Putting people with mental illness at the heart of the mental health researchsystem
Creators: name: Kirkham, Elizabeth
Publisher: name: Nature Awards for Inclusive Health Research
Year: 2024
Subjects: classificationCode: 420602
subject: Health equity

description: <b>Abstract</b>Historically, research about mental illness has not done enough to prioritize the perspectives of people with lived experience. This is particularly prevalent in ‘hard sciences’ like data science and genetics. Our team of researchers at the University of Edinburgh in the UK have brought in new working practices aimed at putting people with mental illness at the heart of our research.This process began with research funding designed to support mental health data science in the UK. We used this funding to work with representatives from multiple mental health organizations, many of whom also had lived experience themselves, to conduct research into what people with mental illness want to happen with their mental health data. Following this, we collaborated with experts in data science who have lived experience of mental illness to produce a best-practice checklist for researchers and organizations working with mental health data.<b>Introduction</b>This case study describes a new way of working developed by our team at the University of Edinburgh. The purpose of this approach is to ensure that perspectives of people with mental illness are represented across the research cycle and throughout the mental health research ecosystem.The catalyst for this work was a Medical Research Council (MRC) Pathfinder grant awarded to Andrew McIntosh in 2018. The grant was focused on leveraging routinely collected and linked cohort research data to study common mental health conditions. Elizabeth Kirkham worked as a postdoctoral researcher on this grant, alongside colleagues including Sue Fletcher-Watson and Iona Beange. This case study focuses on how our team developed an inclusive research ecosystem, beginning with the Pathfinder project and applying it to our subsequent research.We worked with numerous mental health charity and advocacy organizations, including Lanarkshire Links, Bipolar Scotland, VOX Scotland, Young Scot and Health in Mind. These partners helped us design and disseminate our research studies in collaboration with this historically under-served patient group. We also worked with other academics, such as representatives of the Cambridgeshire and Peterborough National Health System Foundation Trust (CPFT), to develop and evidence best practice from a data-science perspective.<b>Who should benefit?</b>People with mental illness represent a historically under-served patient group; the share of the UK health research budget spent on mental health is low (only 6.1% in 2018). This picture is improving, amidst growing recognition of the burden of mental ill health on people and society. However, historically, people with mental illness have rarely been included in scientific research about their health conditions. This means that we risk conducting research based on assumptions which do not reflect the needs or desires of the populations we seek to help. Given the relative scarcity of mental health funding, it is essential that projects which do receive funding have genuine impact.Our team have built a research system that is more inclusive of this historically under-served patient group. We used our position as the public engagement and co-production team on the MRC Pathfinder grant to embed patient perspectives into the research cycle.It was important to work with partners who could bring direct lived experience of mental illness to the research process. As well as working with local organizations, we sought input from teams at other universities who were conducting related research. This included colleagues working at the forefront of mental health data infrastructure in the UK such as the Clinical Record Interactive Search (CRIS) mental healthcare data resource in London, and the SAIL Databank in Swansea. We also gained valuable input from colleagues in the CPFT who followed a patient-led approach when setting up their Clinical Data Linkage Service.Working with our partners, we began to develop new ways of working. This included application of the collaborative Delphi method in which a group of experts (people with mental illness and expertise in data science) worked together with the research team to develop new guidance for mental health data science.<b>Engagement</b>Engagement has been central to our development of a more inclusive research system. Through a stakeholder advisory group, we worked collaboratively with local mental health organizations. The stakeholder advisory meetings fed into our research projects, and also allowed us to share findings from relevant research by colleagues at the University of Edinburgh. Our partner organizations also helped to ensure that research opportunities were shared with the wider community—these collaborations helped us to reach patients groups who would not normally come into contact with university-led research projects.As well as working with the advisory group as a whole, Dr Kirkham<b> </b>also benefited from stakeholders’ individual expertise. For example, one stakeholder who is a peer researcher with lived experience, helped prepare research interviews for participants with mental illness. Her insight provided a perspective that could not have been gained from a generic training session.The research team also benefited from engagement with colleagues in academia and healthcare. Our co-created checklist for best practice in mental health data science includes information on how researchers and organizations can take an approach that centres on expert patient perspectives.Quality co-production is rare in data science research. Our checklist is designed as a resource for the wider scientific community who may be unfamiliar with co-production and can use it to understand expert patient perspectives. To further support the wider research community, we asked organizations who have developed best practice in a specific area of data science to contribute case studies to accompany the checklist. This included CPFT, who submitted case studies detailing how they have embedded patient perspectives in their research database of de-identified health data from patients in Cambridgeshire.<b>Research</b>Mental health data science is a good setting to begin applying these new research approaches, given that it is a fast-growing field which can only succeed if it prioritizes the people whose data it depends upon. Our first step was to set up the stakeholder advisory group comprising representatives from multiple mental health organizations, many of whom also had lived experience of mental illness themselves. Throughout the project, we held stakeholder advisory group meetings in which we asked for input on the design of our studies (such as on the wording of a planned survey, or the direction we should take when planning a Delphi study). We also discussed broader topics which could make mental health research more reflective of people’s perspectives, such as appropriate terminology or images. Stakeholders were particularly keen to move away from the ‘head in hands’ images that are often used to depict mental illness, and our advisory group allowed us to identify alternatives.One challenge we encountered was around ensuring that stakeholders were properly compensated for their input. It is appropriate to pay people with lived experience of mental illness for their expertise, yet organizational finance structures can make this challenging. Furthermore, in the UK, paying people for their involvement in research can affect their entitlement to state benefits, which is particularly relevant for those with mental illness who may be disabled by their condition. It is important that researchers seeking to develop a more inclusive culture are aware of such practicalities.Our new approach to research practice led to the creation of a best-practice checklist for mental health data scientists. These guidelines were co-created by the research team and data science experts who had lived experience of mental illness, using the Delphi method. The checklist brings together best practice for data scientists working now, and best practice for the wider field going forward. The involvement of our stakeholders and experts by experience allowed us to ensure that, from the beginning, the checklist reflected the priorities of people with mental illness. Similarly, the involvement of data science partner organizations, including the Scottish Schools Health and Wellbeing Improvement Research Network (SHINE), allowed us to source concrete examples of existing best practice to illustrate the points made in the checklist.<b>Translating to impact</b>Our best-practice checklist is used by the UK’s new health data research hub for mental health, DATAMIND, which aims to collate data in a manner that supports innovative research.We have applied our learning in a Horizon 2020 research project—investigating comorbid mental ill-health and cardiovascular disease (CoMorMent)—which examines genetic and environmental links between mental and physical health. To include people with mental illness in the process of translating the findings of this work from basic science to health care, we set up a focus group in which core findings were discussed, leading to the co-creation of guidance for health professionals who want to discuss the research with patients. By informing health professionals of the context in which information is being received by patients with mental illness, and providing advice on supportive delivery, this guidance is intended to maximize the ways in which emerging scientific research improves the lives of people living with mental illness.