Co-Designing Community-Led Cancer Awareness Pathways to Improve Early Diagnosis and Research Engagement in Underserved Populations

About the Project

This PhD will co-design AI-enhanced, community-led cancer awareness pathways to improve early diagnosis and research engagement among underserved groups in Merseyside. The candidate will combine participatory co-design, quantitative and qualitative methods, and digital/AI tools to create scalable public health solutions addressing cancer inequalities. There is flexibility within the project, depending on the candidate’s skills and interests.

Early diagnosis is one of the most powerful determinants of cancer survival, yet inequalities in symptom awareness, access to healthcare, and participation in cancer research persist across the UK. In Merseyside, one of the most socioeconomically deprived regions in England, these disparities are intensified by low health literacy, cultural barriers, limited trust in research institutions, and unequal access to diagnostic pathways . Many cancers, including pancreatic, lung, and colorectal cancers, disproportionately affect communities with high deprivation, certain ethnic minority groups, and people with long-term chronic conditions [1, 2]. These inequities contribute to later-stage presentation and reduced engagement in potentially life-saving clinical research.

Early diagnosis is one of the strongest determinants of cancer survival, yet profound inequalities in symptom awareness, access to care, and participation in cancer research persist across the UK. In Merseyside, one of England’s most socioeconomically deprived regions, these disparities are intensified by low health literacy, cultural and language barriers, mistrust of institutions, and unequal access to diagnostic pathways. These factors contribute to later-stage presentation for cancers such as pancreatic, lung, and colorectal cancers, all of which disproportionately affect communities experiencing deprivation and certain ethnic minority groups. This PhD will address these inequities by co-designing culturally relevant cancer awareness pathways in partnership with local residents, widening participation schools, voluntary-sector organisations, and healthcare stakeholders.

The project will use participatory and mixed-methods approaches, including qualitative research, quantitative surveys, behavioural insights, community mapping, and AI-supported analytical tools, to generate scalable, community-led solutions that improve early symptom recognition, confidence in navigating primary care, and readiness to engage in cancer research [3]

Research Aims

1. Identify barriers and facilitators to early cancer awareness and help-seeking behaviour in underserved communities in Merseyside.
2. Co-produce community-led cancer awareness materials and engagement pathways with residents, young people, and stakeholders.
3. Evaluate the impact of co-designed interventions on awareness, trust, help-seeking, and research engagement.

What the Candidate Will Do

The candidate will lead the design, delivery, and evaluation of a community-based mixed-methods project focused on tackling cancer inequalities.

Phase 1 – Discovery & Data Mapping

• Conduct evidence synthesis review to map existing data
• Use quantitative surveys to assess awareness, trust, and help-seeking intentions.
• Qualitative interviews and focus groups with residents, community leaders, healthcare professionals, and voluntary-sector organisations.
• Apply AI-enabled methods towards data collection, data analysis, reporting, and dissemination of results. Natural language processing (NLP) for transcript analysis, geospatial mapping (GIS) to visualise inequalities, and clustering tools to identify underserved population groups.

Phase 2 – Co-Design of Community-Led Interventions

• Facilitate co-design workshops with community members and widening participation schools to develop culturally appropriate, multilingual cancer awareness materials.
• Explore digital engagement tools—potentially including AI-supported chatbots, interactive symptom resources, or mobile-friendly decision aids—depending on candidate expertise.
• Ensure outputs meet health literacy needs and reflect behavioural science principles.

Phase 3 – Implementation & Evaluation

• Pilot interventions in schools, community venues, faith settings, and partner organisations.
• Evaluate impact using pre–post surveys, digital analytics, statistical analysis (e.g., regression or subgroup analysis), and follow-up qualitative interviews.
• Assess changes in awareness, trust, symptom recognition, and willingness to engage with research or trials.

Training, Collaboration & Project Flexibility

The student will receive in-depth training in mixed-methods research, quantitative analysis (Python/R/SPSS), qualitative coding (NVivo), AI and machine learning applications in public health, and participatory co-design.

They will work closely with community partners, NHS stakeholders, and interdisciplinary supervisors, including digital health/AI expertise, to develop strong transferable skills.

While the project has clear overarching aims, there is flexibility for the candidate to shape methodological depth according to their interests. Students with data science strengths may enhance the AI component; those with behavioural or public engagement backgrounds may expand co-design and intervention development. This ensures a focused but adaptable PhD, enabling a distinctive contribution to cancer inequalities research.