Project Title: Impact of changes in the food environment on food and drink purchasing using large-scale secondary data
Poor diets are increasingly linked to a range of chronic diseases, including cardiovascular disease, cancer and stroke as well as dental caries, obesity and type 2 diabetes and contribute substantially to excess morbidity, mortality, and rising health-care costs (1,2). Features of the built environment, such as increased access to fast-food and poorer access to supermarkets and grocery stores, have been, in turn linked with poor diets. Although the existing body of research looking at the influence of built environment risks to diet and diet-related health outcomes is substantial, the evidence remains inconsistent and largely consists of studies from the U.S. (3). Research to date has focused on the direct effects on health and has not investigated how the specific elements of the local food environment drives purchases of specific foods and how changes in the local food environment change purchasing behaviours – a key part of the causal pathway. Part of the reason for this is limited availability of high quality data on household food purchases for consumption inside and outside of the home. This data, where available, is often cross-sectional, lacks granularity and is often unable to distinguish in sufficient detail from which outlets foods have been purchased.
The aim of this project is to explore whether different elements of the local food environment are associated with changes in purchasing of food and drinks for consumption both inside and outside homes over time and how these may influence diet and dietary outcomes.
1. To construct novel time-varying relative and absolute measures of the local food environment and matched with data on household food purchasing.
2. Examine cross-sectional and longitudinal associations between features of the local food environment and household food and drink purchasing for consumption both in- and outside of the home.
3. Analyse whether these effects are distributed and whether they are modified by markers of socio-economic position, product price and household composition.
4. Utilise the effects of changes in purchasing behaviour to estimate the effect on diet-related health outcomes.
To achieve these objectives, the PhD will take advantage of access to a unique highly disaggregated large-scale commercial dataset on household food and drink purchases for consumption inside and outside homes (Kantar Worldpanel). The dataset comprises product-level scanner information on food purchases for 30,000 UK households for the period 2012-2017 (c210 million observations). These data will be linked through geocoding with measures of the social and built food environment exposures as well as undertaking georeferenced sentiment analysis of social media data. A key part of the thesis will be the creation of these novel environmental exposures by the student based on existing geospatial data on food businesses (e.g. OS Points of Interest, FSA outlet data). A range of advanced statistical and economic models will be employed to undertake analyses including the use of longitudinal and spatial regression methods, and methods to undertake health impact modelling.
1. Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 766–81.
2. Dobbs R, Sawers C, Thompson F, et al. Overcoming obesity: an initial economic analysis. London: McKinsey & Company, 2014.
3. Cummins S, & Macintyre S. Food environments and obesity: neighbourhood or nation? International Journal of Obesity 2006; 35(1) 100-104
Subject areas: epidemiology, economics, geography, statistics, population health
Keywords: food environment, diet, obesity, neighbourhood, built environment
We invite applications from outstanding and highly motivated students who have a Masters degree in epidemiology, quantitative social/geographical science, GIScience, data science, social/medical statistics, modelling or similar with a substantial quantitative component. An undergraduate degree (1st class honours or 2.1) in a relevant discipline with demonstrable strong quantitative component is desirable.
The studentship is only open to applicants who meet the Home/EU fee rate requirements. For further information about Fee Status Assessment please see the School’s policy & procedure document.
How to Apply
To apply for this studentship, applicants should submit an application for research degree study in the Faculty of Public Health & Policy, via the LSHTM application portal. Under the ‘Funding Section’ of the application form please clearly state ‘Bloomsbury PhD Studentship’. Instead of uploading a research proposal please upload a one-page cover document stating that you are applying for this project, and outlining why you are a suitable candidate for the PhD.
The deadline for applications is Monday 8 April 2019.
Incomplete applications will not be considered for this studentship.
For any queries regarding the project please contact the Principal Supervisor: firstname.lastname@example.org.
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