OBJECTIVES:

This study aimed to compare health preferences for EQ-5D-5L health states between urban and rural populations in China.

METHODS:

This study used pooled secondary data from two EQ-VT studies. Participants were recruited from Guizhou Province, China using quota sampling. Each participant was interviewed face-to-face to value a set of 15 or 16 out of 30 EQ-5D-5L health states using time trade-off (TTO) methods including cTTO and other two variants. Regression analysis was used to compare health state preferences between urban and rural participants. First, univariate and multivariable linear regression analyses with adjustment of gender, education and ethnicity were used to examine the differences in mean values for individual health states and in the overall mean values between urban and rural participants. Second, we examined the effect of urban/rural on TTO values using the standard, additive 20-parameter main effects model with 20 interaction terms differentiating urban and rural sub-sample. Third, we compared the modelling results using the data from urban and rural participants separately.

RESULTS:

A total of 597 participants (urban: 55.44%; rural: 44.56%) completed the valuation interviews. Both univariate and multivariable linear regression analyses showed that rural participants tended to value health states lower than urban participants regardless of severity of health states. The unadjusted and adjusted overall mean differences between the two groups were -0.041 (p<0.001) and -0.039 (p=0.002), respectively. The model with interaction terms indicated that rural participants put more weight on the pain/discomfort dimension than urban participants. Predictions for the 3125 health states based on rural participants’ health preferences tended to be lower than those based on urban participants’ health preferences.

CONCLUSIONS:

There were small, yet significant, differences in EQ-5D-5L health states preferences between urban and rural populations in China. It is preferable to use a representative population sample to generate a national value set.