Do health preferences differ among Asian populations? A comparison of EQ-5D-5L discrete choice experiments data from 11 Asian studies

The EQ-5D DCE data provided an ideal data source to understand the health preference variations in Asia. By comparing the modeling results, the relative importance of dimensions and levels, we found Asian regions have diverse health preferences.

EQ-5D is a generic preference-based health-related quality of life (HRQoL) questionnaire that is widely used around the world [1, 2]. When value sets are available, EQ-5D data can be converted to health utility [2]. Many countries have established their own EQ-5D value sets proceeding on the basis that health preferences differ among countries/populations [3, 4]. Indeed, studies have found differences between value sets [3, 5, 6]. In developing the value sets of three-level version of EQ-5D (EQ-5D-3L), published studies differed in terms of design, data collection protocol and the choice of model. By comparing these value sets, Norman et al. concluded that these variations in methods could obscure true differences in values [6]. For the latest five-level version of EQ-5D (EQ-5D-5L), the EuroQol Group developed a standardized protocol for data collection in valuation studies, which is named the EuroQol valuation technology protocol (EQ-VT) [7‐9].

With application of the EQ-VT, EQ-5D-5L valuation data can be exploited to study whether important differences in health preferences across populations exist, as the method variations observed in the 3L studies are minimized. The EQ-VT data collection protocol uses both time trade-off (TTO) and discrete choice experiment (DCE) as preference elicitation methods [7]. Currently, all comparison studies of EQ-5D value sets only used the TTO data from the valuation studies [5, 10, 11]. This is partially because the TTO data is considered as the primary preference source in the EQ-VT protocol and some studies estimated their value set using TTO data only, for example, China and South Korea [12, 13]. So far, the DCE data collected using the EQ-VT protocol has not been utilized for the purpose of identifying preference differences across studies. While the TTO valuation data could be subject to interviewer effects as the task relies on the good performance of the interviewers [14], there is minimal interviewer effect for the DCE data.

As a preference elicitation method, DCE has been increasingly used in health preference studies [15]. Based on random utility theory, DCE is designed to ask respondents to choose a preferred multi-dimensional health state from two or more alternatives. The ordinal preference data can be modeled to predict health utility on a latent scale [16]. This means that the coefficients of DCE are not directly comparable across studies and most studies assessed their difference by calculating and comparing the relative importance of five health dimensions [17, 18].

Further, differences in health preferences among Asian populations are not well understood. By comparing the multiplicative model coefficients of the EQ-5D-5L TTO valuation data from seven Asian studies, Wang et al. noticed that there was no consensus about the rank ordering of the five dimensions [10]. Additionally, statistical test suggested most coefficients differed among Asian studies. In the study of Roudijk et al., the authors found that cultural variables (i.e. traditional/rational-secular, survival/self-expression) did not explain the variations of value differences (defined as utility differences between the mild and severe states) among EQ-5D valuation studies, including 10 Asian studies [11]. As stated before, these studies only explored the TTO valuation data.

Following the EQ-VT protocol, 11 studies (China, Indonesia, Japan, South Korea, Malaysia, Singapore, Thailand, Philippines, Vietnam, Hong Kong, Taiwan) have been completed in Asia. Of those studies, China and South Korea did not use the DCE data to model the value sets, and Singapore and Philippines have not yet published their value sets. The rest of the studies modeled the DCE data and TTO data jointly. Notably, no study has compared DCE-derived preference data among Asian populations. Given all studies used the standardized EQ-5D-5L instrument, DCE experimental design, and data collection protocol, it is possible to explore the variations of health preferences in Asia. We hypothesized that the health preferences differed in Asian populations. If this is true, the results of this study could further support the establishment of national/regional value sets for better guidance of health care decision-making and resource allocation rather than using a unified value set designed merely for the continent. In this study, we aim to understand the similarities and differences in Asians’ preferences for EQ-5D-5L health states in 11 Asian DCE datasets collected as part of EQ-5D-5L valuation studies.

The present study compared the DCE based modeling results and relative importance of EQ-5D-5L dimensions and levels of 11 Asian valuation studies. The strength of this study is all 11 studies followed the standardized EQ-VT protocol, which minimized possible noises in identifying the true differences. Based on our results, it is fair to declare that there does not exist a single preference pattern for Asian populations. This is in line with a previous study comparing TTO preference data [10]. A clear distinction between our DCE results and the TTO results is the relative weights for level 3 and level 4 are larger in the TTO study.

Our study first tested the differences of modeling coefficients and then compared the relative importance attached to the dimensions and levels of EQ-5D-5L. Both analyses suggest large health preference heterogeneities among Asians. First, the number of differed coefficients ranged between 2 (Malaysia vs. Taiwan) and 16 (Singapore vs. Malaysia) and the average number is 9.3, suggesting about half of the coefficients differed when pooled two studies’ data for a joint model. Second, both the relative importance of dimensions and levels differed among studies. Only Hong Kong, Taiwan and Malaysia showed the same order of five dimensions. Here we concluded some common patterns that, however, always come with exceptions. First, among the five dimensions, mobility is the most important dimension for every population except for Vietnam. This is similar to the results from a comparison of TTO-only preference data from 7 Asian regions [10]. However, western countries do not value mobility as highly; the Dutch, German, and US populations view mobility as third, fourth, and second most important dimension, respectively [34‐36]. Purba et al. argued that in the western developed countries, problems with mobility had less influence due to better infrastructure provision and less emphasis on manual labor [20]. However, in high income and developed regions such as Singapore and Japan, mobility is still the most valued dimension. Second, the sum of three function dimensions (mobility, self-care and usual activities) were higher than the sum of two symptom dimensions (pain/discomfort and anxiety/depression). Also, either usual activities or self-care is the least important dimension. Indonesia and Philippines are the exceptions. This result agrees with the previous study of comparing TTO data among 7 Asian populations. In that study, Indonesia was the only one who valued pain/discomfort and anxiety/depression the lowest. Third, pain/discomfort was valued more important than anxiety/depression and is the second most important dimensions for 6 studies. These characteristics mark some notable difference between preference pattern from most European, American, and African populations [5, 37‐39].

Despite these similarities, it is clear that a singular preference pattern does not exist for all Asian populations. For example, there is no agreement on the least important dimension in our comparison: 3 studies valued self-care, 2 studies valued anxiety/depression, and 6 studies valued usual activities as the least important. This contrasts to a previous study of comparing health preference pattern for Canada, England, the Netherlands, and Spain. In that study, Olsen et al. found a clear pattern existed for these four western countries and named it western preference pattern (WePP) [5]. In the WePP, four general characteristics were noticed in terms of the relative importance: 1) (PD + AD) ≈ (MO + SC + UA); 2) PD ≈ AD; 3) MO ≈ SC; 4) UA < SC. However, no Asian preferences fit well with these four characteristics. In fact, the sum of pain/discomfort and anxiety/depression was less than the weight of the other three dimensions in all Asian studies: (PD + AD) < (MO + SC + UA), suggesting that compared with the western countries, the Asian placed more weights on the functional dimensions. The second characteristic of ‘PD ≈ AD’ was only observed in the results from Indonesia, Singapore, and Malaysia. The third characteristic was clearly invalid in Asia as mobility was valued as the most important dimension while self-care had less relative importance in 4 studies. For the last characteristic, four Asian populations put similar or higher values for usual activities.

The differences of health preferences can be attributed to several reasons. First, in our sample, 11 populations come from diverse cultural, economic, political and social environments. Although no study has examined how these factors related to health preferences, country specific value set has been established on the notion that these factors shape people’s preference. Second, even though each study followed the same study protocol, their sampling method differed. Quota sampling method was the most used sampling strategy, but the quota varied across study. For example, ethnicity was used in some studies like Malaysia and Singapore, but not in China and South Korea. Similarly, some studies only recruited participants from urban areas, which may not be representative for the whole target population. Studies have shown that the demographic of respondents could influence the health preferences [40, 41]. Hence, different respondents recruited for each study may contribute to the observed differences. Last but not least, the EQ-5D-5L descriptive system was translated into different official languages from English. Though a standardized translation process was conducted to maintain equivalence between the translated questionnaire and its source version, different languages have different ways of expression which maybe inadequately captured [42].

This study has some limitations. First, the point estimates of the relative weights were used to identify the preference pattern. Considering the 95% confidence intervals were overlapped for some dimensions, the relative weight difference between dimensions may not be statistically significant. Assuming a scale length of 1.5 (i.e. 55555 has a value of -0.5, 11111 has a value of 1) and using a MID of 0.05, any relative importance difference over 0.03 should be meaningful. Nevertheless, since we do not know the actual scale length of each study, we did not use this criterion. Second, even though a standardized protocol was used, the demographic questions used for each study was customized by each local study team. Due to these sampling variations, we did not further test how these variations affect preferences. Only the heteroscedastic model shown in Online Appendix 4 demonstrates that the variances was constant for respondents with different ages and gender.

Norman et al. pointed out that differences in methods obscured the true differences in health preferences across countries after comparing published EQ-5D-3L value sets [6]. Our study has shown that using a standardized data collection protocol, study design and modeling choice, there still remained differences in EQ-5D-5L modeling results and the relative importance of dimensions and levels among Asian populations. Therefore, the effort of estimating a combined continental value set that was carried out for European and Western countries [5, 43] should be discouraged for Asia.

By comparing the DCE data modeling results, we found that the rank order of EQ-5D-5L dimensions and the relative weight of levels differed among Asian populations. These findings confirmed the health preference heterogeneity among Asian populations that was observed in previous studies using TTO data. All the evidence suggested the necessity of using local value set for estimating health utility.