Good TREs work
University Of Manchester projects
3 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).
Investigating Causal Effects of Mental Healthcare Provision on Labour Outcomes: A Case Study of Talking Therapies in England — DARS-NIC-739822-Q8R6Y
Type of data: information not disclosed for TRE projects
Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)
Legal basis: Health and Social Care Act 2012 s261(2)(a)
Purposes: No (Academic)
Sensitive: Sensitive
When:DSA runs 2024-09-01 — 2025-08-31 2024.08 — 2024.11.
Access method: System Access
(System access exclusively means data was not disseminated, but was accessed under supervision on NHS Digital's systems)
Data-controller type: THE UNIVERSITY OF MANCHESTER
Sublicensing allowed: No
Datasets:
- Improving Access to Psychological Therapies (IAPT) v2
Objectives:
The University of Manchester requires access to NHS England data for the purpose of the following research project:
Investigating Causal Effects of Mental Healthcare Provision on Labour Outcomes: A Case Study of Talking Therapies in England
The following is a summary of the aims of the research project provided by the University of Manchester:
Identify the care pathway for individuals with mental health conditions/disorders through the expansion of access to talking therapies in England (IAPT);
estimate the impact of supply-side factors on clinical and labour market outcomes of patients receiving IAPT treatment;
investigate what treatment pathway is optimal for improved labour market outcomes for individuals with common mental disorders:
provide evidence of the impact of different care pathways and varying treatments on labour market outcomes of real-world patients who have need of mental healthcare services;
inform service adaptations to better meet the needs of the patient population accessing mental healthcare in England, namely IAPT services;
aid the expansion of mental healthcare services to achieve the objective of reducing lost productivity of workers.
The following NHS England Data will be accessed: Improving Access to Psychological Therapies (IAPT) v2
The data requested from the IAPT dataset captures detailed information on a nationally representative patient population, including patients sociodemographic characteristics, treatment pathway through the IAPT programme, and employment information captured at each interaction with the service. The University of Manchester project team will examine the impact of mental healthcare treatment provided by IAPT services on labour market outcomes of patients with common mental disorders at the individual-level. The project team consists of a Data Analyst who is a PhD student, two research fellows, and the Research Centre Lead.
The level of the Data will be pseudonymised.
The Data will be minimised as follows:
Limited to data between 2020/21 and 2022/23.
The study team will not have access to any other data in the Secure Data Environment (SDE) other than the minimised data as outlined in this Data Sharing Agreement.
The University of Manchester is the sole 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 Centre for Primary Care and Health Services Research at the University of Manchester is a public authority conducting research which influences the provision of healthcare services, and benefits the patients who use them.
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.
There is an identified need for evidence of the impact of mental healthcare treatments on labour market outcomes of real-world patients with common mental disorders. The impact of mental healthcare services on labour market outcomes of patients was expressed as a priority area of research by NHS England's Director of Operational Interests at a 'Lunch and Learn' seminar hosted by NHS England on 18th June 2024. The meeting was attended by University of Manchester colleagues to share information about the research UoM is conducting and discuss the areas of research which are of particular interest to NHS England.
This research study is to aid in improving the service delivery of NHS Talking Therapies for Anxiety and Depression services (formally IAPT), and provide real-world evidence of the effect of mental healthcare treatments on labour market outcomes needed to better understand the relationship of mental health and labour supply in England. By doing so, the NHS may better meet the demands for treatment of the English population, and potentially further reduce the lost productivity caused by mental health conditions.
The funding is provided by the Economic and Social Research Council as part of the health and wellbeing pathway of a doctoral training partnership. The funding is specifically for the project described. Funding is in place until May 2026.
The funder will have no ability to suppress or otherwise limit the publication of findings.
The University of Manchester team engaged with subject matter experts when designing the study, however no other organisations are involved as a controller, acting in an advisory capacity, or part of an oversight/steering committee.
Data will be accessed by a PhD student who is a substantive employee of University of Manchester. The PhD student has completed mandatory data protection and confidentiality training and is subject to the University of Manchesters policies on data protection and confidentiality. The individual accessing the data will do so under the supervision of a substantive employee of the University of Manchester. The University of Manchester would be responsible and liable for any work carried out by the individual. The PhD student would only work on the data for the purposes described in this Data Sharing Agreement (DSA).
Expected Benefits:
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.
An estimated 8 million individuals in England have a need for mental healthcare access but are not in contact with NHS services (Hyde et al, 2023). This research project will contribute to the gap in the literature for examining the impact of mental health treatments on labour market outcomes of a real-world patient population.
The use of the data could:
1. support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).
2. advance understanding of regional and national trends in health and social care needs.
3. lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
4. inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
In relation to expected benefits 1 and 2:
- The project team will develop a causal model that exploits sources of geographical variation in IAPT treatment effects to produce causal estimates of IAPT treatment on patients' labour market outcomes.
- This develops the literature by supporting the creation of policy-relevant and actionable findings through a reproducible econometric model (benefit 1 of the list above).
- This will also advance the understanding of regional trends in the demand for IAPT treatments, the provision of IAPT treatments, and patient labour market outcomes (benefit 2 of the list above).
In relation to expected benefits 3 and 4:
- The IAPT service was created to improve access to psychological therapies, providing evidence-based treatments to patients for their identified symptoms of common mental health problems.
- This service delivery model offered treatments based on the identified symptoms at the assessment stage and the patient's treatment goals.
- The service has expanded to offer more treatments, different treatment modalities, and additional service options for patients (support for individuals with Long-term conditions, employment advice, and self-help resources).
- We will explore variation in patient characteristics as an impacting factor in the success of IAPT treatment on labour market outcomes. This can reveal whether patients select psychotherapy treatment based on perceived idiosyncratic (individualised) gains in labour market outcomes.
- For example, many patients will present symptoms of the same condition at the assessment stage of IAPT, but the causes of these symptoms and how they affect the person can differ significantly.
- These perceived gains from treatment will directly influence the acceptance and adherence to the treatment offered.
- Combining the economic and mental health research fields, we can produce causal estimates of specific treatment choices on labour market outcomes and sub-samples of patients who benefit most from these choices using patient demographic data.
- These findings can be used to identify optimal treatment pathway choices for individuals who declare a goal of their treatment is related to their labour market prospects during their assessment or individuals identified by the assessor who could benefit from employment support (benefit 3 of the list above).
- These findings can also inform the planning of IAPT services across England, such as in areas of higher deprivation and lower labour market prospects, aiding the equitable provision of mental healthcare that accounts for variation in patients needs (benefit 4 of the list above).
The findings of this research project can be used to inform the assessment phase in the IAPT care pathway:
Ensuring patients are provided with adequate information and choice to inform and agree a course of treatment with IAPT practitioners conducting the assessments, and;
if there is an identified link between a mental health condition and labour supply, practitioners will be empowered to make evidence-based recommendations for optimal treatment pathways for improved labour market outcomes based on the CMD condition and socioeconomic demographic indicators.
It is hoped that through publication of findings in appropriate media, the findings of this research will add to the body of evidence that is considered by the bodies, organisations and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.
Recipients of the outputs will need to take action based on the information provided to them in order to realise the potential improvement opportunities. For example, IAPT practitioners may consider changes to consultation questions to improve understanding of CMD patient socioeconomic and demographic circumstances to help practitioners recommend the most appropriate treatment pathways.
Outputs:
The expected outputs of the processing will be:
A report of findings to IAPT service providers and NHS England and Improvement will be produced and disseminated (expected autumn 2025)
Submissions to peer reviewed journals expected in Autumn 2026
A summary of the research project and findings will be included in one chapter of a journal-styled thesis to be submitted to the University of Manchester in September 2026
Presentation at the Health Services Research UK conference to raise awareness in the research community and gain feedback. Interim findings will be presented at conferences throughout the lifetime of the project in order to gain timely engagement and feedback.
Presentations at the Health Economy Study Group, European Health Economic Association Conference, and International Health Economic Association conference.
The outputs will not contain NHS England Data. Technical controls in the Secure Data Environment restrict user exports to aggregated outputs 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
Direct bilateral engagement with provider and commissioner audiences through Integrated Care Systems (ICS) leadership
Briefing documents provided to NHS England.
Reports aimed at IAPT service providers, the NHS R&D Forum, NIHR Applied Research Collaborations (ARCs), Academic Health Science Networks (AHSNs), and the Community Network hosted by the NHS Confederation and NHS Providers which acts as the national voice for community providers.
Social media
The study team expect to disseminate findings in Autumn 2025 and Autumn 2026.
Processing:
No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).
NHS England will grant access to the Data via the Secure Data Environment (SDE). The SDE is a secure data and research analysis platform. It allows approved researchers with approved projects access to pseudonymised data and analytics tools.
NHS England will provide access to the relevant records from the Improving Access to Psychological Therapies (IAPT) v2 dataset. 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.
SDE users can request exportation of aggregated analysis results (suppressed and summarised according to the NHSE SDE Disclosure Control rules) subject to review and approval by the NHS England SDE Output Checking team. The SDE Output Checking team will ensure that no output contains information which could be used either on its own or in conjunction with other data to breach an individual's privacy.
Access to the SDE is controlled via a multi-factor authentication mechanism and access is restricted to the datasets and periods detailed within this agreement. Access and use of the system is fully auditable, and all users must comply with the use of the Data as specified in this DSA.
Users are only authorised to access the Data specified in this DSA and can utilise a variety of analytical tools available within the SDE platform. Users are not permitted to export record-level data from the SDE.
The Data will be stored on servers at NHS England.
Remote processing will be from secure locations within the UK.
The Data will not leave the UK at any time.
SDE user access is restricted to an individual within The Centre for Primary Care and Health Services Research at the University of Manchester. The individual is a PhD student and a substantive employee of the University of Manchester.
All personnel accessing the Data have been appropriately trained in data protection and confidentiality.
The aggregated outputs derived from the Data will be combined with aggregated data from the publicly available IAPT monthly activity data (https://digital.nhs.uk/data-and-information/publications/statistical/psychological-therapies-report-on-the-use-of-iapt-services) to produce provider/area-level IAPT performance metrics of the IAPT providers. This poses no risk of reidentification to patients included in the IAPT dataset.
There will be no requirement and no attempt to reidentify individuals when using the Data.
A PhD student Data Analyst from The Centre for Primary Care and Health Services Research of the University of Manchester will analyse the Data for the purposes described above. Research fellows (also supervisors of the PhD student) will collaborate with the PhD student to produce the expected outputs described in this Data Sharing Agreement.