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 microm 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 microg/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 microg/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.

In the Air Pollution and Health: A European Approach (APHEA2) project, the effects of ambient ozone concentrations on mortality were investigated. Data were collected on daily ozone concentrations, the daily number of deaths, confounders, and potential effect modifiers from 23 cities/areas for at least 3 years since 1990. Effect estimates were obtained for each city with city-specific models and were combined using second-stage regression models. No significant effects were observed during the cold half of the year. For the warm season, an increase in the 1-hour ozone concentration by 10 mug/m3 was associated with a 0.33% (95% confidence interval [CI], 0.17-0.52) increase in the total daily number of deaths, 0.45% (95% CI, 0.22-0.69) in the number of cardiovascular deaths, and 1.13% (95% CI, 0.62-1.48) in the number of respiratory deaths. The corresponding figures for the 8-hour ozone were similar. The associations with total mortality were independent of SO2 and particulate matter with aerodynamic diameter less than 10 mum (PM10) but were somewhat confounded by NO2 and CO. Individual city estimates were heterogeneous for total (a higher standardized mortality rate was associated with larger effects) and cardiovascular mortality (larger effects were observed in southern cities). The dose-response curve of ozone effects on total mortality during the summer did not deviate significantly from linearity.

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.

While studies show that ultrafine and fine particles can be translocated from the lungs to the central nervous system, the possible neurodegenerative effect of air pollution remains largely unexplored. The authors examined the relation between black carbon, a marker for traffic particles, and cognition among 202 Boston, Massachusetts, children (mean age = 9.7 years (standard deviation, 1.7)) in a prospective birth cohort study (1986-2001). Local black carbon levels were estimated using a validated spatiotemporal land-use regression model (mean predicted annual black carbon level, 0.56 mug/m(3) (standard deviation, 0.13)). The Wide Range Assessment of Memory and Learning and the Kaufman Brief Intelligence Test were administered for assessment of cognitive constructs. In analysis adjusting for sociodemographic factors, birth weight, blood lead level, and tobacco smoke exposure, black carbon (per interquartile-range increase) was associated with decreases in the vocabulary (-2.2, 95% confidence interval (CI): -5.5, 1.1), matrices (-4.0, 95% CI: -7.6, -0.5), and composite intelligence quotient (-3.4, 95% CI: -6.6, -0.3) scores of the Kaufman Brief Intelligence Test and with decreases on the visual subscale (-5.4, 95% CI: -8.9, -1.9) and general index (-3.9, 95% CI: -7.5, -0.3) of the Wide Range Assessment of Memory and Learning. Higher levels of black carbon predicted decreased cognitive function across assessments of verbal and nonverbal intelligence and memory constructs.

Short-term changes in ambient particulate matter with aerodynamic diameters < 10 micro m (PM10) have been associated with short-term fluctuations in mortality or morbidity in many studies. In this study, we tested whether those deaths are just advanced by a few days or weeks using a multicity hierarchical modeling approach for all-cause, respiratory, and cardiovascular deaths, for all ages and stratifying by age groups, within the APHEA-2 (Air Pollution and Health: A European Approach) project. We fit a Poisson regression and used an unconstrained distributed lag to model the effect of PM10 exposure on deaths up to 40 days after the exposure. In baseline models using PM10 the day of and day before the death, we found that the overall PM10 effect (per 10 micro g/m3) was 0.74% [95% confidence interval (95% CI), -0.17 to 1.66] for respiratory deaths and 0.69% (95% CI, 0.31-1.08) for cardiovascular deaths. In unrestricted distributed lag models, the effect estimates increased to 4.2% (95% CI, 1.08-7.42) for respiratory deaths and to 1.97% (95% CI, 1.38-2.55) for cardiovascular deaths. Our study confirms that most of the effect of air pollution is not simply advanced by a few weeks and that effects persist for more than a month after exposure. The effect size estimate for PM10 doubles when we considered longer-term effects for all deaths and for cardiovascular deaths and becomes five times higher for respiratory deaths. We found similar effects when stratifying by age groups. These larger effects are important for risk assessment.

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.

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 microM in aerodynamic diameter (PM10) per 10 microg/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.

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modelling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies that were conducted at specific household locations as well as 15 ambient monitoring sites in the area. The models allow for both flexible non-linear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalized spline formulation of the model that relates to generalized kriging of the latent traffic pollution variable and leads to a natural Bayesian Markov chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degrees of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately. Copyright 2007 Royal Statistical Society.