance of the association. If it is due solely to the deaths of frail individuals, which are brought forward by only a brief period of time, the public health implications of the association are fewer than if there is an increase in the number of deaths. Recently, other research has addressed the mortality displacement issue in single-city analysis. We analyzed this issue with a distributed lag model in a multicity hierarchic modeling approach, within the Air Pollution and Health: A European Approach (APHEA-2) study. We fit a Poisson regression model and a polynomial distributed lag model with up to 40 days of delay in each city. In the second stage we combined the city-specific results. We found that the overall effect of particulate matter less than 10 &mgr;M in aerodynamic diameter (PM10) per 10 &mgr;g/m3 for the fourth-degree distributed lag model is a 1.61% increase in daily deaths (95% CI = 1.02–2.20), whereas the mean of PM10 on the same day and the previous day is associated with only a 0.70% increase in deaths (95% CI = 0.43–0.97). This result is unchanged using an unconstrained distributed lag model. Our study confirms that the effects observed in daily time-series studies are not due primarily to short-term mortality displacement. The effect size estimate for airborne particles more than doubles when we consider longer-term effects, which has important implications for risk assessment.

Mortality Risk Associated with Short-Term Exposure to Traffic Particles and Sulfates

Background Many studies have shown that airborne particles are associated with increased risk of death, but attention has more recently focused on the differential toxicity of particles from different sources. Geographic information system (GIS) approaches have recently been used to improve exposure assessment, particularly for traffic particles, but only for long-term exposure. Objectives We analyzed approximately 100,000 deaths from all, cardiovascular, and respiratory causes for the years 1995–2002 using a case–crossover analysis. Methods Estimates of exposure to traffic particles were geocoded to the address of each decedent on the day before death and control days, with these estimates derived from a GIS-based exposure model incorporating deterministic covariates, such as traffic density and meteorologic factors, and a smooth function of latitude and longitude. Results We estimate that an IQR increase in traffic particle exposure on the day before death is associated with a 2.3% increase [95% confidence interval (CI), 1.2 to 3.4%] in all-cause mortality risk. Stroke deaths were particularly elevated (4.4%; 95% CI, −0.2 to 9.3%), as were diabetes deaths (5.7%; 95% CI, −1.7 to 13.7%). Sulfate particles are spatially homogeneous, and using a central monitor, we found that an IQR increase in sulfate levels on the day before death is associated with a 1.1% (95% CI, 0.1 to 2.0%) increase in all-cause mortality risk. Conclusions Both traffic and powerplant particles are associated with increased deaths in Boston, with larger effects for traffic particles.

Measurement error caused by spatial misalignment in environmental epidemiology.

In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Association between traffic-related black carbon exposure and lung function among urban women.

Background Although a number of studies have documented the relationship between lung function and traffic-related pollution among children, few have focused on adult lung function or examined community-based populations. Objective We examined the relationship between black carbon (BC), a surrogate of traffic-related particles, and lung function among women in the Maternal–Infant Smoking Study of East Boston, an urban cohort in Boston, Massachusetts. Methods We estimated local BC levels using a validated spatiotemporal land-use regression model, derived using ambient and indoor monitor data. We examined associations between percent predicted pulmonary function and predicted BC using linear regression, adjusting for sociodemographics (individual and neighborhood levels), smoking status, occupational exposure, type of cooking fuel, and a diagnosis of asthma or chronic bronchitis. Results The sample of 272 women 18–42 years of age included 57% who self-identified as Hispanic versus 43% white, and 18% who were current smokers. Mean ± SD predicted annual BC exposure level was 0.62 ± 0.2 μg/m3. In adjusted analysis, BC (per interquartile range increase) was associated with a 1.1% decrease [95% confidence interval (CI), −2.5% to 0.3%] in forced expiratory volume in 1 sec, a 0.6% decrease (95% CI, −1.9% to 0.6%) in forced vital capacity, and a 3.0% decrease (95% CI, −5.8% to −0.2%) in forced mid-expiratory flow rate. We noted differential effects by smoking status in that former smokers were most affected by BC exposure, whereas current smokers were not affected. Conclusion In this cohort, exposure to traffic-related BC, a component of particulate matter, independently predicted decreased lung function in urban women, when adjusting for tobacco smoke, asthma diagnosis, and socioeconomic status.

Medium-term exposure to traffic-related air pollution and markers of inflammation and endothelial function.

Background Exposure to traffic-related air pollution (TRAP) contributes to increased cardiovascular risk. Land-use regression models can improve exposure assessment for TRAP. Objectives We examined the association between medium-term concentrations of black carbon (BC) estimated by land-use regression and levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1), both markers of inflammatory and endothelial response. Methods We studied 642 elderly men participating in the Veterans Administration (VA) Normative Aging Study with repeated measurements of sICAM-1 and sVCAM-1 during 1999–2008. Daily estimates of BC exposure at each geocoded participant address were derived using a validated spatiotemporal model and averaged to form 4-, 8-, and 12-week exposures. We used linear mixed models to estimate associations, controlling for confounders. We examined effect modification by statin use, obesity, and diabetes. Results We found statistically significant positive associations between BC and sICAM-1 for averages of 4, 8, and 12 weeks. An interquartile-range increase in 8-week BC exposure (0.30 μg/m3) was associated with a 1.58% increase in sICAM-1 (95% confidence interval, 0.18–3.00%). Overall associations between sVCAM-1 and BC exposures were suggestive but not statistically significant. We found a significant interaction with diabetes—where diabetics were more susceptible to the effect of BC—for both sICAM-1 and sVCAM-1. We also observed an interaction with statin use, which was statistically significant for sVCAM-1 and suggestive for sICAM-1. We found no evidence of an interaction with obesity. Conclusion Our results suggest that medium-term exposure to TRAP may induce an increased inflammatory/endothelial response, especially among diabetics and those not using statins.

Residential Proximity to Nearest Major Roadway and Cognitive Function in Community‐Dwelling Seniors: Results from the MOBILIZE Boston Study

To evaluate the association between residential distance to nearest major roadway, as a marker of long‐term exposure to traffic pollution, and cognitive function in older adults.

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

BACKGROUND Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. OBJECTIVES Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. METHODS The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. RESULTS The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. CONCLUSIONS LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Association between long-term exposure to traffic particles and blood pressure in the Veterans Administration Normative Aging Study

Objectives Particulate air pollution is associated with cardiovascular events, but the mechanisms are not fully understood. The main objective was to assess the relationship between long-term exposure to traffic-related air pollution and blood pressure (BP). Methods The authors used longitudinal data from 853 elderly men participating in the Veterans Administration Normative Aging Study, followed during 1996–2008. Long-term average exposures to traffic particles were created from daily predictions of black carbon (BC) exposure at the geocoded address of each subject, using a validated spatiotemporal model based on ambient monitoring at 82 Boston-area locations. The authors examined the association of these exposures with BP using a mixed model. The authors included the following covariates: age, body mass index, smoking, alcohol, fasting glucose, creatinine clearance, use of cardiovascular medication, education, census-level poverty, day of week and season of clinical visit. Results The authors found significant positive associations between 1-year average BC exposure and both systolic and diastolic blood pressure. An IQR increase in 1-year average BC exposure (0.32 μg/m3) was associated with a 2.64 mm Hg increase in systolic blood pressure (95% CI 1.47 to 3.80) and a 2.41 mm Hg increase in diastolic blood pressure (95% CI 1.77 to 3.05). Conclusions Long-term exposure to traffic particles is associated with increased BP, which may explain part of the association with myocardial infarctions and cardiovascular deaths reported in cohort studies.