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

Springer Science and Business Media LLC - 2015
Mette Kjer Kaltoft1, Robin Turner2, Michelle Cunich3, Glenn Salkeld4, Jesper Bo Nielsen1, Jack Dowie5
1Research Unit for General Practice, Department of Public Health, University of Southern Denmark, Odense C, Denmark
2School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
3NHMRC Clinical Trials Centre, Sydney Medical School, Charles Perkins Centre, Camperdown, Australia
4Faculty of Medicine, School of Public Health University of Sydney, Sydney, Australia
5Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK

Tóm tắt

Abstract 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.

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