Does context matter? A discrete choice experiment investigating the impact of palliative context on EQ-5D-5L health state valuation

Each participant undertook two DCEs (DCE 1 in a palliative care needs context and DCE 2 no context), with the order of presentation of DCE 1 and DCE 2 randomised across participants. Each DCE comprised 10 choice sets. In DCE 1, each choice set began with the presentation of a “palliative context needs vignette”, in which the respondent was asked to imagine they had a terminal illness with a specified prognosis or duration of life expectancy (3 months or 2 years) and available level of support (limited or adequate). This was followed by a choice set with three options: two EQ-5D-5L health states and immediate death. Participants were instructed to choose the most and the least preferred option in each choice set, given the context vignette. The context vignette was fixed for each respondent across all 10 choice sets but varied across respondents. Thus, DCE1 had four study arms which presented different context vignettes (Table 1).

In DCE 2 there was no context vignette and respondents were presented with two EQ-5D-5L health states and immediate death and asked to choose the most and least preferred option. Appendix 1 in the supplementary materials provides examples of survey screenshots for both DCEs.

This study followed a four-step iterative process to develop and contextualise vignettes. The aim of Stage 1 involved developing draft vignettes, based on a review of palliative care needs literature and quality of life instruments (for example, ASCOT, ICECAP-SCM). Key considerations included vignette format (story versus self-imagination), terminology (specific diseases versus generic terms), and content scope (symptoms only versus demographic characteristics). Vignettes were designed to be evidence-based while avoiding direct reference to EQ-5D-5L domains. Stage 2 refined the draft vignettes through consultations with seven experts, including three palliative care clinicians, two health economists, and two patient representatives, to ensure clinical accuracy and study appropriateness. Consultations were conducted through a combination of one-to-one interviews and a group discussion, allowing for both individual and collective feedback on the vignettes. Stage 3 involved pilot testing through six cognitive interviews with patient representatives from Voices4Care (UK and Ireland). Voices4Care is a public and patient involvement (PPI) group coordinated by the All Ireland Institute of Hospice and Palliative Care (AIIHPC). The group is composed of people with lived experience of palliative care, including patients, carers, and family members, as well as members of the public with an interest in palliative and end-of-life care. In Stage 3 of vignette development, members of Voices4Care were consulted to ensure that the vignettes were understandable, relevant, and sensitive to the experiences of people affected by palliative care. Subsequently, the vignettes were reviewed by Australian-based palliative care clinicians to ensure its relevance and accuracy for the local context. Based on feedback, vignettes were simplified: palliative duration was reduced to two options (3 months/2 years), ‘feeling prepared to die’ was removed, and ‘terminal illness’ replaced ‘life-threatening condition’ for clarity. Stage 4 finalized the vignettes through ten cognitive interviews in Ireland and a pilot survey with 103 Australian respondents, ensuring comprehension and usability among the general population.

Each choice set consisted of two EQ-5D-5L states and immediate death. The approach to the construction of the health state pairs was to first construct all 3000 pairs of three attributes, each with five levels, with two dimensions better in option A than in option B and with the remaining dimension better in option B than in option A. From this set of 3000 pairs, 1000 random sets of 18 pairs were selected and the set of pairs with the highest efficiency, relative to the efficiency of the full set of 3000 pairs, was chosen. To construct the choice sets, we began with 18 pairs of options, each defined by three attributes with differing levels. Each of these pairs was then expanded into a set of 10 pairs, each involving five attributes. For each set, the original three attributes from the pair determined the levels for those attributes in both options A and B. The remaining two attributes were assigned the same level in both options, ensuring that only the attributes with differing levels contributed to the utility differences between options. This approach allowed us to systematically vary the attribute combinations while maintaining a manageable number of choice sets. While it does not matter at what levels the overlapped attributes are presented, in terms of design efficiency [15]. To ensure balanced representation of attribute levels, we used all 9 possible ordered pairs of the levels 2, 3, and 4 for the two overlapped attributes. Each ordered pair was used in two of the 18 choice sets, so that across the design, each combination was equally represented. The resulting set of choice sets was 92.1% efficient for non-informative priors, compared to the best possible when there must be overlap on two of the five dimensions, and where no dominated pairs are allowed. Finally, these 180 choice sets were grouped into blocks of 10 choice sets each, such that all 10 pairs of overlapped attributes appeared once each in each block and there were 10 different triples for the non-overlapped dimensions. The choice sets are available on request. We chose to overlap on two items as previous studies have shown that respondents find overlap on two dimensions less challenging than having no overlap [16].

While the typical guideline suggests having 20 respondents per choice set [17], empirical studies have shown that 80 respondents per choice set can reduce the standard error by about 50% [18]. Recent research has suggested having 50 respondents per choice set, or even 100 if confidence intervals for ratios are necessary [19]. Hence, for each country, we randomly allocated 100 respondents to each choice set. Within each arm, respondents were presented 20 choice sets in total, 10 choice sets with the palliative context vignette and 10 choice sets with no context vignette.

Respondents were sourced from the panel provider Pureprofile, which maintains representative panels of the general populations in Australia, the UK and Ireland by age groups and sex. All respondent data were anonymised.

Each respondent accessed and completed the survey at their convenience using a web link. The survey began with a series of baseline questions capturing information such as age, sex, partnership status, carer status, two warm-up DCE tasks, the 20 DCE choice sets, followed by additional demographic and socio-economic status questions, including country of birth and country where most time had been spent, religion, education (country specific), employment status, experience with a life-limiting illness, and experience caring for someone who has passed away, experience with severe health problems, ethnic background, whether a language other than English was spoken at home, self-rated health status using EQ-5D-5L, and feedback questions.

This study has been approved by the ethics committees at Trinity College Dublin [03E/2021/07 granted on 07 February 2022], Curtin University [HRE2022-0096 granted on 23 February 2022], University of Technology Sydney [HREC REF NO. ETH22-7049 granted on 12 April 2022], and King’s College London [LRS-21/22-29,058 granted on 02 September 2022].

From the total number of 6,286 individuals who began the survey, 915 did not complete it, resulting in 5,371 completed responses. This group consisted of 2,082 participants from Australia, 1,280 from Ireland, and 2,009 from the UK. The median completion time for this survey was 14.0 min in Australia, 11.2 min in Ireland and 13.0 min in the UK. For our analysis, we used data from the 5,371 respondents who completed the full survey as there was no significant difference in MNL estimates before and after excluding respondents who spent less than 40% of the median completion time and consistently selected the same response for all questions (Appendix 2, Table 1 in supplementary materials; all models included in the sensitivity analysis are available upon request).

Table 2 provides the demographic characteristics of the analysed respondents from each country. Notable demographic differences included Ireland’s younger, more employed population compared to Australia and UK’s older, more retired respondents. Irish respondents also showed higher rates of religious involvement, multilingualism, and better self-reported health (except anxiety/depression). UK and Australian respondents had lower educational attainment and reported more health issues, though UK participants reported less anxiety/depression (Table 2).

Comparison with national statistics showed a higher proportion of younger adults (aged 18–34) and a lower proportion of older adults (aged 75 and above) in the study sample compared to the general populations in Australia, Ireland, and the UK. The gender distribution was similar to national figures, with approximately half of respondents identifying as female in each country. Other characteristics, such as marital status, employment, and education, were broadly comparable to national profiles, though the proportion with a university degree was somewhat higher in the sample.

Overall, the number of health states worse than dead was largest in the Australian sample, while the Irish sample had the lowest number of such states (Fig. 2). In addition, the impact of palliative care contexts was evident in the percentages of health states worse than dead, with the most significant effect in the worst-case scenario (arm 1: the palliative care context with a 3-month life expectancy with limited support). Specifically, under the no context scenario, there were 15.5%, 5,3% and 12.2% health states considered to be worse than dead in Australia, Ireland, and the UK respectively. These percentages increased when respondents were presented with the palliative care scenario (arm 1: a 3-month life expectancy with limited support), with 32.5%, 14.7% and 24.6% in Australia, Ireland, and the UK respectively.

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These findings inform how cost-effectiveness analyses (CEAs) for palliative care interventions should be conducted. For example, using generic value sets in CEAs may underestimate the perceived value of palliative interventions that extend life by even a few months, as respondents placed greater weight on preserving life when less time remained. Policymakers and agencies like NICE could use this evidence to advocate for context-specific modifiers in economic evaluations, ensuring that palliative care interventions—which often focus on quality rather than quantity of life—are assessed using values that reflect societal priorities in end-of-life scenarios. This would align with NICE’s recent emphasis on prioritizing health benefits in severe diseases [8] and could prevent undervaluation of palliative care in funding decisions.

The cross-country variations in valuations (e.g., Ireland’s lower proportion of "worse than dead" states) further highlight the need for localized or subgroup-specific value sets. Demographic factors such as religiosity, caregiving experience, and age—all of which differed across samples—likely shape societal preferences. For instance, Ireland’s younger, more religious sample may reflect cultural attitudes toward end-of-life care that differ from Australia or the UK. Policymakers could use these insights to tailor palliative care resource allocation to populations with distinct demographic profiles, ensuring equity in access and acceptability of services.

By quantifying how context alters health state valuations, this study provides insights for designing patient-centred palliative care models. For example, the strong preference against states perceived as "worse than dead" in short life expectancy scenarios suggests that patients may prioritize interventions minimizing suffering over life extension—a finding that could guide clinical guidelines and advance care planning. Similarly, health systems could use these results to prioritize funding for interventions that align with societal values (e.g., home-based palliative care over aggressive, costly end-of-life hospitalizations).

This study provides the first empirical evidence of the impact of a palliative context on EQ-5D-5L health state values across Australia, Ireland, and the UK. The introduction of a palliative care context increased the number of health states given values worse than dead, across all three countries. The impact was particularly prominent in palliative care contexts with a shorter life expectancy of 3 months, compared to those with a life expectancy of 2 years. This implies that respondents may value health differently in a setting in which they know that death is imminent. One way of interpreting this is that people may value life over death more when the available amount of life is more. This is an idea that has been suggested previously in theoretical and conceptual research [23, 24], but this has not been demonstrated widely in empirical data. It is a crucial point for health state valuation methods to consider, particularly in the context of suggestions that general population values can be used to assess states worse than death in economic evaluation [25].

While the overall impact of a palliative care context on the EQ-5D-5L health state valuation remained consistent across the three countries, variations in the percentages of health states worse than dead were observed. Interestingly, the Irish sample showed the smallest proportion of health states worse than dead among the 3125 health states, irrespective of palliative care needs. This discrepancy may be attributed to dissimilarities in demographic characteristics across the three countries. Specifically, Ireland had a higher percentage of younger and employed respondents, as well as a greater proportion of individuals reporting having adult children, placing importance on religion, and engaging in religious activities to some extent, in comparison to respondents from the UK and Australia. In line with previous research [26‐28], demographic factors, including age, mental health status, employment status, and caregiving experience, may influence preferences regarding the death health state. It is especially relevant when assessing the cost-effectiveness of interventions aimed at specific demographic groups.

The strengths of this study lie in its comprehensive approach to examining the valuation of health states using MNL models with interactions across multiple contexts and regions. However, it also has some limitations. The use of self-reported data and simulated ‘contexts’, while informative, might not fully capture the complexities and emotional depth of real-life palliative care needs situations. While the study covers multiple countries, the cultural diversity within each country is not specifically addressed, which might affect the generalizability of the findings to all sub-populations within those nations. Lastly, the study design assumes that the context provided in the vignettes was interpreted uniformly by all participants, which may not accurately reflect individual differences in perception and valuation of health states. These factors should be carefully considered when applying the study’s findings to policy and practice.

In conclusion, this study contributes to the ongoing debate on the necessity of condition-specific value sets. While conventional practice involves using generic value sets for decision-making, our findings suggest potential variations in these values within a palliative care context. These findings highlight the need for a more nuanced valuation approach, possibly incorporating modifiers tailored to reflect the severity of palliative states, aligning with recent recommendations by NICE to prioritise health benefits in severe diseases, not limited to end-of-life conditions [8]. Alternatively, conducting sensitivity analyses with condition-specific value sets could offer valuable insights into decision-making frameworks within palliative care contexts, ensuring robust and equitable resource allocation decisions.