Early Intervention and Prevention

The UK is a global leader in the delivery of national, evidence-based early intervention in psychosis. Our goal is to extend the breadth of this impact to common mental health disorders, such as depression, bipolar disorder and anxiety disorders. By intervening earlier in the life-course we are taking a truly preventative approach with focus on potential causes including bullying, deprivation, substance misuse and childhood trauma.

The development of early intervention services for psychosis across the UK has been a uniquely effective NHS innovation, led by pioneering service developments in Birmingham and Australia in the 1990’s; aiming to improve clinical outcomes, reduce the societal burden of psychosis and the healthcare costs. The UK has a universal healthcare system, and Birmingham the largest 0-25 mental health service in the UK. Our comprehensive clinical infrastructure has contact with thousands of young people with early psychosis and developing mental health disorders every year and therefore provides a unique opportunity for such impact.

Our current research:

Psychosis Immune Mechanism Stratified Medicine Study

Key people: Professor Rachel Upthegrove

In collaboration with the University of Cambridge, we have recently been awarded a major grant from the Medical Research Council to investigate the link between increased brain inflammation and psychosis. Evidence suggests that inflammation may be present before and during the early stages in some, but not all young people with psychosis. In this multicentre Psychosis Immune Mechanism Stratified Medicine Study, Professor Upthegrove and Dr Khandaker will lead a team of investigators, including University of Birmingham MDS Professors Nicholas Barnes, and George Gkoutos, to examine how immune dysfunction could cause psychosis and use advanced AI techniques to identify who might benefit most from novel immune targeted treatments.

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Preventing depression following a diagnosis of first episode psychosis 

Key people: Professor Rachel Upthegrove

Antidepressant for the prevention of DEPression following first episode Psychosis trial (ADEPP)

Existing treatments for psychosis are only partially effective, with only 30% of young people recovering to previous levels of role and work functioning. First episode of psychosis (FEP), which includes illnesses such as schizophrenia,
typically begins in the late teens or early 20s and is diagnosed by the presence of hallucinations (such as hearing voices) and delusions (unusual beliefs held with conviction). Medication and cognitive behavioural therapy help to treat
these symptoms, but young people struggle to return to previous social and work roles, have suicidal thoughts and are at high risk of relapse. 

We want to know whether antidepressant medication can reduce the risk of depression happening at all after FEP, and whether by preventing depression we can improve recovery and reduce relapse.    

Learn more about the study

Midlands Engine: Mental Health Pilot

Key people: Professor Steven Marwaha

Studies show that at any one time, a sixth of the population in England aged 16 to 64 has a mental health problem, which costs employers between £33 billion and £42 billion a year in lost productivity. Recognising the huge impact mental health issues have on employees’ wellbeing and employers’ productivity. Our Professor Steven Marwaha is part of a consortium of researchers working as part of the Midlands Engine Mental Health and Productivity Pilot to break down the barriers facing people with mental health problems, and facilitate their return to work.

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PARTNERS2: collaborative care for people with severe mental illness

Key people: Professor Steven Marwaha

PARTNERS2 is a five year NIHR programme grant awarded to Birmingham and Solihull Mental Health Foundation Trust in conjunction with the Dept of Primary Care at the University of Birmingham. The study aims to help primary care and community based mental health services work more closely together in order to develop an evidenced based model of care to support individuals with severe mental illness in primary care with secondary care support.

Discover the PARTNERS2 study

Recent projects:

Testing a model of altered memory processes in psychosis and autism-spectrum disorder

Key people: Dr Kareen Heinze

This study looks into the mechanisms and shared neurobiology of memory impairments in young people with a diagnosis or autism and/or psychosis.

Long-term outcomes of children with borderline personality disorder traits at 11-12 years

Key people: Professor Steven Marwaha

The aim of this study was to explore to what extent bipolar disorder (BPD) symptoms at 11-12 years of age could predict negative mental health outcomes in the future and investigate which factors might increase this risk.

Learn more about the study

Our PhD research:


Neuroimaging & Metabolic Biomarker Predictors of Recovery from Depression and Psychosis

Depression is the leading cause of disability world-wide with 300 million people being affected. Adolescents and young people are particularly vulnerable to developing this illness. In the UK, the one-year prevalence of depression in adolescents is 5%. While around 60% of young people with depression fully recover, a large proportion will have ongoing difficulties. Depression is also the most common co-morbidity seen with other mental disorders such as psychosis. Complex psychopathology presents clinicians with the challenge of correctly identifying co-morbidities, avoiding misdiagnoses, and tailoring therapeutic options for the individual. 

Currently, clinicians treat depression with co-morbid psychosis the same way they treat major depressive disorder on the presumption that they have the same neural basis. Data from the PRONIA study, an EUFP7 funded 8 centre study recruiting recent onset depression and recent onset psychosis participants will be used to identify common and distinct neuroimaging, clinical, and metabolic features of depression across diagnostic groups. Features that provide significant information will be used to build predictive machine learning models that predict recovery from depression with co-morbid psychosis as well as diagnostic machine learning models.

PhD student: Paris Lalousis

Supervisors: Professor Rachel UpthegroveProfessor Stephen WoodProfessor Nikolaos KoutsoulerisDr. Lianne SchmaalDr. Renate Reniers

Developing and validating outcome prediction models for people with First Episode Psychosis using Supervised Machine Learning (Precision Medicine)

Psychosis is an illness affecting mainly young people with an incidence rate of 31 per 100,000 people. Evidence from meta-analysis suggests that more than half of young people with First Episode Psychosis have poor outcomes. Despite the heterogeneity in remission and recovery outcomes, there are currently no validated tools available for early identification of these patients. Identifying individual patients with poor outcomes at initial clinical contact would help in stratifying interventions, which may lead to improved outcomes and utilisation of clinical resources.

Previous studies have identified predictors of outcome at group level including socio-demographic variables, clinical and treatment response variables, comorbidity, functional and cognitive deficits with inconsistent reliability. There is a lack of clarity on how or which of these group level factors should be combined for prediction at the individual level, or as to the performance of prediction models in clinical settings that may be very different to the primary research study site. In order to maximise clinical utility, an approach is needed that can be applied to stratify the risk of poor outcome in an individual patient at the initial clinical contact. One solution to this is the use of machine learning, where algorithms can sift through a large array of predictor variables, looking for combinations that can reliably predict individual patient outcomes.

This research will involve empirically testing clinical prediction models in clinical settings to validate their utility in stratifying interventions based on risk prediction of outcomes and whether, by such stratification, outcomes can be improved.

PhD student: Rebecca Lee

Supervisors: Dr Pavan MallikarjunProfessor Georgios GkoutosDr Sarah-Jane FentonProfessor Stephen Wood