The public health problem of obesity and morbid obesity is one of the concerns and topics of research in the UK (Keaver 2020, Hughes, 2017). Obesity increases the risk of Cardiovascular disease, some cancers, and Type 2 Diabetes (Kivimäki, 2017). Promotion of healthy behaviours via public health policy is an important avenue for public health intervention. In particular, obesity has been linked to take root in childhood years in many cases, and to be linked to deprivation ( and families. The proposed study will provide (i) information to policymakers regarding obesity with quantified impacts of key variables, and (ii) new quantitative methodologies to public health researchers, (iii) inform the decision process in the debate on the banning of multibuy snacks, on national policy on food price regulation and on advertising especially in relation to food targeted to young children These would be useful to assess the impact of (and design) better policies as interventions.
Traditionally the impact of interventions has been measured by monitoring changes in certain indicators (e.g., weight=70Kg). These are measured by capturing a snapshot of the variable at a specific point in time, which is then analysed using mathematical models that quantify intervention impact. There is a unique opportunity to improve this approach by making it more data rich. New technologies have made the collection of longitudinal data easier, making it possible to know the change of a variable over time or its “trajectory”. In addition, the geographic location, for example, the Council associated with a participant is an important dimension to include. The proposed research will develop data and models to use this new data structure (to analyse trajectories in space and time), providing more information content and thus more precision to model obesity in the UK and provide evidence to inform debates such as the proposed banning of multibuy snacks and the advertisement of soft drinks. Methodology The methodology will achieve the following objectives: 1. Data augmentation. This will be achieved by the formulation of a multi-state trajectory dataset using the National Child Development Survey data (this survey has been running from 1946). Trajectories for mental health, BMI, Weight, Height, Diet, Smoking history, Mental Health, and Demographics 2. Model the dependent trajectory in the presence of the explanatory trajectories. 3. To summarize in numerical form the trajectories in terms of fluidity and similarity 4. To summarize in graphical form the trajectories in terms of broad typologies. 5. To generate maps of the distribution (across the UK) of access to weight control services and counselling for obesity.
Degree of recognitionLocal