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Objectives: This study aimed to establish the Japanese population norms of the EQ-5D-5L and Health Utilities Index Mark 3 (HUI3) and estimate the disutility associated with diseases and symptoms.Methods: We performed a door-to-door survey of the general population by random sampling. The planned sample size was 10 000 residents (age ≥16 years) of 334 districts in Japan. In addition to the EQ-5D-5L and HUI3 questionnaires, questions regarding demographic factors and self-reported main diseases and symptoms were asked. The EQ-5D-5L and HUI3 responses were converted to index values on the basis of Japanese value sets. Summary values by age and sex were calculated to obtain Japanese normative values. A multiple linear model was used to examine relationships between these values and diseases and symptoms.Results: We collected 10 183 responses from 334 districts. The mean EQ-5D-5L index values were 0.821 (male) and 0.774 (female) in the age group of 80 to 89 years, which were lower compared with 0.978 (male) and 0.967 (female) in the age group of 16 to 19 years. Similar trends were observed for the HUI3 values. Age, sex, household income, and education level had a significant influence on the values of both instruments. When measured with the EQ-5D-5L, Parkinson disease, dementia, and stroke were associated with the largest disutility (>0.2), and the disutility for depression was approximately 0.18. In contrast, the HUI3 disutility values for Parkinson disease and dementia were approximately 0.4.Conclusions: This study established the Japanese population norms of the EQ-5D-5L and HUI3, which can be used in healthcare decision making and contribute to a more reliable analysis of economic evaluations. [ABSTRACT FROM AUTHOR]

Background: Evidence is accumulating for the presence of cognitive impairment in patients with major depressive disorder (MDD). The Prospective Epidemiological Research on Functioning Outcomes Related to Major Depressive Disorder in Japan (PERFORM-J) study is a 6-month, non-interventional, prospective, multicenter, epidemiological study. Using baseline data, the relationship between cognitive symptoms and psychosocial function was analyzed in Japanese patients with MDD.Methods: A total of 518 Japanese outpatients (aged 18-65 years) with MDD initiating new antidepressant monotherapy (first-line or switch from a previous drug) participated. Assessment measures were: physician-rated Montgomery-Asberg Depression Rating Scale (MADRS) (depression severity); Digit Symbol Substitution Test (DSST) (objective cognition); and patient-rated Perceived Deficits Questionnaire - Depression (PDQ-D) (subjective cognition); Sheehan Disability Scale (SDS); Work Productivity and Activity Impairment questionnaire (WPAI) (psychosocial function); and EuroQol-5 Dimension-5 Level (quality of life).Results: Over half of patients exhibited a greater than 1 standard deviation decline below norm in objective cognition, as measured by the DSST. Severity of depressive symptoms correlated positively with subjective (PDQ-D), but not objective (DSST) cognition. Depression severity and subjective cognition were significantly associated with psychosocial function, as measured by the SDS and WPAI (except the absenteeism items). Conversely, the association between objective cognition and psychosocial function was not significant.Limitations: Only outpatients were recruited. Occupations and job-related stress levels were not taken into account.Conclusions: Japanese patients with MDD presented a high incidence of cognitive impairment. The relationships between cognitive and psychosocial function in these patients suggest a need for therapeutics targeting cognitive impairment of MDD. [ABSTRACT FROM AUTHOR]

This study aimed to develop direct and response mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the 5-level version of EQ-5D index based on the gradient boosted tree (GBT), a promising modern machine learning method. We used the Quality of Life Mapping Algorithm for Cancer study data (903 observations from 903 patients) for training GBTs and testing their predictive performance. In the Quality of Life Mapping Algorithm for Cancer study, patients with advanced solid tumor were enrolled, and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 and 5-level version of EQ-5D were simultaneously evaluated. The Japanese value set was used for direct mapping, whereas the Japanese and US value sets were used for response mapping. We trained the GBTs in the training data set (80%) with cross-validation and tested the predictive performance measured by the root mean squared error (RMSE), mean absolute error (MAE), and mean error in the test data set (20%). The RMSE and MAE in the test data set were larger in the GBT approaches than in the previously developed regression-based approaches. The mean error in the test data set tended to be smaller in the GBT approaches than in the previously developed regression-based approaches. The predictive performances in the RMSE and MAE did not improve by the GBT approaches compared with regression approaches. The flexibility of the GBT approaches had the potential to reduce overprediction and underprediction in poor and good health, respectively. Further research is needed to establish the role of machine learning methods in mapping a nonpreference-based measure onto health utility. • Although modern machine learning methods have led to improvements in many fields of medicine, they are rarely applied to mapping from a nonpreference-based measure onto health utility. • This study developed direct and response mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the 5-level version of EQ-5D index using the gradient boosted tree, a promising modern machine learning method. • The gradient boosted tree approach did not improve the predictive performance measured by the root mean squared error and mean absolute error compared with regression approaches but had the potential to reduce overprediction and underprediction in poor and good health, respectively. [ABSTRACT FROM AUTHOR]

Background: The valuation study of the five-level version of the EuroQol five-dimensional questionnaire (EQ-5D-5L) involved composite time trade-off (cTTO) and a discrete choice experiment (DCE). The DCE scores must be anchored to the quality-of-life scale from 0 (death) to 1 (full health). Nevertheless, the characteristics of the statistical methods used for converting the EQ-5D-5L DCE results by using TTO information are not yet clearly known.Objectives: To present the Japanese DCE value set of the EQ-5D-5L and compare three methods for converting latent DCE values.Methods: The survey sampled the general population at five locations in Japan. 1098 respondents were stratified by age and sex. To obtain and compare the value sets of the EQ-5D-5L, the cTTO and DCE data were analyzed by a linear mixed model and conditional logit, respectively. The DCE scores were converted to the quality-of-life scale by anchoring to the worst state using cTTO, mapping DCE onto cTTO, and a hybrid model.Results: The data from 1026 respondents were analyzed. All the coefficients in the cTTO and DCE value sets were consistent throughout all the analyses. Compared with the cTTO algorithm, the mapping and hybrid methods yielded very similar scoring coefficients. The hybrid model results, however, produced a lower root mean square error and fewer health states with errors exceeding 0.05 than did the other models. The DCE anchored to the worst state overestimated the cTTO scores of almost all the health states.Conclusions: Japanese value sets based on DCE were demonstrated. On comparing the observed cTTO scores, we found that the hybrid model was slightly superior to the simpler methods, including the TTO model. [ABSTRACT FROM AUTHOR]