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

心理健康状况不佳在英国和全世界都是一个严重的问题。在英国，精神健康问题是导致残疾的最大单一原因，在任何一年，每四个成年人中就有一个经历至少一种可诊断的精神健康问题。大流行和相关的封锁进一步导致精神健康水平大幅下降。我们预计心理健康将成为从混乱中恢复的政策议程上的首要任务。在英格兰，国民健康服务体系实施了一项史无前例的大规模公共心理健康计划：改善获得心理治疗(IAPT)计划，该计划针对患有抑郁症和焦虑症的成年人，是同类计划中最雄心勃勃的计划之一，以解决人口中的精神健康问题。自2008年在全国范围内实施以来，该方案已被证明是有效的，平均50%的患者在转诊后表现出康复。然而，不同人口群体和地理区域的回收率差别很大。缓解这些差异是NHS的当务之急。它可能变得更加重要，因为大流行已被证明显著恶化了不同患者群体和当地之间现有的精神健康不平等。我们的目标是系统地研究IAPT方案有效性中的这些不平等，帮助政策制定者了解其潜在来源，并为如何减少这些不平等提供基于证据的建议。为此，我们将首先区分方案的因果关系，即治疗效果，与影响治疗结果的其他混杂因素，例如自然恢复率。然后，我们将确定系统地与不同治疗效果相关的因素。确定个体治疗效果和其中的不平等将提供关于哪些服务、治疗和患者特征的组合在精神健康方面产生最显著改善的洞察。我们将把NHS Digital的详细服务和患者数据与当地的地理信息结合起来，这将使我们能够构建财富、教育、种族和其他特征的衡量标准，以研究由当地特征造成的不平等。然后，我们将在服务提供者层面汇总患者水平的结果，以分析是什么导致不同特征的服务的恢复率不平等。这将提供对高和低服务之间的差异以及如何改善低服务的治疗结果的洞察。我们的项目有两个关键创新：第一个是评估IAPT方案的因果影响。对机构间技术援助方案的现有分析是描述性的。我们更进一步，通过使用准实验技术来估计因果平均和异质性治疗效果。特别是，我们将利用等待治疗时间的差异来构建准实验对照组。第二个关键创新是，我们将用最先进的机器学习技术来补充分析治疗效果异质性的标准计量经济学方法，以揭示服务、患者和当地特征之间的复杂关系，以及IAPT计划有效性中的不平等。我们的目标是确定如何改进服务和治疗，使患者和治疗更好地匹配，为如何改善英国和其他国家的精神疾病治疗提供循证政策的信息。

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.