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Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method

机译:通过聚类分析和多标准决策分析解决公共卫生政策中的偏好异质性:方法证明

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The use of subgroups based on biological-clinical and socio-demographic variables to deal with population heterogeneity is well-established in public policy. The use of subgroups based on preferences is rare, except when religion based, and controversial. If it were decided to treat subgroup preferences as valid determinants of public policy, a transparent analytical procedure is needed. In this proof of method study we show how public preferences could be incorporated into policy decisions in a way that respects both the multi-criterial nature of those decisions, and the heterogeneity of the population in relation to the importance assigned to relevant criteria. It involves combining Cluster Analysis (CA), to generate the subgroup sets of preferences, with Multi-Criteria Decision Analysis (MCDA), to provide the policy framework into which the clustered preferences are entered. We employ three techniques of CA to demonstrate that not only do different techniques produce different clusters, but that choosing among techniques (as well as developing the MCDA structure) is an important task to be undertaken in implementing the approach outlined in any specific policy context. Data for the illustrative, not substantive, application are from a Randomized Controlled Trial of online decision aids for Australian men aged 40-69 years considering Prostate-specific Antigen testing for prostate cancer. We show that such analyses can provide policy-makers with insights into the criterion-specific needs of different subgroups. Implementing CA and MCDA in combination to assist in the development of policies on important health and community issues such as drug coverage, reimbursement, and screening programs, poses major challenges -conceptual, methodological, ethical-political, and practical - but most are exposed by the techniques, not created by them. Electronic supplementary material The online version of this article (doi:10.1186/s13561-015-0048-4) contains supplementary material, which is available to authorized users.
机译:在公共政策中,已经建立了基于生物临床和社会人口学变量的亚组来处理人口异质性的做法。除非基于宗教信仰且有争议,否则很少根据喜好使用分组。如果决定将亚组偏好视为公共政策的有效决定因素,则需要一个透明的分析程序。在这种方法研究的证明中,我们展示了如何将公共偏好纳入政策决策中,既要尊重那些决策的多标准性质,又要考虑到与分配给相关标准的重要性有关的人口异质性。它涉及将聚类分析(CA)与多准则决策分析(MCDA)结合以生成优先级子集,以提供将聚类的优先级输入到其中的策略框架。我们使用CA的三种技术来证明,不仅不同的技术会产生不同的集群,而且在技术中进行选择(以及开发MCDA结构)是在任何特定策略环境中实施该方法时要执行的重要任务。说明性而非实质性应用的数据来自40-69岁澳大利亚男性在线决策辅助工具的随机对照试验,考虑对前列腺癌进行前列腺特异性抗原测试。我们表明,此类分析可以为决策者提供对不同子组特定于标准的需求的见解。联合实施CA和MCDA来协助制定有关重要健康和社区问题的政策,例如药物覆盖率,费用报销和筛查计划,这带来了重大挑战-概念,方法,伦理政治和实践-但大多数挑战在于技术,不是他们创造的。电子补充材料本文的在线版本(doi:10.1186 / s13561-015-0048-4)包含补充材料,授权用户可以使用。

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