PREDICT-PD - Identifying People at Risk of Parkinson's Disease and Neurodegenerative Disease in a United Kingdom Cohort

Queen Mary, University of London (QMUL) requires access to NHS England data for the purpose of the following research project: PREDICT-PD (Parkinsons Disease).

The following is a summary of the aims of the research project provided on behalf of QMUL.

Parkinson’s Disease is the second most common neurodegenerative disorder, and the second most common movement disorder. It has a lifetime prevalence of 0.2% and this prevalence increases significantly with age. PREDICT-PD is the first study to systematically combine risk factors for PD in the general population

“PREDICT-PD is a large cohort study led by PIs at QMUL (Noyce) and UCL (Schrag). Fundamentally, it aims to identify individuals at risk of or in the early stages of Parkinson’s disease to facilitate early detection to enrol in programmes to delay progression. To date, the study has evaluated various early (‘prodromal’) symptoms, including anosmia (smell loss), sleep problems, constipation, anxiety, and depression, and related these to subsequent diagnosis of Parkinson’s disease. Findings to date have been amalgamated into an increasingly precise predictive algorithm for Parkinson’s disease. These results have been published previously in the Journal of Neurology, Neurosurgery and Psychiatry (PMID: 23828833), Movement Disorders (PMID: 28090684) and NPJ Parkinson’s disease (PMID: 33795693).

As with all epidemiological cohort studies, increasing statistical power through growth and increasing longitudinal follow-up, brings major logistical difficulties. Indeed, as PREDICT-PD has grown, attrition rates have predictably ballooned. Investigator-instigated follow-up (calls, research clinic appointments and questionnaires) have become largely unfeasible without equivalently large, and unachievable increases in research team size and funding. Data linkage provides an excellent and affordable approach to facilitating accurate longitudinal follow-up, by centralising outcomes of interest from NHS records of study participants into a single dataset. The particular outcomes of interest for PREDICT-PD in this project include the diagnosis of Parkinson’s disease, ‘Parkinson’s plus’ syndromes, other neurodegenerative diseases, and mortality data”

The following NHS England Data will be accessed:

• Hospital Episode Statistics

o Admitted Patient Care –necessary because Parkinson’s disease patients may have a primary presentation to inpatient services. PREDICT-PD needs to capture an accurate presentation time/date and diagnosis time/date of Parkinson’s disease (+ other related neurodegenerative diseases).

o Outpatients – necessary because Parkinson’s disease patients will usually be diagnosed in outpatients secondary care services. PREDICT-PD needs to capture an accurate presentation time/date and diagnosis time/date of Parkinson’s disease (+ other related neurodegenerative diseases).

• Mental Health Services Data Set – necessary because Parkinson’s disease patients may have a primary manifestation which presents to mental health services (cognitive difficulties, depression, others). PREDICT-PD needs to capture an accurate presentation time/date and diagnosis time/date of Parkinson’s disease (+ other related neurodegenerative diseases).

• Civil Registration Mortality – necessary because necessary because mortality from Parkinson’s disease and other neurodegenerative disorders is an important long-term outcome within PREDICT-PD. Establishing an accurate date/time/cause of death is necessary for the study.

The level of the data will be identifiable because QMUL holds the identifying details. However, the data that will be disseminated under this Agreement will contain no identifying details.

The Data will be minimised as follows

• Limited to a study cohort identified by QMUL – approx. 10,000 participants who have consented to be part of the PREDICT-PD Study. This cohort will be provided to NHS England. These participants were recruited from 2018 onwards.

• Limited to data between 2011 - 2024

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 generalisable and publicly available information to inform future decisions over patients’ treatments or care, particularly in the area of Parkinson’s Disease.

The funding is provided by Parkinson’s UK. The funding is specifically for the PREDICT-PD study described. Funding is in place until December 2025.

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

University College London (UCL) is a processor acting under the instructions of QMUL. UCL’s role is limited to analysing the final pseudonymised dataset for analysis under the instruction of QMUL for the purposes of the PREDICT-PD study.

University College London (UCL) is a processor acting under the instructions of QMUL. UCL’s role is limited to analysing the pseudonymised dataset for analysis under the instruction of QMUL for the purposes of the PREDICT-PD study.

PREDICT-PD has a steering committee comprised of Professor Alastair Noyce (QMUL), Professor Anette Schrag (UCL), Dr Jon Bestwick (QMUL), Professor Gavin Giovannoni (QMUL) and Professor Andrew Lees (UCL). The study also has a patient and public involvement and engagement group (PPIE) focus group, “PREDICT-PD Champions”.

Data will be accessed by:

• Undergraduate, Masters and PhD Students enrolled with QMUL or UCL. Any student working with the Data held under this Data Sharing Agreement (DSA) must have completed relevant data protection and confidentiality training and are subject to QMUL’s/UCL’s policies on data protection and confidentiality. Any students accessing the Data will do so under the supervision of a substantive employee of QMUL or UCL. QMUL or UCL would be responsible and liable for any work carried out by students. These students would only work on the Data for the purposes described in this DSA. It is anticipated that there will be approximately 2 students per year accessing data. UCL students will only access the data in UCL's role as data processor.

• Individuals holding an honorary contract under the supervision of a substantive employee of QMUL or UCL for the purposes described in this DSA only. QMUL and UCL 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

PREDICT-PD is a well-established study that began recruitment in April 2011. At the point of study conceptualisation and regularly through, a Public and Patient Involvement and Engagement (PPIE) group (known as the PREDICT-PD champions) helped refine the purpose of the research. The group helped in informing the data collection for PREDICT-PD. As the study has continued, study participants and original PPIE groups are kept up to date with the study through newsletters, phone calls, and a dedicated website.

QMUL will transfer data to NHS England. The data will consist of identifying details (specifically NHS Number, 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 HES APC, HES OP, MHSDS and CRD datasets to QMUL.

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 QMUL. These servers are secure and maintained by dedicated, professional IT Teams at the institution.

The Data will be accessed onsite at the premises of QMUL.

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

The Controller(s) 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 England and Wales The data will not leave England or Wales at any time.

Data may be accessed by individuals with an honorary contract with QMUL or UCL. The individuals will act as an agent of QMUL or UCL at all times under supervision from employees of QMUL or UCL. Aside from this/these individuals, access is restricted to employees or agents of QMUL or UCL who have authorisation from the Principal Investigator at QMUL.

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

The Data will be combined with publicly available data – spatial small area data on environmental exposures, given the well-evidenced links between environmental exposures and Parkinson’s disease. This linkage would enable environmental risk factors to be further assessed, which is necessary in building an accurate predictive algorithm for Parkinson’s disease.

The Data will be linked at person record level with datasets obtained from the study participants. These will include questionnaire and patient reported outcome data directly obtained from the participants.

The identifying details will be stored in a separate database to the linked dataset used for analysis. All analyses will use the pseudonymised dataset. There will be no requirement and no attempt to reidentify individuals when using the pseudonymised dataset.

Researchers from the PREDICT-PD Study Team at QMUL and UCL will analyse the Data for the purposes described above.

The expected outputs of the processing will be:

• A report of findings to the funding body (Parkinsons UK) in accordance with funder protocols

• Submissions to peer reviewed journals within 12 months of receiving data.

• Presentations at appropriate international/national conferences (within 12 months of receiving data)

• Publication of dashboards on the PREDICT-PD website.

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

• Social media (PREDICT-PD social media channels – Facebook, X, Instagram)

• Direct bilateral engagement with study participants (newsletters, phone calls, PPI events with PREDICT-PD Patient Champions, and members of the study cohorts)

• Posters/oral presentations at international/national conferences

• Participant newsletters

All outputs described above will aim to be achieved within 12 months of receiving data. For journal submissions and presentations, this will be dependent on peer-review processes and journal timelines.

The findings of this research study 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 the subject matter of the study.

The use of the data could

• Improve understanding of the health and care needs of populations with Parkinson’s disease, and the wider population of those at risk of Parkinson’s disease.

• Through understanding the prodrome trajectory of Parkinson’s disease, this may lead to the improvement of health and care system design for populations at risk of Parkinson’s disease.

• Advance understanding of the need for, and effectiveness of, preventative health and care measures for patients with, and at risk of, Parkinson’s disease.

• Inform planning health services and programmes, for example to improve equity of access, experience, and outcomes of populations with, and at risk of, Parkinson’s disease.

• Inform funding allocation decisions related to prevention of Parkinson’s disease.

• Support knowledge creation or exploratory research, by creating a hugely valuable dataset for analysis.

The goal of PREDICT-PD is to develop predictive algorithms for Parkinson’s disease, that will ultimately inform prevention and early detection strategies. However, following up patients longitudinally in research settings to identify outcomes of interest (Parkinson’s disease, neurodegenerative diseases and related conditions) is very challenging due to attrition. Data linkage will enable the pulling of outcomes from NHS records to quickly and accurately identify patients who have developed outcomes of interest. This will then increase the power and accuracy of the prediction estimates for Parkinson’s disease, moving towards prevention of Parkinson’s disease. Furthermore, this proposal is contemporaneous with a paradigm shift in blood-based biomarkers for early detection of Parkinson’s disease, which are expected in the next year. Early detection of Parkinson’s disease is thus not merely an academic exercise, but now pragmatically represents a direction to revolutionise clinical services and support.

It is hoped that through publication of findings in appropriate media, the conducted analyses will further refine the existing predictive algorithms for Parkinson’s disease that the PREDICT-PD team have developed. Dissemination of these algorithms through publication and presentation will help to inform prevention strategies and early detection strategies at a healthcare structural level

Alongside keeping study participants and the PPIE group engaged with the study through dissemination of lay output including newsletters and social media posts, relevant charities are aware of the study and proposal, including Parkinson’s UK, the MRC and Cure Parkinson’s.

Yielded Benefits is not a requirement for new applications.