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Aims: To investigate the feasibility of discrete choice experiments for valuing EQ-5D-5L states using computer-based data collection, the consistency of the estimated regression coefficients produced after modeling the preference data, and to examine the similarity of the values derived across countries., Methods: Data were collected in Canada, England, The Netherlands, and the United States (US). Interactive software was developed to standardize the format of the choice tasks across countries, except for face-to-face interviewing in England. The choice task required respondents to choose between 2 suboptimal health states. A Bayesian design was used to generate 200 pairs of states that were randomly grouped into 20 blocks. Each respondent completed 1 block of 10 pairs. A main-effects probit model was used to estimate regression coefficients and to derive values., Results: Approximately 400 respondents participated from each country. The mean time to perform 1 choice task was between 29.2 (US) and 45.2 (England) seconds. All regression coefficients were statistically significant, except level 2 for Usual Activities in The Netherlands (P=0.51). Predictions for the complete set of 3125 EQ-5D-5L health states were similar for the 4 countries. Intraclass correlation coefficients between the countries were high: from 0.80 (England vs. US) through 0.98 (Canada vs., Us) Conclusions: Derivation of value sets from the general population using computer-based choice tasks for the EQ-5D-5L is feasible. Parameter estimates were generally consistent and logical, and health-state values were similar across the 4 countries.

Purpose: To compare self-rated health among the United States general population in 2002 and 2017.Methods: Secondary data were analyzed from two EQ-5D valuation studies conducted in 2002 and 2017. Both studies included the EQ-5D-3L self-classifier and visual analog scale (VAS), where health is rated from 0 (worst imaginable health) to 100 (best imaginable health). VAS scores were compared between time points using regression models, adjusting for sociodemographic factors (Model 1), plus illness (Model 2), and health problems according to the EQ-5D classifier (Model 3).Results: Mean VAS scores in 2002 [84.4 (SD = 16.1)] were not different from 2017 [84.6 (SD = 14.5)] (p = 0.63), nor different after adjusting for demographics (Model 1) or illness (Model 2). However, 2017 VAS mean scores were significantly higher than 2002 [2.2 (95% CI 1.36-3.10)] upon adjusting for the presence of dimension-specific health problems.Conclusions: Self-rated health of the general US adult population in 2017 was similar to 2002, but after adjusting for health problems, scores were slightly higher in 2017. Sociodemographic shifts in age and education explain some of the differences in scores, and by removing health and sociodemographic factors, we found the VAS reveals self-rated health is slightly better in 2017 than 2002. [ABSTRACT FROM AUTHOR]

Objective: The D1 model that was developed to predict US societal preferences for EQ-5D health states addressed several important conceptual and statistical issues. However, it has been criticized for being too complex, failing to account for the nonnormal distribution of health state values, and the transformation of preferences for worse-than-death health states before estimation. This research was conducted to develop an improved model for predicting median preferences for EQ-5D health states for the US population. Methods: Probability-weighted least absolute deviations regression was used to fit models to the time trade-off data collected in the US Valuation of the EQ-5D Health States study. No transformation was applied to the values for states considered worse than death. Several model specifications that differed with respect to explanatory variables were evaluated using two-sample cross-validation. Results: The best-fitting model included only fixed effects for moderate or severe problems in each of the 5 EQ-5D dimensions and excluded a constant. This specification yielded rank correlations between observed and predicted values and median observed and predicted values of 0.635 and 0.991, respectively, as well as a median absolute error of 0.026. The predicted median preferences ranged from 1.00 for full health, to –0.81 for the worst possible health state. Conclusions: Due to its simplicity and robustness, a median model is superior to other models for predicting US population preferences for EQ-5D health states. The predictions of this model are suggested for use in applications that require US societal health state values. [ABSTRACT FROM AUTHOR]