Understanding causal relationships between socio-economic inequalities, multi-morbidity, and cancer treatment and outcomes

The University of Manchester requires access to NHS England data for the purpose of the following research project: Understanding causal relationships between socio-economic inequalities, multi-morbidity, and cancer treatment and outcomes (COSMIC).

The following is a summary of the aims of the research project provided on behalf of The University of Manchester:

This project aims to examine the impact of socio-economic status and comorbidities on cancer patients' experiences, focusing on the complexities of inequality in cancer care.

The study aim will be determined through three key objectives:

1. Identify and measure patterns in comorbidity incidence in cancer patients treated in England.

2. Understand the associations between cancer patient presentation, demographics, comorbidity burden, treatment strategies and outcome.

3. Map cause-and-effect relationships between comorbidities, socio-economic status, and cancer treatment outcomes.

The following NHS England Data will be accessed:

• NDRS Linked Cancer Registration – necessary because it provides comprehensive patient-level data on cancer diagnoses, including cancer type, stage at diagnosis, morphology, and demographic information. This dataset is critical for the study cohort and understanding baseline clinical characteristics.

• NDRS Systemic Anti-Cancer Therapy Dataset (SACT) – necessary to gather information on systemic cancer treatments for the above population, including treatment intent, number of cycles, and regimen details. This data supports analysis of treatment patterns and their associations with patient outcomes and comorbidities.

• NDRS Radiotherapy Dataset (RTDS) – necessary to analyse radiotherapy treatment factors for the above population. This dataset enables assessment of radiotherapy’s role in cancer care and potential treatment-related complications.

• NDRS Linked Hospital Episode Statistics

o Admitted Patient Care – necessary because it captures inpatient hospital admissions, enabling identification of comorbidities prior to cancer diagnosis and toxicities or new comorbidities following treatment. It also includes operative procedures and hospitalizations related to treatment and complications.

o Outpatients necessary to supplement inpatient data by capturing diagnoses and treatment-related encounters in outpatient settings, thus improving the completeness of comorbidity and toxicity profiling.

The level of the Data will be pseudonymised

The Data will be minimised as follows:

• Limited to England

• Limited to the following time periods:

1. Cancer Registry: Records for patients diagnosed with cancer between 01/01/2013 to the latest available , including death information. The data includes calculated time from diagnosis to death.

2. Treatment information where available for SACT for the above population.

3. Treatment information where available for RTDS for the above population.

4. NDRS-Linked HES Admitted Care: Hospital admission records for the above population, including data for operations, from 01/01/2010 to 31/12/2024 (or the most recent data available, typically four to seven months prior to the current month, as stated by NDRS).

5. NDRS-Linked HES Outpatients: To complement hospitalization data for the above population, from 01/01/2010 to 31/12/2024 (or the most recent data available, typically four to seven months prior to the current month, as stated by NDRS).

The University of Manchester is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.

The lawful basis for processing personal data under the UK GDPR is:

Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller.

The lawful basis for processing special category data under the UK GDPR is:

Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

This processing is in the public interest because it addresses a critical need to understand and reduce health inequalities in cancer care. The study uses anonymised, population-level NHS England data to investigate the causal relationships between socio-economic status, multi-morbidity, and cancer treatment outcomes. The findings will inform the development of interventions and healthcare policy improvements, ultimately benefiting public health. All processing is conducted within a secure Trusted Research Environment and follows strict data minimisation and confidentiality safeguards, in line with Article 89(1).

The funding is provided by National Institute for Health and Care Research (NIHR). The funding is specifically for the study described. Funding is in place until 31/07/2025. Funding to continue the work described will be sought on an ongoing basis.

The funder will have no ability to suppress or otherwise limit the publication of findings.

The Christie NHS Foundation Trust provides IT hosting services to The University of Manchester and will store the Data as contracted by The University of Manchester.

A research team, comprising of the Chief Investigator (CI), Co-Investigator (Co-I) will be established. The research database team will convene every six months to review the study's management and development and to ensure the safety of the data within the database.

To define the complex relationships between multimorbidity, socio-economic position (as derived from Index of Multiple Deprivation [IMD]), and cancer care outcomes as a causal inference problem a Patient and Public Involvement and Engagement (PPIE) workshop was conducted. The value of this project has been recognised through a presentation to the Christie Patient Centred Research (CPCR) PPIE group, and this group will continue provided patient insights into the research. The input from these workshops will directly inform the structure of the causal model and help identify key factors for analysis. This ensures that the use of data addresses real-world concerns, aligns with patient priorities, and maximises the public benefit of the research in tackling cancer inequalities.

Where individuals have opted out of disease registration by the National Disease Registration Service (NDRS), their data has been permanently removed from the registry and therefore will not be disseminated under this Data Sharing Agreement (DSA). https://digital.nhs.uk/ndrs/patients/opting-out

No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).

NHS England will provide the relevant records from the NDRS Cancer Registrations, NDRS-Linked HES APC, NDRS-Linked HES OP, NDRS-Linked RTDS and NDRS-Linked SACT datasets to The Christie NHS Foundation Trust. The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.

The Data will not be transferred to any other location.

The Data will be stored on servers at The Christie NHS Foundation Trust.

Data will be backed-up at The Christie NHS Foundation Trust on-site.

The Controller must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.

For remote access:

- Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;

- Access controls granting users the minimum level of access required are in place;

- Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;

- Multifactor authentication (MFA) is required for remote access;

- Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;

- All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.

The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).

Remote processing will be from secure locations within UK. The data will not leave UK at any time.

Access is restricted to employees or agents of The University of Manchester who have authorisation from the Chief Investigator.

No other organisation is permitted to access the Data.

All personnel accessing the Data have been appropriately trained in data protection and confidentiality.

The Data will not be linked with any other data outside of this agreement.

There will be no requirement and no attempt to reidentify individuals when using the Data.

Analysts from The University of Manchester will analyse the Data for the purposes described above.

The expected outputs of the processing will be:

• Submissions to peer-reviewed journals, with the first manuscript expected by Q2 2026, followed by further submissions approximately every 1 year, aligned with major analytical milestones.

• Presentations at national and international conferences, including (but not limited to) The European Society for Radiotherapy and Oncology (ESTRO) and NCRI Cancer Conference, expected annually.

• Reports of findings to internal project stakeholders and funders (e.g. NIHR Manchester BRC) every 6 months, in line with project review meetings.

• Presentations to PPIE groups to share emerging findings and ensure continued public involvement.

• Development of a causal model and analytical framework (based on a Directed Acyclic Graph [DAG]), which may be shared as an open resource with the wider research community, pending review.

The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.

The outputs will be communicated to relevant recipients through the following dissemination channels:

• Journals

• PPIE Workshops to define the complex relationships between multimorbidity, socio-economic position, and cancer care outcomes as a causal inference problem.

The target dates for the dissemination of the different outputs are below:

• Initial exploratory findings: by Q4 2025

• First peer-reviewed journal submission: by Q2 2026

• First conference presentation (e.g. ESTRO): by Q3-2026

• Causal model Directed Acyclic Graph (DAG) and interim analysis outputs: by Q4 2026

• Subsequent publications and presentations: throughout 2026–2027

• Final outputs and project close-out report: by December 2027

The findings of this research will be instrumental in informing evidence-based interventions, reducing health inequalities, and ultimately improving cancer care on a broader scale. By understanding how multi-morbidity and deprivation influence treatment pathways and outcomes, the study will contribute to policy change, service planning, and resource allocation, helping improve equity in access, quality, and effectiveness of cancer care across the NHS.

The expected benefits to patients are

• More personalised and equitable treatment, particularly for those with high comorbidity burden or from deprived backgrounds.

• Improved clinical guidelines that take into account real-world complexity such as multimorbidity.

• Earlier identification and management of new comorbidities or treatment toxicities, leading to better survivorship outcomes.

• Enhanced focus on underserved populations who are currently underrepresented in clinical trials but heavily impacted by cancer disparities.

Through the publication of findings in peer-reviewed journals, presentations at national and international forums, and engagement with clinical and policy stakeholders, this research will drive greater awareness and evidence-informed change. The development of a causal model Directed Acyclic Graph (DAG) will provide a foundational tool for further research and support the design of targeted interventions aimed at mitigating inequities.

To maximise public benefit, the study team will:

• Engage PPIE groups throughout to ensure findings are aligned with patient priorities.

• Disseminate results via lay summaries and open-access platforms hosted by The Christie NHS Foundation Trust.

• Present findings to relevant bodies including NHSE, NIHR, and professional societies (e.g., ESTRO, NCRI).

• Explore collaboration opportunities with cancer charities and health equity organisations to ensure findings inform outreach, support services, and advocacy efforts.

Yielded Benefits is not a requirement for new applications.