NHS Digital Data Release Register - reformatted

Imperial College Healthcare NHS Trust projects

26 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).


Indigo (Investigating Digital Outcomes) - Participant led electronic completion of PROMs and PREMs for patients living with and beyond cancer — DARS-NIC-747046-T7C5C

Opt outs honoured: (Excuses: Consent (Reasonable Expectation))

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (NHS Trust)

Sensitive: Non-Sensitive, and Sensitive

When:DSA runs 2025-03 – 2028-03

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. Mental Health Services Data Set (MHSDS)
  2. NDRS Cancer Consolidated Data Set

Type of data: Anonymised - ICO Code Compliant

Objectives:

Imperial College London requires access to NHS England data for the purpose of the following research project: Indigo (Investigating Digital Outcomes) - Participant led electronic completion of PROMs and PREMs for patients living with and beyond cancer.

The following is a summary of the aims of the trial provided by Imperial College London:

The Indigo Community project is a digital clinical trial that aims to assess the feasibility of mass recruitment to a community cancer survivor study via a large-scale online platform using participant self-enrolment. The ambition is to develop a firm, pragmatic evidence based on how to collect patient reported data for people living in the community who have previously been treated for cancer.

A key aspect of the trial is directly asking participants if they consent to the Data controller having access to their NHS registry data so that the Data controller can link their patient reported outcome data to their cancer journey to allow analysis.

The outcome measures of the trial are as follows

Co-primary outcome measures
1. To assess the feasibility of recruiting to a community digital Patient Reported Outcomes Measures (PROMs) study via the primary care research network.
2. Feasibility of linking participants PROMs to regional and national data sets.

Secondary outcome measures
3. Feasibility of different methods of communication to trigger participant self-identification and self-enrolment into a digitally administered community cancer
PROMs study.
4. To assess which of three PROMs participants identify as most useful to them in combination with EQ-5D-5L.

Tertiary outcome measures
5. The feasibility of collecting, filtering, grouping, and interpreting free text responses in the context of a digital community-based PROM study.
6. The feasibility of developing a national cohort of people living with and beyond cancer with linked registry datasets who can be followed longitudinally with repeat sampling.

The Data are required to deliver one of the trial outcomes - 'feasibility of linkage' and building the data and analytical pipeline ready for when there will be much larger numbers of datasets after national roll out.

Patient demographics and tumour details are captured in the national cancer registry. Although clinical and NHS administrative data are widely collected, PROMs data are largely missing even with the National Cancer Patient Experience Survey (NCPES). Indeed, the sending of the NCPES is triggered in all adult NHS patients (aged 16 and over), with a confirmed primary diagnosis of cancer, discharged from an NHS Trust after an inpatient episode or day case attendance for cancer-related treatment over a 3-month period each year. Therefore, patients who did not use healthcare services during that time are not sampled. Furthermore, the survey is paper-based and can be complex as the respondents must understand the logic of the questions by themselves although telephone helpline support is available. Although the survey focuses on the use of healthcare services, it does not use well-validated questionnaires. However, Public Health England (now UKHSA and OHID) has successfully linked patients between the survey and the national cancer registry with patients’ year of birth, sex, ethnicity and postcode. By being able to link patients’ PROMs data to the cancer registry, Indigo Community project team can then link symptoms and side effects to clinical data on a national scale without relying on clinical trials data which is usually subject to selection bias. This can help clinicians and patients improve their understanding of treatments and overcome the bias in clinical trial recruitments. This then reduces the patients’ burden to find clinical information and exact dates to help researchers

The following NHS England Data will be accessed:
• NDRS Cancer Consolidated Data – necessary to compare self-reported data to what the NHS captured and what the participants understood of their diagnosis.
• Mental Health Services Data Set (MHSDS) – necessary to understand the impact of the cancer diagnosis and treatment on participants’ mental health and if they needed further support that their clinical team could not provide.

The level of the Data will be;
• Pseudonymised

The Data will be minimised as follows:
• Limited to the study cohort of participants who consented to participate. Approximately 1,600
Imperial College London 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 adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce comprehensive available information about PROMs and the impact cancer treatments had on routinely treated cancer patients.

The funding is provided by Brain Tumour Research Campaign and Macmillan Cancer Support.

The funding is specifically for the trial described.

Funding to continue the work described will be sought on an ongoing basis.

Imperial College Healthcare NHS Trust is a processor acting under the instructions of Imperial college London.

Data will be accessed by:
• Individuals holding an honorary contract under the supervision of a substantive employee of Imperial College London for the purposes described in this DSA only.

Imperial College London must maintain records in a single location that cover the following details of each individual given access under an honorary contract:
• Their substantive employer;
• Their role in respect of the purpose for the processing specified in the DSA;
• The start date and end date of the duration in which the Data will be accessed by the individual under an honorary contract;
• The necessity for the Data to be accessed by the person(s) holding an honorary contract, instead of a substantive employee of an organisation named as controller or a processor in this DSA;
• Confirmation that an appropriate contract is in place which follows the relevant guidance and is countersigned by the substantive employer of the honorary contract holder.

Imperial College London has developed the trial from conception with Patient Public Partners. Their expert advisory group is composed of representatives of Leeds Becket University, Macmillan Cancer Support, Independent Cancer Patients' Voice, The National Institute for Health and Care Excellence (NICE), Guy’s Cancer Centre, Patient Partner, The Word Desk Ltd, Royal College of Radiology and Imperial College London.

Expected Benefits:

The findings of this trial are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment and experience of health care users relevant to cancer.

The use of the Data could:
• Help the system to better understand the health and care needs of populations.
• Advance understanding of regional and national trends in health and social care needs.
• Advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions such as cancer.
• Inform planning health services and programmes, for example to improve equity of access, experience and outcomes.

There is a small benefit for the participants in signposting to resources at the end of the study, it helps address aspects of quality of life that they may reflect they would like to improve. Ultimately, It is hoped that the INDIGO Community will help develop a firm, applicable, pragmatic evidence base on how to collect PROMS, and service use on patients who are living with or beyond cancer in the long term. It is hoped that utilising this novel approach the Data Controller will be able to lower the barriers to community PROMS collection such that it can become a standard of care and that service provision can better reflect patients long term unmet needs. By exploring willingness to link data it may be possible to reduce the questionnaire burden in future community PROMS studies. Patients will get a summary of the questions and their answers at the end of the questionnaire, after submission. If they choose, they can bring a copy to their GP, keep a record of it, or send it to their healthcare professionals. There may be a value in facilitating conversations with friends, family, healthcare professionals about ongoing unmet needs and symptoms.

There is little to no research of impact of pan-cancer on a national level across different ethnicity, age groups and geographically. Without knowing how patients are managed and what are the outcomes, it is difficult to understand what’s the best practice to apply to newly diagnosed patients. Nationally, the NICE guidelines used clinical trials to understand outcomes. However, clinical trials do not reflect the routinely treated cancer population. Clinical research teams enrol fitter and younger patients than the national cancer population. Therefore, it is difficult to apply outcomes from a young population to an older population.

The study will be registered through publication of the study protocol in an open access journal, highlighting on our public webpage and publicising the study aims and objectives before we have results through conference presentation.

The results will be reported and disseminated through blog posts, social media, publications in peer reviewed scientific journals and conference presentations.

Macmillan and other relevant charities (e.g., Brain Tumour Charity, Breast Cancer Now) have been contacted and heard about this clinical trial.

Outputs:

The expected outputs of the processing will be:
• Submissions to peer reviewed journals
• Presentations at appropriate conferences

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 datasets from which the information was derived.

The outputs will be communicated to relevant recipients through the following dissemination
channels:
• Peer-reviewed journals
• Webinars open to PROMs professionals, patients, scientific committees,
cancer communities
• Social media
• Public reports
• Public events such as conferences
• Posters displayed at appropriate conferences
• Press/media engagement
• Reports aimed at participants/patients

Target Dates
- Preliminary Results: Expected publication of preliminary results in 2024.
- Linked Data Analysis: Full analysis and publication of work using linked data are anticipated within 6 to 12 months following data receipt.

Processing:

Imperial College Healthcare NHS Trust will transfer data to NHS England. The data will consist of identifying details (specifically First Name, Last Name, Date of Birth, Postcode, Gender and a unique person ID) for the cohort to be linked with NHS England Data.

NHS England will provide the relevant records from the list of datasets listed above to Imperial College London. The Data will;
• contain no direct identifying data items but will contain a unique person ID which can be used to link the Data with other record level data already held by the recipient.

The Data will not be transferred to any other location.

The Data will be stored on servers at Imperial College London in the BDAU (Big Data and Analytical Unit)

The Data will be accessed by authorised personnel via remote access.

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).

The Data will not leave England/Wales at any time.

Remote processing will be from secure locations within England/Wales.

Data will be accessed by individuals with an honorary contract with Imperial College London.

The individuals will act as an agent of Imperial College London at all times under supervision from employees of Imperial College London.

Aside from these individuals, access is restricted to employees or agents of Imperial College London from the clinical trial research team (known as the Computational Oncology Laboratory, affiliated with Imperial College London and Imperial College Healthcare NHS Trust) who have authorisation from the Principal Investigator

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

The Data will not be linked with any other data.

Identifying details will be stored in a separate database at Imperial College Healthcare NHS Trust to the linked dataset used for analysis. All analyses will use the pseudonymised dataset. Imperial College London will not have the technical ability to re-identify individuals.


Braina CaVa: Care, variation, outcomes, and costs in patients with brain tumours in England. (ODR1819_236) — DARS-NIC-656838-J7H7S

Opt outs honoured: (Excuses: Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (NHS Trust)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2023-06 – 2024-06

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS Linked Cancer Waiting Times (Treatments only)
  3. NDRS Linked DIDs
  4. NDRS Linked HES AE
  5. NDRS Linked HES APC
  6. NDRS Linked HES Outpatient
  7. NDRS National Cancer Patient Experience Survey (CPES)
  8. NDRS National Radiotherapy Dataset (RTDS)
  9. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Type of data: Anonymised - ICO Code Compliant

Objectives:

Primary brain tumours are the leading cause of cancer death in the under the 40s and have the highest average number of years of life lost. Although primary brain metastases are rare, they are the leading cause of cancer death in the under the 40s, and 10 – 15% of all patients with extra-cranial cancers develop brain metastases, with a poor prognosis.
The aims of this project are to:
• To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary Central Nervous System (CNS) tumours in England.
• To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
• To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.
Reviewed and Approved

In line with the National data opt-out policy, opt-outs are not applied because the data is not Confidential Patient Information as defined in section 251(10) and (11) of the National Health Service Act 2006

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

Yielded Benefits:

The study team have explored: - costs of treatments and non treatments of patients diagnosed with a glioblastoma (WHO Grade IV brain tumours) or with a meningioma (WHO Grade I); - 30-day complication following a major resection; - the incidence, treatments and admissions of patients diagnosed with a glioblastoma; - end-of-life care for patients diagnosed with a primary brain tumour; - effect of dyads on outcomes and survival.

Expected Benefits:

To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary, secondary brain tumours or with a suspicion of brain tumour in England.
To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.

Outputs:

To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary, secondary brain tumours or with a suspicion of brain tumour in England.
To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.
Since the data extraction (August 2020), we had two more updates, in May 2021 and October 2022 as Public Health England, NHS Digital and NHS England realised the data sent was incomplete, hence this data extension.

Processing:

The study team will analyse summary treatment patterns at centre level and at centre-dyad level. Centre-dyads will be defined based on patterns of co-care; two centres that share >=33% of their patients is a centre-dyad. Each centre-dyad will be modelled as a single unit (the partner-actor interaction model is not appropriate here). Analyses will examine rates of histological diagnosis, surgical extent, 30 day mortality and prolonged admission (>75th centile duration of admission), rates of maximal treatment and patient safety events (Objectives A, B, C). Patient safety events will be based on the translated AHRQ patient safety indicators 14,15. Novel endpoints will be defined in conjunction with our charity partners, patients and carers.

The study team will conduct sensitivity analyses based on geographic patterns of referral (in-areas only vs. all) 16. Estimates of absolute cost but cannot be used to calculate relative cost-effectiveness; but within each pathway the study team will calculate the relative costs of treatment vs. non-treatment inpatient care (Objective D).


As an illustrative example, if the study team assume the impact on oncology volume is the same as the impact of surgical volume, the study team would expect to find a difference in HR of ~0.6 between low-volume (<20) and high volume (>80) centres 6. 12 month mortality then might be expected to vary between 50% and 30%, with 95% CIs of +/- < 4%. The study team are therefore confident that the study will have the power to detect differences that exist, in part due to the poor prognosis, and high event rates, for patients primary or secondary with brain tumours. In addition, these provide a direct route to improving outcomes in the short-term: this magnitude of difference would equate to ~ 200 patients per year in the GBM cohort alone.

7.2 Statistical methods

The study team will evaluate the impact of patient, tumour and treatment factors on outcomes in patients with primary or secondary brain tumours in England. The study team will carry out analyses using patient-level data, but analyse volume effects for centres and dyads using a centre/dyad volumes in a random-effects model; Assessment of risks and outcomes will be carried out at individual patient level on all the patients in the dataset 17.

The study team will include sex and exact age, tumour diagnosis using combined ICD-10 and ICD-O3 (5 categories), Charlson index derived from secondary ICD-10 diagnostic codes, presence of 16 comorbidities included in the Charlson index, ethnic group, deprivation quintile and distance from both oncology and surgical centre. All these data items are in, or derivable from, our linked dataset.

The study team will construct proportional hazards models. The models will be used to predict the n-year probability of event for every patient, and aggregated to construct n-year probabilities of event by (hospital/ surgeon/ region). The adequacy of the proportional hazards model will be assessed using martingale residual plots and Schoenberg residual plots. If there the model does not meet the criteria for a Cox model, we will consider other modelling approaches (e.g. AFT models); in all cases we will construct parsimonious models using Akaike’s information criterion. We will use binary indicators for mortality at 30 and 90 days after diagnosis and use regression coefficients to predict expected outcome.

The study team will assess the impact of biology and treatment (Objective E) in carefully defined sub-cohorts, then further adjusted using propensity methods. Comparisons of the consequences of a brain tumour diagnosis will be made between primary and secondary diagnosis with the comparison inversely weighted by a propensity score calculated from relevant confounding variables such as age and gender. The study team will use the inverse PS method as none of the groups are “unexposed”. The study will conduct multiple independent analyses in an attempt to reduce bias, and report methods and results in line with recommendations 17.

7.3 Health Economics

The study team will use reference costs from NHS Tariffs for 2015. We will estimate direct costs for 3 months before diagnosis and 12 months after diagnosis. We will distinguish between costs incurred in the in-patient and outpatient setting, planned and emergency care, during and after treatment. We will use NHS reference costs, length of stay and other measures of resource (e.g. intensive care days and treatment procedures) to estimate costs and will examine the impact on care and patient characteristics on total pathway costs.

The study team will distinguish direct treatment costs – neurosurgery, radiotherapy and chemotherapy from indirect treatment costs, based on procedure codes (HES) and data on chemotherapy and radiotherapy (RTDS and SACT)