Neighbourhood / socioenvironmental predictors of outcomes in psychosis

Lead Supervisor
Dr Jayati Das-Munshi
Senior Lecturer / Clinician ScientistInstitute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London

Dr Margaret Heslin
Health Service and Population Research Department, King’s College London

Project Details

Psychosis (schizophrenia and related disorders, bipolar disorder, depressive psychosis) are long-standing conditions with substantial impacts on individuals across the life-course (Malla, 1999). Psychosis is ranked fifth and sixth in men and women respectively as a cause of years lived with disability (Lora, 2011). However, outcomes in people with psychosis show high individual variability from discrete episodes followed by prolonged recovery in some people, to a chronic and highly disabling course in others (Morgan, 2014). Little is known about environmental predictors of prognosis (such as employment, mortality and admissions) following the onset of disorder. Recent work has indicated that although premature mortality is elevated in people with psychoses, this may be lower in ethnic minority groups resident in areas of higher ethnic density, as individuals with psychosis may be less socially isolated in these areas (Das-Munshi et al 2017, 2019). In addition, residency in more deprived areas may increase the risk of admissions in people with severe mental illnesses (Thornicroft, 1991, Croudace, 2000, Boardman, 1997).  The interplay of social factors at the individual level with neighbourhood level characteristics remain unclear but could form important targets for intervention if better understood (Dragt, 2010).  

This project will aim to assess individual-level and area-level associations for outcomes (mortality, employment and admissions) in a cohort of 20,000+ people with clinically defined severe mental illnesses. The clinical data is from the NIHR-funded Clinical Record Interactive Search (CRIS) which provides de-identified records on over 350,000 mental health service users from a geographic catchment of 1.3 million residents. This will be linked to detailed individual-level data from UK Census conducted in 2011. The data linkage is the first of its kind in the UK and will bring in important individual-level social information into the clinical record. 

The project would suit a student with a good MSc or equivalent qualification in epidemiology, statistics, population health and/ or other data sciences related disciplines with some experience in working with statistical software such as STATA, MPLUS, R or equivalent.  An understanding of mental illness and psychiatry is desirable and an enthusiasm for the topic area essential.  The student will be provided with training/ support in advanced data linkage and quantitative data analyses methods and will be encouraged to attend in-house training at KCL or elsewhere as appropriate. The lead supervisor is affiliated with the newly established ESRC-funded Centre for Society and Mental Health, and the student will be strongly encouraged to attend relevant interdisciplinary seminars linked to this centre. Due to the location of the linked records (at a secure research site (SRS) in central London) the student will be expected to undertake ONS approved researcher training and spend part of the time accessing and analysing sensitive linked data at the SRS.

Indicative timeline:
Year 1- Undertake training, gain all necessary approvals and complete systematic reviews. Begin data analysis. PhD Upgrade. 
Year 2- Undertake/ complete main analyses.
Year 3- Finalise analyses and complete PhD write up. Submit papers for publication. PhD viva/ completion.


The dataset has all necessary ethical approvals (from NHS REC, ONS NSDEC and CRIS Oversight committee). The student will have to gain approved researcher status from the ONS (attend a training and submit an application) – which the supervisors will support. The linked data will be accessible in a secure research site after all approvals have been gained. 


severe mental illness; psychosis; outcomes; data linkage; social psychiatry; social epidemiology; inequalities; psychosis