2.1. Study design and sample
We conducted a CV survey on general Chinese population between June 1st, 2019 and August 10th, 2019. A relatively low response rate was observed in the pilot study of the probability sample survey. Hence, quota sampling was used in the final survey with quotas based on sex, age, and income. First, study participants were recruited in-person by trained interviewers, then we interviewed those who satisfied the quotas. A questionnaire that measures maximum WTP per QALY for various hypothetical scenarios was used in this research. This survey was carried out with trained interviewers through telephone (a mobile app “WeChat”). Five different health statuses were defined using five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) descriptions [24, 25], including three treatment settings and two end-of-life scenarios. More details will be discussed in the next section. All subjects were asked for their full consent to participate in the study and no financial incentives were offered.
2.2. Questionnaire
The full questionnaire contained 22 questions concerning quality of life, WTP, and demographic items as well as health-related issues. The demographic section included questions about age, sex, marital status, education, and family income. First, we evaluated the individuals’ present health state using the EQ-5D-5L. Part 2 consisted of a hypothetical health state and a WTP exercise in which we asked individuals to state the maximum amount he or she would be willing to pay for treatment of a hypothetical condition. An example of part 2 can be found in the Additional file 1 [see Additional file 1]. To avert possible extreme WTP values and reveal general treatment in each scenario, small QALY gains, 0.2 QALY and 0.4 QALY, were applied in this research. Altogether, 10 eliciting scenarios were constructed (see Table 1).
Table 1 Scenarios of questionnaire
|
Health state
|
No
|
EQ-5D-5L description
|
QALY gain
|
Period (months)†
|
Treatment scenario
|
Mild
|
1
|
12122
|
I have no problems in walking about; I have slight problems washing or dressing myself; I have no problems doing my usual activities; I have slight pain or discomfort; I am slightly anxious or depressed
|
0.2
|
15
|
Mild
|
2
|
0.4
|
31
|
Moderate
|
3
|
23332
|
I have slight problems in walking about; I have moderate problems washing or dressing myself; I have moderate problems doing my usual activities; I have moderate pain or discomfort; I am slightly anxious or depressed
|
0.2
|
5
|
Moderate
|
4
|
0.4
|
10
|
Severe
|
5
|
44332
|
I have severe problems in walking about; I have severe problems washing or dressing myself; I have moderate problems doing my usual activities; I have moderate pain or discomfort; I am slightly anxious or depressed
|
0.2
|
3
|
Severe
|
6
|
0.4
|
6
|
Terminal illness
|
7
|
0.2
|
15
|
8
|
0.4
|
26
|
Immediate death
|
9
|
11115
|
I have no problems in walking about; I have no problems washing or dressing myself; I have no problems doing my usual activities; I have no pain or discomfort; I am extremely anxious or depressed
|
0.2
|
3
|
10
|
0.4
|
6
|
† Since QALY=the period of life length (year) * utility of health state, the period was calculated as follows:
For treatment scenarios, the period (month)=QALY gain/(utility of health state after treatment - utility of health state before treatment)*12,
Health state after treatment is perfect health, hence, the period (month) = QALY gain/ (1- utility of health state before treatment) * 12
For terminal illness and immediate death, the treatment can prolong life expectancy in assumed health state, which should result in 0.2 or 0.4 QALY gain. Hence, for terminal illness the period (month) = QALY gain/utility of health state*12+3. For immediate death, QALY gain/utility of health state*12
For treatment scenarios, a hypothetical scenario with description of EQ-5D-5L (the health states mentioned in Table 1) was explained to participants. Without any treatment, they would live with the described health state for XX months. After XX months, they would fully recover. For each hypothetical health state, the WTP value was measured by the respondents’ willingness to purchase the treatment.
We also specified the following conditions to each respondent to clarify the assumed situation; (a) the treatment was not reimbursed by public health insurance, the full amount had to be paid beforehand; (b) loss of income due to the illness need not be considered (it is compensated by social security.); and (c) payment for the treatment will influence the respondents’ household.
“Terminal illness scenario” reflected the assumption that participants suffered a terminal disease with 3 months in severe health state (EQ-5D-5L description: 44332). A newly developed treatment could prolong life expectancy by 12 months (0.2 QALY) or 24 months (0.4 QALY) in that severe health state. For “immediate death scenario”, we assumed that because of fatal sickness, the respondents would die immediately. However, in this scenario we hypothesized that there was a treatment that could prolong life expectancy by 3 months (0.2 QALY) or 6 months (0.4 QALY) in health state 11115[1].
The WTP payment was defined as the amount of out-of-pocket expense to purchase an assumed intervention. Participants were asked if he or she would pay for the treatment. Those who replied “No” were then asked to give their reasons. If the answer was “yes”, the participant was requested to provide the maximum amount they were willing to pay out of pocket. The PC had the following categories: 3200 RMB (5 percent of Chinese GDP per capita, USD 457), 6450 RMB (10 percent of Chinese GDP per capita, USD 922), 12,900 RMB (20 percent of Chinese GDP per capita, USD 1,844), 25,800 RMB (40 percent of Chinese GDP per capita, USD 3,688), 51,600 RMB (80 percent of Chinese GDP per capita, USD 7,376), 77,400 RMB (120 percent of Chinese GDP per capita, USD 11,064), 103,200 RMB (160 percent of Chinese GDP per capita, USD 14,753).
2.3. Data analysis
Previous studies have applied two different methods of converting the data on WTP and QALY gains into WTP per QALY estimates, namely aggregated method and disaggregated method. The aggregated approach calculates the ratio by dividing the mean of WTP by the mean of QALY, whereas the disaggregated method estimates WTP/QALY for individuals, and subsequently estimates the mean value of WTP/QALY, which was proved to be a more appropriate method as it takes account of heterogeneity in preferences as well as individual’s marginal rate of substitution between health and money [26, 27]. Hence, the disaggregated method was applied in this research.
Descriptive statistics (mean, SD, median, inter-quartile range, minimum, maximum) for the WTP values of the PC and the OE formats were computed. Zero response of each format were compared and excluded for further analysis. In order to reduce the impact of outliers, the top 1% of values in both the OE and PC formats were trimmed for additional comparison. First, we compared the mean and the median WTP/QALY obtained from the two elicitation methods using a two-sample equality test with bootstrapping. Additionally, a subgroup analysis of diverse scenarios was conducted.
Linear multiple regression and log-linear multiple regression were carried out to control observed heterogeneity and test theoretical validity. In a broad sense, the theoretical validity of WTP/QALY estimates refers to whether the estimates concur with the underlying theory. The subsequent variables[2] were selected for regression analysis in conformity with previous researches [11-13]: age, income, hypothetical health state, and QALY gain. Age was proven to be a significant factor of WTP/QALY in previous research [11], indicating that being younger led to a higher WTP/QALY. Income is positively associated with WTP/QALY [12] and thus should be captured in the regression analysis. Furthermore, we also assumed that worse health state scenario [13] and smaller QALY gain should lead to a higher WTP/QALY [9]. Categorical variables were coded with dummy variables. Statistical analysis was performed with IBM SPSS version 23.0.
Footnotes:
[1] We used perfect health (11111) in the pilot study, which was believed to be to ideal since most people feel extreme anxious in face of death. Hence, 11115 (5 means extreme anxious or depressed) was used for the immediate death scenario.
[2] The regressors were chosen based on the bivariate analysis (the Mann-Whitney U-tests for dichotomous variables, Kruskal-Wallis H tests for polychromous variables, Spearman’s rank correlation coefficient for the continuous variable) as well as previous researches