4-year PhD Studentship: Using supermarket loyalty cards data for cancer risk factor prediction

Shopping history records, collected via purchases tracked on loyalty cards, can provide a new perspective on lifestyle choices and behaviours and how these relate to health outcomes such as cancer. Shopping history data can provide information, which is otherwise difficult to measure such as granular, population level, objective data on lifestyle behaviours and risk factors (e.g., smoking, alcohol consumption) that can be tracked longitudinally. However, shopping history data also have inherent biases. For example, despite providing details on purchasing habits and basic individual characteristics, patterns in the data could be explained by other factors (e.g., the gap between purchase and consumption). Reliability of health information that is derived from shopping history data can be assessed through integrating these data with detailed self-reports of behaviour collected through more traditional methods like diary studies. This work will improve detection of cancer risk as well assess validity of integrated data sources in risk prediction.

Aims and objectives

The overall aim of this PhD is to integrate supermarket loyalty cards data with conventional epidemiological measures (eg questionnaires, biomarkers, etc) in Avon Longitudinal Study of Parents and Children (ALSPAC) to predict risk factors for cancer. The innovative aspect is the use of supermarket loyalty cards data, which provide higher-density time-series data with different biases from conventional questionnaire/interview data. The ability to predict risk factors using such data could produce novel insights of early cancer symptoms and associated consumption patterns.

Methodology

Identify patterns in standalone shopping history data that can be reflective of consumption association with known risks of cancer (Years 1 – 2). Use statistical methods (e.g., linear and logistic regression) to validate shopping histories patterns through conventional self-report/biomedical data in ALSPAC (Years 2-3). Use statistical and machine learning methods to predict cancer risk factors in the ALSPAC dataset in a sample of thousands of ALSPAC participants as well as standalone supermarket loyalty cards data in population-wide sample of millions of supermarket customers (Years 2-4)

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.

Please visit the Faculty of Health Sciences website for details of how to apply