Belgian population norms for the EQ-5D-5L, 2018

Health-related quality of life (HRQoL) refers to a person’s self-reported physical, mental, and social functioning and is an important outcome measure to support health care decisions [1]. Several instruments have been proposed to elicit HRQoL scores. One of the most commonly used generic multi-attribute classification systems accompanied by preference weights is the EQ-5D. The EQ-5D consists of a descriptive system of self-perceived health status along five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) and a visual analogue scale (VAS) which provides a self-rating of the general health status on a scale from 0 (worst imaginable state of health) to 100 (best imaginable state of health). The original EQ-5D descriptive system, the EQ-5D-3L, defined three severity levels (no problems, some or moderate problems, extreme problems/unable to) per EQ-5D dimension. In 2009, the more sensitive EQ-5D-5L was launched, with five severity levels (no problems, slight problems, moderate problems, severe problems, extreme problems/unable to) per EQ-5D dimension [2]. Translating the EQ-5D dimension scores into a single index value, or utility, requires an algorithm that can attach values to all possible EQ-5D health states. A utility ranges between 0 (death) and 1 (perfect health), but it can also include negative values for health states perceived worse than death [3]. Recently, the Belgian value set for the EQ-5D-5L has been developed based on health states preferences from the general population of Belgium [4].

In order to evaluate disease-associated loss in HRQoL for a given patient, reference data in terms of population norms, i.e., HRQoL data for the average person in the general population in a similar age and/or gender group, are required [5]. Several countries therefore now include generic HRQoL measures in their national health surveys to obtain such population norms [6‐8]. The growing availability of population norms also offers an additional way of assessing and monitoring population health and health inequalities [9]. Indeed, with aging populations and the growing importance of non-fatal diseases, evidence-based public health policies require knowledge of population health in terms of morbidity and mortality [10].

In Belgium, National Health Interview Surveys (BHIS) have been organized since 1997 [11]. In the BHIS, information is collected on the health status, lifestyle, and health care utilization of a representative sample of the total Belgian population (including the regions of Brussels, Wallonia, and Flanders, which make up 10%, 32%, and 58% of the population, respectively). Since 2013, the EQ-5D-5L is included. We aim to present Belgian population norms for the EQ-5D-5L by age, sex, and region, and analyze its association with educational attainment using the most recent data from BHIS 2018, and compare changes in population norms between 2013 and 2018.

Based on the BHIS 2018, we estimated nationally representative population norms for the EQ-5D-5L by sex, age, region, and educational attainment. In addition to being indispensable for cost-utility analyses, as currently recommended by Belgian guidelines for economic evaluations in health care [16], these population norms also present novel insights on the self-perceived health status of the Belgian population. These results may serve as a benchmark for follow-up in future BHIS.

Consistent with the evidence regarding population norms, our results show that female gender and older age were significantly associated with lower self-perceived health status. We identified a gender association with both the EQ-5D dimensions, particularly on anxiety/depression and pain/discomfort, and the EQ-5D index values. The pattern of women having a higher prevalence of reporting problems as well as having lower index values than men has already been observed in many countries [17‐19]. The significant reduction with age for the index values is also consistent with the population norms from other countries [20‐23]. Among the EQ-5D dimensions, problems of mobility, usual activities, and self-care showed a clearer-cut direct association with increasing age (prevalence < 10% in teenagers versus > 50% in octogenarians). This observation is in line with the increasing prevalence of disability at older ages previously documented in Belgium [24].

We found that higher education was significantly associated with a lower prevalence of reporting problems (except for anxiety/depression) and was related to higher index values. These results emphasize the socioeconomic inequalities in health in terms of educational attainment, which has been shown in Europe for decades [22]. According to region, our results show a significant higher prevalence of reporting problems on mobility, pain/discomfort, and anxiety/depression and significantly lower index values for people living in Brussels or Wallonia compared to Flanders. The associations with region on the other EQ-5D dimensions were less pronounced. Explanations for regional effects should be searched in socioeconomic characteristics, differences in lifestyles and in inside/outside air pollution, and differences in health policies and in health care management [25]. In addition, differences can also be attributed to the language version of the EQ-5D-5L used across the three regions (Flanders: 95.3% Dutch version, Brussels: 95.9% French version, Wallonia: 97.2% French version).

The Belgian population norms for the EQ-5D-5L appear to be consistent with those from other European countries, which also found that problems with pain/discomfort had the highest prevalence and problems with self-care had the lowest prevalence. The prevalence of reporting problems with pain/discomfort in the BHIS is, however, higher than the prevalence reported in Spain (28%) [26], Germany (32%) [27], England (42%) [28], Italy (43%) [29], and the Netherlands (49%) [30]. Only Poland reported a comparable high prevalence of pain/discomfort (52%). The second highest prevalence was reported in problems with anxiety/depression with nearly one in four Belgians reporting these problems. Among other European countries, the prevalence of reporting problems of anxiety/depression in Belgium was lower than in Poland (42%) [31] and Italy (38%) [29], but reported to be higher than in Spain (16%) [26], Germany (18%) [27], the Netherlands (21%) [30], and England (24%) [28]. Several possible explanations exist explaining the difference in prevalence between countries, such as different sociodemographic structures and cultural differences in health perceptions [23, 32]. The EQ-5D index values are furthermore affected by differences in valuation methods, which reflect the preferences of the country of elicitation [33, 34]. Indeed, the average index value (0.84) was lower than in other European countries, where it ranged from 0.87 (the Netherlands) to 0.92 (Italy) [26, 31, 30, 31, 35]. The Belgian VAS score (77.1) was lower than in Italy (78.2) [29], England (78.4) [28], the Netherlands (80.6) [30], and Germany (84.3) [27], but higher than in France (73.4) [35] and Spain (75.7) [26].

It should be acknowledged that the significant differences in EQ-5D index values or VAS scores, e.g., between sexes or across countries, may not be meaningfully different from each other. Indeed, interpreting the relevance of differences is not straightforward. Several authors have attempted to estimate the smallest difference in utility scores that an individual can recognize and appreciate, often termed the minimal important difference (MID) [36]. Walters and Brazier [37] estimated a mean MID for the EQ-5D of 0.074 [37]; likewise, Pickard et al. [38] estimated an MID for VAS scores of 7 [38]. MID values however depend on the characteristics of the study populations they were derived from, as well as the computational methods used to calculate the values. A review showed that MID values for the EQ-5D ranged widely, from 0.03 to 0.52 [36]. Furthermore, what constitutes an important difference is necessarily context-dependent, as even small differences in quality-adjusted life years may lead to large differences in incremental cost-effectiveness ratios. Interpreting cross-country differences in index values is further hampered by the fact that the values are the result of both the dimension scores and the value set used [39].

Our estimates represent the first Belgian HRQoL population norms by age, sex, and region, and the first based on the EQ-5D-5L. Earlier Belgian population norms were based on smaller sample sizes and used the EQ-5D-3L. König et al. [40] estimated population norms based on 2411 respondents who completed the EQ-5D between 2001 and 2003 [40], resulting in an average EQ-5D-3L index value of 0.883 [17]. Interestingly, only 6.6% of their respondents reported problems with anxiety/depression, compared to 31% in the BHIS. This discrepancy may be due to the more sensitive and precise nature of the EQ-5D-5L as compared to the EQ-5D-3L [41]. More recently, Bilcke et al. [42] used a representative sample of 1774 persons surveyed between 2010 and 2011 to estimate Flemish population norms for all ages and to explore potential associations of these health status measures with several potential determinants [42]. As in our study, older age and lower education was significantly associated with lower self-perceived health status, however, no association with sex was found, which contrasts the existing literature. Bilcke et al. [42] also found significant associations with smoking behavior, pet ownership, past experience with severe disease (in oneself or family members), working or having worked in health care, household size, and people aged 60 + years living on their own [42].

Modeling health status responses is not straightforward. The index value and VAS score have a non-normal distribution, with fixed boundaries and a highly right-skewed distribution, as the majority of the general population reported good self-perceived health. Basu and Manca [43] proposed the use of a (one-inflated) beta regression model to better capture these idiosyncrasies [43]. However, Bilcke et al. [42] noted that this approach would result in a limitation in the type of models that can be fit, as complex models with a beta error distribution are currently not readily available in standard statistical software [42]. In our case, the ideal model would allow taking three characteristics into account, i.e., the survey design, the non-linear age relationships, and the non-standard response distributions of the index values and VAS scores. Since such a model is not available in current statistical software, we had to resort to models that do not meet all criteria, thereby making trade-offs between good estimations of point estimates versus good estimations of standard errors. We used a survey-weighted linear regression model with natural cubic splines to model the age-specific index values and VAS scores, which allowed to properly take the survey design into account and to model non-linear age relationships, but used a Gaussian response distribution instead of a theoretically more suitable Beta one-inflated distribution [44]. To assess the possible impact of this limitation, we performed sensitivity analyses using alternative model definitions, including Beta one-inflated models. These sensitivity analyses revealed similar point estimates across model definitions, reinforcing the use of a survey-weighted linear regression model, yielding both accurate point estimates and standard errors. Differences across models mainly seemed to occur for the ages ≥ 80 years. This is a result of the lower sample size at these older ages, as also reflected by the larger confidence intervals of the individual models. Estimates of HRQoL for the very old based on the BHIS should therefore be treated with caution.

One of the limitations of the current study is that it considered only four potential determinants of self-perceived health status: age, sex, region, and educational attainment. All four of these turn out to be significant; however, despite the addition of these important covariates and the flexible, non-linear association with age, a lot of the variability has been left unexplained, resulting in relatively low goodness-of-fit estimates and trends in the diagnostic plots. Nonetheless, for the purpose of health economic evaluations, age, gender, and region are by far the most relevant categories to distinguish for population norms informing cost-effectiveness in the Belgian context. The main objective of this study was to provide population norms by age, gender, region, and combinations thereof. Future analyses will focus on explaining HRQoL by exploring associations with other determinants, including chronic disease status, and thus finding the model with the most optimal goodness-of-fit.

Certain limitations of the BHIS also apply to this study. First, although the survey included people in nursing homes, it excluded specific institutionalized populations such as prisoners and people residing in mental hospitals. Although these populations only represent a small fraction of the total population (less than 0.1%, of which more than 90% are men), they are likely to report impaired health status due to higher rates of physical and mental problems [45]. The results may also be biased toward a more healthy population by excluding participants aged ≥ 15 years for whom a proxy interview was conducted because they were not able to answer themselves. These participants are usually in worse health. In general, BHIS participation itself may be affected by a healthy volunteer effect. Second, the BHIS did not collect EQ-5D information from children under the age of 15 years. Estimating and interpreting HRQoL in children is not straightforward, as it often relies on responses from proxies. Wille et al. [46] proposed a youth version of the EQ-5D (EQ-5D-Y), which can be applied to children aged 8–15, either directly or via a proxy [46]. However, they also noted that existing EQ-5D value sets could not be assumed appropriate preference weights for the EQ-5D-Y. A third limitation of the BHIS, as in many other health surveys, is the potential bias due to educational differences in survey participation and the willingness and ability to answer the self-administered questionnaire [47]. To account for unequal representation of educational strata, Van der Heyden et al. [48] recommended including education in the calculation of post-stratification weights [48], which may be explored in future iterations of the BHIS; however, it should be kept in mind that weighting does not solve a potential selection bias. A final limitation is the exclusion of the EQ-5D VAS in the BHIS 2018 due to practical restrictions. Therefore, evolution over time for the EQ-5D VAS could not be assessed, although this could be of interest.

Future studies may build on the results presented here to gain additional insights in the self-reported health of the Belgian population by characterizing and analyzing inequalities in HRQoL and losses in HRQoL associated with self-reported chronic disease status or health determinants [49, 50]. Furthermore, linking the BHIS with mortality data would allow quantifying quality-adjusted life expectancy, as a summary measure that encompasses both quantity and quality of life [51]. Finally, by continuing to include the EQ-5D in future BHIS, it will become possible to further monitor self-perceived health status and inequalities therein over time.

We estimated, to the best of our knowledge for the first time, nationally representative HRQoL population norms for the Belgian population by age, sex, and region using the EQ-5D-5L. Older age, female gender, Brussels or Walloon residents, and low educational attainment were significantly associated with a lower self-perceived health status. Inclusion of the EQ-5D in future BHIS will allow monitoring self-reported health, disease burden, health inequalities, and quality-adjusted life expectancy.