Valuation of the SF-6D Health States Is Feasible, Acceptable, Reliable, and Valid in a Chinese Population.

OBJECTIVES The SF-6D is a preference-based measure of health (PBMH) derived from the SF-36 for economic evaluation. The aim of this study was to find out whether it was feasible, acceptable, reliable, and valid to use the standard gamble (SG) method to generate preference-based values for the SF-6D in a Chinese population. METHODS The SF-6D was translated into Chinese by forward and backward translations. Forty-nine states defined by the SF-6D were selected using an orthogonal design and grouped into seven sets. An age-sex stratified sample of 126 Chinese adults with low education levels valued a set of 7 and the pits (worst) SF-6D health states by the SG method. The data were modeled at the individual and mean levels to predict preference values for all SF-6D states. The quality of data and the predictive power of the models were compared with results from the United Kingdom. RESULTS All respondents completed the interviews with 3% finding the process very difficult and 21% felt some degree of irritation or boredom. A total of 907 SG valuations (90% out of 1008 observations) were useable for econometric modeling. There was no significant change in the test-retest values from 21 subjects. The main mean effect models achieved a good fit with a mean absolute error of 0.054. Some differences between the Chinese and UK preference coefficients were found especially in the physical functioning dimension. The range of SG values predicted by the HK function is slightly longer, with the pits state having a value of 0.152 compared to 0.271 in the UK. CONCLUSION It was feasible, acceptable, reliable, and valid to value the SF-6D with the SG method in a Chinese population with relatively low education levels. The results supported the feasibility and validity of valuing PBMH in Asian populations. Further studies are required to determine whether the differences in the SF-6D scoring algorithms between the British and Chinese populations are important.

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