Retail Loyalty Data for Early Cancer Detection: Researchers Launch CLOCS-2 Using Shopping Habits

🔬 Launch of the Cancer Loyalty Card Study 2 (CLOCS-2)

Researchers at Imperial College London have kicked off an exciting new initiative called the Cancer Loyalty Card Study 2, or CLOCS-2, which aims to harness everyday shopping data from retail loyalty cards to uncover early signs of cancer. Led by Professor James Flanagan from the Department of Surgery and Cancer at Imperial, this study builds on promising previous work and could transform how we approach cancer detection. By analyzing patterns in over-the-counter (OTC) medicine purchases—such as painkillers, indigestion remedies, and laxatives—the team hopes to identify subtle shifts in consumer behavior that signal the onset of disease months before traditional symptoms prompt a doctor's visit.

The study, funded by Cancer Research UK, involves partnerships with major UK retailers Boots and Tesco, whose Advantage Card and Clubcard programs provide a treasure trove of anonymized data. Over a quarter of the UK population holds a Boots Advantage Card, making this data incredibly representative. CLOCS-2 is recruiting nearly 3,000 volunteers across the UK: 1,450 individuals recently diagnosed with one of ten specific cancers and an equal number of healthy controls. Participants, sourced through general practitioners (GPs) and the National Institute for Health and Care Research's (NIHR) Be Part of Research platform, will share up to six years of their loyalty card purchase history.

This isn't science fiction; it's grounded in real-world data science applied to public health. Imagine your weekly shop at the supermarket revealing health insights that could save lives. Professor Flanagan emphasizes the revolutionary potential: "This study ultimately has the potential to revolutionise how we can use everyday data to understand and improve people’s health."

📜 Building on the Success of CLOCS-1: Lessons from Ovarian Cancer

The foundation for CLOCS-2 comes from the original Cancer Loyalty Card Study (CLOCS-1), which focused solely on ovarian cancer. That feasibility study, also led by Imperial College London, examined nearly 300 women and found striking differences in shopping habits. Women who later received an ovarian cancer diagnosis purchased significantly more pain relief and indigestion medications—up to eight months before their formal diagnosis—compared to healthy controls.

These findings were free from recall bias because the data was prospective and captured automatically through loyalty cards. For instance, combined purchases of painkillers like paracetamol or ibuprofen alongside antacids like Gaviscon or Rennie showed elevated patterns in cases. This proof-of-concept demonstrated that self-medication for vague symptoms—such as bloating, abdominal discomfort, or persistent fatigue—leaves a digital footprint in retail data.

CLOCS-1 faced challenges like participant verification for data access under General Data Protection Regulation (GDPR) compliance, but retention rates were solid at around 88% for cases. It paved the way for larger-scale validation, highlighting the feasibility of using transactional data to study pre-diagnostic behaviors without relying on self-reported surveys.

🛒 How the Study Works: Methods and Data Analysis

At its core, CLOCS-2 is a retrospective observational case-control study. Here's how it unfolds:

• Recruitment and Consent: Volunteers complete a brief online questionnaire via REDCap about health, lifestyle, and clinical history. They then authorize access to their Boots and Tesco loyalty card data.
• Data Extraction: Retailers provide anonymized transactional records spanning up to six years, focusing on OTC products linked to cancer symptoms.
• Comparison: Researchers compare purchasing patterns between cancer cases and controls to pinpoint differences. Key metrics include frequency, volume, and timing of buys.
• Threshold Definition: The goal is to establish a 'purchasing threshold'—a quantifiable benchmark that flags potential cancer-related changes—while identifying specific products per cancer type.

Advanced analytics, likely involving machine learning algorithms, will detect trends invisible to the naked eye. Collaborators from the University of Birmingham, University of Nottingham, and University of Lancashire bring expertise in statistics, epidemiology, and oncology. All data handling adheres strictly to GDPR, with ethical approvals ensuring privacy.

For more on the study protocol, check the official details on ClinicalTrials.gov.

🎯 The Ten Cancers in Focus and Their Subtle Symptoms

CLOCS-2 targets ten hard-to-detect cancers that often masquerade with non-specific symptoms managed via OTC remedies. These 'hard-to-spot' cancers account for significant mortality because diagnosis comes too late. Here's the list:

• Bladder cancer (e.g., urinary pain relievers)
• Bowel (colorectal) cancer (laxatives, anti-diarrheals)
• Endometrial cancer (pain meds for pelvic discomfort)
• Liver cancer (indigestion, fatigue aids)
• Oesophageal cancer (antacids for reflux)
• Ovarian cancer (painkillers, indigestion tablets)
• Pancreatic cancer (digestive enzymes, pain relief)
• Stomach (gastric) cancer (anti-acids, nausea remedies)
• Uterine cancer (similar to endometrial)
• Vulval cancer (topical creams, pain relief)

These symptoms—indigestion, bloating, fatigue, bowel changes—affect millions annually and are rarely immediately alarming. Self-medication delays GP visits, but aggregated loyalty data could change that by spotting population-level signals.

Explore the full project scope at Imperial College London's announcement.

📈 Potential Impact: Earlier Detection and Better Outcomes

If CLOCS-2 succeeds, it could usher in a new era of opportunistic screening via everyday data. Early-stage cancers have survival rates up to 90% for some types, plummeting to under 20% if advanced. Detecting signals eight months earlier could prompt timely investigations, saving lives and reducing NHS burdens.

Future phases envision real-time monitoring: consenting shoppers receive digital nudges like "Noticed more antacid buys? Consider a check-up." Dr. Talisia Quallo from Cancer Research UK notes: "Changes in what we shop for... could become a powerful tool to find cancer at an earlier stage."

This aligns with broader health tech trends, where big data meets precision medicine. Boots' Marc Donovan highlights collaboration: "Everyday shopping data... a powerful tool in helping customers spot early healthcare warning signs."

🔒 Privacy, Ethics, and Public Trust

Critics might worry about surveillance, but CLOCS-2 prioritizes ethics. Data is anonymized, used solely for research, and participants control access. GDPR-compliant processes include two-step verification and dynamic consent. Previous studies showed high public willingness to share, with recruitment via trusted channels building trust.

Retailers like Tesco emphasize responsible use: "We hope... more lives can be saved by detecting certain cancers early." Balancing innovation with privacy will be key to scaling.

Read Boots' perspective in their support statement.

🎓 Career Opportunities in Health Data Research

This study exemplifies the growing intersection of data science, oncology, and public health—fields ripe for innovation. Researchers with skills in epidemiology, machine learning, and bioinformatics are in demand. For those eyeing academia or industry, opportunities abound in analyzing real-world evidence for disease prediction.

Consider roles in clinical research or higher education, where you could contribute to projects like CLOCS. Platforms like clinical research jobs and research jobs list openings at top universities. Aspiring postdocs might find inspiration in higher ed postdoc positions, while career advice is available at higher ed career advice.

Students and professors in these areas can share insights on Rate My Professor or explore faculty openings via higher ed faculty jobs.

💡 Looking Ahead: What This Means for Cancer Prevention

CLOCS-2 isn't just about one study; it's a blueprint for using consumer data ethically to prevent disease. As Professor Flanagan puts it, looking back at shopping history gives clues to how conditions emerge. For the public, it underscores paying attention to persistent symptom management—don't ignore that extra pack of antacids.

Have your say in the comments below, and check out resources like Rate My Professor for expert views, higher ed jobs for career paths, university jobs, or post your opening at recruitment. Stay informed on breakthroughs shaping tomorrow's healthcare.

For the original CLOCS-1 findings, see the feasibility report.