Application of Bayesian Networks to Problems within Obesity Epidemiology

Obesity is a significant public health problem in the United Kingdom and many other parts of the world, including some low-income settings. Although obesity prevalence has been rising for several decades, governments have been slow to implement policies that may have an impact at a population level. Numerous socio-demographic factors have been linked with obesity, but are highly intercorrelated, and identifying relevant factors or at-risk population groups is difficult.This thesis uses a graphical modelling approach, specifically Bayesian networks, to model the joint distribution of socio-demographic factors and obesity related behaviour. The key advantages of graphical models in this context are their ability to model highly correlated data, and to represent complex relationships efficiently as network structure.Three separate pieces of work comprise this thesis. The first uses a sampling technique to identify the networks that best explain the observed data, and employs the common structural features of these networks to infer conditional dependencies present between socio-demographic variables and obesity related behaviour indicators. We find determinants of recreational physical activity differ between males and females, and age and ethnicity have a significant influence on snacking behaviour. The second piece of work usesBayesian networks to build a model of health behaviour given socio demographic input, and then applies this to data from the 2001 census in order to provide an estimate of the health behaviour of a real population. The final analysis uses Bayesian network structure to explore potential determinants of body fat deposition patterns and compares the results tothose derived from a Generalized Linear Model (GLM). Our approach successfully identifies the main determinants, age and Body Mass Index, although is not a genuine alternative due to a lack of sensitivity to less important determinants.Beyond the application to obesity, results of this thesis are of a wider relevance to epidemiology as the field moves towards an increased use of Machine Learning techniques. The work conducted has also met and overcome several technical issues that are likely to be of relevance to others exploring similar approaches. C# Metropolis Hastings implementation available from:http://personalpages.manchester.ac.uk/postgrad/nicholas.harding/njharding_thesis_samplingoverBNtopologies.rar