Comparison of Caregiver and General Population Preferences for Dependency‑Related Health States

Vol.:(0123456789) Applied Health Economics and Health Policy (2025) 23:105–117 https://doi.org/10.1007/s40258-024-00908-x ORIGINAL RESEARCH ARTICLE Comparison ofCaregiver andGeneral Population Preferences forDependency‑Related Health States EvaRodríguez‑Míguez1,2 · AntonioSampayo3 Accepted: 8 August 2024 / Published online: 10 September 2024 © The Author(s) 2024 Abstract Objective We assess whether the preferences regarding dependency-related health states as stated by informal caregivers are aligned with those expressed by the general population. Methods The preferences of a sample of 139 Spanish informal caregivers of dependent patients are compared with those obtained via a sample of 312 persons, also from the Spanish general population. We assess 24 dependency states extracted from the DEP-6D using the time trade-off method. Descriptive statistics and regression methods are used to explore differences between the two samples. Results Mean difference tests establish that, for all but one of the 24 states, there are no significant differences between the samples. The estimated mean values ranged from − 0.64 to 0.60 for the caregiver sample and from − 0.60 to 0.65 for the general population sample, with a correlation of 0.96. On average, the classification of states as better or worse than dead matched in both samples (except for one state). Regression models also show that sample type does not have a significant average impact. After we introduce interaction effects, only the most severe level of two dimensions, cognitive problems and housework, result in significant differences—with the caregiver sample reporting higher values for the former, and lower values for the latter. Conclusion Caregivers and the general population exhibit quite similar preferences concerning dependency-related health states. This suggests that the results of cost-utility analyses, and the resource allocation decisions based on them, would likewise not be significantly affected by the preferences used to generate the weighting algorithm. Key Points for Decision Makers The caregivers’ preferences for dependency-related health states are very similar, on average, to the preferences of the general population. The analysis by dimension shows that the cognitive problems generate more disutility for the general population than for carers, and the opposite is true for the housework dimension. Cost-utility analysis and public decision making would hardly be affected by the preferences used to generate the dependency states values, and the use of preferences from general population is recommended. 1 Introduction The rapid ageing of the population will bring new challenges, one of which will be to delay as much as possible the loss of health-related quality of life (QoL) for older people. A frequent consequence of this deterioration in health associated with ageing is the loss of the ability to carry out basic activities of daily living, resulting in the need for help from others. This situation of dependency, not necessarily associated with ageing, translates into an enormous cost of care, which is added to the health expenditure generated by these situations. In fact, it is common for funding decisions to change when these costs are included [1, 2]. * Eva Rodríguez-Míguez [email protected] Antonio Sampayo ar.sampay[email protected] 1 ECOBAS, Universidade de Vigo, GRIEE, 36310Vigo, Spain 2 Instituto de Investigación Sanitaria Galicia Sur, Hospital Álvaro Cunqueiro, 36213Vigo, Spain 3 ECOBAS, Universidade de Santiago de Compostela, 15782SantiagodeCompostela, Spain 106 E.Rodríguez-Míguez, A.Sampayo There are multiple strategies—both pharmacologic [3, 4] and non-pharmacologic [5, 6]—to promote health for older adults, which contribute both to prevent or delay situations of dependency, and to decrease the degree of dependency when it occurs (preventing strokes, delaying both the onset and the progression of Alzheimer’s disease and other dementias, rehabilitation, technical aids, education programmes to prevent falls, etc.). The benefits and costs of these strategies and others expected to emerge in the future need to be quantified to make informed decisions. Several instruments are available to assess the QoL associated with a situation of dependency—dependencyrelated QoL—that make it possible to estimate the effectiveness of such programmes, some of which are widely used, such as the Katz scale [7], the Barthel Index [8] or the Lawton and Brody scale [9]. However, these instruments are not preference-based and, in consequence, do not allow to measure the variation of QoL using scales recommended for economic evaluation, such as qualityadjusted life years (QALYs). To overcome these limitations, generic preference-based instruments can be used, with the EQ-5D being the most frequently mentioned in guidelines from national health technology assessment agencies [10]. However, the main limitation of these instruments is that generic measures, as opposed to specific measures of dependency, may not be sensitive enough to identify relevant changes in dependency status [11, 12]. For example, regaining the ability to eat on one's own in a Parkinson's sufferer (either through a tremor-reducing drug or an electronic device that compensates for tremors) is a major change at the individual and family level, but would rarely lead to a change in any of the generic preference-based measures,in patientswithmultiplecomorbidities. The weak correlation between the Barthel index and the EQ-5D found in the literature supports this evidence [13–15]. In the context of dependency, there is also a growing interest in the Adult Social Care Outcomes Toolkit (ASCOT) [16, 17]. The ASCOT is another preference-based instrument that can be applied to economic evaluation. However, this tool does not measure changes in the severity of dependency but rather changes in the social care–related QoL. The DEP-6D, is a specific instrument designed to measure changes in dependency-related QoL, which integrates the discriminatory capacity of instruments designed specifically for dependency with the desirable properties for use in economic evaluation [18]. As the authors point out “the proposed instrument provides complementary utilities to generic measures with a wider focus and should be used alongside these to provide a more accurate measurement”. This instrument provides classical QALYs anchored on a full health-dead scale as commonly used in cost-utility analysis and it has been used in different contexts, such as the estimation of disability-adjusted life expectancy [19], or in measuring the impact of severity of dependency on caregiver burden [20]. Using the COSMIN grid in a systematic review of generic and specific instruments used to estimate QALYs, Touré etal. [21] found that 63.2% of the quality criteria assessed were rated as very good in the DEP-6D, compared to 56% of the mean for all instruments assessed. The DEP-6D amounts to a system for classifying dependency states along six dimensions of three or four levels each, and it incorporates a preference-based scoring algorithm that assigns weights to each of the dimension levels. The weights are based on preferences of Spanish citizens obtained by way of the time trade-off (TTO) method. The use of general population preferences to estimate these weights is a strategy often recommended in the economic evaluation of health programmes [22, 23]. However, it would be desirable to analyse whether the results would be similar if based on the preferences of other involved groups. The DEP-6D describes, in general, more severe problems than those usually described by means of generic preference-based measures, with the minimum utility score predicted by the DEP-6D algorithm being −0.84 (valued on the1-0fullhealth–deadQALY scale), which is considerably lower than the minimum value predicted by the Spanish algorithms of the most widely used generic instruments, such as the EQ-5D-3L [24], the EQ5D-5L [25] and the SF-6D [26], whose minimum values are − 0.65, − 0.25 and − 0.36, respectively. The authors also find that more than half (14 from 24) of the directly rated dependency states had a negative value—that is, they were considered to be worse than dead. Given the low values obtained, one wonders whether greater knowledge based on personal experience might lead to a different assessment of these states, producing an algorithm with different weights. Following Cubi-Molla etal. [27], different perspectives can be adopted to incorporate experience in the assessment of health states. One of them uses patients to assess their own state of health. However, it has strong limitations if we want to assess dependency situations that are very severe or linked to cognitive problems. While it is true that other studies have surveyed people with mild or moderate cognitive problems, the tasks to be performed were aimed at positioning themselves in one of the levels of the dimensions assessed (for example in the EQ-5D multi-attribute classification system). When utility weights need to be estimated, as in our study, only patients with a mild cognitive impairment can participate [28]. It should be noted that the methodology commonly used to obtain utility weights, for example the TTO method, requires very demanding tasks from a cognitive point of view, which makes it impossible for many of the health conditions described by the DEP-6D to be assessed by patients experiencing such a health condition. Another perspective that can also be considered to evaluate health states is that provided by dependent people 107 Preferences of Caregivers and the Public for Dependency States evaluating hypothetical situations. In this case, dependent persons without cognitive problems would be able to assess the 24 health states previously assessed by the general population. However, this option has also been rejected in our study for several reasons. On the one hand, there may be an asymmetric bias between assessments of health states that are more similar to those of the patient being interviewed, in which adaptation may play a role, and those health states that are more different [29, 30]. On the other hand, the elicitation of experience-based values would be compromised in the case of more severe hypothetical states. Another group of interest widely used in the literature to incorporate experience-based values into the analysis is caregivers of dependent persons (vicarious experience) [31, 32]. This is the group we use in our study. The answers of informal caregivers can be a good proxy because they are familiar with the limitations, needs, and feelings of the individuals for whom they care and as the National Institute for Health and Clinical Excellence (NICE) points out, when “it is not possible to get measurements directly from patients, these should come from the person who acts as their carer” [22]. In addition, using the caregiver’s perspective allows for the incorporation of experience in a wide range of dependency situations, both in terms of current and past experience—it is common for the same caregiver to have experience in different dependency situations that the cared-for person has been through. Given that our aim is to analyse whether the preferences on dependency-related health states expressed by informal caregivers coincide with those expressed by the general population, as described in Rodríguez-Míguez etal., the assessment of the same 24 hypothetical health states also evaluated by the general population seems to us to be the most appropriate strategy [18]. Furthermore, there is evidence that when it comes to hypothetical health states, patient and caregiver preferences do not differ significantly [31]. There is, a priori, no clear expectation. On the one hand, care provision may induce a process of adaptation that leads to higher evaluations of these states; this phenomenon is frequently reported in the literature comparing the preferences of patients with those of the general population [33]. Although this “disability paradox” refers to patients’ preferences, a similar effect might arise when caregivers’ preferences are analysed. On the other hand, close contact with dependent persons may provide more information about the limitations and losses of well-being that such situations produce, which in turn could exacerbate the negative perception of these states [28]. Finally, it may be that the two groups express similar preferences, reinforcing the recommendation (from studies of economic evaluation) to use general population samples. 2 Method 2.1 The DEP‑6D Instrument The DEP-6D is an instrument used to characterise the level of daily dependence on others [18]. It comprises a questionnaire and a weighting algorithm that assigns a score to each of the responses provided. The DEP-6D classification system is based on six attributes or dimensions—eating, incontinence, personal care, mobility, housework, and cognition problems—each having either three or four levels (see Table1). Correlation analysis and focus groups were used to select dimensions and levels, and the TTO method was used to estimate a preference-based scoring algorithm. A major advantage of this dependency assessment tool is that it enables the estimation of QALYs, which underpin costutility analysis. 2.2 Samples andQuestionnaire This study uses two cross-sectional samples from two groups: the general population and caregivers. Microdata from the general population sample were provided by Rodríguez-Míguez etal. [18]. This sample consists of 312 citizens drawn from the general population of Galicia (a region in Spain) recruited at home in 2011 by means of stratified random sampling. Caregivers’ preferences were obtained in 2016 from a convenience sample of 139 primary informal caregivers of chronically ill dependent adults who need help with activities of daily living. The caregivers were contacted in primary care centres in Pontevedra (Galicia) and interviewed by health and social workers. In both samples, the interviewers were trained to conduct the interview as follows; first, the content of the questionnaire was explained in detail and then a mock interview was conducted with one of the researchers. Face-to-face interviews were conducted in the participant's home in both samples. In order to reproduce as closely as possible, the interview with the general population, a pen-and-paper interview was also conducted with the sample of caregivers. Only interviews that, due to interviewer error, did not follow the pre-established TTO sequence (explained below) were eliminated—in these situations the TTO values cannot be obtained. The questionnaire begins with the presentation of the dimensions and levels of the DEP-6D. Since the questionnaire is designed so that in many cases it can be self-completed, no additional explanation is provided, unless requested by the participant. Next, participants were asked to value six DEP-6D dependency states. To increase comparability, the questionnaire and experimentaldesignused for the 108 E.Rodríguez-Míguez, A.Sampayo caregiver sample were identical to the one used previously for the general population sample. It included a total of 24 dependency states, divided into four blocks of size six (see Table4 in the Results section to follow); the states and blocks were obtained in the seminal work using an optimal design that captures only main effects, which means that interaction effects cannot be tested. The blocks and the states were randomly assigned to the participants. Assessment of the dependency states was carried out using the TTO method. After rating the six states in sequence, each participant was shown the six states together (on separate cards) and asked to rank them from most to least preferred. Participants’ socioeconomic characteristics were also collected. 2.3 Utility Valuation Method The traditional TTO method was used to assess the dependency states. Given that the assessment of health states may vary depending on the framing of the TTO questions [34–37], to avoid confounding effects when comparing the preference of caregivers to the general population, we used the same framework as in the original study. Participants were asked to place themselves in a hypothetical situation in which a doctor informed them that they suffer from a serious illness which, if not treated urgently, will degenerate and rapidly lead to death. The doctor also informed them that there was a treatment that, although it would not cure the disease, would allow them to live 10 more years in a certain state of dependency (at this moment, one of the six states to be evaluated is shown). Participants were asked to choose between being treated or not. If they choose the treatment, this means that the state is regarded as better than dead (BTD); if the treatment is rejected, then the state is considered worse than dead (WTD). Depending on the answer given to this initial question, the following line of inquiry is adopted. • If they accept treatment, then an iterative up–down procedure was applied to find the number of years, Y∗ BTD , in full heath (FH) at which participants were indifferent between 10 years in the state being evaluated and Y∗ BTD in FH. The starting value for YBTD was set equal to 5 years. Thus, participants must choose between two options: A, 10 years in the evaluated state; or B, YBTD =5 years in FH. Depending on the answer provided, YBTD is adjusted up or down until the indifference point—or the interval containing it—( Y∗ BTD ) is bounded as described next (indifference is allowed in all choices, in which case the process ends). – If participant chooses A → YBTD =8 : 1) if A → YBTD =9 ; 2) if B → YBTD =7 → if B → YBTD =6. Table 1 Levels and dimensions of DEP-6D Levels of dimensions Dimensions 1. Does not need assistance to eat or drink 2. Needs partial aid to eat or drink (cutting, serving, etc.) 3. Needs to be given food and drink Feeding 1. Does not have incontinence or does not need help 2. Has urinary (or faecal) incontinence and needs help for hygiene 3. Has both urinary and faecal incontinence and needs help for hygiene Incontinence 1. Does not need help for personal care: bathing, grooming, dressing, etc. 2. Needs help only to bathe but not for the rest of personal care 3. Needs help for most personal care activities 4. Is incapable of carrying out personal care; needs someone else to perform this activity Personal care 1. Moves independently 2. Does not need help to move within the home but does out of home 3. Needs help to move both in and out of home 4. Is incapable of changing position; bed-ridden or chair-ridden Mobility 1. Does not need help to carry out daily housework (making food, cleaning crockery, etc.) 2. Needs daily help for daily housework 3. Is incapable of carrying out most daily housework Housework 1. Does not need help due to cognitive/mental problems or has not these problems 2. Needs assistance to manage money, medication or to take some basic everyday decisions; generally collaborative attitude with the caregiver 3. Incapable of taking basic decisions; cannot live alone. Does not resist help 4. Incapable of taking basic decisions; cannot live alone. Does not collaborate and usually offers resistance to help Cognition problems 109 Preferences of Caregivers and the Public for Dependency States – If participant chooses B → YBTD =2 : 1) if A → YBTD =4 → if B → YBTD =3 ; 2) if B → YBTD =1 . • If they refuse treatment, the next choice was between: A, YWTD years in full health followed by 10 −YWTD years in the state being evaluated; or B, dying. As before, the value for YWTD as initially set to 5 years and then moved up or down until the convergence process terminated. – If participant chooses A → YWTD = 2 : 1) if A → YWTD = 1 ; 2) if B → YWTD = 4 → if B → YWTD = 3. – If participant chooses B → YBTD = 8 : 1) if A → YWTD = 7 → if B → YWTD = 6 ; 2) if B → YBTD = 9. . Utility for state S, denoted U(S) , was obtained by using the QALY model assumptions for chronic health states – that is, the value of existing Y years in state S is equal to U(S)×Y . By convention, the utility of FH is 1 and the utility of death is 0; hence U(S) is obtained as follows. If a state is regarded as BTD then, under the previous assumptions, U(S)=Y∗ BTD∕10 . For states that are regarded as WTD, U( S )=− Y ∗ WTD∕( 10 − Y ∗ WTD) . However, because the negative utilities calculated in this way do not have a lower bound (and so result in distributions that are skewed strongly to the left), we bound negative values at −1 via applying the transformation suggested by Patrick etal. [38]: U(S)=−Y∗ WTD∕10 . Owing to our iterative up–down procedure, usually a range of values (and not an indifference value) is obtained for both Y∗ BTD and Y∗ WTD . The following example illustrates the procedure used to obtain U(S) . Suppose a participant, who considers a state better than dead, has chosen option A when YBTD =5 , option B when YBTD =8 , and option A when YBTD =7 . In that event, YBTD lies in the interval (0.7,0.8). Visual aids (identical for both samples) were used in all questions to help subjects understand the questions posed; as an example, Fig.1 shows the visual aid presented immediately after the state has been found to be considered BTD by the participant. 2.4 Statistical Analysis Several analyses were conducted to determine whether the preferences of caregivers are different from those of the general population. First, we checked for whether the level of inconsistency—both internal and external—and invariance of the responses is similar in both samples. Internal consistency is analysed by identifying non-compliance with the dominance test, which is violated when a respondent places more value on a state that is logically worse in the sense that none of its dimensions presents a better situation. External consistency is measured as the extent of correlation between the ranking of states as derived from the TTO exercises and the direct ranking provided by participants. With regard to invariance, we identify those participants who assign the same value to all states, a particular case of which involves individuals who simply do not trade off ( U(S)=1 for all states). Second, for each of the 24 states assessed we determined the mean utility by sample (general population vs caregivers) and checked for significant differences between those two samples. There is no consensus on how best to proceed with inconsistent participants [39, 40]; here we followed other authors in opting to exclude participants with more than one inconsistency. In this way, we seek to distinguish between systematic departures from dominance—as exhibited by the excluded participants—and random error [18, 41]. We report the states’ mean values for the full sample and also for the reduced sample (hereinafter referred to as the “consistent participants”). Only the sample of consistent participants is used for all other aspects of the statistical analyses. Third, random-effects interval-data regression models were used to obtain the weighting algorithms for each of the samples, and also to look for significant betweensample differences in preferences concerning dependency-related health states. These regression models allow us to consider both uncertainty about the exact value of indifference and the presence of left-censored value [25]. On the one hand, in most of the ratings we do not obtain a point of indifference, but an interval in which the unknown value lies. On the other hand, by construction, the values obtained had a minimum TTO value bounded at −1, but respondents could hypothetically continue trading beyond the left bound at−1. Therefore, we relax this lower bound assumption and consider responses at the lower bound (−1) to be censored. The model also contains a random effect to account for the dependence of repeated observations within respondents. The dependent variable for all models was U(S) . In model 1 and model 2 we estimate separately the weighting algorithms for each of the samples (caregivers and general population), and the independent variables are the severity levels of the dimensions. To facilitate comparisons, the relative importance of each dimension is obtained by dividing its range by the sum of the ranges of all attributes. Model 3 uses the full data set; in this model, the independent variables are the severity levels of dimensions and a dummy variable set to 1 for caregiver respondents or to 0 for respondents from the general population. Model 4 reproduces model 3 but 110 E.Rodríguez-Míguez, A.Sampayo controls for the most relevant sociodemographic variables (gender, age, and educational level). Finally, the independent variables in model 5 are the interactions between severity levels and sample type; thus, we explore whether (and, if so, how) the sample’s effects differ among the dimensions. Written informed consent was obtained from each participant, and the Committee on Ethics of Clinical Research in Galicia approved the study. 3 Results In the sample of caregivers, all questions have correctly followed the established itinerary to obtain TTO values— in the general population sample only 2 observations had been eliminated. Table2 presents a descriptive analysis of the sample. As expected, the sample of caregivers differs markedly from that of the general population. In particular, the former consists mostly of women and has a higher mean age than the general population. The caregiver sample is also characterized by a lower educational level and a lower frequency of people working, with a quarter of the sample being retired people. The distribution of family income (excluding missing data) is similar across the samples. Table3 reports the level of consistency and invariance by sample. The two samples yield similar results, with no significant differences being observed. There is a high percentage of participants who meet all the consistency tests, and if we include those who commit no more than one inconsistency then this percentage rises to 94% in both samples. The correlation between the direct ranking and the resulting TTO method is only moderate yet is similar in both samples. The number of participants who always provide the same answer for all states (invariant utility) is higher in the general population than in caregivers, although the difference is not statistically significant (p = 0.228). Table4 shows the mean values of the 24 states that were directly evaluated. The labelling of the states refers to the levels in which the state is found in each of the six dimensions that make up the DEP-6D (e.g., a dependent in state 122222 does not need assistance to eat or drink but has a level-2 severity in all the other dimensions). Figure2 plots the mean values, by sample, ordered according to the sum of the (ordinal) Fig. 1 Example of visual aid Translation: Now suppose that you are in thestate you have chosen (see card) and you will liveinthat situationfor the next 10 years. On anew visittothe doctor, you are informedthat thereisanew treatmentthat wouldallowyou to regainyour health, but you will livefewer years. Therefore, thesituationyou face is the following: WITHOUT TREATMENT: 10 YEARS WITH THOSE LIMITATIONS WITH TREATMENT: 5 YEARS IN GOOD HEALTH Would you undergo the treatment? 111 Preferences of Caregivers and the Public for Dependency States values of the levels that make up each state. According to the mean difference tests whose results are reported in Table4, there are no significant differences between the samples (caregivers vs general population)—except for the state 112132 (p = 0.008 in the full samples and p = 0.013 in the samples of consistent participants). Pearson’s coefficient of correlation between the mean values of the two samples is 0.96. Mean values of the consistent samples ranged from − 0.69 to 0.64 for the caregiver’s sample and from − 0.62 to 0.66 for the general population sample. On average, the classification of states as better or worse than dead coincides in both of those samples (except for state 212223). Table5 (models 1 and 2) displays the impact of dimension levels on the utility weights (weighting algorithm) by sample. For the estimation, the two most severe levels of the dimension “personal care” (level 3 and level 4 in Table1) have been combined into a single level. The reason is that, in the general population sample, level 4 of personal care had less of an effect on QoL than level 3 of personal care (although the two parameters are not significantly different, p = 0.568); hence, we followed the approach of Rodríguez-Míguez etal. [18] in grouping them together. We also grouped them in the caregiver sample to facilitate comparisons between samples; in this case, the parameters reflected the hypothesized effects—level 3 of personal care had less of an effect on QoL than level 4 of personal care—(p = 0.058). The remaining parameters have the expected sign and direction: as the situation in a given dimension worsens, its utility declines. Comparing models 1 and 2 reveals that the largest differences are observed in the most severe level of the “housework” dimension (caregivers report greater disutility than the general population) and in the severest level of “cognitive problems” (for which caregivers report less disutility). Table6 shows the relative importance of the 6 dimensions, as well as their confidence intervals, for each of the samples. The relative importance is quite similar between samples for all dimensions except for the housework and cognitive problems dimensions, both with a difference of around 9 percentage points. Models 1 and 2 allow the estimation of the utility score associated with any dependency situation described by the DEP-6D by adding to the constant the estimated value of each of its levels. We can see from model 3 that, at the aggregate level, the average score in the sample of caregivers is 0.063 points less than the general population sample—although this difference is not significant (p = 0.36). Model 4 establishes that these results do not change substantially when we control for sociodemographic variables. And in model 5, where we allowed for differences by dimension, again there are no significant differences between samples except for the most severe level of cognitive problems (− 0.607 for the general population and − 0.444 for caregivers; p = 0.032) and the most severe level of housework (− 0.104 for the general population and − 0.240 for caregivers; p = 0.031). These results do not change substantially when we control for sociodemographic variables (results not displayed). Table 2 Characteristics of the two samples (%) CD close dependent Caregivers (n = 139) General population (n = 312) Female 91.4 47.4 Age (mean) 55.0 41.5 Education Primary studies or less 59.7 37.5 Secondary 30.2 39.4 University 10.1 23.1 Lives with partner 33.8 39.9 Labor status Employed 20.1 59.6 Pensioner/retired 25.9 10.9 Unemployed 41.0 15.7 Domestic tasks 10.1 8.3 Student/others 2.9 5.5 Home income (€ monthly) < 500 1.4 5.9 500–999 15.8 13.2 1000–1499 29.5 30.5 1500–1999 25.9 25.7 2000–2999 19.4 16.9 ≥ 3000 7.9 7.7 # Household members (mean) 3.1 3.5 CD No CD 0.0 53.2 CD (not living together) 16.55 40.1 CD (living together) 83.45 6.7 Table 3 Consistency and invariance by sample TTO time trade-off, U(S) utility for state Caregivers (n = 139) General population (n = 312) Meet all dominance tests 80.6% 79.2% Fail only one dominance test 13.7% 14.7% Individual Spearman’s rho between TTO and direct ranking (mean) 0.65 0.66 U(S) = 1 for all states (no trade-off) 0% 0% Same utility for all states 3.6% 6.4% 112 E.Rodríguez-Míguez, A.Sampayo Table 4 Descriptive statistics of observed TTO values by sample SD standard deviation, TTO time trade-off a The first number of the state refers to the level of the first dimension, and so on StateaAll participants Consistent participants Caregivers General population p value Caregivers General population p value nMean SD nMean SD nMean SD nMean SD Block 1 122222 34 0.09 0.65 82 0.17 0.54 0.465 32 0.12 0.66 77 0.18 0.53 0.607 133334 34 − 0.37 0.56 82 − 0.45 0.47 0.403 32 0.39 0.56 77 − 0.49 0.45 0.335 211121 34 0.60 0.49 82 0.58 0.47 0.888 32 0.64 0.45 77 0.60 0.46 0.696 214232 34 0.00 0.64 82 0.06 0.59 0.605 32 0.02 0.65 77 0.04 0.60 0.871 313331 34 0.11 0.71 82 0.17 0.60 0.614 32 0.15 0.71 77 0.17 0.61 0.873 323433 34 − 0.42 0.54 82 − 0.49 0.51 0.480 32 − 0.41 0.55 77 − 0.53 0.48 0.252 Block 2 111221 36 0.60 0.48 78 0.65 0.42 0.518 35 0.62 0.46 77 0.66 0.42 0.664 112132 36 0.02 0.68 78 0.35 0.57 0.008 35 0.04 0.68 77 0.35 0.57 0.013 112211 36 0.51 0.58 78 0.60 0.45 0.355 35 0.50 0.58 77 0.60 0.45 0.322 223234 36 − 0.44 0.52 78 − 0.47 0.54 0.822 35 − 0.43 0.52 77 − 0.48 0.53 0.610 234333 36 − 0.54 0.43 78 − 0.42 0.51 0.235 35 − 0.57 0.40 77 − 0.44 0.50 0.185 333122 36 − 0.37 0.55 78 − 0.23 0.62 0.259 35 − 0.37 0.55 77 − 0.24 0.61 0.304 Block 3 111112 33 0.38 0.49 75 0.40 0.57 0.905 30 0.44 0.46 66 0.50 0.49 0.594 113233 33 − 0.27 0.60 75 − 0.12 0.66 0.263 30 − 0.31 0.60 66 − 0.12 0.66 0.202 213322 33 − 0.26 0.60 75 − 0.07 0.64 0.144 30 − 0.30 0.59 66 − 0.07 0.64 0.098 222131 33 0.08 0.58 75 0.24 0.60 0.191 30 0.10 0.58 66 0.26 0.60 0.235 234431 33 − 0.57 0.49 75 − 0.37 0.62 0.102 30 − 0.60 0.48 66 − 0.37 0.62 0.075 334234 33 − 0.64 0.40 75 − 0.54 0.51 0.293 30 − 0.69 0.35 66 − 0.55 0.51 0.179 Block 4 123121 36 0.29 0.57 77 0.30 0.57 0.984 34 0.31 0.59 73 0.32 0.58 0.917 212223 36 0.02 0.66 77 − 0.16 0.67 0.198 34 0.00 0.67 73 − 0.15 0.68 0.301 233432 36 − 0.46 0.53 77 − 0.45 0.58 0.946 34 − 0.52 0.48 73 − 0.48 0.57 0.743 314434 36 − 0.55 0.45 77 − 0.60 0.51 0.563 34 − 0.58 0.42 73 − 0.62 0.50 0.686 324332 36 − 0.45 0.49 77 − 0.32 0.61 0.234 34 − 0.46 0.50 73 − 0.32 0.61 0.244 333231 36 − 0.21 0.65 77 − 0.19 0.64 0.841 34 − 0.26 0.64 73 − 0.21 0.65 0.709 Fig. 2 Values of DEP-6D states by sample 113 Preferences of Caregivers and the Public for Dependency States Table 5 DEP-6D models: Comparing caregivers and the general population (GP) Inconsistent observations have been excluded in all models. Bootstrap standard errors in all models Coef. coefficient, SE standard error MODEL 1 (carers) MODEL 2 (GP) MODEL 3 (full data) MODEL 4 (full data) MODEL 5 (full data) Coef. SE pCoef. SE pCoef. SE pCoef. SE pCoef. SE p Cons 0.839 0.068 <0.001 0.776 0.047 <0.001 0.875 0.097 <0.001 0.837 0.152 <0.001 Cons 0.794 0.040 <0.001 Feeding_2 − 0.095 0.031 0.002 − 0.159 0.031 <0.001 − 0.139 0.023 <0.001 − 0.139 0.026 <0.001 Feeding _2 × GP − 0.158 0.031 <0.001 Feeding _3 − 0.182 0.038 <0.001 − 0.220 0.037 <0.001 − 0.207 0.028 <0.001 − 0.207 0.029 <0.001 Feeding _2 × carer − 0.095 0.036 0.008 Incontinence_2 − 0.113 0.046 0.014 − 0.134 0.031 <0.001 − 0.129 0.023 <0.001 − 0.129 0.027 <0.001 Feeding _3 × GP − 0.219 0.039 <0.001 Incontinence_3 − 0.300 0.040 <0.001 − 0.286 0.034 <0.001 − 0.293 0.026 <0.001 − 0.293 0.027 <0.001 Feeding _3 × carer − 0.179 0.046 <0.001 Personal_2 − 0.230 0.067 <0.001 − 0.122 0.044 0.006 − 0.153 0.031 <0.001 − 0.153 0.034 <0.001 Incontinence_2 × GP − 0.133 0.029 <0.001 Personal_3+4 − 0.327 0.067 <0.001 − 0.249 0.057 <0.001 − 0.271 0.042 <0.001 − 0.271 0.043 <0.001 Incontinence_2 × carer − 0.113 0.041 0.006 Mobility_2 − 0.071 0.049 0.147 − 0.099 0.037 0.008 − 0.090 0.031 0.003 − 0.090 0.023 <0.001 Incontinence_3 × GP − 0.285 0.030 <0.001 Mobility_3 − 0.163 0.058 0.005 − 0.144 0.043 0.001 − 0.150 0.035 <0.001 − 0.150 0.030 <0.001 Incontinence_3 × carer − 0.303 0.036 <0.001 Mobility_4 − 0.289 0.068 <0.001 − 0.363 0.048 <0.001 − 0.337 0.042 <0.001 − 0.336 0.036 <0.001 Personal_2 × GP − 0.126 0.039 <0.001 Housework_2 − 0.169 0.059 0.004 − 0.067 0.043 0.117 − 0.096 0.030 <0.001 − 0.096 0.030 0.001 Personal_2 × carer − 0.221 0.054 <0.001 Housework_3 − 0.260 0.061 <0.001 − 0.096 0.045 0.035 − 0.146 0.030 <0.001 − 0.145 0.033 <0.001 Personal_3+4 × GP − 0.250 0.057 <0.001 Cognition_2 − 0.278 0.040 <0.001 − 0.249 0.028 <0.001 − 0.257 0.020 <0.001 − 0.257 0.027 <0.001 Personal_3+4 × carer − 0.322 0.065 <0.001 Cognition_3 − 0.349 0.046 <0.001 − 0.437 0.035 <0.001 − 0.410 0.026 <0.001 − 0.410 0.027 <0.001 Mobility_2 × GP − 0.101 0.031 0.001 Cognition_4 − 0.442 0.046 <0.001 − 0.609 0.038 <0.001 − 0.554 0.033 <0.001 − 0.554 0.034 <0.001 Mobility_2 × carer − 0.063 0.042 0.133 Carer − 0.063 0.069 0.360 –0.074 0.061 0.228 Mobility_3 × GP − 0.147 0.038 <0.001 Age 0.000 0.002 0.856 Mobility_3 × carer − 0.156 0.043 <0.001 Female 0.043 0.049 0.377 Mobility_4 × GP − 0.364 0.050 <0.001 Secondary –0.015 0.068 0.829 Mobility_4 × carer − 0.280 0.069 <0.001 University 0.030 0.068 0.663 Housework_2 × GP − 0.077 0.033 0.021 Housework_2 × carer − 0.144 0.044 0.001 Housework_3 × GP − 0.104 0.036 0.004 Housework_3 × carer − 0.240 0.063 <0.001 Cognition_2 × GP − 0.250 0.023 <0.001 Cognition_2 × carer − 0.275 0.036 <0.001 Cognition_3 × GP − 0.435 0.038 <0.001 Cognition_3 × carer − 0.352 0.055 <0.001 Cognition_4 × GP − 0.607 0.038 <0.001 Cognition_4 × carer − 0.444 0.057 <0.001 # Participants 131 293 424 424 424 # Observations 786 1758 2544 2544 2544 # Left-cens. Obs. 112 316 428 428 428 Wald χ21095.23 906.39 1797.28 2239.40 3786.02 Rho 0.640 0.037 0.509 0.030 0.547 0.024 0.546 0.032 0.550 0.021