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Daily Mortality (daily + mortality)
Selected AbstractsBayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily MortalityBIOMETRICS, Issue 1 2009L. 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 the effects of ultrafine particulate matter while accounting for human exposureENVIRONMETRICS, Issue 2 2009B. J. REICH Abstract Particulate matter (PM) has been associated with mortality in several epidemiological studies. The US EPA currently regulates PM10 and PM2.5 (mass concentration of particles with diameter less than 10 and 2.5 µm, respectively), but it is not clear which size of particles are most responsible for adverse heath outcomes. A current hypothesis is that ultrafine particles with diameter less than 0.1 µm are particularly harmful because their small size allows them to deeply penetrate the lungs. This paper investigates the association between exposure to particles of varying diameter and daily mortality. We propose a new dynamic factor analysis model to relate the ambient concentrations of several sizes of particles with diameters ranging from 0.01 to 0.40 µm with mortality. We introduce a Bayesian model that converts ambient concentrations into simulated personal exposure using the EPA's Stochastic Human Exposure and Dose Simulator, and relates simulated exposure with mortality. Using new data from Fresno, CA, we find that the 4-day lag of particles with diameter between 0.02 and 0.08 µm is associated with mortality. This is consistent with the small particles hypothesis. Copyright © 2008 John Wiley & Sons, Ltd. [source] Seasonal confounding and residual correlation in analyses of health effects of air pollutionENVIRONMETRICS, Issue 4 2007Isabella R. Ghement Abstract To investigate the health effects of air pollution via a partially linear model, one must choose an appropriate amount of smoothing for accurate estimation of the linear pollution effects. This choice is complicated by the dependencies between the various covariates and by the potential residual correlation. Most existing approaches to making this choice are inadequate, as they neither target accurate estimation of the linear pollutant effects nor handle residual correlation. In this paper, we illustrate two new adaptive and objective methods for determining an appropriate amount of smoothing. We construct valid confidence intervals for the linear pollutant effects, intervals that account for residual correlation. We use our inferential methods to investigate the same-day effects of PM10 on daily mortality in two data sets for the period 1994 to 1996: one collected in Mexico City, an urban area with high levels of air pollution, and the other collected in Vancouver, British Columbia, an urban area with low levels of air pollution. For Mexico City, our methodology does not detect a PM10 effect. In contrast, for Vancouver, a PM10 effect corresponding to an expected 2.4% increase (95% confidence interval ranging from 0.0% to 4.7%) in daily mortality for every 10,µg/m3 increase in PM10 is identified. Copyright © 2006 John Wiley & Sons, Ltd. [source] Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategyJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 3 2000Francesca Dominici Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution,mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log-linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution,mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM10 (particulate matter less than 10 ,m in aerodynamic diameter) and by performing univariate and bivariate analyses with PM10 and ozone (O3) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM10 for each of the 20 cities by using semiparametric log-linear models. The second stage of the model describes between-city variation in the true relative rates as a function of selected city-specific covariates. We also fit two variations of a spatial model with the goal of exploring the spatial correlation of the pollutant-specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. All posterior distributions from the second stage are estimated by using Markov chain Monte Carlo techniques. In univariate analyses using concurrent day pollution values to predict mortality, we find that an increase of 10 ,g m -3 in PM10 on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92). With adjustment for the O3 level the PM10 -coefficient is slightly higher. The results are largely insensitive to the specific choice of vague but proper prior distribution. The models and estimation methods are general and can be used for any number of locations and pollutant measurements and have potential applications to other environmental agents. [source] The short-term effects of air pollution on daily mortality in four Australian citiesAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 3 2005Rod Simpson Objective: To examine the short-term health effects of air pollution on daily mortality in four Australian cities (Brisbane, Melbourne, Perth and Sydney), where more than 50% of Australians reside. Methods: The study used a similar protocol to APHEA2 (Air Pollution and Health: A European Approach) study and derived single-city and pooled estimates. Results: The results derived from the different approaches for the 1996-99 period showed consistent results for different statistical models used. There were significant effects on total mortality, (RR=1.0284 per 1 unit increase in nephelometry [10 -4. m -1], RR=1.0011 per 1ppb increase in NO2), and on respiratory mortality (RR=1.0022 per 1ppb increase in O3). No significant differences between cities were found, but the NO2 and particle effects may refer to the same impacts. Meta-analyses carried out for three cities yielded estimates for the increase in the daily total number of deaths of 0.2% (-0.8% to 1.2%) for a 10 ,g/m3 increase in PM10 concentration, and 0.9% (-0.7% to 2.5%) for a 10 ,g/m3 increase in PM2.5 concentration. Conclusions: Air pollutants in Australian cities have significant effects on mortality. [source] | ||||||||||