Inequality and heterogeneity in health-related quality of life: findings based on a large sample of cross-sectional EQ-5D-5L data from the Swedish general population

Assessing population health status, and its distribution in subgroups, and exploring health inequalities associated with social determinants of health [1] is important in determining targets for and evaluating efficiency and equity aspects of health policy [2]. Health is a multi-dimensional concept and policy makers may need more specific information on which health dimensions are most affected in different groups in the population to better guide the investments in health policy. Although, a multi-dimensional instrument may be preferable for such purposes, it may, at the same time, be desirable to also convert the results into a single measure of health, weighing together the different dimensions .

The generic Health-Related Quality of Life (HRQoL) instrument EQ-5D-5L allows respondents to report their health in five dimension and five severity levels (yielding 3125 health states) and rate their overall health perception on the EQ visual analogue scale (EQ VAS) [3, 4]. This version was developed to increase discrimination between less severe health problems and to recognize small changes in health status, compared to the EQ-5D-3L with three severity levels, a welcomed improvement in granularity when used for economic evaluation as well as in population health status surveys (4). How problems are reported by the EQ-5D-5L descriptive system and how rating on EQ VAS differ by subgroups of the population reflect the distribution of health in the population. To assign a single index value for each health state, value sets can be applied using different valuation methods (e.g., time trade-off (TTO) and VAS valuations) and sources of valuations (based on own experience or on hypothetical states) [5‐7]. Index values, or weights, the quality component corresponding to the 0‒1 scale (dead‒full health) required for calculation of Quality-Adjusted Life Years (QALYs) obtained through the EQ-5D-5L instrument can be used in economic evaluation [8].

Large sets of EQ-5D-5L data, commonly based on cross-sectional general population surveys, can serve as important population reference data, known also as norms data [9]. Population reference data for EQ-5D-5L can provide useful guidance regarding interpretation of results when the instrument is used in specific patient groups, for instance by comparing their results with data for the average person of the same sex and age in the general population [3, 9]. Subgroup reference data may be important for decision makers for example to understand the burden of disease in a specific patient population. They could also provide a reference to quantify the overall inequalities in health status [10]. Population reference data, with sex- and age-specific QALY-weights, are also important in any economic evaluation modelling long-term health outcomes of specific interventions.

Population reference data using the three-level severity version EQ-5D-3L [11, 12], as well as reference data for adolescents using the youth version EQ-5D-Y-3L [13] are available for Sweden. The EQ-5D-5L is increasingly used in Sweden – in health care and in quality registers which underlines the need of reference data also for the EQ-5D-5L [14]. However, until now, Sweden lacks large-scale EQ-5D-5L results for the general population stratified by subgroups.

Population reference data for the EQ-5D-5L are available for several countries at national or regional level. Recently, data have been presented for United States [15], Canada [16], Bulgaria [17], Russia [18], and Slovenia [19], adding to previous data for China [20], Germany [21‐23], Indonesia [24], Ireland [25], South Australia [26], Spain [27, 28], Trinidad and Tobago [29], and Uruguay [30].

In Sweden, the EQ-5D-5L was included in the large population-based health survey to more than 56,000 individuals [31], enabling comprehensive and stratified analyses. These data were used for estimation of the Swedish experience-based value sets [32].

In the present study, we use these data to describe HRQoL in the population, by different subgroups and individual characteristics. The study investigates both systematic inequalities in HRQoL related to characteristics such as sex, age, education, income, and economic activity, and individual heterogeneity linked to medical and behavioural characteristics such as self-reported diseases and health conditions, and health-related behaviours. In addition, the paper provides EQ-5D-5L population reference data for Sweden.

Data from 25,867 individuals were used in the present study. The mean age was 64.3 years, 52.6% were women, and 42.5% were diagnosed with at least one disease (Online Resource Table S2). In total, 896 health states (28.7% of the 3125 possible states) were reported; 174 by ten or more, and 291 by five or more respondents (Online Resource Table S3). No problems on any dimension (state 11111) was reported by 24.1%; extreme problems on all dimensions (state 55555) was reported by six respondents (0.02%).

This study describes how HRQoL based on the EQ-5D-5L varies across subgroups and individual characteristics in the population, and investigates socioeconomic inequalities and individual heterogeneity in HRQoL, using data from 25,867 respondents aged 30‒104 [31], to the best of our knowledge one of the largest population-based surveys using EQ-5D-5L.

Generally, women reported worse health status than men, and health deteriorated with age, although the age gradient was bell-shaped, similar to a previous EQ-5D-3L population study in Sweden [12]. The present study shows evidence of the well-known socioeconomic gradient in health status [35]; more problems reported and lower mean observed EQ VAS scores, TTO and VAS index values among those with lower educational level, in lower income quintiles and those unemployed. These results are in line with findings of other studies investigating these relationships using EQ-5D-5L [e.g., 16, 26, 28]. Adjusting for health-related behaviours, diseases and self-reported conditions slightly reduced the differences between educational groups and income groups, but the socioeconomic differences largely remained.

Having a disease or a health condition was negatively associated with HRQoL, with the strongest association between EQ VAS score and those with depression diagnosed by a physician, or reporting conditions related to mental health and stress. These findings were also seen among adolescents in Sweden in Åström et al. [13] and in other studies [12, 36]. The mean EQ VAS score decreased with increasing number of diseases, findings similar to other studies see [e.g., 36, 37]. Daily smoking and being less physically active reduced HRQoL, as did deviations from normal BMI. Most problems were reported in the pain/discomfort dimension, similarly to findings in the Swedish population reference EQ-5D-3L [12] and EQ-5D-Y-3L [13] studies and other EQ-5D-5L reference studies summarised by Prevolnik Rupel et al. [19]. However, in the reference study for adolescents, girls reported most problems in the mood dimension [13].

In the present study, the proportion of the respondents reporting no problem on all dimensions (state 11111) was 24.1, which was much lower than in most other studies. For instance, in Trinidad and Tobago the proportion was 72% [29], in German studies 62% [22] and 31% [23], Bulgaria 50% [17], South Australia 43% [26], and in Slovenia 28% [19]. A lower proportion, 21%, reporting no problems was found in Quebec, Canada [16]. Among other factors influencing reporting problems, the age ranges differ across studies that makes it difficult to directly compare the proportions. As noted, our sample includes participants up to 104 years and health problems increase steeply among the oldest. Comparing to Swedish population studies, in the EQ-5D-3L study the proportion was 46% [12] and in the EQ-5D-Y-3L reference data for adolescents aged 13‒18 years the proportion was 45% [13].

The population reference data for the EQ-5D-5L in Sweden reported in the present study enable comparisons of results from other studies using EQ-5D-5L, and they also provide valuable inputs into health economic evaluations based on decision-analytic modelling. The HRQoL of the general population, by sex and age, is required in any model containing a disease-free, or “well”, health state. Furthermore, reductions in HRQoL associated with clinical events or health states, are often modelled as decrements from a population reference value.

As a future use, results derived by this study may also serve as socioeconomically and medically stratified population references for individual monitoring of HRQoL. Approaches using the EQ-5D instrument are under development for patient groups in oncology, asthma and diabetes [38‐40]. With monitoring extending to other patient and population groups, population reference values are set to increase in relevance.

Another aspect, methods to value a health state may influence the burden of disease indicated for a population subgroup, as the following example shows. Compared to the overall population mean (Online Resource Table S3), people with asthma experience a loss of − 0.035 using the TTO index value but a loss of − 5.9 using the EQ VAS score; people with depression experience a loss of − 0.148 using the TTO index value but a loss of − 19.0 using the EQ VAS score (Table S4). With the EQ VAS score, the loss by depression exceeds that by asthma by a factor of 3.2, while with the TTO index value, this factor is 4.2, indicating a much higher relative burden. The valuation method may thus impact the relative burden for subgroups, and therefore the choice of method may impact decision making.

In this study, we identify important variation in problem levels reported as well as in overall valuation both related to socioeconomic inequality and to individual heterogeneity in health and health behaviour. The two components are also relevant from a policy perspective: systematic variation in HRQoL due to inequality requires responses directed at the socioeconomically disadvantaged groups, heterogeneity in the medical and behavioral factors requires search for more effective health promotion.

The equity implications of the findings of this study may be considerable and is an area to explore further. Adding a quality component to mortality differences across different subgroups in the population may reveal dramatic differences in life-time health prospects (see e.g., [41‐44]). As an example, comparing highest income quintile vs. lowest shows a (crude) difference in 0.07‒0.10 depending on the method of measurement. A loss of 0.10 in HRQoL for 10 years may be translated to one year in full health lost. If lower income is also associated with increased mortality, the expected health loss over a lifetime may be substantial. For any health care system, taking health inequalities seriously such analyses will provide important input to health policy.

This study is larger and more comprehensive than studies only reporting standard population reference data, such as results by sex and broader age groups. Our study allows stratified analyses by 5-year age groups by sex, and analyses by socioeconomic characteristics, BMI and SRH, stratified by sex. Linking individual survey data to register data to obtain sociodemographic characteristics for each respondent is a strength. It was also possible to present results over the five severity levels, not being restricted to dichotomise into no problems vs any problems, by collapsing slight, moderate, severe and extreme problems into any problems. A shortcoming is that the descriptive analyses on education, income and economic activity were not stratified by age and that no sensitivity analyses with different dichotomisation of the severity levels were done. The fact that the sociodemographic composition of the CDUST Region is similar to the composition of the overall Swedish population implies that our results can be generalised to Sweden as a whole with respect to the studied age groups [32]. Due to the comprehensive data set, this study takes the advantage of the five levels in the EQ-5D-5L, which was developed to reduce the ceiling effect that has been found with the EQ-5D-3L [4]. To present HRQoL among the oldest old (95‒104 years) is another strength. However, as the survey did not include the EQ-5D-5L to those below 30 years, the lack of results for the youngest is a shortcoming of this study. A possible limitation could be the internal non-response rate in some groups; but due to the weak relationship between the specified variables and the response status, the implications are likely to be minor. The present study is based on the same data source as that used for the Swedish TTO and VAS EQ-5D-5L value sets [32], which is a strength as no other population is introduced. The individual respondents experiencing the health state report and value the health states. The cross-sectional design limits studying causality. Variables have been attributed to indicate either inequity or heterogeneity. Yet, other definitions may be used in specific research or policy contexts, e.g., when focusing on the biological aspect of age as one of heterogeneity, or when attributing multimorbidity to equity issues.

Findings of this study point out substantial socioeconomic inequalities and individual heterogeneity in HRQoL, which ought to be considered in decision making and in research. Thus, results add information that is especially important for equity considerations, an aspect often neglected in economic evaluation studies and in evidence-based decision making in health care [45].

Based on a large population-based survey with 25,867 respondents aged 30‒104 years, EQ-5D-5L population reference data are now available for Sweden. This study investigates socioeconomic inequalities and individual heterogeneity in HRQoL, relating HRQoL to health-related behaviours, diseases and self-reported conditions. It presents respondents rating of their own overall health on the EQ VAS and results for different ways to assign a single value for each health state applying population-based value sets based on VAS valuation, and on TTO valuation allowing for calculation of QALYs. Yet, all measures refer to the individual’s own experienced health state. The results may be useful not only for comparisons with disease-specific populations and as inputs into health economic evaluation studies, but also for policy makers, concerned with both efficiency and equity aspects. The findings of inequality in HRQoL between socioeconomic groups indicate that the latter may be of importance in determining, and in implementing health policy targets.