Evaluating the Heterogeneous Impacts of the Improving Access to Psychological Therapies (IAPT) Programme

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Poor mental health is a serious concern both in the UK and worldwide. In the UK, mental health problems are the largest single cause of disability, with one in four adults experiencing at least one diagnosable mental health problem in any given year. The pandemic and associated lockdowns have further led to substantial reductions in mental wellbeing. We expect mental health to be a top priority on the policy agenda in the recovery from the pandemic.In England, mental health problems in the population have been addressed by an unprecedented, large-scale public mental health programme implemented by the NHS: the Improving Access to Psychological Therapies (IAPT) programme, which targets adults with depression and anxiety disorders and which is one of the most ambitious schemes of its kind. Since its nationwide implementation in 2008, the programme has been shown to be effective and, on average, 50% of patients show recovery after referral. However, recovery rates differ significantly between different population groups and geographical areas. Mitigating these differences is a top priority for the NHS. It is likely to become even more important as the pandemic has been shown to significantly worsen existing mental health inequalities between different patient groups and local areas.We aim at studying these inequalities in the effectiveness of the IAPT programme systematically, to help policy-makers understand its underlying sources and to inform evidence-based recommendations on how to reduce them. To do so, we will first distinguish the causal effect of the programme, i.e. the treatment effect, from other confounding factors that influence treatment outcomes, e.g. natural recovery rates. Then, we will identify the factors that are systematically associated with heterogeneous treatment effects.Identifying individual treatment effects and inequalities therein will provide insights into which combinations of service, treatment, and patient characteristics yield the most significant improvements in mental health. We will combine detailed service and patient data from NHS Digital with geographical information on local areas, which will enable us to construct measures of wealth, education, ethnic, and other characteristics, to study inequalities resulting from local area characteristics. We will then aggregate patient-level outcomes at the service-provider level to analyse what drives inequalities in the recovery rates of services with different characteristics. This will provide insights into the differences between high and low-performing services and how treatment outcomes of low-performing services can be improved.Our project has two key innovations: the first is to evaluate the causal impacts of the IAPT programme. Existing analyses of the IAPT programme are descriptive. We take this further, by using quasi-experimental techniques to estimate causal average and heterogeneous treatment effects. In particular, we will exploit differences in waiting times for treatment to construct quasi-experimental control groups. The second key innovation is that we will complement standard econometric approaches to analysing treatment effect heterogeneity with state-of-the-art machine learning techniques to uncover complex relations between service, patient, and local area characteristics and inequalities in the effectiveness of the IAPT programme.Our goal is to identify how services and treatments can be improved and patients and treatments better matched, informing evidence-based policy on how to improve treatment of mental ill-health in the UK and beyond.