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Purpose: The EQ-5D is the most widely applied generic preference-based measure (GPBM) of health-related quality of life (HRQoL). Much concern has been raised that its descriptive system is lacking psycho-social dimensions. A recent paper in this journal provided theoretical support for four dimensions to fill this gap. The current paper aims to provide empirical support for these suggested bolt-on dimensions to the EQ-5D., Methods: We use data from the comprehensive Multi-Instrument-Comparison (MIC) study. The four proposed bolt-on dimensions (Vitality, Sleep, Social Relationships, and Community Connectedness) were selected from the Assessment of Quality of Life (AQoL)-8D. We investigate the relative importance of these four dimensions as compared to the five EQ-5D-5L dimensions on explaining HRQoL (measured by a visual analogue scale; N = 7846) or global life satisfaction (measured by the Satisfaction With Life Scale; N = 8005), using the Shorrocks-Shapely decomposition analysis. Robustness analyses on Vitality was conducted using data from the United States National Health Measurement Study (NHMS) (N = 3812)., Results: All five EQ-5D-5L dimensions and four bolt-on dimensions significantly explained the variance of HRQoL. Among them, Vitality was found to be the most important dimension with regard to the HRQoL (relative contribution based on the Shorrocks-Shapely decomposition of R 2 : 23.0%), followed by Usual Activities (15.1%). Self-Care was the least important dimension (relative contribution: 5.4%). As a comparison, when explaining global life satisfaction, Social Relationships was the most important dimension (relative contribution: 24.0%), followed by Anxiety/Depression (23.2%), while Self-Care remained the least important (relative contribution: 1.6%). The importance of the Vitality dimension in explaining HRQoL was supported in the robustness analysis using the NHMS data (relative contribution: 23.7%)., Conclusions: We provide empirical support for complementing the current EQ-5D-5L descriptive system with a coherent set of four bolt-on dimensions that will fill its psycho-social gap. Such an extended health state classification system would in particular be relevant for programme evaluations within the expanding fields of mental health and community care.

This general population-based study examined associations between violence and mental health, musculoskeletal pain, and early disability pension. The prevalence and consequences of good vs. poor adjustment (resilience vs. vulnerability) following encounters with violence were also examined. Data were based on the sixth wave of the "Tromsø Study" (N = 12,981; 65.7% response rate, 53.4% women, M-age = 57.5 years, SD-age = 12.7 years). Self-reported data on psychological (threats) and physical violence (beaten/kicked), mental health (anxiety/depression), musculoskeletal pain (MSP), and granting of disability pension (DP) were collected. Men suffered more violent events during childhood than women did, and vice versa during adulthood. Psychological violence implied poorer mental health and slightly more MSP than physical violence. The risk of MSP was highest for violence occurring during childhood in women and during the last year for men. A dose-response relationship between an increasing number of violent encounters and poorer health was observed. About 58% of individuals reported no negative impact of violence (hence, resilience group), whereas 42% considered themselves as more vulnerable following encounters with violence. Regression analyses indicated comparable mental health but slightly more MSP in the resilience group compared to the unexposed group, whereas the vulnerable group had significantly worse health overall and a higher risk of early granting of DP. Resilience is not an all-or-nothing matter, as physical ailments may characterize individuals adapting well following encounters with violence.

Purpose: Quality-adjusted life years (QALYs) represent a critical metric in economic evaluations impacting key healthcare decisions in many countries. However, there is widespread disagreement as to which is the best of the health state utility (HSU) instruments that are designed to measure the Q in the QALY. Instruments differ in their descriptive systems as well as their valuation methodologies; that is, they simply measure different things. We propose a visual framework that can be utilized to make meaningful comparisons across HSU instruments. Methods: The framework expands on existing HRQoL models, by incorporating four distinctive continua, and by putting HRQoL within the broader notion of subjective well-being (SWB). Using this conceptual map, we locate the five most widely used HSU-instruments (EQ-5D, SF-6D, HUI, 15D, AQoL). Results: By individually mapping dimensions onto this visual framework, we provide a clear picture of the significant conceptual and operational differences between instruments. Moreover, the conceptual map demonstrates the varying extent to which each instrument moves outside the traditional biomedical focus of physical health, to also incorporate indicators of mental health and social well-being. Conclusion: Our visual comparison provides useful insights to assess the suitability of different instruments for particular purposes. Following on from this comparative analyses, we extract some important lessons for a new instrument that cover the domains of physical, mental and social aspects of health, i.e. it is in alignment with the seminal 1948 WHO definition of health. [ABSTRACT FROM AUTHOR]

Purpose: To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.Methods: Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r2. Cross-validation with fivefold approach was conducted to test the generalizability of each model.Results: The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models.Conclusions: The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries. [ABSTRACT FROM AUTHOR]

The mechanisms by which childhood socioeconomic status (CSES) affects adult mental health, general health, and well-being are not clear. Moreover, the analytical assumptions employed when assessing mediation in social and psychiatric epidemiology are rarely explained. The aim of this paper was to explain the intermediate confounding assumption, and to quantify differential recall bias in the association between CSES, childhood abuse, and mental health (SCL-10), general health (EQ-5D), and subjective well-being (SWLS). Furthermore, we assessed the mediating role of psychological and physical abuse in the association between CSES and mental health, general health, and well-being; and the influence of differential recall bias in the estimation of total effects, direct effects, and proportion of mediated effects. The assumptions employed when assessing mediation are explained with reference to a causal diagram. Poisson regression models (relative risk, RR and 99% CIs) were used to assess the association between CSES and psychological and physical abuse in childhood. Mediation analysis (difference method) was used to assess the indirect effect of CSES (through psychological and physical abuse in childhood) on mental health, general health, and well-being. Exposure (CSES) was measured at two time points. Mediation was assessed with both cross-sectional and longitudinal data. Psychological abuse and physical abuse mediated the association between CSES and adult mental health, general health, and well-being (6-16% among men and 7-14% among women, p < 0.001). The results suggest that up to 27% of the association between CSES and childhood abuse, 23% of the association between childhood abuse, and adult mental health, general health, and well-being, and 44% of the association between CSES and adult mental health, general health, and wellbeing is driven by differential recall bias. Assessing mediation with cross-sectional data (exposure, mediator, and outcome measured at the same time) showed that the total effects and direct effects were vastly overestimated (biased upwards). Consequently, the proportion of mediated effects were underestimated (biased downwards). If there is a true (unobserved) direct or indirect effect, and the direction of the differential recall bias is predictable, then the results of cross-sectional analyses should be discussed in light of that. [ABSTRACT FROM AUTHOR]

There is much evidence that health, income and social relationships are important for our well-being, but little evidence on their relative importance. This study makes an integrative analysis of the relative influence of health related quality of life (HRQoL), household income and social relationships for subjective well-being (SWB), where SWB is measured by the first three of the five items on the satisfaction with life scale (SWLS). In a comprehensive 2012 survey from six countries, seven disease groups and representative healthy samples (N = 7933) reported their health along several measures of HRQoL. A Shapley value decomposition method measures the relative importance of health, income and social relationships, while a quantile regression model tests how the effects of each of the three predictors vary across different points of SWB distributions. Results are compared with the standard regression. The respective marginal contribution of social relationships, health and income to SWB (as a share of goodness-of-fit) is 50.2, 19.3 and 7.3% when EQ-5D-5L is used as a measure of health. These findings are consistent across models based on five alternative measures of HRQoL. The influence of the key determinants varied significantly between low and high levels of the SWB distribution, with health and income having stronger influence among those with relatively lower SWB. Consistent with several studies, income has a significantly positive association with SWB, but with diminishing importance. [ABSTRACT FROM AUTHOR]