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

利用机器学习、因果推理和运筹学方法定制卫生政策以改善结果

• 批准号: MR/T04487X/1
• 负责人: Noemi Kreif
• 金额: $ 51.91万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT04487X%2F1
• 关键词: Tailoring; health; policies; improve; outcomes

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.

为了最大限度地发挥卫生政策对人口健康的影响，改善健康的公平分配，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？谁受益最多？该政策是否减少了健康不平等？谁有资格参加某个方案？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，并考虑不同类型的个人之间的影响如何不同（亚组之间的治疗效果异质性）。虽然这种亚组分析以前已经做过，以前使用的方法是有限的，因为它们打开了大门，研究人员樱桃挑选亚组的基础上，结果是统计上显着的估计。相比之下，机器学习技术--从数据中学习的自动算法--可以揭示政策影响的模式，这可能是事先没有预料到的。这对于需要了解谁从所实施的政策中受益最多（谁受益较少或根本没有受益）的政策制定者来说很重要。一旦我们能够评估一项特定政策如何影响人口的不同亚群体以及影响的程度，我们就可以利用这些见解并设计卫生政策的资格标准，以便最大限度地实现决策者的目标，例如在固定的卫生保健预算下产生最大的总健康效益。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，特别是它们在了解“治疗效果异质性”和政策目标方面的应用。因此，在如何将这些最新工具应用于卫生政策评价方面没有方法学指导。拟议的研究旨在通过评估和扩展现有方法来应对评估卫生政策时通常出现的具体挑战。这些挑战包括统计方面的挑战，例如需要考虑由于治疗组和对照组之间观察到的和未观察到的差异而产生的潜在偏倚;但也面临着使评估与决策相关的挑战，不仅要考虑干预的好处，还要考虑干预的成本（成本效益），以及在设计政策应针对哪些人口亚群时考虑到预算限制或公平因素。本项目建议应对这些挑战，通过在卫生政策评价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。这将有助于支持显著改善人口健康，并缩小国家内部富人和穷人之间的健康差距。方法的发展是由两个低收入和中等收入国家背景下的案例研究，其中在改善健康和减少健康不平等方面的收益特别大。案例研究集中在两个大规模的卫生政策与持续相关性：主要的公共医疗保险改革在印度尼西亚和全国范围内的家庭健康Programme在巴西，以最大限度地提高当前的卫生政策制定的影响，在案例研究中的具体研究问题的设计将受益于印尼和巴西的合作者以及决策者的持续投入。通过广泛的交流和影响活动，该项目将为学术界及其他领域从事卫生政策评估的研究人员提供方法学见解。

项目成果

Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia

• DOI: 10.1007/s10742-021-00259-3
• 发表时间: 2021-11
• 期刊: Health Services and Outcomes Research Methodology
• 影响因子: 1.5
• 作者: N. Kreif;K. DiazOrdaz;R. Moreno-Serra;A. Mirelman;Taufik Hidayat-;M. Suhrcke

Policy Learning with Rare Outcomes

政策学习取得罕见成果

• 发表时间: 2023
• 作者: Hatamyar J

Integrating decision modelling and machine learning to inform treatment stratification

集成决策建模和机器学习以告知治疗分层

• 发表时间: 2023
• 作者: Glynn D

Handbook of Research Methods and Applications in Empirical Microeconomics

实证微观经济学研究方法与应用手册

• DOI: 10.4337/9781788976480.00025
• 发表时间: 2021
• 作者: Shah V

Integrating machine learning estimates of heterogeneous treatment effects and decision modelling

整合异质治疗效果的机器学习估计和决策建模

• 发表时间: 2022
• 作者: David Glynn