Japanese preference weights of the Adult Social Care Outcomes Toolkit for Carers (ASCOT-Carer)

The Japanese version of ASCOT-Carer [6] was used in this study, with permission from and in collaboration with the developer of the original measure—the ASCOT team of the Personal Social Services Research Unit (PSSRU) at the University of Kent. The ASCOT-Carer consists of seven domains: (1) occupation, (2) control over daily life, (3) looking after oneself, (4) personal safety, (5) social participation and involvement, (6) space and time to be yourself, and (7) feeling supported and encouraged. Each domain is represented by one item and has four response options. The first level among the four responses indicates the best health state, and the fourth level indicates the worst health state.

We used best–worst scaling (BWS) and composite time trade-off (cTTO) to measure the preference weights of ASCOT-Carer states in the general population. The TTO values were applied to convert the BWS scores to utility values. In the UK survey, the cTTO values were not measured, and the BWS scores were converted by assuming the utility value of the worst state as zero. In this study, we showed those taking part in the TTO survey a profile including each ASCOT-Carer domain. Respondents were asked to put themselves in an imaginary state (described by the profile) of being caregivers and then select the best, worst, second best, and second worst domains from the profile. The selected domains were greyed out on the screen, and the remaining domains were presented for the next choice. In the BWS phase, four blocks consisting of eight ASCOT-Carer profiles were randomly allocated to each respondent; 32 profiles were selected from all 4⁸ = 65,536 profiles using a fractional–factorial design.

In the TTO survey, participants always started with a conventional TTO task assuming that they were informal caregivers: living for 10 years in a health state described by the ASCOT-Carer, or living x years in full health. If the participants considered the presented ASCOT-Carer state to be better than immediate death (i.e., x > 0), the value of x was varied until indifference was reached. If the participants considered immediate death to be better than living for 10 years in the ASCOT-Carer state (i.e., x < 0), a lead-time TTO [17, 18] was started that allowed estimation of negative values. In lead-time TTO, a series of choices were offered between “y years of life in full health” and a life of “10 years in full health followed by 10 years in the presented ASCOT-Carer state.” The value of y was varied until indifference was reached. In the cTTO phase, four blocks were similarly allocated to each respondent. Each block consisted of eight ASCOT states. However, only the worst states [4444444] were included in the two blocks. In total, 31 ASCOT-Carer profiles (= 4 × 8 − 1) were used for the TTO survey.

An online survey was conducted during the BWS phase. Respondents (aged 20 to 79 years) were recruited through a Japanese web panel based on quota sampling by sex and age. The sample size was 1000. The Japanese sample number was not based on any statistical considerations but was selected with reference to a UK survey [15].

First, the respondents self-assessed their own QoL using the ASCOT-Carer. Then, the respondent was asked to value the eight ASCOT-Carer profiles based on the BWS. Each domain in a profile is shown line by line. The position of each domain was randomized between the people to avoid positioning effects. The order in which the eight health states in the block were presented was randomized. After the BWS tasks were completed, the degree of understanding of the BWS questions, experience with social care, and demographic data were collected from the respondents. The response times of the BWS tasks were recorded.

For the TTO, face-to-face surveys were performed because the TTO tasks are more complex, and it was considered that a web-based survey might create some biases [19]. The subjects were different from the respondents in the BWS tasks. The respondents (aged 20 to 64 years) were recruited through a panel owned by a research company based on non-random quota sampling by sex and age. As it was difficult to recruit elderly people for this survey because of the COVID-19 outbreak, respondents aged more than 65 years were not included. The inclusion criteria were as follows: (1) aged 20 to 65 years; (2) current Japanese residency; (3) ability to visit the survey room in Tokyo; (4) ability to provide informed consent; and (5) ability to complete the tasks in Japanese. The target sample number was approximately 200. This was not based on statistical considerations, but 50 responses per health state were collected. Respondents were asked to visit a survey center in Tokyo. Computer-assisted personal interviewing (CAPI) was performed with interviewers’ support, with a one-on-one setting over intervals of 30 to 60 min at the survey center. Subsequently, three training TTO tasks were completed before the actual TTO tasks; “in a wheelchair,” “much better than being in a wheelchair,” and “much worse than being in a wheelchair, so bad that one would prefer to die immediately” Are the responses that were collected automatically as electronic data.

The survey was conducted from January 2021 to March 2021. Prior to conducting the survey, the investigators at each location received training for approximately half a day. Screenshots of the BWS and TTO surveys are provided in the Appendix.

A mixed logit model (MIXL) [20] was used to analyze the BWS data. The best, worst, second best, and second worst data were pooled for the analysis. A mixed logit model can analyze the heterogeneity of coefficients by relaxing the assumption of independence of irrelevant alternatives (IIA), whereas a simple multinomial logit (MNL) model assumes that all responses are independent.

Mixed logit models include 7 (domain level) + 3 × 8 − 1 (item level; level 1 to 3 in each domain excluding the reference level: the third level of “space and time to be yourself”) = 30 dummy variables to be estimated. When choices are analyzed based on random utility theory, U_ij (utility respondent j derives from choosing item i) is divided into an explainable component (V_ij) and a random component (ε_ij).where β_p denotes the common effect of the pth ASCOT domain, and β_pq denotes the effect of the qth (1 ≤ q ≤ 3) level of the pth domain. β_p are random parameters and β_pq are fixed parameters. In the mixed logit model, if β_p^m and β_p^s are used to represent the mean and scale parameters, respectively, for the random coefficient β_p,where η is a stochastic component with normal distribution.

X_i and X_ij indicate the choice of information. For example, if respondents selected the first level of the second domain as the best item, X₂ = 1, X₂₁ = 1, and others = 0. In the case of the worst and second worst choices, − 1 was used instead of 1 [27] parameters in the utility function were estimated with mixlogit in STATA 16. Respondents with a total BWS time of < 4.5 min, which was considered too short based on the pre-test results of a valuation survey in the UK, were excluded.

Regarding the TTO data, when the respondents equated 10 years of life with a better-than-dead ASCOT-Carer state to x years of life in perfect health, the TTO value was calculated as x/10. Conversely, when y years of life with a perfect ASCOT-Carer state was equated to “life with a perfect ASCOT-Carer state for 10 years, followed by life with a worse-than-dead ASCOT-Carer state for 10 years,” then the TTO value was calculated as y/10 − 1. Summary statistics of the TTO values of the 31 ASCOT-Carer states were calculated.

Finally, to convert the latent BWS scores to utility values, the function f(∙) between the latent BWS scores and TTO values of the 31 ASCOT-Carer states was estimated as TTO_i = f (BWS_i) + ε_i, where TTO_i denotes the observed mean TTO value, and BWS_i denotes the latent BWS score for the ith ASCOT-Carer state (1 ≤ i ≤ 31).

The respondents’ background characteristics in the BWS and TTO populations are shown in Table 1. The median household income of the BWS population ranged from JPY 5 million to JPY 7 million. When compared with the household incomes of all Japanese families of JPY 4.4 million in 2019, [2] the household income of the BWS population was high. According to the 2019 Labour Force Survey, [28] full-time workers accounted for 31.6% of all workers, and part-time workers accounted for 13.7%. Of the total, 24.3% of Japanese individuals graduated from a university or graduate school in 2017; 61.3% of the Japanese people were married and 31.6% were unmarried in 2015. Many factors (excluding the age category) were comparable with observations in the general population.

Table 2 presents the estimated coefficients using the mixed logit model. The fourth level of “space and time to be yourself” was used as the reference, similar to that in the UK model, and all the coefficients were positive. Table 3 presents the mean TTO values of ASCOT-Carer states. The worst TTO value was 0.011 [4444444], and the best value was 0.867 [1112121]. Only one health state, [4344444], was evaluated as “worse than death” (− 0.01); however, the absolute value was small. We confirmed the face validity of the TTO survey, as shown in Fig. 1. As the misery score (the sum of the level scores across all dimensions) increased, the mean TTO value decreased, and the standard deviation increased as the misery score increased. Figure 2 shows the relationships between the latent BWS scores and TTO values of the 31 states. Based on the linear relationship between latent BWS scores and TTO values, the BWS scores can be converted to utility values using the following formula:

(R² = 0.98 and mean square error = 0.09).

The preference weights for calculating the latent BWS scores of the ASCOT-Carer states listed in Table 4. These values were calculated using the BWS coefficients listed in Table 2 and Eq. (1). All the coefficients in Table 4 were consistent; weights at the higher level in the same domain were higher, and those at the lower level were lower. By adding the weight of each domain, the utility value of the ASCOT-Carer states can be calculated. For example, in the case that a response to ASCOT-Carer was [2234134,] the utility can be calculated using Table 4 as follows: 0.166 + 0.133 + 0.049 + 0.012 + 0.121 + 0.063 + 0.011 − 0.069 (intercept: always need to include except full QoL status) = 0.486. The utility values of the ASCOT-Carer states were distributed between 1.00 and 0.02. The estimated utility value of the worst states by UK weighting was the same at − 0.001.

Figure 3 shows a comparison between the weights of the UK and the Japanese. The most preferred item was Level 1 in the “space and time to be yourself.” Respondents showed a strong preference for the “Occupation” and “Looking after yourself” domains. In the UK, the most preferred item was Level 1 in the “occupation” domain. In Japan, the least preferred items were level 4 in the “space and time to be yourself” and “control over daily life” domains. In the UK weighting, the fourth level of the “control over daily life” item was the least preferred. The weights of level 3 in the “control over daily life” domains differed greatly between the Japanese and UK weights.