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

Journal Article


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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.

UOW Authors


  •   Kaltoft, Mette Kjer. (external author)
  •   Turner, Robin (external author)
  •   Cunich, Michelle (external author)
  •   Salkeld, Glenn
  •   Nielsen, Jesper Bo. (external author)
  •   Dowie, Jack (external author)

Publication Date


  • 2015

Citation


  • Kaltoft, M. Kjer., Turner, R., Cunich, M., Salkeld, G., Nielsen, J. Bo. & Dowie, J. (2015). Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method. Health Economics Review, 5 (10), 1-11.

Scopus Eid


  • 2-s2.0-84971324181

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3588&context=sspapers

Ro Metadata Url


  • http://ro.uow.edu.au/sspapers/2587

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 5

Issue


  • 10

Place Of Publication


  • Germany

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.

UOW Authors


  •   Kaltoft, Mette Kjer. (external author)
  •   Turner, Robin (external author)
  •   Cunich, Michelle (external author)
  •   Salkeld, Glenn
  •   Nielsen, Jesper Bo. (external author)
  •   Dowie, Jack (external author)

Publication Date


  • 2015

Citation


  • Kaltoft, M. Kjer., Turner, R., Cunich, M., Salkeld, G., Nielsen, J. Bo. & Dowie, J. (2015). Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method. Health Economics Review, 5 (10), 1-11.

Scopus Eid


  • 2-s2.0-84971324181

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3588&context=sspapers

Ro Metadata Url


  • http://ro.uow.edu.au/sspapers/2587

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 5

Issue


  • 10

Place Of Publication


  • Germany