Title: “The Bigger Picture” How can we make psychological therapy more helpful for people in more deprived areas?
Funder: Health Education England

Abstract: Background Mental illness is a major cause of worldwide disease burden, creating annual socio-economic costs of 100 billion in England. Improving Access to Psychological Therapies (IAPT) is a large-scale government initiative designed to provide effective, evidence based psychological interventions for people with common mental health problems. IAPT treats one million patients annually. Despite efforts to standardise treatments and outcomes, there remains significant variability in effectiveness between IAPT services. A third of referred patients never access treatment, and another third don't complete treatment. Deprivation has well-established links with mental health, and evidence suggests that area-level deprivation predicts poorer treatment access and outcomes, independent of individual socioeconomic status. Current provision models may therefore be maintaining or exacerbating health inequalities. However, the causal mechanisms and relative impact of neighbourhood versus service is unclear. Objectives Quantify the extent of variability in IAPT clinical effectiveness, access, and completion attributable to (a) neighbourhood and (b) service. Determine the extent of this variability explainable by area-level deprivation related variables, after controlling for case-mix. Develop an in-depth qualitative understanding of mechanisms linking deprivation with effectiveness, to inform a theoretical model. Collaborate with stakeholders to identify intervention, policy, and commissioning recommendations, and produce an actionable outcome prediction and benchmarking tool.Plan The research will utilise a mixed-methods sequential design known as ethnographic residual analysis. Work package 1 will comprise a quantitative, cross-classified multilevel analysis of retrospective routine anonymous clinical data from 30-40 IAPT services (estimated 600+ therapists, 12000+ patients). Data will be obtained via the 'iaptus' software provider. All IAPT services collect standardised measures of depression (PHQ-9) and generalised anxiety (GAD-7) symptoms at every appointment. Clinical data will be linked with publicly available area-level socio-demographic and socio-economic data. Multilevel models will partition variance and test variables. The primary outcome is IAPT-defined clinical recovery. Secondary outcomes are treatment access and completion. IAPT uses a stepped-care approach - separate analyses will investigate high intensity provision (psychotherapy/counselling) and low intensity provision (guided self-help). Work package 2 will use a qualitative study of semi-structured interviews to understand causal processes. Patients and stakeholders from two services in deprived areas with poor predicted outcomes will be selected -h; one accurately predicted as under-performing, and one where outcomes are better than expected ('positive deviance'). The estimated sample will include 24-30 patients and 4-8 stakeholders. I will produce an outcome prediction/benchmarking tool with stakeholder involvement, and hold a half-day impact workshop with stakeholders, patients and the public to identify how to implement change. Benefits IAPT is 'fundamental to the implementation and success of the Government's mental health strategy', and research 'to improve [its] efficiency and effectiveness' is a government priority (Department of Health, 2012). Identifying the causal mechanisms by which specific (e.g. deprived) groups or communities find IAPT more or less effective helps us to reduce outcome inequality by a) achieving better engagement with groups at risk of disengagement and poor outcome, b) delivering responsive, targeted interventions congruent to community needs (via IAPT and/or other complementary interventions), and c) improving needs-based service commissioning and psychopolitical validity.

Lay abstract: This research aims to find out how we can make 'psychological therapy' more helpful for people living in poorer areas. Why? Mental illness affects 1 in 4 people and costs 100 billion each year in England. One of the main treatments is psychological therapy. This involves talking with a therapist to help people make positive changes. Improving Access to Psychological Therapies (IAPT) is a government programme offering psychological therapy to a million patients each year. IAPT aims to offer the same therapy to everyone. But, evidence suggests there are big differences in how helpful different IAPT services are ('effectiveness'). Also, many people never attend their appointment ('access'), or stop treatment early ('non-completion'). Patients have told me it is important to understand why this happens. Evidence suggests that differences in access and effectiveness might involve how poor the area is. But, it's not clear whether this is related to the neighbourhood where people live, or how the service provides the therapy. Either way, this might be increasing health differences between rich and poor people (a 'postcode lottery'). My research has three questions: To what extent are differences in effectiveness, access, and completion related to neighbourhood, compared with service? How much of these differences can be explained by how poor the area is (using the government's 'Index of Multiple Deprivation')? How and why is this happening, and what we can do to make therapy more helpful for these groups of people?Answering these questions will tell us which groups of people aren't finding therapy as helpful, and help us to provide services that are designed specifically for them, so that: they can better access treatment, the treatment is appropriate for their needs, money is better spent to help people.How? The research will have two main parts. All IAPT patients complete questionnaires about how they are feeling to measure effectiveness. Part 1 of the research will use non-identifiable information ('data') from over 12000 patients in 30-40 IAPT services. This data has already been collected. I will link this with public data about how poor the area is. I will use mathematical methods to explain differences in effectiveness, access, and completion, accounting for individual patient characteristics. Part 2 will interview patients, therapists and service managers ('participants'). Results from part 1 will indicate where to interview participants from. All participants will come from poorer areas where therapy is predicted to be less effective. But half of these participants will come from areas where therapy is actually more effective than predicted. This way we can learn from what is going right (for example, best practice) as well as what isn't working. I will ask each participant about their experiences of giving or receiving therapy, and use this to learn what isn't working, and what good practice we can share. I will create a computer program to predict and compare access, completion, and effectiveness of therapy in different situations. People will be able to compare what's predicted with what's actually happening. This will help professionals make decisions about how services are provided. I will also meet with professionals, patients, and the public at an event to discuss how to make positive changes to services. Using this discussion, I will work with the public and IAPT to produce a guide to help services improve. Research findings will also be reported using a range of other methods to help reach a wide audience. Patients and the public will help to decide how to report these findings, and may help to write reports. Patients and the public will also give me feedback throughout the project, using meetings and email.

Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH