Time trade-off with someone to live for: impact of having significant others on time trade-off valuations of hypothetical health states

The study used data from the Norwegian EQ-5D-5L valuation study. Data collection started in November 2019, but stopped in March 2020 due to the COVID-19 pandemic, at which point 542 interviews were completed. The study intentionally oversampled selected groups typically hard to reach, including ethnic minorities, those with lower socio-economic status and parents of young children [22].

Respondents were invited to the study via randomly sampled locations within different geographic areas in Norway and location type strata aimed at reaching different respondent groups. Contact persons at each location assisted, where feasible, to meet quotas according to gender and age. Child day-care facilities and primary schools were sampled to increase the number of respondents with young children.

Data were collected by one-to-one pc-assisted interviews, following standard EQ-VT protocol version 2.1 [9], and guided by a trained interviewer. See the original study protocol for more details on training and use of valuation technology [22]. Interviews were completed at sampled locations, for example libraries, schools, workplaces or recreational centres. Where possible, interviews were completed in separate rooms. Standard EuroQol quality controls (QC) were assessed throughout data collection [23], with flags related to time spent on the task, the introduction to lead-time TTO, and inconsistent valuations of the worst possible health state. Protocol compliance was found to be excellent, with few interviews flagged for poor data quality.

Interviews were conducted using the EQ-PVT, a portable version of the EQ-VT software developed by EuroQol. The EQ-PVT provides a similar visual presentation of the TTO tasks as the EQ-VT software, presented as two horizontal scales indicating number of years in Life A (a life in full health) and Life B (a life in the health state to be valued). The respondent values each health state by choosing between Life A and Life B in an iterative process until the respondent perceives the two lives to be of about the same value.

Following EQ-VT protocol, composite time trade-off (cTTO) was administered [8, 24]. The cTTO is a modified version of the TTO, where lead-time TTO is used for the valuation of states identified as worse than being dead (WTD). When states are judged to be WTD, the respondent is offered an additional 10 years in full health lead-time in Life B, a total of 20 years (10 years in full health, followed by 10 years in the health state to be valued), as an alternative to 10 or fewer years in full health in Life A.

Interviewers followed a standardised interview guide with scripted introduction and recommended responses, introducing all parts of the cTTO task, including the concept of WTD, and how to give such values using the cTTO. Respondents practiced by valuing three practice states before completing 10 cTTO valuations.

The interviewer guided the respondent through the entire interview and answered questions respondents had throughout. The interviewer was instructed to not comment on seemingly illogical responses, but to encourage respondents to think aloud and carefully consider each health state presented. After completing all TTO tasks, respondents were asked to review responses and flag any they deemed inconsistent in a feedback module, without comment from the interviewer.

In addition to the valuation tasks, each respondent defined their own health with the EQ-5D-5L and VAS, and completed a paper questionnaire with items describing their background. The standard EuroQol visual analogue scale (EQ VAS) from 0 to 100 was used, with 100 representing best imaginable health and 0 worst imaginable health. Respondents defined their own health state prior to completing valuation tasks, and to conclude completed the rest of the questionnaire, including questions about significant others.

Information on significant others was collected from questionnaire items where respondents indicated how many children under 18 years of age they had responsibility for, as well as their marital/partner status. The items were formulated as “Do you have responsibility for children under the age of 18?”, where respondents indicated the number of children for whom they were responsible, and “What is your marital status?”, with the response categories “Single”, “Married”, “Cohabiting”, “Divorced/separated”, “Widowed”. Responses for these items were recoded to “with children under 18 years of age” if they stated that they had responsibility for at least one child under 18 years of age, and with a partner if they indicated that they were either married or co-habiting.

Descriptive statistics summarized respondent characteristics. Linear regression models assessed the association of the main analysis variables with use of the feedback module and QC flags.

Each respondent provided ten individual TTO valuations, all of which were included in the primary analyses, irrespective of flagging in the feedback module. To account for the nested nature of the data, a mixed model with a random intercept at the respondent level was used to estimate the effects of having significant others on willingness to trade. We used disutility (= 1-utility) in the analyses. Values elicited using the cTTO procedure are left-censored at − 1. Correspondingly, disutility values were handled as being right-censored at 2, i.e. a Tobit model. The effect of age on elicited values was explored prior to final modelling using descriptive methods and loess regression (Supplementary Fig. 1).

We tested five different models. Model 1 included only having children as a significant other, as well as age, sex, and higher education. Model 2 was similar to Model 1, but with a dummy variable for having a partner instead of children as significant other. Model 3 included both variables for having children and having a partner, and Model 4 included an interaction between the two. Model 5 included only a dummy variable for any significant other, indicating either children or a partner, in addition to age, sex and higher education, as in previous models. We defined dummy variables coded 1 for: individuals with children < 18 years (CHILD); individuals living with a partner or married (PART); individuals with either children or a partner (SIGNIF); female respondents (FEM); individuals with higher education (EDU). Age in years was included as a continuous variable (AGE). For more flexible modelling and to account for the non-linear relationship of age and disutility, we made use of natural splines; a form of flexible interpolation by use of a pre-defined set of polynomials. In the equation, ns represents a function for cubic (3-knot) natural splines. Knots were placed at the quartiles of age in the data, giving four estimates in total. Final number of knots was determined by the Akaike information criterion. The five models were defined as following:

\({\text{Model 1: }}\,disutility \sim \alpha + \beta_{{ns\left( {AGE} \right)}} + \beta_{EDU} + \beta_{FEM} + \beta_{CHILD } + b_{0id}\).

\({\text{Model 2: }}\,disutility \sim \alpha + \beta_{{ns\left( {AGE} \right)}} + \beta_{EDU} + \beta_{FEM} + \beta_{PART } + b_{0id}\).

\({\text{Model 3: }}\,disutility \sim \alpha + \beta_{{ns\left( {AGE} \right)}} + \beta_{EDU} + \beta_{FEM} + \beta_{CHILD} + \beta_{PART } + b_{0id}\).

\({\text{Model 4: }}\,disutility \sim \alpha + \beta_{{ns\left( {AGE} \right)}} + \beta_{EDU} + \beta_{FEM} + \beta_{CHILD} + \beta_{PART } + \beta_{CHILD:PART} + b_{0id}\).

\({\text{Model 5: }}\,disutility \sim \alpha + \beta_{{ns\left( {AGE} \right)}} + \beta_{EDU} + \beta_{FEM} + \beta_{SIGNIF } + b_{0id}\).

Sensitivity analyses controlled for interviewer effects, being married versus co-habiting, the health state valued, respondent’s self-reported health (EQ VAS), and included respondents with missing values for number of children, and excluded responses flagged in the feedback module. We coded missing values for children as not having indicated any children under the age of 18. To control for the health state we performed two analyses, including dummy variables per level per dimension of the health state, as well the level sum score (representing the deviation from full health) as a measure of the health state’s general severity.

R version 3.6.2 was used for the statistical analyses [25]. We chose a 5% significance level, using two-sided tests.

The Regional Committee for Medical and Research Ethics reviewed the study and stated that their approval was not required. The Norwegian Institute of Public Health approved the Data Protection Impact Assessment for the study 30^th September 2019.

Of the 542 interviews completed, responses from 430 respondents were included, with 10 responses per respondent. Respondents with missing responses, either missing TTO responses (n = 31), completely missing paper questionnaire responses (n = 5), or missing response for item on number of children (n = 76), were excluded from the analyses. The mean age of the sample was 44 years; 58% of respondents were female, and 61% had completed higher education (Table 1). Almost half (48%) indicated having responsibility for at least one child under the age of 18 years, and 68% had a partner. In total, 25% of respondents indicated that they had neither children under the age of 18 nor a partner, whilst 35% indicated that they had both. Respondents had a mean VAS score of 78.8, where those with a partner, either with children (79.8) or without (80.7), scored their own health today as slightly higher than those without a partner (with children = 72.6, without children = 76.9).

Of included responses, 445 had been flagged by respondents in the feedback module as inconsistent [median 1 flagged per respondent, min 0 (n = 179), max 5 (n = 4)]. Fifteen interviews were flagged for data quality concerns; most of these (n = 9) were inconsistent valuations of the worst possible health state. Regression models showed no significant association between having significant others and use of the feedback module or QC flags (Supplementary Table 6).

On average, respondents traded 5.9 years per health state. Those with neither children nor partner traded mean 7.2 years and those with both 5.4 years (Fig. 1). 335 observations were right-censored with a maximum disutility of 2. Respondents without significant others defined 22% of valuations as WTD (respondents with significant others: 15%). Logistic regression showed that those with a partner were less likely to value health states as WTD (p < 0.05) (Supplementary Table 7).

Both having children and having a partner were found to have a significant effect on willingness to trade (p < 0.05), with respondents with either a partner or at least one child under the age of 18 assigning lower disutility to presented health states (Table 2). All models included age, sex, and higher education as independent variables, and having significant others had a larger effect than of both sex and higher education.

Predicted values based on the final models indicated that those without any significant other were on average willing to trade at least two more life years than those with significant others (Fig. 2).

The effect of having children and/or a partner remained statistically significant in all sensitivity analyses and with comparable effect size as found in the main analyses. Findings were robust after excluding responses flagged in the feedback module (Supplementary Table 5). Including those with missing values for number of children, resulted in a significant though slightly reduced effect of having significant others (Supplementary Table 4). Analyses exploring how best to model age, showed that the effect of age seemed to depend on whether the respondent had significant others (Supplementary Fig. 1). Analyses without a random intercept at the respondent level resulted in a significant interaction between age and partner, where those with a partner valued health states with lower disutility even in older age. This interaction was not statistically significant when the random intercept was included and was not included in the final models.

A strength of this study was the use of trained interviewers and face-to-face personal interviews. Interviewer training is important because the TTO can be difficult to comprehend and complete without interviewer guidance [20, 21]. Respondents included in this study completed TTO tasks before indicating their marital status and number of children under the age of 18. Therefore, interviewers were blinded to this information during the TTO exercises.

Some limitations should be noted. Respondents indicated only number of children under the age of 18 years for whom they had responsibility, and we had no information about older children, grandchildren, or other children in their family. In 2020, average age for first-time parenthood in Norway was approximately 30 years (mean age of new mothers 29.9 years; new fathers 32.1) [31]. Not surprisingly, only one respondent under the age of 25 years, and few respondents over the age of 65 (n = 4), indicated having responsibility for a child under the age of 18. Older respondents may however have grandchildren or older children, which could be influencing their responses. A considerable number of respondents (n = 76) chose not to answer the survey item for children, and one could speculate as to the reason for this. Respondents with children over the age of 18 years, or those without children may have deemed the item irrelevant, or they may have had children but not wished to answer, thus muddying the results when this group was included. Mean disutility per health state was lower for respondents in middle age (e.g. 40–60 years of age), as shown by a decrease in years traded for all groups including those without significant others. Given the formulation of the questionnaire item for having children and the restriction to children under the age of 18, lower willingness to trade in this age group may be an expression of respondents typically having older children at this age, which would be not captured being by the survey item.

Respondents indicated their marital status, with single, married, divorced, co-habiting, and widow as response options, yet significant others may fall outside the categories provided, for example those living with other family members. The study included no information on the age of the children in question. Without the age, it is not possible to explore whether the effect of being a parent on willingness to trade changes over time, or is a passing effect, as has been suggested by some studies arguing that the effect of children may be an intrinsic effect, or, as with risk aversion, present prior to parenthood then decreasing over time [15].