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公平分配，政策制定者需要回答以下问题：政策对预期的接受者有效吗？谁受益最大？这项政策是否减少了健康不平等？谁应该有资格参加一项计划？为了产生回答这些问题的证据，政策评估需要超越平均人口影响，并考虑不同类型的个人的影响如何不同(治疗效果在不同亚组中的异质性)。虽然以前已经进行过这样的子组分析，但以前使用的方法是有限的，因为它们为研究人员打开了大门，根据在估计中被证明具有统计意义的东西来挑选子组。相比之下，机器学习技术--从数据中学习的自动算法--可以揭示政策影响中的模式，这可能是事先没有预料到的。这对于需要了解谁从相关政策中受益最多(以及谁做得更少，或者根本不做)的政策制定者来说很重要。一旦我们能够评估一项给定的政策如何影响人口的不同亚群以及影响程度，我们就可以利用这些洞察力来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，通过在给定固定的卫生保健预算的情况下产生最大的总卫生福利。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，特别是它们在学习“治疗效果异质性”和政策针对性方面的应用。因此，没有关于如何在卫生政策评估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做出贡献，以应对在评估卫生政策时通常出现的具体挑战。这些挑战包括统计学上的挑战，如需要考虑由于观察到的和未观察到的治疗组和对照组之间的差异而造成的潜在偏差；以及通过不仅考虑干预的好处而且考虑干预的成本(成本效益)，以及在设计政策针对哪些人口亚组时考虑预算限制或公平考虑，使评估与决策相关的挑战。该项目建议通过在卫生政策评估的背景下评估和推广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，来解决这些挑战。通过成功应对这些挑战，该项目将提供方法，帮助研究人员和政策制定者对全国卫生政策进行更全面的评估。这有助于支持人口健康的显著改善，并缩小各国内富人和穷人之间的健康差距。这些方法的发展受到低收入和中等收入国家背景下的两个案例研究的推动，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持续相关的大规模医疗政策：印度尼西亚的重大公共医疗保险改革和巴西的全国家庭健康计划。为了最大限度地影响当前的医疗政策制定，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的合作者以及政策制定者的持续投入。通过广泛的交流和影响活动，该项目将向从事卫生政策评估的研究人员、学术界和其他方面提供其方法论见解。

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.