EORTC QLU-C10D value sets for Austria, Italy, and Poland

Cost-utility analyses (CUAs) are an essential source of information for rational decision-making in resource allocation in health care. Their primary outcome is quality adjusted life years (QALYs), a parameter which integrates survival time and the “value” of a specific health state: the health utility. Health utilities are cardinal values that represent an individual’s preferences for specific health states, with “0” considered equivalent to death and “1” reflecting perfect health. Among the different preference-based methods used to obtain utilities, multi-attribute utility instruments (MAUIs) are popular. Well-known and frequently used MAUIs include the EQ-5D [1, 2] and the SF-6D [3]. Like most MAUIs, these are generic, i.e. they cover very general health aspects (such as mobility, pain, or self-care) which makes them applicable to a broad range of different health conditions. This breadth is valuable, but may come at the price of missing specific health issues relevant to certain conditions, such as nausea and vomiting in cancer [4, 5]. However, utility instruments using a disease-specific health state description system are relatively scarce.

Non-preference-based disease-specific quality of life (QOL) measures are widely used in clinical research. By definition, they include aspects of health relevant to a certain disease, which arguably may be more sensitive to clinical changes [5]. However, they do not allow utility scoring, and therefore cannot be used in cost-utility analysis.

The recent development of the EORTC Quality of Life Utility-Core 10 Dimensions (QLU-C10D) [6] contributes to closing this gap, as it is a MAUI based on the EORTC Quality of Life Questionnaire Core 30 (QLQ-C30) [7], the most widely used QOL profile measure in clinical oncology research [8]. It helps to overcome the lack of disease-specific utility instruments in the field of oncology by providing a health state classification system and utility algorithm using 13 key items of the parent instrument, covering 10 QOL dimensions.

QLU-C10D valuations are currently being performed for a range of countries and tariffs have already been published for Australia [9], Canada [10], Germany [11], and the UK [12]. In the present study, we aim to determine the utility weights for health states of the Austrian, Italian, and Polish version of the QLU-C10D. Furthermore, we descriptively compare the utilities of the three countries for selected QLU-C10D health states.

For QLU-C10 valuations a standardised survey has been developed consisting of a discrete choice experiment (DCE) for the valuation of health states as its core element, and of feedback questions on the participants’ experience of the DCE, self-report instruments on QOL (EORTC QLQ-C30, EQ-5D) and distress (Kessler K-10), and questions on basic socio-demographic and basic clinical information [13]. Before adopting the approach as method of choice for QLU-C10D valuations, feasibility and reliability have been established using quantitative and qualitative methods. The wording of the DCE tasks and different layouts have been pre-tested in general population respondents which showed that although perceived difficult the tasks were considered manageable [14]. Utility weights resulting from the DCE showed to be unbiased by the ordering of attributes in the DCE [15] and stable within respondents over time [16].

The QLU-C10D descriptive system [6] consist of 13 of the 30 items of the parent instrument EORTC QLQ-C30 [17] covering the 10 QOL domains physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue, sleep disturbances, appetite loss, nausea, and bowel problems. Each can take on 4 levels from the best level “not at all” (coded 1) to the worst level “very much” (coded 4) (see Table 1 for entire health state classification system). For example a health state with “very much” problems in physical and role functioning and “no problems” on other QLU-C10D domains would be coded 4411111111. The combination of domain and level therefore is able to describe a total of 4¹⁰ = 1,048,576 unique health states. These are the health states for which preferences need to be obtained in the valuation using the DCE.

The DCE asks respondents to choose between pairs of (hypothetical) health states which are described by 11 attributes: the 10 domains of the QLU-C10D (in a randomised order across participants to control for any potential dimension ordering effect) and a survival time of 1, 2, 5, or 10 years. These durations were selected to be plausible to most respondents, with enough spread to ensure discrimination between them. Each respondent is presented with 16 binary choice sets randomly selected from a choice of 960 sets. Only five attributes differ between the health states in each choice set to minimise the cognitive burden. An example choice set as presented to respondents is shown in Fig. 1 for details on the DCE design please refer to King et al. (2018) [9].

As with previous EORTC QLU-C10D valuations in other countries, recruitment and survey administration were performed web-based by survey engine (www.​surveyengine.​com), a survey company specialising in choice experiments. The survey was sent out as a weblink for the respondents to complete at their leisure. The approached potential respondents were members of an online panel of persons willing to complete surveys for a small payment. Survey Engine and its panel providers comply with the International Code on Market, Opinion and Social Research and Data Analytics (www.​esomar.​org).

In each country, we aimed to recruit 1000 respondents from the general population, aged between 18 and 80. Quota sampling by age and sex was applied to ensure that these variables were representative. The representativeness of educational level, marital status, and chronic disease (yes/no) were checked a posteriori by comparison with national census data. Data collection for all countries were performed in the years 2016–2017.

For the present study, validated translations of all required questionnaires were available. Also the entire Austrian survey was already available in German (see [11]). With regard to the QLU-C10D health state description system attribute descriptions were already available in all languages as they were taken from the parent instrument QLQ-C30. The respective questions are in past tense and were changed into a statement in the present tense (“Did you feel tired?” to “You feel tired.”). This and the translation of the remaining survey text (section headings and brief section summaries, DCE task descriptions, thank you notes) was contracted to translators, forward and backward translations were performed including in-country persons and pilot testing in convenience samples of 3–5 persons were conducted.

It has to be noted that for Austria, a revised German response format of the QLQ-C30 was used which has also been used for QLU-C10D valuations in Germany [11]. The reason for revision was that the original German wording for the category “quite a bit” (“mäßig”) is suspected to express a lower severity than the English version [18] and a revised German wording for this response level (“ziemlich”) is currently being investigated within the EORTC QLG [19]. The new translation appears to be a closer approximation of the severity level expressed by “quite a bit” (personal communication with study PI) but results are not yet published. The health states for QLU-C10D valuations for Austria are already based on the adapted German version, and consequently the utility weights presented here for Austria are valid for this version, which is abbreviated as “QLU-C10D Austria V2”.

Utilities score across countries were descriptively compared across a selection of QLU-C10D health states covering a continuum between best (i.e. 1111111111) and worst (i.e. 4444444444).

The major advantage of the EORTC QLU-C10D is that it is based on the EORTC QLQ-C30, and therefore, cancer-specific utilities can be determined using data previously or prospectively collected with the QLQ-C30 in addition to its traditional QOL profile scoring.

The current study provides value sets for Austria, Italy, and Poland. In all three countries, respective guidelines consider QALYs to be one of the preferred outcome measures in economic evaluations [28]. Normally, generic instruments are used to obtain utilities for QALY calculation as a result of conceptual considerations; however, disease-specific measures can be more sensitive to clinical differences in the respective diseases [5]. Whether this assumption holds true for the QLU-C10D requires clinical validity evaluation. An indication for the potential relevance of cancer-specific symptoms are the decrements found for nausea, bowel problems and fatigue which are consistent with the Australian QLU-C10D valuation [9] as well as those in Germany, the UK [12], and Canada [10].

In overall, we found that the impact of some QOL dimensions on QLU-C10D utilities appeared to differ across countries. This was especially true for emotional functioning, for which decrements were clearly higher in Italy than in Austria and Poland. Austrian utilities tended to be systematically lower in moderately and severely impaired health states.

Possible explanations of observed differences between countries may include different health care systems or culture-specific attitudes towards health, which could impact the willingness to trade-off life time for QOL. A possible methodological cause may arise from slight differences in meaning through translation into different languages. Although the EORTC follows a rigorous translation procedures, there is some tangential evidence for differential item function (DIF) analyses conducted by Scott et al. [18, 29, 30], who found that some QLQ-C30 items functioned slightly differently in a range of countries compared to the original English version. Region-related differences in how items function have also been found in other utility instruments, such as the EQ-5D [31, 32] and the SF-36 [33]. If utility differences do arise from differences in meaning as a result of translation, then they may very well disappear when using country-specific weights on the respective national data, which would be subject to the same translation effect. In fact, combined evidence of investigations by Scott et al. do suggest an important role of the lack of translation equivalence but also cultural DIF cannot be excluded. Further investigation on these issues is warranted, especially on the relative contribution of translation versus real differences in culture-specific attitudes towards health. Although these are complex topics to research, they are the key to truly understanding the inter-country differences we also have observed in the valuations presented here. A standardised and evaluated QLU-C10D valuation methodology is being used we will in future be able study utility differences between countries in a more sophisticated way.

A potential limitation of our study is that levels of education in the samples were slightly higher than in the respective general populations. The same was observed in the Australian valuation study [9] and is typical for online panels [34]. However, this would only be a problem if health valuation differed by education level. There is evidence that education level does not affect health valuation in different time trade of tasks [35, 36], however, this has not yet been investigated for DCEs. We intend to explore this with our data, but that is beyond the scope of the current paper.

The results from debriefing questions at the end of the valuation surveys indicated that clarity was not an issue in the vast majority of respondents. Task difficulty ratings do not raise severe concerns but require more caution in interpretation. If DCE tasks are too difficult or too easy they might be countering the required trade-off. The literature on the perceived difficulty of making decisions in health DCEs is somewhat scarce. The numbers we found compare well to the results from the QLU-C10D feasibility study [14] and are well within the range of what has been reported and considered acceptable in the literature so far. Mulhern et al. 2016 directly compared a DCE with a time-trade-off (TTO) for the EQ-5D-5L resulting in 57% considering the DCE tasks difficult to answer and 63% considering the TTO tasks difficult to answer [37]. In a study by Norman et al. (2013), a much lower number of 11% of respondents rated tasks of a DCE for the valuation of EQ-5D-5L to be either difficult or very difficult [38]. Other examples of DCEs in the context of health are Skedgel et al. (2013) who investigated societal preferences for the allocation of health care resources an found 65% to rate the presented DCE questions somewhat or extremely difficult to answer [39], and Green and Gerard (2009), likewise a societal DCE, where as many as 68% considered it fairly difficult or very difficult to complete the DCE tasks [40].

We conclude that the QLU-C10D enables the incorporation of QOL data collected via the QLQ-C30 into economic evaluation in Europe. This is especially important when QOL is a significant outcome of an investigated health intervention. Based on our results, we advise the use of country-specific QLU-C10D value sets for the evaluation of treatment effects whenever possible. In line with a recent suggestion recently also made for the EQ-5D [41], we advocate further investigation and discussion of the compatibility of translations and value sets, especially when used in multinational clinical studies. Future research will show how QLU-C10D cancer-specific utility values compare to generic ones in terms of sensitivity and responsiveness to clinical differences, and whether the choice of instrument will impact cost-utility ratios.