People with severe mental illnesses like schizophrenia or bipolar disorder are more likely to recover when they have stable housing, supportive relationships, and meaningful work—yet most NHS treatments ignore these social factors entirely. This research hub aims to change that by putting social determinants at the centre of mental health science. Current treatments focus on medication and therapy, but they miss why some people recover while others do not, even with identical clinical care. The gap is social: the conditions of daily life—where someone lives, who they talk to, whether they feel connected—powerfully shape illness trajectories. This project will generate new knowledge on exactly how those factors work, and whether protective social conditions can be deliberately strengthened to build resilience. If successful, the research could reshape how mental health services are designed. Instead of prescribing only pills or talking therapies, clinicians might prescribe community activities, social groups, or housing support—interventions that target the root social causes of poor outcomes. The work also opens the door to policy changes in housing, employment, and social care that could collectively reduce health inequalities for people with severe mental illness.
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Severe Mental Illnesses (SMIs) arise from a complex interplay of biological, psychological, and social factors. Yet most frontline treatments for SMIs target biological and psychological factors. This hub will focus on "social determinants": the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems that shape the conditions of daily life. We will draw on multi-disciplinary and cross-sectoral expertise and experience, interweaving these insights with lived experiences to a) generate new knowledge on the role of social determinants in influencing the course and outcomes of SMIs and b) explore how we can leverage "protective" social factors to build resilience and recovery in people with SMIs. Three workstreams are planned. Workstream 1 will use machine learning predictive models to analyse longitudinal, secondary datasets containing data on individuals with various SMIs. We will explore how locality-based predictors of structural social determinants cluster and interact to predict the course and outcomes of various SMIs. Workstream 2 will collect new information from a cohort of patients with SMIs to measure how the quantity and quality of interpersonal exchanges, relationships and support networks across social spheres contributes to the recovery journeys of people with SMIs. We will explore the biological and cognitive mechanisms by which social factors exert their effects. Workstream 3 will explore the potential of social prescribing and community organising in supporting patients with SMIs. Creative workshops and sandpits with lived experience participants, community organisations, and other stakeholders will allow participants to share their experiences to shape the development of these protocols/programmes. Addressing key social determinants of SMIs tackles health inequalities invites an opportunity for policy change to collectively support people with SMIs.
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