Valuing Health at the End of Life: A Stated Preference Discrete Choice Experiment

Social Science & Medicine, 124, pp. 58-56 (2015)

59 Pages Posted: 19 Aug 2015

See all articles by Koonal Shah

Koonal Shah

Office of Health Economics; University of Sheffield - School of Health and Related Research (ScHARR)

Aki Tsuchiya

University of Sheffield - School of Health and Related Research (ScHARR)

Arne Risa Hole

University of Sheffield - Department of Economics

Allan J. Wailoo

University of Sheffield - Health Economics and Decision Science Research Group (HEDS)

Date Written: 2015

Abstract

In 2009, NICE issued supplementary advice to its Appraisal Committees to be taken into account when appraising life extending, ‘end of life’ treatments. The advice indicates that under certain circumstances it may be appropriate to recommend the use of such treatments even if their base case cost effectiveness estimates exceed the range normally considered acceptable.

However, the consultation carried out by NICE revealed concerns that there is little evidence to support the premise that society is prepared to fund end of life treatments that would not meet the cost effectiveness criteria used for other treatments. The study described here seeks to address this gap in the evidence.

A discrete choice experiment (DCE) was used to elicit the preferences of a sample of members of the general public in England and Wales over a range of priority setting scenarios. Each choice task involved asking respondents which of two hypothetical patients they thought should be treated, assuming the health service has enough funds to treat one but not both of them. The patients were described in terms of their life expectancy and quality of life without treatment, and the life expectancy and quality of life gains achievable from treating them.

In addition, the survey included two ‘extension tasks’ designed to examine the extent to which respondents’ priority setting choices are influenced by information about how long the patients have known about their illness.

The DCE was carried out using a web‐based survey. A total of 3,969 respondents successfully completed the survey, each completing 10 DCE tasks plus two extension tasks. The sample is representative of the general population in terms of age and gender, and covers a range of social grades.

The conditional logit model was used for modelling. The best fitting model analysed main effects plus three interactions: (i) life expectancy without treatment against life expectancy gain; (ii) life expectancy without treatment against quality of life gain; and (iii) life expectancy gain against quality of life gain.

Using the model results, utility scores were calculated for all of the 110 possible profiles (combinations of attribute levels) in the full factorial design, as well as the predicted probability of choosing each profile from the full set of profiles. The highest ranked profiles (those with the greatest probability of being chosen) were those that involved substantial life expectancy and quality of life gains from treatment. There is a clear positive relationship between the size of the QALY gains from treatment in a given profile and the predicted probability of that profile being chosen. By comparison, whilst there is little evidence to suggest that profiles involving shorter life expectancy without treatment are more likely to be chosen than those involving longer life expectancy without treatment – the observed patterns are noisy.

Overall, the results indicate that choices about which patient to treat are influenced more by the sizes of the health gains achievable from treatment than by patients’ life expectancy or quality of life in absence of treatment. The extent to which patients are at their end of life does not appear to be the driving factor, although it should be noted that all of the scenarios in this study involve relatively poor prognoses (across all profiles, the patient who is ‘best off’ without treatment would still die within five years).

Some respondents appear to support a QALY‐maximisation type objective throughout, whilst a small minority always seek to treat those who are worse off without treatment. The majority of respondents, however, seem to advocate a mixture of the two approaches.

Overall, the results call into question whether a policy of giving higher priority to end of life treatments than to other types of treatments is supported by the public, particularly if the health gains offered by the treatments being ‘de‐prioritised’ are larger than those offered by the end of life treatments. The results also suggest that the focus on life extensions and absence of quality of life improvements in the current NICE end of life criteria may be consistent with public preferences.

Results from the extension tasks show that including information about the amount of time patients have known about their prognosis has a clear impact on preferences. All other thing being equal, respondents are less likely to choose to treat a patient if they have known about their illness for two years than if they have only just found out about their illness. Further investigation of this factor is recommended.

Suggested Citation

Shah, Koonal and Tsuchiya, Aki and Hole, Arne Risa and Wailoo, Allan J., Valuing Health at the End of Life: A Stated Preference Discrete Choice Experiment (2015). Social Science & Medicine, 124, pp. 58-56 (2015), Available at SSRN: https://ssrn.com/abstract=2631592 or http://dx.doi.org/10.2139/ssrn.2631592

Koonal Shah (Contact Author)

Office of Health Economics ( email )

7th floor Southside
105 Victoria Street
London, SW1E 6QT
United Kingdom

University of Sheffield - School of Health and Related Research (ScHARR) ( email )

Regent Court
30 Regent Street
Sheffield S1 4DA
United Kingdom

Aki Tsuchiya

University of Sheffield - School of Health and Related Research (ScHARR) ( email )

Regent Court
30 Regent Street
Sheffield S1 4DA
United Kingdom

Arne Risa Hole

University of Sheffield - Department of Economics ( email )

9 Mappin Street
Sheffield, S1 4DT
United Kingdom

Allan J. Wailoo

University of Sheffield - Health Economics and Decision Science Research Group (HEDS) ( email )

Sheffield, S10 2TN
United Kingdom

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