title: Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method.
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.
authors.affiliation: Research Unit for General Practice, Department of Public Health University of Southern Denmark, J.B. Winsløws Vej 9 B, 5000 Odense C, Denmark.
authors.forename: Mette Kjer
authors.initials: MK
authors.lastname: Kaltoft
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052 Australia.
authors.forename: Robin
authors.initials: R
authors.lastname: Turner
authors.affiliation: NHMRC Clinical Trials Centre, Sydney Medical School, Charles Perkins Centre, Johns Hopkins Drive, Camperdown, NSW 2050 Australia.
authors.forename: Michelle
authors.initials: M
authors.lastname: Cunich
authors.affiliation: Faculty of Medicine, School of Public Health University of Sydney, Edward Ford Building (A27), Sydney, NSW 2006 Australia.
authors.forename: Glenn
authors.initials: G
authors.lastname: Salkeld
authors.affiliation: Research Unit for General Practice, Department of Public Health University of Southern Denmark, J.B. Winsløws Vej 9 B, 5000 Odense C, Denmark.
authors.forename: Jesper Bo
authors.initials: JB
authors.lastname: Nielsen
authors.affiliation: Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH UK.
authors.forename: Jack
authors.initials: J
authors.lastname: Dowie
keywords: Cluster analysis; Heterogeneity; Multi-criteria decision analysis; Preference subgroups