4-year PhD Studentship: Using supermarket loyalty cards data to study dietary patterns associated with known risk factors for cancer

The incidence of early-onset cancer has surged by nearly 80% since 1990, with high and middle-income countries, including the UK, experiencing the most significant increase. Dietary choices have been identified as the primary risk factor associated with this rise. Previous research has primarily focused on specific foods such as fruits, vegetables, sugary snacks, and red meats, but data collection has often relied on self-reported questionnaires, introducing potential biases.

To address these limitations, the proposed research aims to leverage shopping history records collected through supermarket loyalty card programs. In the UK, where 85% of families purchase groceries from supermarkets, these records offer a promising alternative for studying dietary patterns and their links to cancer outcomes and risk factors. Although shopping history data provide granular, objective, and longitudinal insights into lifestyle behaviours and risk factors, they are not without their biases, including the gap between purchase and consumption.

Despite these challenges, commercially collected transactional records represent a unique opportunity to gain time-series objective information on dietary habits at the population level. This research project will focus on assessing the utility of shopping history data for cancer research, aiming to enhance our understanding of the relationship between dietary choices and known risk factors for cancer.

Aims and objectives

This research aims to assess the value of shopping history data in understanding the relationship between dietary choices and cancer risk factors. The project will:

1. Analyze shopping data to identify dietary patterns over time.
2. Link patterns in shopping data with known risk factors for cancer through cross-sectional samples.
3. Analyse patterns in the shopping data which are linked to known risk factors on national datasets of supermarkets
4. Address potential data biases.
5. Provide policy and health recommendations for cancer prevention.
6. Disseminate findings to inform both the scientific community and the public.

Methodology

The project will use both cross-sectional data collection where participants will be requested to donate their shopping data and respond to surveys about their diet (e.g., via food frequency questionnaire; and anonymous population level data access from industry partners. The student will utilize standard statistical methods to analyze shopping data (e.g., regression) as well as machine learning categorisation methods with the aim to identifying shopping data patterns related to dietary choices. The project will identify and mitigate potential biases, such as time gaps between purchase and consumption, to ensure the validity of research findings.

The student will acquire skills in working with very large dataset that include data cleaning and curation, as well as advance data analytic skills. They will enhance their programming skills and knowledge of standard statistical methods.

Supervisors: Prof Richard Martin (primary supervisor), Dr Anya Skatova, nan

Funding available

This project is open for Bristol PGR scholarship applications (closing date 1st December 2023)

The Bristol PGR scholarship funds tuition fees, the costs of carrying out your research and a maintenance stipend (at the minimum UKRI rate) for the duration of a PhD (four years).

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol. Use this information to search for the relevant programme in our online application system.

Please visit the Faculty of Health Sciences website for details of how to apply, the information you must include in your application, and for information about our online Application Workshop to help you submit a competitive application.