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

Copyright @ 2009 Gryparis et al - Published by Oxford University Press.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.This research was supported by NIEHS grants ES012044 (AG, BAC), ES009825 (JS, BAC), ES007142 (CJP), and ES000002 (CJP), and EPA grant R-832416 (JS, BAC)

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling 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 conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear 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 penalised spline formation of the model that relates to generalised kringing 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 degress 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 separatel

Medium-Term Exposure to Traffic-Related Air Pollution and Markers of Inflammation and Endothelial Function

Bac k g r o u n d: 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. Me t h o d s: 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. Re s u l t s: 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 association

Πίνακες Bézout και εφαρμογές

Οι πίνακες χρησιμοποιούνται ευρέως σε πολλούς κλάδους των μαθηματικών, ειδικότερα στη Γραμμική Άλγεβρα και την Αριθμητική Ανάλυση. Μια γνωστή, συχνή και απλή εφαρμογή των πινάκων είναι στην επίλυση συστήματος γραμμικών εξισώσεων. Αν ένας πίνακας είναι τετραγωνικός, είναι δυνατόν να συμπεράνουμε μερικές από τις ιδιότητές του υπολογίζοντας την ορίζουσα του. Αν ο πίνακας είναι συμμετρικός, τότε έχουμε επιπλέον σημαντικές ιδιότητες. Η παρούσα διπλωματική εργασία σχετίζεται με τους πίνακες Bézout και τις εφαρμογές τους. Το σημαντικό στοιχείο αυτών των πινάκων είναι πως είναι συμμετρικοί, που μας δίνει μεγάλο πλεονέκτημα στις εφαρμογές τους συγκριτικά με άλλους πίνακες, και μας μειώνει το χρόνο εκτέλεσης υπολογισμών (πολυπλοκότητα). Στο πρώτο μέρος παρουσιάζονται τα μαθηματικά εργαλεία, τα οποία είναι χρήσιμα για τον υπολογισμό και τις εφαρμογές των πινάκων Bézout . Στο δεύτερο μέρος παρουσιάζουμε αναλυτικά τους πίνακες Bézout, δίνοντας τον ορισμό, θεωρητικά και αριθμητικά παραδείγματα, τις ιδιότητές του και τις συναρτήσεις υπολογισμού αυτών των πινάκων, μέσω των αριθμητικών υπολογιστικών περιβάλλοντων Matlab (έκδοση R2015a) και Maple (έκδοση 2016). Το τρίτο μέρος αποτελείται από πολλά θεωρήματα, τα οποία συνδέουν τους πίνακες Bézout με τον Μέγιστο Κοινό Διαιρέτη (GCD) δύο πολυωνύμων μίας μεταβλητής, όπου μέσω των πινάκων βρίσκουμε τους συντελεστές του GCD. Επιπλέον δίνονται πολλά αριθμητικά παραδείγματα προς επαλήθευση των θεωρημάτων. Στα δύο τελευταία μέρη δίνονται πολλές αριθμητικές εφαρμογές, τα συμπεράσματα της παρούσας εργασίας και οι χρήσεις των πινάκων Bézout και του GCD δύο πολυωνύμων. Αξίζει να αναφερθεί ότι ο στόχος είναι η εύρεση του GCD δύο πολυωνύμων μέσω των πινάκων Bézout να είναι της τάξης O(n^2).The matrices are widely used in many fields of mathematics, especially in Linear Algebra and Arithmetic Analysis. A well-known and simple application of the matrices is to solve a system of linear equations. If a matrix is square, it is possible to deduce some of the properties of calculating its determinant. If the matrix is symmetric, then we have additional important properties. This thesis researches Bézout matrices and their applications. The important element of these matrices is that they are symmetric, which it gives us a great advantage in applications comparing to other matrices. In addition these matrices reduce our complexity. The first part presents the mathematical tools, which are useful for the calculation and applications of Bézout matrices. In the second part we present the definition of Bézout matrices, theoretical and numerical examples and the properties. Finally, the calculation functions of these tables via two numerical computing environments, Matlab (version R2015a) and Maple (version 2016) are introduced. The third part consists of several theorems which connect the Bézout matrices with the Greatest Common Divisor (GCD) of two univariate polynomials. It is possible to calculate both the degree and the coefficients of GCD via the application of these theorems. Furthermore many examples are provided to verify these theorems. In the final two sections a number numerical applications are presented, the conclusions of this thesis, and the use of both these matrices and GCD of two univariate polynomials. It is worth mentioning that the goal is to calculate the GCD via Bézout matrix so that the complexity will be of the order O(n^2)

Atmospheric circulation types and daily mortality in Athens, Greece.

We investigated the short-term effects of synoptic and mesoscale atmospheric circulation types on mortality in Athens, Greece. The synoptic patterns in the lower troposphere were classified in 8 a priori defined categories. The mesoscale weather types were classified into 11 categories, using meteorologic parameters from the Athens area surface monitoring network; the daily number of deaths was available for 1987-1991. We applied generalized additive models (GAM), extending Poisson regression, using a LOESS smoother to control for the confounding effects of seasonal patterns. We adjusted for long-term trends, day of the week, ambient particle concentrations, and additional temperature effects. Both classifications, synoptic and mesoscale, explain the daily variation of mortality to a statistically significant degree. The highest daily mortality was observed on days characterized by southeasterly flow [increase 10%; 95% confidence interval (CI), 6.1-13.9% compared to the high-low pressure system), followed by zonal flow (5.8%; 95% CI, 1.8-10%). The high-low pressure system and the northwesterly flow are associated with the lowest mortality. The seasonal patterns are consistent with the annual pattern. For mesoscale categories, in the cold period the highest mortality is observed during days characterized by the easterly flow category (increase 9.4%; 95% CI, 1.0-18.5% compared to flow without the main component). In the warm period, the highest mortality occurs during the strong southerly flow category (8.5% increase; 95% CI, 2.0-15.4% compared again to flow without the main component). Adjusting for ambient particle levels leaves the estimated associations unchanged for the synoptic categories and slightly increases the effects of mesoscale categories. In conclusion, synoptic and mesoscale weather classification is a useful tool for studying the weather-health associations in a warm Mediterranean climate situation

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects

Traffic particles and occurrence of acute myocardial infarction: a case–control analysis

OBJECTIVES: We modelled exposure to traffic particles using a latent variable approach and investigated whether long-term exposure to traffic particles is associated with an increase in the occurrence of acute myocardial infarction (AMI) using data from a population-based coronary disease registry. METHODS: Cases of individually validated AMI were identified between 1995 and 2003 as part of the Worcester Heart Attack Study. Population controls were selected from Massachusetts, USA, resident lists. NO(2) and PM(2.5) filter absorbance were measured at 36 locations throughout the study area. The air pollution data were used to estimate exposure to traffic particles using a semiparametric latent variable regression model. Conditional logistic models were used to estimate the association between exposure to traffic particles and occurrence of AMI. RESULTS: Modelled exposure to traffic particles was highest near the city of Worcester. Cases of AMI were more exposed to traffic and traffic particles compared to controls. An interquartile range increase in modelled traffic particles was associated with a 10% (95% CI 4% to 16%) increase in the odds of AMI. Accounting for spatial dependence at the census tract, but not block group, scale substantially attenuated this association. CONCLUSIONS: These results provide some support for an association between long-term exposure to traffic particles and risk of AMI. The results were sensitive to the scale selected for the analysis of spatial dependence, an issue that requires further investigation. The latent variable model captured variation in exposure, although on a relatively large spatial scale

Long-term Exposure to Black Carbon and Carotid Intima-Media Thickness: The Normative Aging Study

Background: Evidence suggests that air pollution is associated with atherosclerosis and that traffic-related particles are a particularly important contributor to the association. Objectives: We investigated the association between long-term exposure to black carbon, a correlate of traffic particles, and intima-media thickness of the common carotid artery (CIMT) in elderly men residing in the greater Boston, Massachusetts, area. Methods: We estimated 1-year average exposures to black carbon at the home addresses of Normative Aging Study participants before their first CIMT measurement. The association between estimated black carbon levels and CIMT was estimated using mixed effects models to account for repeated outcome measures. In secondary analyses, we examined whether living close to a major road or average daily traffic within 100 m of residence was associated with CIMT. Results: There were 380 participants (97% self-reported white race) with an initial visit between 2004 and 2008. Two or three follow-up CIMT measurements 1.5 years apart were available for 340 (89%) and 260 (68%) men, respectively. At first examination, the average ± SD age was 76 ± 6.4 years and the mean ± SD CIMT was 0.99 ± 0.18 mm. A one-interquartile range increase in 1-year average black carbon (0.26 µg/m3) was associated with a 1.1% higher CIMT (95% CI: 0.4, 1.7%) based on a fully adjusted model. Conclusions: Annual mean black carbon concentration based on spatially resolved exposure estimates was associated with CIMT in a population of elderly men. These findings support an association between long-term air pollution exposure and atherosclerosis. Citation: Wilker EH, Mittleman MA, Coull BA, Gryparis A, Bots ML, Schwartz J, Sparrow D. 2013. Long-term exposure to black carbon and carotid intima-media thickness: the Normative Aging Study. Environ Health Perspect 121:1061–1067; http://dx.doi.org/10.1289/ehp.1104845 [Online 2 July 2013

The effects of particulate and ozone pollution on mortality in Moscow, Russia

The objectives of this study were (1) to evaluate how acute mortality responds to changes in particulate and ozone (O3) pollution levels, (2) to identify vulnerable population groups by age and cause of death, and (3) to address the problem of interaction between the effects of O3 and particulate pollution. Time-series of daily mortality counts, air pollution, and air temperature were obtained for the city of Moscow during a 3-year period (2003–2005). To estimate the pollution-mortality relationships, we used a log-linear model that controlled for potential confounding by daily air temperature and longer term trends. The effects of 10 μg/m3 increases in daily average measures of particulate matter ≤10 μm in aerodynamic diameter (PM10) and O3 were, respectively, (1) a 0.33% [95% confidence interval (CI) 0.09–0.57] and 1.09% (95% CI 0.71–1.47) increase in all-cause non-accidental mortality in Moscow; (2) a 0.66% (0.30–1.02) and 1.61% (1.01–2.21) increase in mortality from ischemic heart disease; (3) a 0.48% (0.02–0.94) and 1.28% (0.54–2.02) increase in mortality from cerebrovascular diseases. In the age group >75 years, mortality increments were consistently higher, typically by factor of 1.2 – 1.5, depending upon the cause of death. PM10-mortality relationships were significantly modified by O3 levels. On the days with O3 concentrations above the 90th percentile, PM10 risk for all-cause mortality was threefold greater and PM10 risk for cerebrovascular disease mortality was fourfold greater than the unadjusted risk estimate