Modern causal methods to estimate the impact of Individual and Group Health Policies using routinely collected data

决策者感兴趣的是评估在初级或二级卫生保健或社会保健机构（全科医生做法、医院或住宅/疗养院）采用的卫生和保健政策的因果效应和成本效益。这类政策通常是在没有首先进行随机研究的情况下引入的，因此政策评估通常依赖于回顾性观察数据，这些数据通常是常规收集的，例如来自全科医生和医院信托的电子健康记录（EHRs）。在使用电子病历数据进行政策评估方面存在一些挑战，需要解决这些挑战，以便对政策影响进行公正的估计。一个关键的挑战是要解释这样一个事实，即受政策影响的人可能在多个方面与未受政策影响的人存在系统性差异。同时与兴趣结果和暴露相关的变量被称为混杂因素。未能对混杂因素进行调整，可能导致对政策效果的估计存在重大偏差。当使用电子病历时，其他偏差的来源源于不准确地定义研究人群、感兴趣的干预措施和时间起源。数据的特殊性也带来了额外的挑战：数据量大，通常记录不一致，增加了识别混杂因素和影响调节因素的复杂性。在高维环境中（即有许多协变量），正确建模复杂的混杂模式是困难的，特别是考虑到它们通常具有时间依赖性。结果、暴露和确定的混杂因素之间关系的错误说明也是一个严重的问题，因为因果推断不可避免地需要样本外推断；错误的描述可能导致难以诊断的误导性结论。近年来，“因果推理”领域提供了概念、工具和统计分析方法，便于从观测数据中估计因果效应。然而，该工具包中仍然存在许多空白。其中一个差距是，文献主要关注个人层面的干预，而不是群体层面的政策干预。此外，为适应电子病历数据的特殊复杂性而扩展的方法直到最近才开始出现，尚未得到广泛使用。拟议的项目是LSHTM医学统计部门和健康基金会（THF）主持的改进分析单位（IAU）之间的合作。IAU是THF与英格兰国民保健服务体系和改进国民保健服务体系之间的一个伙伴关系，旨在评估从地方到国家一级实施的复杂卫生保健政策，以支持决策并为国家政策提供信息。IAU使用新的患者级关联数据集，其中包括二级保健使用信息、死亡率以及社会人口信息，并在适当情况下与初级保健、社会或社区保健或其他地方服务的数据相关联。该单位的工作计划随着时间的推移而变化，但目前的重点是(1)数字初级保健模式对一般实践和随后的医院使用的影响，(2)综合护理计划对二级护理结果的长期影响，以及(3)COVID家庭血氧仪计划对临床有效性和COVID死亡率的影响。拟议的博士项目将利用电子病历数据开发群体层面政策干预的因果推理方法，并将其应用于评估这三个项目的政策影响。由此产生的政策评估估计在承认群体结构、考虑大量混杂因素和模型不确定性方面将更加可靠。本研究提出的方法将为未来的分析人员提供更好的工具，用于基于证据的政策评估，从而更好地支持政策制定者和卫生服务管理者寻求提供更有价值的高质量医疗服务。

Policy makers are interested in evaluating the causal effects and cost-effectiveness of health and care policies introduced in primary or secondary health or social care settings (GP practices, hospitals or residential/nursing homes). Such policies are often introduced without having first conducted a randomised study and therefore policy evaluation usually relies on retrospective observational data, usually routinely collected, such as electronic health records (EHRs) from GP practices and hospital trusts. There are several challenges in using EHR data for policy evaluation, which need to be addressed to enable unbiased estimates of policy impacts. A key challenge is to account for the fact that those exposed to the policy may systematically differ from those unexposed in multiple respects. Variables simultaneously associated with the outcome of interest and the exposure are known as confounders. Failure to adjust for confounding can lead to substantial bias in estimates of policy effects. Other sources of bias when using EHRs stem from inaccurately defining the study populations, interventions of interest and time origins. The specific nature of the data also brings additional challenges: the large volume of data, often inconsistently recorded, increases the complexity of identifying confounders and effect moderators. In high-dimensional settings (i.e. with many covariates), modelling complex confounding patterns correctly, especially given their often time-dependent nature, is difficult. Misspecification of the relationships between an outcome, the exposure and identified confounders is also a serious concern, because causal inference inevitably requires out-of-sample extrapolation; misspecification may result in misleading conclusions that are difficult to diagnose.In recent years, the field of 'causal inference' has provided concepts, tools and statistical analysis methods that facilitate estimation of causal effects from observational data. However, there remain a number of gaps in the toolkit. One such gap is that the literature has focused primarily on individuallevel interventions, rather than policy interventions at a group level. Furthermore, extensions of methods to accommodate the particular complexities of EHR data have only more recently started to emerge and have not yet come into wide use. The proposed project is a collaboration between the LSHTM Department of Medical Statistics, and the Improvement Analytics Unit (IAU) hosted at The Health Foundation (THF). The IAU is a partnership between THF and NHS England and NHS Improvement, created to evaluate complex health care policies implemented at local to national level to support decision making as well as inform national policy. The IAU uses novel patient-level linked datasets, which include information on secondary care use, mortality, as well as socio-demographic information, where appropriate linked with data from primary care, social or community care, or other local services. The unit's work programme varies over time, but at present focusses on (1) the impact of Digital First Primary Care models on general practice and subsequent hospital use, (2) the long term impact of integrated care initiatives on secondary care outcomes, and (3) the impact of the COVID Oximetry at home programme on clinical effectiveness and COVID-19 mortality. The proposed PhD project will develop causal inference methodology for group-level policy interventions using EHR data, and apply this to evaluate the policy impact of these 3 projects. The resulting policy evaluation estimates will be more reliable in terms of acknowledging the groupstructure, accounting for high number of confounders and model uncertainty. The methods developed in this proposed research will equip future analysts with better tools for evidence based policy evaluation that will better support policy-makers and health services managers in their quest to deliver better-value high-quality care.