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Activity Number: 116
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #317487 View Presentation
Title: The Effects of Exposure Misspecification in Spatio-Temporal Epidemiological Studies
Author(s): Gavin Shaddick* and James Zidek and Yi Liu
Companies: University of Bath and The University of British Columbia and University of Bath
Keywords: spatio-temporal modelling ; epidemiology ; preferential sampling ; INLA ; high-dimensional data
Abstract:

In order to estimate risks of environmental hazards on human health there is a requirement for accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and often the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. In addition, there may be 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, however the large amounts of data that can arise from environmental networks mean that inference using MCMC might not be computational feasible. Here we use Integrated Nested Laplace Approximation (INLA) to implement spatio-temporal exposure models.


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