Air pollution studies such as time series use measures of ambient concentration to approximate aggregate personal exposures. The resulting difference in health effects can be evaluated using survey data on the time people spend in different environments, with differing concentrations of pollutants.
We present a Bayesian hierarchical model that incorporates time activity data to obtain an adjusted distribution of air pollution exposure for categories of individuals (group exposure); its implementation is illustrated using ambient data from large US cities (NMMAPS database) and diaries of daily activities from the CHAD database.
We then use a simulation study to quantify the differences on the relative risks that arise when ambient concentration is used instead of time activity adjusted group exposure.
Keywords: Individual and group exposure; PM10; Bayesian model; Attenuation
Biography: I have a degree in Statistics from the University of Milan-Bicocca (Italy) and a PhD in Applied Statistics from the University of Florence (Italy); during my PhD I started working on Bayesian methods for gene expression data.
In 2005 I moved to England to start a postDoc in Genomics and Biostatistics at the Department of Epidemiology and Biostatistics, Imperial College, London.
In 2007 I started working on environmental statistics and at the beginning of 2010 I became a lecturer in Biostatistics, as part of the MRC-HPA centre for Environment and Health, established in 2009 and held jointly between Imperial College London and King's College London.
My research interests cover mainly Bayesian hierarchical models for environmental health, integration of different sources of data and measurement error models.