Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life

Huynh, E; Coast, J; Rose, J; Kinghorn, P; Flynn, T

Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life

Huynh, E Coast, J Rose, J Kinghorn, P Flynn, T

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Publication Type:: Journal Article
Citation:: Social Science and Medicine, 2017, 189 pp. 114 - 128
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Huynh, E	en_US
dc.contributor.author	Coast, J	en_US
dc.contributor.author	Rose, J	en_US
dc.contributor.author	Kinghorn, P	en_US
dc.contributor.author	Flynn, T	en_US
dc.date.available	2017-07-19	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	Social Science and Medicine, 2017, 189 pp. 114 - 128	en_US
dc.identifier.issn	0277-9536	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116840
dc.description.abstract	© 2017 Elsevier Ltd End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.	en_US
dc.relation.ispartof	Social Science and Medicine	en_US
dc.relation.isbasedon	10.1016/j.socscimed.2017.07.012	en_US
dc.subject.classification	Public Health	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Terminal Care	en_US
dc.subject.mesh	Choice Behavior	en_US
dc.subject.mesh	Health Status	en_US
dc.subject.mesh	Quality-Adjusted Life Years	en_US
dc.subject.mesh	Quality of Life	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Cost-Benefit Analysis	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Surveys and Questionnaires	en_US
dc.subject.mesh	United Kingdom	en_US
dc.title	Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life	en_US
dc.type	Journal Article
utslib.citation.volume	189	en_US
utslib.for	11 Medical and Health Sciences	en_US
utslib.for	14 Economics	en_US
utslib.for	16 Studies in Human Society	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Strength - BIDA - Business Intelligence and Data Analytics
pubs.organisational-group	/University of Technology Sydney/Strength - CENSOC - Study of Choice
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	189	en_US

Abstract:

© 2017 Elsevier Ltd End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116840