Article

# Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality.

Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, 680 North Lake Shore Drive, Suite 1102, Chicago, Illinois 60611, USA.

Biometrics (Impact Factor: 1.41). 05/2008; 65(1):282-91. DOI: 10.1111/j.1541-0420.2007.01039.x Source: PubMed

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**ABSTRACT:**As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.Environmental Health 10/2013; 12(1):85. · 2.71 Impact Factor -
##### Article: Mortality due to myocardial infarction in the Bavarian population during World Cup Soccer 2006.

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**ABSTRACT:**Previously, we had demonstrated that the World Cup Soccer 2006 provoked levels of emotional stress sufficient to increase the incidence of acute cardiovascular events. We sought to assess whether mortality was also increased as a result. We analyzed daily data on mortality due to myocardial infarction (MI) and total mortality using data from the Bavarian State Office for Statistics. We retrospectively assessed study periods from 2006, 2005 and 2003. Quasi-Poisson regression with a log link to model the number of daily deaths was used. To be able to account for a possible delay, we also fitted a cubic distributed lag quasi-Poisson model for both 1 and 2 weeks post-exposure. A total of 6,699 deaths due to MI were investigated. No increase in death was found on days of World Cup matches either with or without German participation compared to the matched control periods. In addition, none of the analyses showed a significant effect of the (lagged) exposure to the risk period. Likewise, total mortality rates remained unchanged over the entire period of our analysis. During World Cup Soccer, the number of deaths due to myocardial infarction was not measurably increased compared to a matched control period. Thus, we could not demonstrate a translation of a stress-induced increase of cardiac morbidity into a noticeable increase in mortality. However, our findings are based on a public mortality registry, which may be flawed in many ways, regarding ascertainment of causes of death, in particular.Clinical Research in Cardiology 03/2011; 100(9):731-6. · 3.67 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Summary The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We specify modified formulations of DLMs to provide computationally attractive, flexible varying-coefficient models that are applicable in any setting in which lagged covariates are regressed on a time-dependent response. We investigate the application of such models to rainfall and river flow and in particular their role in understanding the impact of hidden variables at work in river systems. We apply two models to data from a Scottish mountain river, and we fit to some simulated data to check the efficacy of our model approach. During heavy rainfall conditions, changes in the influence of rainfall on flow arise through a complex interaction between antecedent ground wetness and a time-delay in rainfall. The models identify subtle changes in responsiveness to rainfall, particularly in the location of peak influence in the lag structure.Biometrics 02/2013; · 1.41 Impact Factor

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