EQ-5D-5L norms for the urban Chinese population in China

This norm study drew data from a large EQ-5D-5L valuation study in China [3]. In the valuation study, respondents reported: 1) valuation data and; 2) self-reported health status data and demographic data. The valuation data was used to establish the EQ-5D-5L value set for China and was reported elsewhere [3]. This study used the self-reported health status data and demographic data to establish a population norm. The sample size was decided by the EQ-5D-5L valuation protocol, which was aiming at constructing country specific EQ-5D-5L value set [9]. Members of the general population were randomly recruited from five urban areas of five cities (Beijing, Shenyang, Nanjing, Chengdu, and Guiyang). From each city, respondents were recruited from at least five difference administrative districts and at different time of day. Specific recruitment sites included library, hospital, university, local community, park and shopping areas etc. [10]. These five cities were selected as representative urban areas in terms of size of population, geographical region and economic development status in China [3]. Within each city, quotas were set to recruit equal numbers of participants from each city and to ensure the study sample resembled the general Chinese urban adult population with respect to age, gender, and education level according to the Sixth National Population Census [10]. In each city, members of the general public who were at least 16 years old, and were literate and able to understand survey questions, were recruited through personal invitation [3]. Response rate was calculated.

Each respondent was interviewed face-to-face by a trained interviewer using the EuroQol valuation technology (EQ-VT) [3, 11]. EQ-VT is a standardized software design by the EuroQol Group in order to facilitate the data collection for valuation study [9]. The interview had four sections. The first section was for respondents to report their own health using the EQ-5D-5L questionnaire: the five-dimensional descriptive system and the EQ-VAS. In the second section respondents were asked to value 10 different EQ-5D-5L health states using a composite time trade-off (cTTO) method [8]. The third section contained 7 pairs of EQ-5D-5L discrete choice tasks. The fourth section assessed respondents’ socio-economic and other background characteristics. This paper used data collected in the first and fourth sections only.

The EQ-5D-5L descriptive system consists of five dimensions (mobility, self-care, usual activities, pain/ discomfort and anxiety/depression) with five ordinal severity levels each (no problems, slight problems, moderate problems, severe problems, and extreme problems/unable to), thus defining 3125 (5⁵) distinct health states [2]. The respondent is asked to indicate his/her health state against the most appropriate statement in each of the 5 dimensions and this leads to a 1-digit number expressing the level selected for each dimension [1], i.e. 12211 means the respondent had no problems in mobility, pain/discomfort, and anxiety/depression, but had slight problems in self-care and usual activities. A VAS was used in the interview, with anchor points 0 (‘worst imaginable health state’) and 100 (‘best imaginable health state’). Respondents first report their own health state using the EQ-5D-5L descriptive system and then their overall health on the EQ-VAS based on their health on the day of survey.

In 2012, the Chinese version of EQ-5D-5L was translated using a response scaling method by Luo et al. [2].The translation process followed the standard translation protocol developed by the EuroQol Group and had mainly 3 stages [2]. First, different candidate labels were generated by direct translation and reviewing existing questionnaire; second, 50 native Chinese speaking respondents were interviewed to rank and value potential candidates labels; third, the final labels were selected to achieve comparable measurement properties to the English/Spanish version of EQ-5D-5L. The translated Chinese EQ-5D-5L demonstrated validity and increased sensitivity in diabetes and hepatitis B patients [12, 13].

For each respondent, the EQ-5D-5L health state and the EQ-VAS were directly observed from respondent’s’ self-report questionnaire while the EQ index score was derived from the Chinese EQ-5D-5L value set [3]. In the EQ-5D-5L value set, the EQ index score of all 3125 health states were estimated [3]. For each respondent, we derived their corresponding EQ index score from their self-reported health states.

First, descriptive statistics of EQ-5D-5L health state, EQ-VAS and EQ index score were calculated for the whole sample and by different demographic variables and cities (age, gender, employment status etc.). For comparison, we categorized age into age groups following other countries’ population norm studies [6, 14‐17]. As our sample was recruited from urban areas only, we used ‘Residence of origin’ as a proxy to study the possible difference of HRQoL between urban and rural residents. ‘Residence of origin’ referred to the birth place of the respondent and was classified into three groups (city, county, township or village) based on China’s administrative levels. ‘Health insurance’ referred to social health insurance types in China’s healthcare system [18]. For each demographic variable, the percentage of reported problem in EQ-5D dimension, the means (and 95% confidence interval) of EQ-VAS and EQ index scores were calculated for each subgroup and the difference were tested statistically. Second, we used multivariable analysis to examine the associations between demographic characteristics with reported problems in EQ-5D-5L, EQ-VAS and EQ index scores respectively. For the reported problems in each dimension, we used logistic regression (‘no problems’ coded as 0; ‘slight problems’, ‘moderate problems’, ‘severe problems’, or ‘extreme problems/unable’ coded as 1) [1]. For EQ-VAS and EQ index scores, we used linear regression. All demographic variables including age and education level were entered into the models as categorical variables. For better interpretation, age groups were collapsed into three categories (young age: <=29 years, middle age: 30–59 years; older age: > = 60 years) [14]. Multivariable analysis was used to identify significant demographic characteristics using a backward selection procedure to remove covariates with p > 0.05. Odds ratio was reported for logistic regression and coefficient was reported for linear regression respectively, the corresponding 95% CI was calculated using robust standard error.

For this study, ethical approval was not needed in China at the time of data collection. A waiver of the informed consent was approved as this study did not provide any intervention to participants. Participants can withdraw at any time without any consequences.