Until recently, deterministic methods have been used to estimate food risk. These methods give one risk estimate for the whole population, mostly ignore the variability in the consumption data and produce unrealistic results. Recently there has been an increase in the use of probabilistic models for food risk assessment. Some of these probabilistic models are purely based on empirical distributions and others on probability models in a frequentist or Bayesian framework. In this thesis we improve on existing Bayesian models for food consumption data collected on successive days, and propose possible models to assess various types of food risk. Bayesian hierarchical modelling provides a natural framework for risk assessment, and allows us to account for the various sources of variability present in the data. We discuss general problems associated for the various sources of variability present in the data. We discuss general problems associated with dietary data such as large proportions of zeros and extreme intakes, and suggest models to account for these. We look at ways to model intake of several food products which may all contain the same pesticide, and continue this with pesticide residue data for exposure assessment for that pesticide. We also discuss a non-Bayesian approach to study extreme intakes using Extreme-value theory. We use our models to produce predicted probabilities of exceeding recommended and safe levels of consumption for individual days and longer periods.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:642763 |
| Date | January 2005 |
| Creators | Chatterjee, Ayona |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/14388 |
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