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Health Society

Social and Economic Predictors of the Severe Mental Disorders: The SEP-MD Data Linkage Study

Despite previous work focussed on the social determinants of psychoses and other severe mental health conditions' onset, course and outcomes, key questions remain. There has been limited research on the impacts of social and environmental conditions, and few studies have focussed specifically on the experiences of racially minoritised groups.

The UK census is conducted every ten years and collects detailed information on social indicators like migration status, housing, education and employment. Our project links data from the Census with clinical records from one of Europe’s largest secondary mental health providers, South London & Maudsley Trust (SLaM Trust). SLaM provides mental health care to an ethnically diverse, urbanised location in South East London, with a catchment area of 1.3 million people.

This data linkage is unique because it provides details on social and economic conditions which are often not included, missing or captured inadequately in standard electronic health record systems. Through advanced statistical methods and informatics, we will analyse the data to better understand relationships between mental health, employment, ethnicity and overlapping forms of disadvantage. Our research questions will inform current service, treatment and policy concerns relating to the severe mental illnesses.

Project Phases

In Phase 1, we completed ethics applications and established a linkage of Census 2011 records to electronic health records of service users from South London & Maudsley Trust (though the NIHR Maudsley BRC Clinical Record Interactive Search (CRIS) system). This has led to a linked cohort of more than 20,000 people with severe mental health conditions such as schizophrenia, bipolar affective disorders, and major depression. Each case was matched to 5 population controls, who did not have these conditions. The linkage will enable us to examine the social determinants of onset, course, and outcomes (admissions, worklessness and mortality) in people living with severe mental health conditions. We also developed a network of individuals from universities, commissioners, policymakers and third sector organisations to increase the reach and impact of our work.

Phase 2 is dedicated to data analysis using advanced statistical methods. We will engage community members and partners around the methods of analysis and accessibility of research processes. Our findings will be shared in collaboration with our partners, through email, social media, meetings, and conferences.

Aims

We will explore how neighbourhood and individual-level factors are related to mortality, in-patient admissions and long-term worklessness in people with severe mental health conditions. We will also study ethnic inequalities to deepen our understanding of disparities in outcomes. 

The study is designed to answer the following research questions:

 

  1. In an urban and ethnically diverse sample with SMI, what are the associations of individual-level and neighbourhood factors with mortality, in-patient admissions and long-term worklessness?
  2. Is disadvantage across multiple indicators (at individual and neighbourhood-level) compared to disadvantage on single indicators associated with a greater risk of mortality, in-patient admissions and long-term worklessness?
  3. How do the associations identified in questions 1 and 2, vary according to ethnicity?

 

We will build on existing community partnerships and make all findings and methodological procedures publicly accessible. We aim to produce a range of outputs including (1) academic papers, (2) a linked dataset accessible for further research, (3) a handbook detailing methodologies, (4) plain English summaries and reports for online dissemination, and (5) presentations for and with community partners and at conferences.

Methods

South London & Maudsley Trust (SLaM) allows access to anonymised health records through the Clinical Record Interactive Search (CRIS) system. The clinical dataset includes the records of 19,808 individuals with a diagnosis of SMI, including Schizophrenia-spectrum disorders, Bipolar Affective Disorders and non-organic psychoses. Social indicators are provided by the linked Census datasets.

Following ethical applications and approvals, individual-level Census records from 2011 were linked to records from CRIS and to death registrations. In addition, 5 controls from the surrounding area who had completed Census questions but who did not have a history of severe mental health problems, were matched to participant with severe mental health problems. The linked dataset is hosted in an ONS secure setting and is accessible to accredited researchers working on the study, subject to completing an approvals process.

Data analyses will be focussed on the following outcomes:

  1. Cause of death
  2. Mental health hospital admissions
  3. Employment status and long-term worklessness

We are using STATA and Mplus software to analyse the data.

Project status: Ongoing

Principal investigators

jayati das-munshi

Jayati Das-Munshi

Clinical Reader

Investigators

Professor Robert Stewart

Professor of Psychiatric Epidemiology & Clinical Informatics

Craig-Morgan-IoPPN

Craig Morgan

Professor of Social Epidemiology

Michael Dewey

Emeritus Professor of Statistical Epidemiology

Funding

  • Funding body: Economic and Social Research Council (ESRC)
  • Amount: £200,000
  • Period: January 2020 - June 2023

Keywords

  • Data
  • linkage
  • severe
  • mental
  • illness
  • (SMI)
  • ethnicity
  • inequalities
  • worklessness