Dr Shaddick is a Reader in Statistics in the Department of Mathematical Sciences at the University of Bath. He has a PhD from Imperial College London in statistics and epidemiology and a Masters from University College London in applied stochastic systems. His research interests include the theory and application of Bayesian statistics to the areas of spatial epidemiology, environmental health risk and the modelling of spatio-temporal fields of environmental hazards. He was a co-author of the Oxford Handbook of Epidemiology for Clinicians which was Highly Commended in the Basis of Medicine Category, BMA Book Awards 2013. Together with Jim Zidek, he has a recently written a book `Spatio-temporal methods in environmental epidemiology’ which will be published in July 2015.
Smog, smoke and standards: modelling the effects of air pollution in space and time
From the famous London smogs in the 1950s to air quality in megacities today, the potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support environmental policy and to estimaterisks of environmental hazards on human health study there is a requirement for accurate estimates of exposures that might be experienced by the populations at risk. For epidemiological studies these exposures must be linked to health outcomes but health and exposure data may not match at all locations in space and time. In such cases a direct comparison of exposures and health outcomes is often not possible without an underlying model to align the two in the spatial and temporal domains. In addition, there may be periods of missing data and preferential sampling, where monitoring locations in environmental networks may be located in areas where levels are expected to be high. Biased estimates of exposures may lead to biased estimates of risk. The Bayesian approach provides a natural framework for modelling complexities within exposure data and provides a means of incorporating the results within health models. However, the large amounts of data that can arise from environmental networks mean that inference using MCMC may not be computational feasible. Here we use Integrated Nested Laplace Approximations (INLA) to implement spatio-temporal exposure models and show how the results can be used to reduce the potential biases that may occur when estimating levels of air pollution and the associated risks to human health.