Towards early identification of adolescent mental health problems

Many aspects of a child or young person's life can affect their mental health. If someone has a serious mental health problem their general practitioner (GP) may refer them to mental health (psychiatry) services for assessment and treatment by professionals. Mental health services are stretched so often intervene late, leaving people to suffer unnecessarily with problems that therefore may last longer, be more severe, or be harder to treat.

Early warning signs of mental health problems may be noticed by the person themselves or by others (e.g. school staff, social workers). Many things can suggest a mental health problem, such as difficult early experiences, bullying, changes in behaviour, poor school attendance or grades, or risk-taking. Not all who experience one or more of these will have a mental health problem, so we need to take them together to spot patterns that show who is developing problems and may need professional help. However, this information (data) is stored in different places, e.g. by schools, GPs and social workers and so it may be impossible to spot problems early.

Some researchers have joined data from two or more sources to find patterns suggesting mental health problems. Their success indicates good potential in this approach, but they have not made a practical difference for two main reasons: 1) the models are not yet accurate enough, probably because they omit many factors that can lead to problems; 2) the results cannot be used directly to help young people as they are based on anonymous data.

We will develop a system that can be used by health, education, or social workers to identify adolescents showing early signs of mental health problems, to offer them help sooner. At the same time we want to provide better anonymous data for research into predicting mental health problems.

Data must be held securely (most likely in the NHS), and only people involved in a person's care should be able to see it, but we need to understand how best to do this. To use data for research while protecting privacy it will be anonymised, removing anything that directly identifies a person (e.g. name, address, date of birth, NHS number) and access will be restricted to approved researchers. But we do not yet know what technical problems there may be in linking the databases, or what data the system will need in order to detect people showing early signs of a problem. The final challenge is how to make this work within the NHS, schools, and social care settings to enable earlier identification of young sufferers of mental health problems.

Over the next year, we want to tackle these challenges by creating a group including mental health researchers, psychologists, schools, the NHS, councils, computer scientists, security experts, mathematicians, people who provide services, and policy makers, many of whom are doing ground-breaking work in other areas. We want to turn their attention to jointly solving these problems. We must involve young people, their carers, and people with lived experience: it is their data and we need to understand their views. We would like their help thinking about which professionals can see their data, and what should happen when a young person is thought to be developing mental health problems.

We will hold workshops about these questions. We also have permission to create an initial data set with data from health, social services, and education. We will anonymise these, and practise linking and analysing them. These will help us understand the challenges, so that our final plan will be more detailed and likely to succeed.

In the future we want to test if a computer program makes it easier to identify mental health problems and offer young people treatments earlier, and if they get better quicker because of this. This might have a range of benefits including helping with school, relationships, home life, and getting jobs or into university, and we want to test this theory.

To support our aim to build a novel adolescent mental health early identification tool, we will carry out foundational work through a new cross-disciplinary research team.

A networking event (Apr 2020) will be followed by 1 year of workshops exploring young people's views, data requirements, and implementation challenges.

Working in an NHS- and social care-approved secure environment, a pilot will link social care, mental health, and acute health data in Cambridgeshire & Peterborough through a pseudonymised 7 year dataset for children (0-18y), to be provided by 31 Mar 2020 (see letters of support from STP, CPFT, LA and Acute Trusts). Hashing functions will be used to convert identifiable information (e.g. NHS numbers, dates of birth) into anonymous form in a uniform manner so allowing data from different providers to be linked but remain non-identifiable throughout the process.

As a framework for integrating these heterogeneous data we will create software to convert each data source into time-stamped pseudonymised "events" useful for research and future near-real-time clinical event processing.

Such structured "events" (including e.g. event type, time, place, anonymous person) will be related to unstructured data (e.g. anonymised clinical notes) using a document store. Natural language processing will detect predictors of mental health problems (e.g. bullying, domestic violence, or behavioural problems).

We will use InterMine to facilitate data exploration and statistical analysis, and simplify the creation of approved anonymised study-specific data subsets linked to e.g. Cambridge BioResource genetic data.

Outputs will include 1) a peer-reviewed protocol; 2) publication of young people's views of early identification and data linkage, and 3) a vision statement and system requirements for implementation, with implications for existing pathways. This will form the basis of further grant applications including a career development award.

Please see the Communications Plan for details of how we will promptly publish our findings and the resulting protocol.

Please see the Academic Beneficiaries section to see how the academic community will benefit from this proposal. Below we outline potential Economic and Societal benefits.

Through the work described in this proposal:

Adolescents and young people with mental health problems and their carers have the potential to benefit through more timely interventions, with the prospect of consequent reduced duration and severity of suffering.

Regional Health & Social Care including the new Children's Hospital have the potential to benefit through providing care more efficiently, and through reduced burden due to earlier intervention. The efforts to develop a shared care record should also be of benefit.

National Health, Social Care and Education providers elsewhere in the UK have the potential to benefit through reusing the freely available open source tools and methods developed in order to replicate the overall approach to early identification of adolescent mental health problems.

Regional and National NHS Healthcare and Social Care providers potentially can improve efficiency by applying the tools and resources developed to areas of healthcare other than adolescent mental health.

Education providers potentially can more effectively recognise which children would benefit from referral to mental health services assessment.

Epidemiologists, social scientists, educationalists, health service researchers, psychiatrists, paediatricians and other care providers will benefit from simpler routes to apply for secure access to the integrated 7 year dataset covering ~200,000 children and young people assembled as part of the proposed pilot, as well as improved tools to facilitate analysis.

Society may benefit through improved understanding of young people's views on the use of early identification tools and the linking of electronic health and other records to support early intervention, as well as having greater awareness of the economic and societal importance of big data to modern healthcare.

Dr Moore will benefit from the experience of building and leading a consortium, which will increase her opportunities for subsequent funding including a career development award.

The collaboration partners will benefit, through the opportunity to create new multi-disciplinary links and forge a strong collaboration in preparation for further funding applications.

The InterMine team will benefit by the opportunity to adapt their technology to work in a de-identified/pseudonymised medical setting with the potential for real-world impact.