Title: Causation in a complex social system: understanding schizophrenia, bipolar disorder, self-harm, and suicide in socially excluded groups
Funder: Department of Health and Social Care

Abstract: Background: Severe mental illness (SMI) including schizophrenia, bipolar disorder, self-harm, and suicide contribute a large burden of mortality and morbidity in England. The distribution of SMI is unequal across the population, with elevated rates in socially excluded groups, making it an important public health priority. While research has revealed aetiological clues for SMI, there is limited evidence on the social and environmental causes. These causes exist with a complex system, making estimating the causal associations with SMI challenging. Better understanding of the modifiable social causes of SMI is needed to develop effective public health interventions.Aim: The aim is to explore the causal architecture of SMI in socially excluded populations using recent advances in causal inference, epidemiology, and systems science. By elucidating the causal factors in the social environment for SMI, I will generate evidence to inform public health planning. I will use a range of methods across three workstreams:Workstream 1: Causal analysis of neighbourhood factors and SMI using natural experiments In the absence of randomised trials on neighbourhood factors and SMI, natural experiments create opportunities to explore how factors in the social environment are causally associated with SMI. The Swedish refugee resettlement programme creates a natural experiment as selected refugees are placed into neighbourhoods, emulating a trial-based design. I will estimate the causal effect of neighbourhood factors on SMI, providing important insights. Workstream 2: Microsimulation, agent-based models, and economic evaluation to explore regional inequalities in social exclusion and SMI I will use data from electronic health records, national statistics, and cohort studies to explore how SMI is geographically distributed. Using static and dynamic microsimulation, I will explore the distribution of SMI and model the impact of counterfactual scenarios to estimate the impact that reducing inequality would have on rates SMI. I will develop an agent-based model to estimate the causal association between social exclusion and SMI. I will conduct a cost-effectiveness analysis of interventions which reduce inequalities. I will create an online tool where practitioners can enter details of their local area into the simulation and economic models to estimate the impact that interventions of reducing inequalities might have on rates of SMI.Workstream 3: Predicting suicide clusters to prevent self-harm and suicideWhile suicide prevention is an important public health priority, identifying suicide clusters remains difficult. In this workstream, I will use data from electronic health records, suicide helpline calls, and Google Trends data to predict suicide clusters. I will use agent-based modelling to estimate what early identification of suicide clusters, and resulting timely mental health support, might have on reducing rates of self-harm and suicide. These models will provide proof-of-concept for the use of modelling for public health planning, which could be applied to a wide range of questions to prevent mental illnesses and support outcomes for those with SMI.This fellowship will allow me to apply cutting-edge methods to augment my epidemiological skills. It will allow me to work with members of the public and practitioners, supporting my development as a future research leader.

Lay abstract: Schizophrenia, bipolar disorder, self-harm, and suicide are severe mental health illnesses that have a large impact on individuals, their families, and carers. To prevent severe mental health problems, we need to understand the causes. There are many causes of severe mental illnesses and they are connected to each other in complex ways. Many people with mental illnesses are socially excluded, meaning they do not have the resources to fully participate in society. This could be because of poverty, discrimination, neighbourhood factors, or inequality. The aim of this research is to identify the social and environmental factors which cause severe mental illnesses. I will focus on groups who experience social exclusion. I will use the latest tools from epidemiology and data science to explore these questions. The ultimate goal of this research is to provide useful evidence for public health decision making. This research has three parts: 1. Do neighbourhood factors cause severe mental illness?Neighbourhoods vary on in many ways: How urban or rural is the area? Is affordable housing available? Are there good public transportation services? Is there good internet connectivity? Are there high-quality health and social services available? Do residents have access to green spaces?Some people choose to live in an area with high-quality schools, green spaces, or public transportation. Others may live in the area they grew up in or where their family lives. Others are limited to neighbourhoods where there is affordable housing, even if it does not have the amenities they prefer. Neighbourhoods have different resources and opportunities, making them important to social exclusion. Since there are many factors which influence where someone lives, it is hard to measure how neighbourhood factors are linked to mental illnesses. The best way to test the causal relationship between neighbourhood factors and severe mental illnesses would be to randomly assign people to live in different neighbourhoods. Since we cannot usually assign people to different neighbourhoods, I have identified a 'natural experiment.' The Swedish refugee resettlement programme assigns individuals to neighbourhoods. This creates a chance to look at which neighbourhood factors cause severe mental illness. 2. Are there geographic differences in rates of severe mental illnesses? Are these differences due to higher levels of social exclusion in these areas? If we were able to make England more equal, how many cases of severe mental illnesses would we prevent? How much would this save health and social services? Rates of mental health problems vary across England, with northern areas having higher rates compared to southern regions. Levels of social exclusion mental illnesses also varies between regions. I will look at rates of mental illnesses and social exclusion factors by region. I will use simulation models to explore how many cases of severe mental illnesses could be prevented if we reduced inequalities. I will calculate how much would be saved if we prevented mental illnesses by reducing inequality. I will create a tool where practitioners can enter details of their local area into these models to get information about the impact of interventions. 3. Can we predict suicide clusters in England? Can we prevent self-harm and suicide through early identification of clusters?Suicide clusters are geographic areas or periods of time with unusually high rates of suicide. Suicide clusters are hard to identify, but new data may help identify clusters more accurately. I will use health care statistics, information from suicide helplines, and Google Trends data to identify suicide clusters. These models may allow us to identify suicide clusters more quickly and accurately. This could enable local authorities provide timely mental health support, preventing further self-harm and suicide.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: MENTAL HEALTH