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Particulate Matter Air Pollution (particulate + matter_air_pollution)

Distribution by Scientific Domains

Mathematics and Statistics	66%

Selected Abstracts

Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality

BIOMETRICS, Issue 1 2009
L. J. Welty
Summary A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online. [source]

Analysis of particulate matter air pollution using Markov random field models of spatial dependence

ENVIRONMETRICS, Issue 5-6 2002
Mark S. Kaiser
Abstract Researchers are beginning to realize the need to take spatial structure into account when modeling data on air pollutants. We develop several models for particulate matter in an urban region that allow spatial dependence to be represented in different manners over a time period of one year. The models are based on a Markov random field approach, and a conceptualization of observed data as arising from two random processes, a conditionally independent observation process and a spatially dependent latent pollution process. Optimal predictors are developed for both of these processes, and predictions of the observation process are used for model assessment. Copyright © 2002 John Wiley & Sons, Ltd. [source]

A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2009
Roger D. Peng
Summary., Time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased hospitalizations, typically at a single lag of 0, 1 or 2 days after an air pollution episode. Two important scientific objectives are to understand better how the risk of hospitalization that is associated with a given day's air pollution increase is distributed over multiple days in the future and to estimate the cumulative short-term health effect of an air pollution episode over the same multiday period. We propose a Bayesian hierarchical distributed lag model that integrates information from national health and air pollution databases with prior beliefs of the time course of risk of hospitalization after an air pollution episode. This model is applied to air pollution and health data on 6.3 million enrollees of the US Medicare system living in 94 counties covering the years 1999,2002. We obtain estimates of the distributed lag functions relating fine particulate matter pollution to hospitalizations for both ischaemic heart disease and acute exacerbation of chronic obstructive pulmonary disease, and we use our model to explore regional variation in the health risks across the USA. [source]

Influences of study design and location on the relationship between particulate matter air pollution and birthweight

PAEDIATRIC & PERINATAL EPIDEMIOLOGY, Issue 3 2008
Jennifer D. Parker
Summary A large number of studies have identified a relationship between particulate matter air pollution and birthweight. Although reported associations are small and varied, they have been identified in studies from places around the world. Exposure assignment, covariates and study inclusion criteria vary among studies. To examine the effect of these and other study characteristics on associations between particulate matter and birthweight, US birth records for singletons delivered at 40 weeks gestation in 2001,03 during the months of March, June, September and December were linked to quarterly estimates of pollution exposure, both particulate matter exposure and exposure to multiple pollutants, by county of residence and month of birth. Annual, 9-month and trimester-specific exposures were assigned. Among births linked to particulate matter exposure there was a small association between coarse particle exposure and birthweight (beta ,13 g per 10 µg/m3 increase [95% CI ,18.3 g, ,7.6 g]) after controlling for maternal factors; this association was attenuated slightly and remained statistically significant after further adjustment for contextual factors, year of birth, region, or urban,rural status. The associations were slightly weaker among births linked to multiple pollutant exposure than among births linked to just particulate matter exposure. The association varied markedly by region, ranging from a decrement of 43 g per 10 µg/m3[95% CI ,58.6 g, ,27.6 g] in the north-west to a null association in the south-west. Trimester findings were smaller, yet remained significant and varied regionally. The association between fine particle exposure and birthweight varied considerably, with an overall small positive association that became null after control for region. This study found that wide regional differences in association may contribute to the varied published findings. The association between coarse particle exposure and birthweight appeared robust, if small; fine particles had no overall association with birthweight. [source]