Background Sub-health which is the state between health and disease is a major global public health challenge. As a reversible stage, sub-health can work as a effective tool for the early detection or prevention of chronic disease. The EQ-5D-5L (5L) is a widely used, generic preference-based instrument while its validity in measuring sub-health is not clear. The aim of the study was thus to assess its measurement properties in individuals with sub-health in China. Methods The data used were from a nationwide cross-sectional survey conducted among primary health care workers who were selected on the basis of convenience and voluntariness. The questionnaire was composited of 5L, Sub-Health Measurement Scale V1.0 (SHMS V1.0), social-demographic characteristics and a question assessing the presence of disease. Missing values and ceiling effects of 5L were calculated. The convergent validity of 5L utility and VAS scores was tested by assessing their correlations with SHMS V1.0 using Spearman’s correlation coefficient. The known-groups validity of 5L utility and VAS scores was assessed by comparing their values between subgroups defined by SHMS V1.0 scores using the Kruskal–Wallis test. We also did an analysis in subgroups according to different regions of China. Results A total of 2063 respondents were included in the analysis. No missing data were observed for the 5L dimensions and only one missing value was for the VAS score. 5L showed strong overall ceiling effects (71.1%). The ceiling effects were slightly weaker on the “pain/discomfort” (82.3%) and “anxiety/depression” (79.5%) dimensions compared with the other three dimensions (nearly 100%). The 5L weakly correlated with SHMS V1.0: the correlation coefficients were mainly between 0.2 and 0.3 for the two scores. 5L was yet not sensitive in distinguishing subgroups of respondents with different levels of sub-health, especially the subgroups with adjacent health status (p > 0.05). The results of subgroup analysis were generally consistent with those of the full sample. Conclusions It appears that the measurement properties of EQ-5D-5L in individuals with sub-health are not satisfactory in China. We thus should be cautious to use it in the population.

ObjectiveThis research aimed to develop the more accurate mapping algorithms from health assessment questionnaire disability index (HAQ-DI) onto EQ-5D-5L based on Chinese Rheumatoid Arthritis patients.MethodsThe cross-sectional data of Chinese RA patients from 8 tertiary hospitals across four provincial capitals was used for constructing the mapping algorithms. Direct mapping using Ordinary least squares regression (OLS), the general linear regression model (GLM), MM-estimator model (MM), Tobit regression model (Tobit), Beta regression model (Beta) and the adjusted limited dependent variable mixture model (ALDVMM) and response mapping using Multivariate Ordered Probit regression model (MV-Probit) were carried out. HAQ-DI score, age, gender, BMI, DAS28-ESR and PtAAP were included as the explanatory variables. The bootstrap was used for validation of mapping algorithms. The average ranking of mean absolute error (MAE), root mean square error (RMSE), adjusted R2 (adjR2) and concordance correlation coefficient (CCC) were used to assess the predictive ability of the mapping algorithms.ResultsAccording to the average ranking of MAE, RMSE, adjR2, and CCC, the mapping algorithm based on Beta performed the best. The mapping algorithm would perform better as the number of variables increasing.ConclusionThe mapping algorithms provided in this research can help researchers to obtain the health utility values more accurately. Researchers can choose the mapping algorithms under different combinations of variables based on the actual data.

ObjectiveThe study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China.MethodsA sample of 2,152 patients with FD completed both the CHU-9D and Peds QL 4.0 instruments. A total of six regression models were used to develop the mapping algorithm, including ordinary least squares regression (OLS), the generalized linear regression model (GLM), MM-estimator model (MM), Tobit regression (Tobit) and Beta regression (Beta) for direct mapping, and multinomial logistic regression (MLOGIT) for response mapping. Peds QL 4.0 total score, Peds QL 4.0 dimension scores, Peds QL 4.0 item scores, gender, and age were used as independent variables according to the Spearman correlation coefficient. The ranking of indicators, including the mean absolute error (MAE), root mean squared error (RMSE), adjusted R2, and consistent correlation coefficient (CCC), was used to assess the predictive ability of the models.ResultsThe Tobit model with selected Peds QL 4.0 item scores, gender and age as the independent variable predicted the most accurate. The best-performing models for other possible combinations of variables were also shown.ConclusionThe mapping algorithm helps to transform Peds QL 4.0 data into health utility value. It is valuable for conducting health technology evaluations within clinical studies that have only collected Peds QL 4.0 data.