Tailoring health policies to improve outcomes using machine learning, causal inference and operations research methods

为了最大限度地发挥卫生政策对人口健康的影响并改善卫生的公平分配，政策制定者需要回答以下问题：政策是否对预期的接受者有效？谁是最大的受益者？该政策是否减少了卫生不平等？谁有资格参加课程？为了产生证据来回答这些问题，政策评估需要超越平均人口影响，并考虑不同类型个体之间的影响差异（亚组间治疗效果的异质性）。虽然这样的亚组分析以前已经做过了，但以前使用的方法是有限的，因为它们为研究人员打开了一扇门，让他们根据在估计中具有统计意义的东西来挑选亚组。相比之下，机器学习技术——从数据中学习的自动算法——可以揭示事先可能无法预料的政策影响模式。这对决策者来说很重要，因为他们需要了解谁从所实施的政策中受益最多（谁受益较少，或者根本没有）。一旦我们能够评估特定政策如何影响人口的不同子群体以及影响程度，我们就可以利用这些见解并设计卫生政策的资格标准，以便使决策者的目标最大化，例如，在固定的卫生保健预算下产生最大的总卫生效益。将机器学习与可以估计政策因果影响的方法相结合是相对较新的研究领域，特别是将其应用于了解“治疗效果异质性”和政策目标。因此，没有关于如何在卫生政策评价中应用这些最新工具的方法学指导。拟议的研究旨在通过评估和扩展现有方法来解决在评估卫生政策时通常出现的具体挑战，从而作出贡献。这些挑战包括统计方面的挑战，例如需要解释治疗组和对照组之间观察到的和未观察到的差异所导致的潜在偏差；但是，通过不仅考虑干预的利益而且考虑干预的成本（成本效益），以及在设计政策应针对哪些人口分组时考虑预算限制或公平考虑，使评价与决策相关也是一项挑战。该项目旨在应对这些挑战，方法是在卫生政策评估的背景下评估和扩展最近提出的机器学习和因果推理方法，并首次结合来自不同学科的工具：因果推理、机器学习和成本效益建模。通过成功应对这些挑战，该项目将提供有助于研究人员和决策者对全国卫生政策进行更全面评估的方法。这有助于支持显著改善人口健康，缩小国家内部贫富之间的健康差距。方法发展的动力来自低收入和中等收入国家的两个案例研究，这些国家在改善健康和减少健康不平等方面的收益特别大。案例研究的重点是两项具有持续相关性的大规模保健政策：印度尼西亚的重大公共健康保险改革和巴西的全国家庭保健方案。为了最大限度地影响当前的卫生政策制定，案例研究中具体研究问题的设计将受益于印度尼西亚和巴西合作者以及政策制定者的持续投入。通过广泛的沟通和影响活动，该项目将向学术界和其他领域从事卫生政策评价工作的研究人员提供方法见解。

To maximise the impact of health policies on population health and improve the equitable distribution of health, policymakers require answers to questions such as: does the policy work for the intended recipients? Who benefits most? Does the policy reduce health inequalities? Who should be eligible for a programme? To generate evidence to answer these questions, policy evaluations need to go beyond the average population impact and consider how impacts differ across different types of individuals (treatment effect heterogeneity across subgroups). While such subgroup analysis has been done before, the previously used approaches are limited in that they open the door to the researcher cherry-picking the subgroups on the basis of what turns out as statistically significant in the estimates. By contrast, machine learning techniques - automated algorithms that learn from the data - can reveal patterns in the policy impact that may not be expected beforehand. This is important for policymakers who need to understand who benefits most (and who does less, or not at all) from the implemented policy in question. Once we are able to assess how a given policy affects different sub-groups of the population and by how much, we can take these insights and design the eligibility criteria of a health policies, so that they maximise a decision maker's objective, for example by generating the most total health benefit given a fixed health care budget. The combination of machine learning with methods that can estimate causal impacts of policy is relatively recent area of research, in particular their application to learn about "treatment effect heterogeneity" and the targeting of policies. Hence, there is no methodological guidance available on how to apply these recent tools in health policy evaluations. The proposed research aims to make a contribution by assessing and extending the available approaches to address the specific challenges that typically arise when evaluating health policies. These include statistical challenges, such as the need to account for potential biases due to observed and unobserved differences between the treated and control groups; but also challenges to make evaluations relevant to decision making, by considering not just the benefits but also the costs of an intervention (cost effectiveness), and also considering budget constraints or considerations of equity when designing which population subgroups should be targeted with a policy.This project proposes to address these challenges, by assessing and extending recently proposed machine learning and causal inference methods in the context of health policy evaluations and also by combining tools from different disciplines: causal inference, machine learning and cost-effectiveness modelling, for the first time. By successfully addressing these challenges, this project will deliver methods that will help researchers and policymakers carry-out more comprehensive evaluations of country-wide health policies. This could help support significant improvements to population health and reduce the health gap between the rich and poor within countries. The methodological developments are motivated by two case studies from a low- and middle-income country context, where the gains in terms of improving health and reducing health inequalities are particularly large. The case studies focus on two large scale health policies with ongoing relevance: major public health insurance reform in Indonesia and the country-wide Family Health Programme in Brazil.To maximise impact on current health policy making, design of the specific research questions in the case studies will benefit from on-going input from Indonesian and Brazilian collaborators as well as policymakers. With extensive communication and impact activities, this project will make its methodological insights available for researchers working on health policy evaluations, in academia and beyond.