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.

The recently published EQ-5D-5L value sets from Canada, England, Japan, Korea, the Netherlands, Spain, and Uruguay are compared with an aim to identify any similarities in preference pattern. We identify some striking similarities for Canada, England, the Netherlands, and Spain in terms of (a) the relative importance of the 5 dimensions; (b) the relative utility decrements across the 5 levels; and (c) the scale length. On the basis of the observed similarities across these 4 Western countries, we develop an amalgam model, WePP (western preference pattern), and compare it with these 4 value sets. The values generated by this model show a high degree of concordance with those of England, Canada, and Spain. Patient level data were obtained from the Multi-Instrument Comparison project, which includes participants from 6 countries in 7 disease groups (N = 7,933): The WePP values lie within the confidence intervals for the value sets in Canada, England, and Spain across the whole severity distribution. We suggest that the WePP model represents a useful "common currency" for (Western) countries that have not yet developed their own value sets. Further research is needed to disentangle the differences between value sets due to preference heterogeneity from those stemming from methodological differences.

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, generalized 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 normalized root mean square error (NRMSE), normalized mean absolute error (NMAE) and adjusted-r 2 . Cross-validation with 5-fold 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-r 2 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.

2 AbstractPurpose Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated non-linear relationships. This paper inquires into the degree of non-linearity across the four most widely used HSUinstruments, and proposes exchange rates that differ depending on the severity levels of the health state utility scale.Method Overall, 7933 respondents from six countries; 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of non-linear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange Rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument.Result Quantile regressions reveal significant non-linear relationships across all four HSUinstruments. The degrees of non-linearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.19, whilst at the upper end it is 0.35. ConclusionComparisons at different utility levels illustrates the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of non-linear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument 3 been used. Accounting for non-linearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY-gains have been based on different HSU-instruments.

Purpose: Compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities, and estimate the mapping algorithms that best predict these health state utilities.Methods: 772 cancer patients described their health along the cancer-specific instrument (QLQ-C30) and two generic preference-based instruments (EQ-5D-5L and SF-6D). Seven alternative regression models were applied: ordinary least squares (OLS), generalized linear model (GLM), extended estimating equations (EEE), fractional regression model (FRM), beta binomial (BB) regression, logistic quantile regression (LQR), and censored least absolute deviation (CLAD). Normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), r-squared (r 2 ) and concordance correlation coefficient (CCC) were used as model performance criteria. Cross-validation was conducted by randomly splitting internal dataset into two equally sized groups to test the generalizability of each model. Results:In predicting EQ-5D-5L utilities, the BB regression performed best. It gave better predictive accuracy in terms of all criteria in the full sample, as well as in the validation sample. In predicting SF-6D, the EEE performed best. It outperformed in all criteria: NRMSE = 0.1004, NMAE = 0.0798, CCC = 0.842 and r 2 =72.7% in the full sample, and NRMSE=0.1037, NMAE=0.0821, CCC = 0.8345 and r 2 =71.4% in cross-validation. Conclusions:When only QLQ-C30 data are available, mapping provides an alternative approach to obtain health state utility data for use in cost-effectiveness analyses. Among seven alternative regression models, the BB and the EEE gave the most accurate predictions for EQ-5D-5L and SF-6D, respectively.

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Overall, preference weighting has minimal effect if the unweighted values are anchored on the same scale as the preference-weighted value sets.

Background Expansion of designated cycling networks increases cycling for transport that, in turn, increases physical activity, contributing to improvement in public health. This paper aims to determine whether cycle-network construction in a large city is cost-effective when compared to the status-quo. We developed a cycle-network investment model (CIM) for Oslo and explored its impact on overall health and wellbeing resulting from the increased physical activity. Methods First, we applied a regression technique on cycling data from 123 major European cities to model the effect of additional cycle-networks on the share of cyclists. Second, we used a Markov model to capture health benefits from increased cycling for people starting to ride cycle at the age of 30 over the next 25 years. All health gains were measured in quality-adjusted life years (QALYs). Costs were estimated in US dollars. Other data to populate the model were derived from a comprehensive literature search of epidemiological and economic evaluation studies. Uncertainty was assessed using deterministic and probabilistic sensitivity analyses. Results Our regression analysis reveals that a 100 km new cycle network construction in Oslo city would increase cycling share by 3%. Under the base-case assumptions, where the benefits of the cycle-network investment relating to increased physical activity are sustained over 25 years, the predicted average increases in costs and QALYs per person are $416 and 0.019, respectively. Thus, the incremental costs are $22,350 per QALY gained. This is considered highly cost-effective in a Norwegian setting. Conclusions The results support the use of CIM as part of a public health program to improve physical activity and consequently avert morbidity and mortality. CIM is affordable and has a long-term effect on physical activity that in turn has a positive impact on health improvement.