Background Previous literature has examined the causal effect of specific diseases on individuals' labour market participation, including cardiovascular diseases, stroke, heart attacks or cancer. However, different diseases, with varying severities, are likely to affect labour market participation differently. Moreover, there is considerable uncertainty regarding heterogeneity across educational attainment that may contribute to explain differences in labour market participation. This paper compares the differential effects from acute heart attack, stroke and cancer, and analyse heterogeneity by educational attainment. Moreover, the paper categorize cancer by severity, which is unique in the context of labour market participation. Method We combined self-reported data from the Tromsø study with register-based data from the State Register of Employers and Employees and the Norwegian Patient Register (NPR). The State Register provided annual data on individuals’ labour market participation from 2007 to 2018, while the NPR provided medical information, including ICD-10 codes (International Classification of Diseases, 10th version), and date of hospitalization for the period between 2006 and 2018. We treated acute heart attack, stroke, and cancer as separate diseases. Cancer was further categorized into three severity levels by their five-year survival rate prognosis (good prognosis, intermediate prognosis, and poor prognosis) provided by the Cancer Registry of Norway. We organized the data into a panel format where each panel represented one year in the study period from 2007 to 2018. We used an event study approach to model labour market participation, where we estimated linear models with correlated random effects. Results Labour market participation was most negatively impacted by cancer with a poor survival prognosis, followed by cancer with an intermediate survival prognosis and stroke, while cancer with a good survival prognosis and acute heart attack showed the least impacts on labour market participation. Furthermore, significant heterogeneities were observed across educational attainment and sex. Conclusion The impact on labour market participation of a sudden health reduction depends on the disease and its severity, as well as the affected individuals’ sex and educational attainment.

A previous study found that individuals with identical EQ-5D-5L profiles reported systematically higher EQ VAS scores with increasing educational attainment, which suggests a ‘hidden’ socioeconomic gradient not captured by the EQ-5D-5L. This study examines the robustness and generalisability of these findings using multi-country data. We analysed data from 32,327 respondents aged 25 to 79 years across eight high-income countries: Australia, Canada, France, Germany, the Netherlands, New Zealand, the UK, and the US. The data came from the EQ-DAPHNIE study. Within ten selected EQ-5D-5L health profiles, we used linear regression models to estimate the associations between EQ VAS scores and educational attainment or subjective income status, adjusting for age, sex, and country. We observed a consistent educational gradient in EQ VAS scores across most EQ-5D-5L profiles. Tertiary education was associated with higher scores in all ten profiles, with effects statistically significant at p < 0.10 in seven, of which four at p < 0.01. Income status showed an even stronger gradient, with significant associations in nine of the ten profiles. These patterns were evident in all eight countries. These multi-country findings provide robust evidence of a socioeconomic gradient in EQ VAS scores among respondents who report identical EQ-5D-5L health profiles, over and above what is reflected in the five EQ-5D-5L dimensions. This pattern has implications for the use of EQ-5D-5L values in equity-informative health technology assessment and population health monitoring.

Studies of health inequalities often examine the association between a single health indicator and a single socioeconomic indicator. Recent research has increasingly explored social disparities in self-reported quality of life. However, most evidences are based on cross-sectional survey and the role of health behaviours is frequently overlooked in these analyses. This study aims to integrate broader sets of socioeconomic and behavioural variables to better explain health disparities that emerge in later adulthood. Using longitudinal data from the Norwegian population-based Tromsø Study, we followed 11,313 adults aged 25–54 at baseline over 21 years. A series of self-reported outcomes were studied, particularly EQ-5D-5L values, EQ-VAS scores, and Self-Rated Health. Predictors included three socioeconomic indicators (childhood living standard, own and spouse’s educational attainment) and three health behaviours (smoking, physical activity, obesity). Shapley value decompositions were used to quantify each predictor’s contribution to explained variance. For all three health outcomes, health behaviours accounted for the majority of the explained variance compared to socioeconomic factors: 80

OBJECTIVES:Population aging is a major public health concern. Preference-weighted instruments are widely used to assess health-related quality of life and inform cost-effectiveness analyses. Given the range of available measures, it is important to identify those most suitable for older adults. This study systematically reviewed evidence on the psychometric properties of preference-weighted measures in older adults. METHODS:Four databases (Medline, Embase, PsycINFO, and CINAHL) were searched, supplemented by citation tracking. Peer-reviewed studies assessing the psychometric properties of preference-weighted measures in adults aged ≥60 were included. Two reviewers independently screened and extracted data. Methodological quality was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instrument risk of bias checklist, and evidence was graded using a modified GRADE approach. RESULTS:A total of 106 studies were included, covering EQ-5D-3L (4), EQ-5D-5L (34), ICECAP-O (29), ASCOT (15), QOL-ACC (9), HUI3 (8), HUI2 (7), QWB (5), EQ-HWB-9 (5), WOOP (3), SF-6D (3), AQoL-8D (2), AQoL-4D (1), and 15D (1). EQ-5D versions showed acceptable performance on some criteria, although ceiling effects, limited interrater reliability, and inconsistent responsiveness were reported. ICECAP-O and ASCOT showed more consistent evidence, but both need further testing on responsiveness. Limited and mixed findings were available for other measures. Early evidence for QOL-ACC and WOOP was promising but incomplete. Although interrater reliability and responsiveness evidence remained limited across all measures, specific measures performed generally better than generic measures but had fewer studies. CONCLUSIONS:Preference-weighted measures are widely used with older adults, but psychometric evidence remains incomplete because of limited assessment of responsiveness, few head-to-head comparisons, and lack of age-group-specific analyses.

AIMS:The aim of this study was to assess how early-life and adult lifestyle factors influence the education gradient in health, measured by health-related quality of life, and to estimate their relative contributions to the social gradient in health. METHODS:In this cohort study, we used data on an adult sample (N=8903, aged ⩾32 years at baseline) who participated in two waves of the Tromsø Study (2007/08 and 2015/16). Educational attainment was measured along four levels of completed education. Early-life factors included childhood financial circumstances, height, parental somatic and mental health, and parental substance abuse. Lifestyle factors were measured by smoking, physical activity and body mass index in the two waves. We used two measures of health-related quality of life: EQ-5D-5L and EQ VAS. Ordinary least squares regression analyses were used to estimate the education-health gradient. Shapley-Owen value decomposition estimated the relative contribution of education, early-life factors and lifestyle factors. RESULTS:The education-health gradient remained nearly unaffected by the inclusion of early-life factors. However, the inclusion of longitudinal data on lifestyle factors substantially attenuated the education-health gradient. Lifestyle factors accounted for 45% and 57% of the share of explained variation in EQ-5D-5L and EQ VAS, respectively, whereas early-life factors explained 43% and 19%, respectively. CONCLUSIONS:Beyond educational attainment, early-life factors represent an important complementary set of determinants to explain health inequalities, while lifestyle factors likely mediate the association between education and health. This highlights the need for public health measures addressing childhood circumstances.

Subjective measures of social status often explain variations in health better than the typical objective measures of education, occupation, and income. This raises the question: if status affects health, then what affects status? To answer this, we ran a survey using representative samples of adult populations in the UK, US and Canada (n = 3,431) to gather data on respondents' subjective social status (SSS) and health-related quality of life (HRQoL), alongside an extensive, rarely gathered set of socioeconomic variables: education, occupation, income, comparative income, wealth, childhood circumstances, parents' education, partner's education, and social and cultural capital. We conduct Shapley-Owen decompositions to identify the relative contributions of these variables in explaining variation in SSS and HRQoL and use RIF (recentered influence function) -regressions to go beyond the mean and identify how these contributions change across the quantiles of SSS and HRQoL. Results show that education, occupation, and income explain relatively little of the explained variation in SSS (26%), while comparative income, wealth and childhood circumstances together explain more than 60%. We find that at higher quantiles of SSS and HRQoL the more subjective and relativistic measures of socioeconomic status contribute more to the explained variation, whilst at lower quantiles, variation is better explained by the more objective socioeconomic variables (i.e. education, occupation, income and wealth). These findings shed light on how policy makers could consider intervening to reduce health inequalities.

The intersectoral workplace intervention “health in work” (HIW), developed by the Norwegian healthcare service and labour and welfare administration, targets common musculoskeletal and mental health conditions by addressing both health and work environment factors. This study assessed the effectiveness of HIW on workers’ health-related quality of life (HRQoL) and subjective wellbeing (SWB) compared to standard inclusive work measures (IWM). A pragmatic cluster randomised controlled trial including 97 workplaces, randomized to either the HIW or IWM intervention over 12 months. HRQoL was measured using the EQ-5D-5L and the EQ-VAS, and SWB by using the satisfaction with life scale and a question on meaningful life. Measurements were taken at baseline, post-intervention period, and at a 12-month follow-up. EQ-5D-5L data were analysed using mixed-effects generalized linear models. No statistically significant difference-in-difference in HRQoL or SWB were found between the HIW and IWM groups at any time point. Participants in both groups reported high baseline levels of HRQoL and SWB. Although HIW did not yield significant improvements or detriments in HRQoL or SWB, this study contributes to addressing the knowledge gap regarding intersectoral collaboration in enhancing work and health. Further research is needed to assess broader outcomes such as healthcare utilisation and sick leave. Trial registration: The trial was prospectively registered with ClinicalTrials.gov on June 24, 2019, under the identifier NCT04000035.

The EQ-5D is increasingly being used in studies of health inequalities, providing further evidence of a social gradient in health; i.e. consistent positive associations between a socioeconomic indicator and health. However, the steepness in the social gradients in HRQoL differs depending on which of the two EQ-5D measures is used; whether based on respondents’ EQ-5D-5L descriptions or by their direct valuation in EQ-VAS. This study aims to provide new knowledge as to why the two HRQoL measures suggest different degrees of health inequalities. Based on two large unique data sets (Tromsø Study Wave 7, N = 21,083; MIC study, N = 8022), cross-sectional analyses were conducted. We identified the most prevalent EQ-5D-5L profiles. Within each of ten EQ-5D-5L profile groups, we examine response heterogeneities in EQ-VAS scores using linear regressions, as explained by respondents’ level of educational attainment, controlling for age and sex. We showed significantly increasing EQ-VAS scores along with educational attainments. For instance, in the most prevalent health state (11121), a consistent education-health gradient was observed: compared to individuals with primary education, the EQ-VAS was 1.7 higher among those with secondary education; 2.6 higher among individuals with short tertiary education; and, 3.7 higher among individuals with long tertiary education. This paper provides new insights into the use of EQ-5D in health inequality studies by suggesting an additional underlying education gradient in HRQoL than what is revealed through the EQ-5D-5L values. Broader psychosocial domains and aspects of adaptation should be considered when monitoring health inequalities. The existence of social inequalities in life expectancies is widely documented. With more health survey data becoming available, there is now an increasing interest in studying social inequalities, or the social gradient, in quality of life. The EQ-5D is the most widely used health utility instrument, providing two different quality of life measures: one based on respondents’ EQ-5D-5L description and another based on their direct valuation on a visual analogue scale (EQ-VAS).Recent studies have shown the magnitude of health inequalities, or the steepness in the social gradients, to differ depending on which of the two measures is used. This study aims to provide new knowledge as to why this is so.Based on two large data sets, we identified the most prevalent health states, described by EQ-5D-5L profiles. We showed that individuals who have described their health state identically still differ in how they value their health on the VAS: individuals with better educational achievement generally report higher VAS-scores.This paper provides new insights into the use of EQ-5D in health inequality studies by showing an additional underlying education-health gradient than what is revealed through the EQ-5D-5L values. In short, the EQ-5D-5L appears to underestimate the inequality in quality of life.

Hip fractures are a significant public health concern due to increasing numbers, high mortality and negative impact on health-related quality of life (HRQoL). Socioeconomic position (SEP) affects various health outcomes, but the specific impact on HRQoL and satisfaction after hip fracture remains underexplored. This study assesses whether education and household income influence patient-reported outcome measures (PROMs) after hip fractures, measured by three visual analog scales: EQ-VAS, pain-VAS, and satisfaction-VAS. This was a nationwide retrospective cohort study using linked data from the Norwegian Hip Fracture Register and Statistics Norway. PROMs assessed at 4, 12, and 36 months postoperatively in 35,206 hip fracture patients from 2015 to 2018 were included. The SEP data included household income and education levels. Covariance analyses were conducted to evaluate differences in mean VAS scores for general health (EQ-VAS), pain from the operated hip (Pain-VAS), and satisfaction with the result of the operation (Satisfaction-VAS). Analyses adjusted for age, sex, vital status, cognitive impairment, treatment type, and education or income when not used as independent variable. The study included 23,649 women (67.2

Since the introduction of the EQ-5D-Y-3L valuation protocol, a considerable number of EQ-5D-Y-3L value sets have been published. This provides an opportunity to explore the differences and similarities between EQ-5D-Y-3L value sets across countries, and their similarity to their EQ-5D-5L counterparts. EQ-5D-Y-3L value set publications for 11 countries identified key methodological, sampling and value set characteristics. Similarity between value sets was assessed using kernel density plots and other key characteristics. Preference patterns between groups of value sets were explored. EQ-5D-Y-3L value set properties were compared with those of EQ-5D-5L value sets from the same country. All EQ-5D-Y-3L valuation studies used the same DCE design. Six studies used expanded health state designs in the composite Time Trade Off. Analytical strategies differed between studies. Values for state 33333 ranged from − 0.691 (Slovenia) to 0.289 (Japan); the number of negative values ranged from 0 to 21

Objective: To estimate the effectiveness and costs of Rehabilitation for Life (RFL) compared with usual rehabilitation and care after hip fracture to determine which course offered the most value for money. Design: Cost-utility analysis. Patient: Community-dwelling patients aged 65+ after hip fracture. Method: 123 intervention and 122 control patients were included. Data was collected at 5 points from discharge to 1-year follow-up. Cost analysis included expenses to hospital, general practice, specialist services, medications, rehabilitation, home and informal care, transport, and waiting times. The primary outcome was the incremental cost per quality-adjusted life year (QALY). Results: The intervention group experienced a statistically significant mean QALY gain of 0.02 -compared with the control group. The intervention was more costly by €4,224, resulting in an incremental cost of €159,990 per QALY gained. Two municipalities had several patients in respite care, yielding an imbalance. A subanalysis excluding these patients demonstrated QALY gain at 0.03 and the cost difference of €2,586 was not statistically significant. Conclusion: The intervention demonstrated a slight improvement in effectiveness over the control but was costly. For patients not requiring respite care, the intervention effect was slightly higher, and the cost differences statistically insignificant. In total 91% received informal care and the economic contribution of informal care exceeded the municipal home care services.

The link between educational attainment and multiple health behaviours has been explained in various ways. This paper provides new insights into the social patterning in health behaviours by investigating the influence of parents' and partners' educational attainments on a composite indicator that integrates the four commonly studied lifestyle behaviours (smoking, alcohol, physical activity and BMI). Two key outcome indicators of interests were created to reflect both ends of the "healthy - unhealthy spectrum". Data was drawn from The Tromso Study, conducted in 2015/16 (N = 21,083, aged 40-93 years). We controlled for two indicators of early life human capital and one personality trait variable. Partners' education attainments are relatively more important for avoiding unhealthy behaviour than choosing healthy behaviour; on the contrary, parents' education is more important for healthy behaviour. Heterogeneity by sex and age was also evident. The influences of partner's education on widening the socioeconomic contrasts in health behaviours were much stronger in the younger (40-59 years) age group. In conclusion, our results support the hypothesis that own health behaviour is affected by the educational attainments of our 'nearest and dearest' (i.e. spouse, mother, and father), net of own education. This study facilitates a better understanding of education -health behaviours nexus from a life course perspective and supports the importance of family -based interventions to improve healthy behaviours.

Background Health inequalities are often assessed in terms of life expectancy or health-related quality of life (HRQoL). Few studies combine both aspects into quality-adjusted life expectancy (QALE) to derive comprehensive estimates of lifetime health inequality. Furthermore, little is known about the sensitivity of estimated inequalities in QALE to different sources of HRQoL information. This study assesses inequalities in QALE by educational attainment in Norway using two different measures of HRQoL. Methods We combine full population life tables from Statistics Norway with survey data from the Tromsø study, a representative sample of the Norwegian population aged ≥ 40. HRQoL is measured using the EQ-5D-5L and EQ-VAS instruments. Life expectancy and QALE at 40 years of age are calculated using the Sullivan-Chiang method and are stratified by educational attainment. Inequality is measured as the absolute and relative gap between individuals with lowest (i.e. primary school) and highest (university degree 4 + years) educational attainment. Results People with the highest educational attainment can expect to live longer lives (men: + 17.9% (95%CI: 16.4 to 19.5%), women: + 13.0% (95%CI: 10.6 to 15.5%)) and have higher QALE (men: + 22.4% (95%CI: 20.4 to 24.4%), women: + 18.3% (95%CI: 15.2 to 21.6%); measured using EQ-5D-5L) than individuals with primary school education. Relative inequality is larger when HRQoL is measured using EQ-VAS. Conclusion Health inequalities by educational attainment become wider when measured in QALE rather than LE, and the degree of this widening is larger when measuring HRQoL by EQ-VAS than by EQ-5D-5L. We find a sizable educational gradient in lifetime health in Norway, one of the most developed and egalitarian societies in the world. Our estimates provide a benchmark against which other countries can be compared.

Positive associations between own educational attainment and own health have been extensively documented. Studies have also shown spousal educational attainment to be associated with own health. This paper investigates the extent to which spousal education contributes to the social gradient in health, net of own education; and whether parts of a seeming spousal education effect are attributable to differences in early-life human capital, as measured by respondents' height and childhood living standard. Furthermore, we investigate the relative contribution of predictors in the regression analysis by use of Shapley value decomposition. We use data from a comprehensive health survey from Northern Norway (conducted in 2015/16, N = 21,083, aged 40 and above). We apply three alternative health outcome measures: the EQ-5D-5L index, a visual analogue scale (EQ-VAS) and self-rated health. In all models considered, spousal education is generally positively significant for both men and women. The results also suggest that spousal education is generally more important for men than women. In the sub-sample of individuals having a spouse, decomposition analyses showed that the relative contribution of spousal education to the goodness-of-fit in men's (women's) health was 13% (14%) with the EQ-5D-5L; 25% (20%) with the EQ-VAS and; 30% (21%) with self-rated health. Heterogeneity analyses showed stronger spousal education effects in younger age groups. In conclusion, we have provided empirical evidence that spousal education may contribute to explaining the amplified health gradient in an egalitarian country like Norway.

BackgroundIndicators of socioeconomic position (SEP) and health behaviours (HB) are widely used predictors of health variations. Their relative importance is hard to establish, because HB takes a mediating role in the link between SEP and health. We aim to provide new knowledge on how SEP and HB are related to health and wellbeing.MethodsThe analysis considered 14,713 Norwegians aged 40-63. Separate regressions were performed using two outcomes for health-related quality of life (EQ-5D-5 L; EQ-VAS), and one for subjective wellbeing (Satisfaction with Life Scale). As predictors, we used educational attainment and a composite measure of HB - both categorized into four levels. We adjusted for differences in childhood financial circumstances, sex and age. We estimated the percentage share of each predictor in total explained variation, and the relative contributions of HB in the education-health association.ResultsThe reference case model, excluding HB, suggests consistent stepwise education gradients in health-related quality of life. The gap between the lowest and highest education was 0.042 on the EQ-5D-5 L, and 0.062 on the EQ-VAS. When including HB, the education effects were much attenuated, making HB take the lion share of the explained health variance. HB contributes 29% of the education-health gradient when health is measured by EQ-5D-5 L, and 40% when measured by EQ-VAS. For subjective wellbeing, we observed a strong HB-gradient, but no education gradient.ConclusionIn the institutional context of a rich egalitarian country, variations in health and wellbeing are to a larger extent explained by health behaviours than educational attainment.

Regional variations in healthcare utilisation rates are ubiquitous and persistent. In settings where an aggregate national health service budget is allocated primarily on a per capita basis, little regional variation in total healthcare utilisation rates will be observed. However, for specific treatments, large variations in utilisation rates are observed, iymplying a substitution effect at some point in service delivery. The current paper investigates the extent to which this substitution effect occurs within or between specialties, particularly distinguishing between emergency versus elective care. We used data from Statistics Norway and the Norwegian Patient Registry on eight somatic surgeries for all patients treated from 2010 to 2015. We calculated Diagnosis-Related Group (DRG) -weight per capita in 19 hospital regions. We applied principal component analysis (PCA) to demonstrate patterns in DRG-weight, annual relative changes in DRG-weight, and DRG-weight production for elective care. We show that treatments with similar characteristics cluster within regions. Treatment frequency explains 29% of the total variation in treatment rates. In a dynamic model, treatments with a high degree of emergency care are negatively correlated with treatments with a high degree of elective care. Furthermore, when considering only elective care treatments, the substitution effect occurs between specialties and explains 49% of the variation. When designing policies aimed at reducing regional variations in healthcare utilisation, a distinction between elective and emergency care as well as substitution effects need to be considered.

AbstractThis chapter aims to explore the differences in EQ-5D-5L value sets between countries/areas, and to investigate whether common patterns can be identified between them. EQ-5D-5L value sets for 25 countries/areas were extracted from published literature. These national value sets were compared on key characteristics, such as: the relative importance of the EQ-5D-5L dimensions; the value scale length and the distribution of values over the value scale. Using these characteristics, distinct preference patterns were identified for Asian, Eastern European and Western countries/areas. The Asian countries/areas were split into East Asian and Southeast Asian countries/areas, as these subgroups shared similar characteristics. Using mean values for countries/areas with similar preference patterns, several aggregate value sets were generated. These aggregate value sets describe mean values for all 3125 health states described by the EQ-5D-5L for countries/areas with similar preference patterns. Applying these values to EQ-5D-5L profile data for 7933 respondents in an international survey showed that these aggregate value sets represent the individual national value sets relatively well. This chapter identified large differences between value sets, yet was able to identify common preference patterns between selected countries/areas.

Background In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of education and income in estimating subjective SEP, and examine how this score performs in predicting inequalities in health-related quality of life (HRQoL). Methods We used data from a comprehensive health survey from Northern Norway, conducted in 2015/16 ( N = 21,083). A composite SEP score was developed using adjacent-category logistic regression of subjective SEP as a function of four education and four household income levels. Weights were derived based on these indicators’ coefficients in explaining variations in respondents’ subjective SEP. The composite SEP score was further applied to predict inequalities in HRQoL, measured by the EQ-5D and a visual analogue scale. Results Education seemed to influence SEP the most, while income added weight primarily for the highest income category. The weights demonstrated clear non-linearities, with large jumps from the middle to the higher SEP score levels. Analyses of the composite SEP score indicated a clear social gradient in both HRQoL measures. Conclusions We provide new insights into the relative contribution of education and income as sources of SEP, both separately and in combination. Combining education and income into a composite SEP score produces more comprehensive estimates of the social gradient in health. A similar approach can be applied in any cohort study that includes education and income data.

Background The literature on Inequality of opportunity (IOp) in health distinguishes between circumstances that lie outside of own control vs . efforts that – to varying extents – are within one’s control. From the perspective of IOp, this paper aims to explain variations in individuals’ health-related quality of life (HRQoL) by focusing on two separate sets of variables that clearly lie outside of own control: Parents’ health is measured by their experience of somatic diseases, psychological problems and any substance abuse, while parents’ wealth is indicated by childhood financial conditions (CFC). We further include own educational attainment which may represent a circumstance, or an effort, and examine associations of IOp for different health outcomes. HRQoL are measured by EQ-5D-5L utility scores, as well as the probability of reporting limitations on specific HRQoL-dimensions (mobility, self-care, usual-activities, pain & discomfort, and anxiety and depression). Method We use unique survey data ( N = 20,150) from the egalitarian country of Norway to investigate if differences in circumstances produce unfair inequalities in health. We estimate cross-sectional regression models which include age and sex as covariates. We estimate two model specifications. The first represents a narrow IOp by estimating the contributions of parents’ health and wealth on HRQoL, while the second includes own education and thus represents a broader IOp, alternatively it provides a comparison of the relative contributions of an effort variable and the two sets of circumstance variables. Results We find strong associations between the circumstance variables and HRQoL. A more detailed examination showed particularly strong associations between parental psychological problems and respondents’ anxiety and depression. Our Shapley decomposition analysis suggests that parents’ health and wealth are each as important as own educational attainment for explaining inequalities in adult HRQoL. Conclusion We provide evidence for the presence of the lasting effect of early life circumstances on adult health that persists even in one of the most egalitarian countries in the world. This suggests that there may be an upper limit to how much a generous welfare state can contribute to equal opportunities.

Objectives The EQ-5D is the most widely applied preference-based health-related quality of life measure. However, concerns have been raised that the existing dimensional structure lacks sufficient components of mental and social aspects of health. This study empirically explored the performance of a coherent set of four psycho-social bolt-ons: Vitality; Sleep; Personal relationships; and Social isolation. Methods Cross-sectional surveys were conducted with online panel members from five countries (Australia, Canada, Norway, UK, US) (total N = 4786). Four bolt-ons were described using terms aligned with EQ nomenclature. Latent structures among all nine dimensions are studied using an exploratory factor analysis (EFA). The Shorrocks-Shapely decomposition analyses are conducted to illustrate the relative importance of the nine dimensions in explaining two outcome measures for health (EQ-VAS, satisfaction with health) and two for subjective well-being (the hedonic approach of global life satisfaction and an eudemonic item on meaningfulness). Sub-group analyses are performed on older adults (65 +) and socially disadvantaged groups. Results Strength of correlations among four bolt-ons ranges from 0.34 to 0.49. As for their correlations with the EQ-5D dimensions, they are generally much less correlated with four physical health dimensions than with mental health dimensions (ranged from 0.21 to 0.50). The EFA identifies two latent factors. When explaining health, Vitality is the most important. When explaining subjective well-being, Social isolation is second most important, after Anxiety/depression. Conclusion We provide evidence that further complementing the current EQ-5D-5L health state classification system with a coherent set of four bolt-on dimensions that will fill its psycho-social gap.