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Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method
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 prefere...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429422/ https://www.ncbi.nlm.nih.gov/pubmed/25992305 http://dx.doi.org/10.1186/s13561-015-0048-4 |
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author | Kaltoft, Mette Kjer Turner, Robin Cunich, Michelle Salkeld, Glenn Nielsen, Jesper Bo Dowie, Jack |
author_facet | Kaltoft, Mette Kjer Turner, Robin Cunich, Michelle Salkeld, Glenn Nielsen, Jesper Bo Dowie, Jack |
author_sort | Kaltoft, Mette Kjer |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4429422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-44294222015-05-19 Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method Kaltoft, Mette Kjer Turner, Robin Cunich, Michelle Salkeld, Glenn Nielsen, Jesper Bo Dowie, Jack Health Econ Rev Research 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. Springer Berlin Heidelberg 2015-05-14 /pmc/articles/PMC4429422/ /pubmed/25992305 http://dx.doi.org/10.1186/s13561-015-0048-4 Text en © Kaltoft et al.; licensee Springer. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Research Kaltoft, Mette Kjer Turner, Robin Cunich, Michelle Salkeld, Glenn Nielsen, Jesper Bo Dowie, Jack Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title | Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title_full | Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title_fullStr | Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title_full_unstemmed | Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title_short | Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method |
title_sort | addressing preference heterogeneity in public health policy by combining cluster analysis and multi-criteria decision analysis: proof of method |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429422/ https://www.ncbi.nlm.nih.gov/pubmed/25992305 http://dx.doi.org/10.1186/s13561-015-0048-4 |
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