Introduction
In the preceding chapters we have detailed the development of DEMQOL-U and DEMQOL-Proxy-U, dementia-specific preference-based single-index measures from DEMQOL and DEMQOL-Proxy. In Chapter 5 we reported the estimation of a preference-based single index for each classification system using values obtained from the general population. However, such values can also be obtained from other sources including patients, carers and health-care professionals. Values are typically obtained from the general population in accordance with recommendations from agencies such as NICE and the Washington Panel. General population values are often advocated because public preferences are considered appropriate when health care is publicly funded (partly or fully, depending on a country's health system). In addition, the general population is considered to have no vested interest as they do not have prior knowledge about the future health states they may experience. However, it can also be argued that the general population do not fully understand hypothetical health states because of their lack of experience of similar health states, meaning that patients are more able to provide accurate valuations. This is important because patients often provide different values from those provided by the general population. This means that the source of the values may affect the values obtained and thus have an impact on any economic evaluation based on such data.79
A recent review and meta-analysis found that patient values of health states were significantly lower than general population values using the TTO elicitation technique.80 However, other papers have found that patients provide higher values for health states using a variety of valuation techniques.79,81 Reasons for these differences can be summarised as (1) valuing different states because of differences in understanding or interpretation of the description; (2) different scales of measurement because of a response shift in different populations; and (3) adaptation to the state by patients. Patients may better understand what it is like to be in the health state than a member of the general population who has to imagine the health state. Furthermore, patients may value the health state differently because they are bringing to the valuation additional information that is not included in the health-state description. However, patients may also not recall experiences of full health or mild health states, and may have lowered their expectations as a result. Knowledge and consideration of adaptation is one important difference between patients and members of the general population. Patients may also have different sociodemographic characteristics from the general population and this may also affect how they value health states.
Much of the literature examining differences between patient and general population preferences has focused on physical health states, and the relationship may differ for conditions affecting mental health and cognition. In two studies people experiencing a range of different health states gave mental health greater weight than physical health, which was the opposite for members of the general public trying to imagine the health states.82 Public attitudes and understandings of dementia in particular may mean that patient and general population values differ for dementia health states. There may also be differences in values provided by carers of patients with dementia, as carers have experience of how the condition impacts on the patient without the health problems experienced by the patient that may affect their understanding of the valuation techniques. The contrast in weightings of physical and mental health by the general population and patients is important for policy. If general population values are used to inform policy and these are higher than patient values for mental health and cognition states, then mental health and cognition may be given lower priority than people with these conditions feel is warranted. The higher relative weighting of physical health to mental health by the general population potentially prioritises physical health at the expense of mental health.
The literature is also limited as typically studies ask respondents to value only a small number of states, meaning that the data cannot be examined to determine whether or not there are systematic differences across all health states or whether or not the difference varies by health-state severity. Even those studies that value a large sample of health states have not examined whether or not differences vary by severity. For example, one study estimated different value sets for patients and the general population for EQ-5D but when combining data from both populations to estimate a combined value set included only a dummy variable to capture patient effects rather than including interaction effects for the severity levels of each dimension.83 However, one study did find that differences in visual analogue scale (VAS) values vary by the severity of the health state between members of the general population with no health problems, those with mild health problems and those with moderate health problems.84 It is also important to control for sociodemographic characteristics of the samples84 as it has been found that elicited TTO values vary by sociodemographic characteristics,85 and it is possible that this explains some of the variation in values across populations.
To address these general limitations and to investigate the difference in values that might be obtained between the general population and people with dementia and their carers we undertook a comparative study comparing health-state utility values from samples of the general population, people with dementia and carers of people with dementia for a range of dementia health states of differing severity. The analysis here explores whether population, health-state severity and respondent sociodemographic characteristics impact on elicited utility values.
Methods
Health-state description
Health-state descriptions were generated using the DEMQOL-U and DEMQOL-Proxy-U classification systems as described in Chapter 3. The DEMQOL-U classification system is derived from the DEMQOL questionnaire, which is completed by people with dementia, whereas the DEMQOL-Proxy-U classification system is derived from the DEMQOL-Proxy questionnaire, which is completed by carers using proxy report. These classification systems are presented in Table 14.
Selection of health states
In Chapter 5 we described the selection of health states and blocks of combinations of states that were included in the general population valuation study used to estimate a preference-based single index. We selected one of these blocks each for DEMQOL-U and DEMQOL-Proxy-U. For each classification system we chose the block that contained the best health state defined by the classification system (states 11111 and 1111 respectively) and so along with the worst state this ensured that the selected health states covered the full severity range of the classification system.
Samples
To maintain the involvement of each population in the valuation of each classification system, health states for each classification were valued by the population who completed the questionnaire and who were involved in the initial development of the questionnaire. DEMQOL-U was valued by patients and DEMQOL-Proxy-U was valued by carers. In addition, both classification systems were valued by the general population in phase 2 of the project as described in Chapter 5. Sample size was chosen to ensure sufficient power for comparison of mean health-state utility values across the different populations for each classification system using simple t-tests. This required a total of 71 completed interviews per population per classification system, assuming a power of 0.8, significance level of 0.05, SD of 0.3 and an expected difference of 0.1.
General population
The general population sample and recruitment process is described in detail in Chapter 5. The analysis conducted in this chapter uses a subset sample of the 600 general population respondents, consisting of all respondents valuing our selected card block. To ensure that there were no systematic differences across the geodemographic profiles of the subsamples used here interviewers worked systematically through all card blocks.
People with dementia and their carers
To recruit the sample of people with mild dementia and their carers, two research workers visited clinical teams who were part of the Mental Health for Older Adults and Dementia Clinical Service at the South London and Maudsley NHS Foundation Trust. This included the Croydon Memory Service, the Southwark and Lambeth Memory Service and the community mental health teams in Croydon and Lewisham. The research workers explained the study to the clinical teams and invited team members to refer suitable patients. Letters and information sheets were then sent to all referred patients, after which the research workers contacted them by telephone to arrange appointments. Participants who agreed to take part in the study were visited in their own homes by both research workers. If both the person with dementia and his or her carer were participating, interviews were conducted separately, each in a different room where possible. The research workers received the same training as the interviewers who carried out the general population survey.
Valuation task
For the valuation survey the TTO technique was chosen in accordance with the UK valuation study design of the EQ-5D,54 which also meets the reference case recommended by NICE (see Chapter 5 for a detailed overview of the TTO technique and Appendix 1 for a sample script for one health state). For each classification system all respondents valued the same health states.
At the interview respondents first self-completed the EQ-5D and then whichever classification system would be used subsequently in the valuation task (DEMQOL-U or DEMQOL-Proxy-U). This familiarises respondents with the classification system by using the system to describe their own health. General population respondents then undertook a ranking task of eight health states plus full health and dead. People with dementia and carers did not undertake a rank task because of concerns that this task would be too complex. Ranking requires the respondent to consider all health states simultaneously and, given that dementia is a condition that affects memory, this was considered inappropriate for patients with dementia. Although carers may have been able to successfully undertake the ranking task, the same protocol was used for both people with dementia and carers, for consistency and to ensure that people with dementia felt that they were treated in the same way as their carers.
To familiarise respondents with the TTO task, respondents undertook a practice TTO task using a hypothetical ‘practice’ state and subsequently valued eight states using TTO. The MVH study version of TTO was used, including a visual prop designed by the MVH group (University of York) (see ). Finally, respondents rated how difficult they found the rank and TTO tasks and answered questions about their sociodemographic characteristics and health service use.
Before conducting the full survey a small number of interviews were conducted and the interviewers discussed their findings with one of the research team. As no problems were identified with the survey, the main survey was started without amendment and these interviews were included in the main sample. The person with dementia and carer survey was approved by the London Research Ethics Committee and the general population survey was approved by the ScHARR Research Ethics Committee at the University of Sheffield.
Analysis of time trade-off data
Significant differences in the observed variation of respondent characteristics across the different populations were analysed for each measure using a factorial ANOVA estimated using a generalised linear model. Descriptive statistics of health-state utility values across the different populations (general population, people with dementia, carers) were presented for each measure. Mean health-state utility values were compared across the different populations for each measure using simple t-tests and Wilcoxon rank-sum tests.
Regression analysis was used to examine the impact of population (general population or people with dementia, general population or carer) on elicited health-state utility values. The analysis further determined the impact of population and health-state severity while controlling for the sociodemographic characteristics of respondents. The standard model specification was:
where i = 1, 2, …, n represents individual respondents and j = 1, 2, …, m represents the eight health states. The dependent variable, y, represents the TTO utility value, s represents the vector of dummy variables for the health states, q represents the dummy variable capturing the population (equals 1 for persons with dementia and carers, 0 for general population), r represents the vector of interaction terms to jointly capture population and severity effects (e.g. in the DEMQOL-U regression analysis a dummy variable equals 1 for state 12231 valued by a person with dementia), z represents the vector of sociodemographic characteristics of respondents and ϵij represents the error term. OLS regressions were estimated but these do not take into account the structure of the data, as there are multiple observations per individual, and these models are not reported here. Random-effects and fixed-effects GLS models were estimated as these take into account the structure of the data when all respondents have multiple observations.86 Refer to Chapter 5 for an overview of random-effects and fixed-effects models. The choice of whether to use random- or fixed-effects models was determined empirically using the Hausman test.
Four model specifications were estimated in which each model contained additional explanatory variables: first, regressions were estimated containing only health-state dummies as independent variables; second, the dummy variable capturing population was added to the model specification; third, interaction terms capturing population and severity effects were added; and finally, sociodemographic characteristics of respondents were added. This procedure was undertaken to determine the additive impact of population across all states regardless of severity, and to then determine whether or not model performance was improved by expanding the model specification to include interactions that allowed for impact of population to vary by state severity. This procedure was also used to determine whether or not population effects are important when the differences in sociodemographic characteristics of respondents and across population samples are controlled for, as some differences in values across populations may be due to the difference in sociodemographic composition of the samples rather than the populations per se. The final model specification was also estimated using a sample excluding respondents whose understanding of the TTO task was doubted by the interviewers.
The selection of sociodemographic characteristics for inclusion in the models was informed using the ANOVA analysis (as described above), Spearman rank correlation coefficients, significance of coefficients and performance of the regression models (described below). Spearman rank correlation coefficients were used to indicate the sociodemographic variables that were appropriate for inclusion in the models (which are variables most highly correlated with the TTO utility value) and to indicate sociodemographic variables that should not appear in the models alongside each other (which are variables with high correlations with other variables, for example age and different categories of employment status).
Performance of the regression models was assessed using within R-squared, between R-squared, overall R-squared, RMSE of predictions and the Wald chi-squared test. Stata version 11 was used for all regression analysis (StatCorp LP, College Station, TX, USA) and SPSS version 15 was used for the descriptive statistical analysis.
Results
Samples
Recruitment of the general population sample is summarised in Chapter 5. The samples of people with dementia and carers were recruited between 7 November 2010 and 23 May 2011. During this time the teams referred a total of 196 people diagnosed with dementia. Of these, 93 patients and 73 carers agreed to be interviewed and were assessed as suitable by the research workers. On some occasions patients were seen without carers, and a few carers participated alone. Recruitment continued until 71 patients and 71 carers had completed the interview. During the process 21 partial interviews were undertaken, 19 with patients and two with carers, and three patients were not able to participate at the time of the arranged interview. These partial interviews were terminated before the end of the TTO tasks because of respondent fatigue, misunderstanding or distress, and these interviews are therefore excluded from the analysis. Of those that completed the full TTO study, 49 were patient/carer dyads.
Respondents who valued the worst state higher than all other states, who valued all states worse than being dead or who valued all states identically but < 1 were excluded from the analysis. No patients with dementia or carers were excluded but one general population respondent was excluded for valuing all states identically but < 1.
shows a comparison of respondents by population and classification system. The comparison of respondents valuing the DEMQOL-U classification system shows that the patient population sample is significantly older (at the 10% level) using the chi-squared p-value, with a mean (SD) age of 78.4 (7.7) years, than the general population sample, with a mean (SD) age of 49.6 (17.0) years. The samples have significant differences in employment status and education, with the patient sample having a much higher proportion of retired individuals and individuals for whom secondary school was their highest level of education. EQ-5D scores were similar between the general population and patient DEMQOL-U samples.
Characteristics of respondents by population and classification system
The comparison of respondents valuing the DEMQOL-Proxy-U classification system shows that the carer population sample was significantly older, with a mean (SD) age of 69.8 (12.7) years in comparison to a mean (SD) age of 50.7 (16.2) years for the general population sample. The samples have significant differences in employment status (using chi-squared p-values), marital status and homeownership (using ANOVA), with the carer sample including a significantly lower proportion of employed and unemployed individuals and students, but a significantly higher proportion of retired individuals, married individuals and individuals owning their own home. EQ-5D scores were similar between the general population and carer DEMQOL-Proxy-U samples.
The differences in sociodemographics may affect elicited values and these differences can be taken into account when modelling the data using the inclusion of sociodemographic characteristics in the model specification. Overall, the person with dementia and carer samples had a significantly higher proportion of respondents for whom the interviewers reported that it was doubtful that they understood the TTO tasks. This can be taken into account by estimating the preferred regression model excluding respondents for whom it was doubtful that they understood the TTO tasks.
Descriptive statistics of health-state utility values
presents descriptive statistics of observed TTO values for DEMQOL-U elicited from the general population (as also reported in Chapter 5) and from people with dementia. For every health state, the mean and median TTO values are higher for the general population sample than for the patient sample. The range of mean utility values is larger for the patient sample (from 0.816 to –0.023) than for the general population sample (from 0.955 to 0.190). The ordering of states differs by population. The ordering is logically consistent for each population as for many states it is not possible to determine a logical ordering as one state is not consistently better across all dimensions.
Descriptive statistics of observed TTO values for DEMQOL-U
reports descriptive statistics of observed TTO values for DEMQOL-Proxy-U elicited from the general population (as also reported in Chapter 5) and from carers of people with dementia. For every health-state mean and median TTO values are higher for the general population sample than for the carer sample. The range of mean utility values is larger for the carer sample (from 0.857 to 0.049) than for the general population sample (from 0.918 to 0.370). The ordering of states differs by population, but the ordering is logically consistent for each population. shows differences in the distribution of TTO values by population for each measure. General population TTO values are negatively skewed with a large proportion of responses at 1, whereas patient and carer TTO values are also negatively skewed but peak at 0.5.
Descriptive statistics of observed TTO values for DEMQOL-Proxy-U
Histogram of observed TTO values: (a) DEMQOL-U valued by the general population sample; (b) DEMQOL-U valued by the patient sample; (c) DEMQOL-Proxy-U valued by the general population sample; (d) DEMQOL-Proxy-U valued by the carer sample.
Simple t-tests revealed that health-state utility values for the general population and for people with dementia are significantly different for DEMQOL-U [general population mean (SD) 0.604 (0.410), person with dementia mean (SD) 0.398 (0.447), t = 8.279, degrees of freedom (df) = 1153, p < 0.001] and health-state utility values for the general population and for carers are significantly different for DEMQOL-Proxy-U [general population mean (SD) 0.707 (0.350), carer mean (SD) 0.531 (0.410), t = 7.946, df = 1120, p < 0.001]. Wilcoxon rank-sum tests reached the same conclusions (p < 0.001 and p < 0.001 respectively).
Regression analysis
Regression analysis examining the relationship between elicited health-state utility values, population, health-state severity and sociodemographic characteristics of respondents is presented in for DEMQOL-U and for DEMQOL-Proxy-U. Random-effects models are reported as the Hausman test confirmed for both classification systems that fixed-effects models would produce similar estimates at reduced efficiency. For the DEMQOL-U classification system, TTO values were regressed on state-level dummy variables in model (1); state-level dummy variables and a population dummy variable in model (2); state-level dummy variables and interaction terms to reflect the interaction between the specific health state and population in model (3); and explanatory variables used in model (3) while controlling for sociodemographic characteristics in model (4). Model (5) used the same specification as model (4) and was estimated on a sample excluding respondents whose understanding of the TTO task was doubted by the interviewers. Models (6), (7), (8), (9) and (10) estimated for the DEMQOL-Proxy-U classification system have the same specification and criteria for sample selection as models (1), (2), (3), (4) and (5) respectively. Models using a range of sociodemographic variables as explanatory variables were estimated and the best models (using significance of coefficients, within R-squared, between R-squared, overall R-squared, RMSE of predictions and Wald chi-squared) are presented here.
Regression analysis of DEMQOL-U health-state values across general population and patient respondents
Regression analysis of DEMQOL-Proxy-U health-state values across general population and carer respondents
Spearman correlation coefficients were used to indicate correlations among the sociodemographic characteristics that can be used as potential explanatory variables. All sociodemographic variables included in the regression had poor intercorrelation (< |0.3|) with the exception of moderate correlation (< |0.7|) between retired and patient variables for DEMQOL-U (correlation 0.67) and between renting and unemployed variables for DEMQOL-Proxy-U (correlation 0.32). This means that there was no problem of multicollinearity in the explanatory variables included in the regressions. Age was not included as an explanatory variable as it was correlated with the employment status predictors, which had a greater improvement in model performance than age and age-squared. Health status using EQ-5D was not included as an explanatory variable because of a concern that it was not an accurate measure of health status for the patient population. Furthermore, inclusion of health status was not significant and did not improve model performance for the DEMQOL-Proxy-U models. The population variable was not included in models (3), (4), (5), (8), (9) and (10) because of perfect collinearity between the population label and the eight population × state interaction terms.
The results show that health-state dummy variables were significant at the 1% level in all models except for state 12231 in models (3), (4) and (5) and state 1222 in models (8), (9) and (10). The size of the coefficients was consistent as the decrement in the elicited utility value was larger for more severe health states (indicated using the modelled utility values from the original valuation study). The only exception is an inconsistency between health states 3112 and 1341 in models (8), (9) and (10) for DEMQOL-Proxy-U.
The inclusion of a dummy variable to represent population in models (2) and (7) was significant at the 1% level and improved model performance across all goodness of fit statistics. The coefficient was negative, demonstrating that respondents with dementia and carers value states significantly lower than the general population.
Interaction effects reflecting the interaction between the specific health state and the person with dementia or carer population had negative coefficients, meaning that there is a reduction in utility value for respondents from the patient or carer populations. The inclusion of the interaction terms reduced the absolute size of the coefficient for the health-state dummy variables with the exception of DEMQOL-U state 23424 in model (3). The size of the interaction coefficients varied by health state, indicating that the impact of population differs by health-state severity, but there was not a clear pattern. Interaction effects for DEMQOL-U health states valued by people with dementia with more severe health problems in the (fourth) negative emotion dimension (health states 12231, 32143, 43442, 44444) had larger coefficients than the other health states, but this could be due to coincidence. Interaction effects for DEMQOL-Proxy-U health states valued by carers with the most severe level in one or more dimensions (health states 1341, 2424, 3234, 4411, 4444) had larger coefficients than other states, but again this could be due to coincidence. The coefficient for the interaction effect for the worst health state for DEMQOL-Proxy-U valued by carers is noticeably larger than those for the other interaction effects. The inclusion of interaction effects improved model performance as measured using within and overall R-squared and Wald chi-squared.
The addition of the sociodemographic characteristics in models (4) and (9) had only a minor impact on the coefficients of the other variables and led to only a small improvement in model performance. Few sociodemographic variables were significant. For the DEMQOL-U models, employment status variables of ‘homemaker’ and ‘retired’ were significant in model (1) but not in models (2) and (3). No sociodemographic variables were significant in model (4) while the variables ‘female’ and ‘renting accommodation’ were significant in model (5). For the DEMQOL-Proxy-U models the employment status variable ‘long-term sick’ was significant in models (9) and (10) (although note that the number of respondents in this category was small). This suggests that, although the sociodemographics are different for the different samples, as found in Table 1, these differences do not impact on elicited health-state utility values.
Excluding respondents whose understanding of the TTO task was doubted by the interviewers did not have a noticeable impact on coefficients for health-state severity and interaction effects. However, the ‘female’ and ‘renting accommodation’ sociodemographic variables became significant in model (5) for DEMQOL-U.
Discussion
The results demonstrate that the population used to value dementia health states affects health-state utility values elicited using TTO. This is in accordance with the literature.80 Health-state utility values elicited from people with dementia and carers of people with dementia were lower than health-state utility values elicited from the general population. This is different from many studies which have found that patients provide higher values than the general population for other health states using a variety of valuation techniques, but is consistent with a recent review and meta-analysis which found that patient values were significantly lower using the TTO elicitation technique in particular.80 Furthermore, the majority of the literature focuses on physical health states whereas this study focused on mental health and cognition, with previous research suggesting that patients and the general population weight problems in physical and mental health differently.82
Modelling of the health-state utility values indicated that the differences in values were due to the population per se and not to differences in the sociodemographic composition of the populations. This was demonstrated because controlling for sociodemographic characteristics had a minimal impact on the coefficients of the other variables and resulted in only a minor improvement in model performance and few of the sociodemographic coefficients were significant. Regression analysis and descriptive statistics of observed values indicated that the difference in values by population differed for each health state, sometimes indicating a different ordering of the health states. This different ordering of health states is not an inconsistency across populations; it represents differences in the valuation of and trade-off between different dimensions. This suggests that the choice of whose values are used to produce utility values for use in cost-effectiveness analysis will have an impact on the results and could potentially affect resource allocation decisions. This suggests the need to produce a full value set for all DEMQOL-U and DEMQOL-Proxy-U health states using values elicited from people with dementia and from carers of people with dementia respectively.
The finding that general population values are higher than patient values for dementia states suggests that members of the general population may be systematically undervaluing the impact on quality of life arising from problems with cognition and mental health. This may reflect public perception in general of health problems such as dementia. However, it is important to note that it may be due to the wording and labelling of the classification system. General population respondents were not informed at any point that the study was about dementia, whereas people with dementia and carers knew that the study was about dementia. This contextualised health states for the people with dementia and carers, meaning that they had a greater understanding of the health states, but it also meant that they knew the underlying cause of the health state. A recent study examining labelling effects found that introducing condition labels into health-state descriptions impacted on health-state utility values elicited from the general population, and that the impact differed by condition and health-state severity.37,71 It can be argued that the inclusion of a condition label can better inform the general population about the health state because respondents may value differently, for example, feeling frustration as a result of epilepsy and feeling frustration as a result of dementia. However, it can also be argued that the inclusion of a condition label may mean that respondents take into account preconceptions of the condition or mortality of the condition, and these are factors that should not be taken into account in the elicitation of health-state utility values. As the aim of the general population valuation survey was to elicit values to inform resource allocation decisions across all conditions and patient groups, there must be comparability between the health-state descriptions used here and in other valuation surveys, regardless of the underlying condition. Otherwise, this could imply that, for example, a given generic EQ-5D state has a lower utility value for a patient with epilepsy than for a patient with diabetes. The exclusion of a dementia health-state label in the general population study may therefore have impacted on elicited utility values, and there is a possibility that if general population values were obtained with the inclusion of a dementia label these values may be closer to the values elicited from people with dementia and carers. However, an important difference is likely to remain: the difference in the valuation of and trade-off between the different dimensions in the classification system. Qualitative research or a full-scale valuation study similar to the study outlined in Chapter 5 for people with dementia and carers would indicate differences in the trade-off between and relative importance of the dimensions in comparison with the general population. Further research in this area is encouraged but is likely to be constrained by the difficulties of conducting this type of research in these populations.
One potential limitation in the comparisons made between the general population values and the values from people with dementia and carers is that the general population study involved ranking as a warm-up task prior to the TTO task whereas this was excluded from the survey for people with dementia and carers. Starting the TTO task without the rank task, which helps respondents think about how they would value different health states, may have affected the understanding of the TTO task and may also have affected the values provided by people with dementia and carers. However, it could be argued that people with dementia and carers knew that the states were dementia states and may already have an idea before the interview about how they value different dementia health states.
Among people with dementia and carers there were significantly higher proportions of respondents who were reported by the interviewers as doubtful that they understood the TTO task, but at 12.7% and 7.0% of respondents, respectively, these proportions are not especially high. The preferred regression models were also estimated on samples excluding respondents whose understanding of the TTO task was doubted by the interviewers. The exclusion of these respondents did not have a large impact on the coefficients for health-state severity and interaction effects but some sociodemographic characteristic variables became significant.
Another potential limitation of this study is the involvement of people with dementia with neuropsychological and cognitive problems to value health states using a cognitively demanding elicitation technique. The study was designed at all stages to take into consideration the health and competencies of the patient population: the team was engaged in conversation with the Alzheimer's Society through its Quality Research in Dementia panel prior to designing the study; patients were referred to the study by clinicians specialising in dementia; and the interviews were immediately terminated if the person with dementia suffered from fatigue, misunderstanding or distress. For these reasons the samples are made up of patients with mild dementia and carers of people with mild dementia. This means that the samples may not be representative of people with more severe dementia and their carers but this is a constraint of conducting a valuation study for this patient group. The MVH TTO protocol has been widely used in valuation studies of the general population but may be more challenging for respondents with cognitive problems. The TTO is no more challenging than many other elicitation techniques, such as SG or person trade-off, and arguably is less cognitively demanding than ranking, which requires the simultaneous consideration of multiple health states. Other options that may be less cognitively demanding include ordinal techniques such as discrete choice techniques and best–worst scaling. However, these require many more data and either increase the burden on each respondent and/or require a much larger sample. There are also challenges regarding the anchoring of these values onto the 1–0 full health–dead scale (see reference 19 for an overview). A VAS may be cognitively easier for respondents, but to obtain values anchored onto the 1–0 full health–dead scale would require the valuation of dead alongside each state.
The study used the same valuation protocol as the EQ-5D as recommended by NICE to ensure that elicited utilities are comparable with the UK tariff of the EQ-5D. However, the choice of TTO and the valuation protocol may have impacted on results. A recent meta-analysis of patient and general population values found that results differed by valuation technique80 and other research has found that valuation results may in general differ by protocol.87 Further research in this area is needed.
Conclusion
Dementia health-state utility values elicited using TTO differ by the population used to elicit the utility values, with people with dementia and carers of people with dementia giving systematically lower utility values than members of the general population. These differences in values were due to the population per se and not to differences in the sociodemographic characteristics of the populations. The general population underestimated the impact of dementia compared with people with dementia and their carers. The ordering of health states also differed by population for some health states, indicating a difference in the valuation of and trade-off between different dimensions. These results suggest that the population used to produce dementia health-state utility values could impact on the results of cost-effectiveness analysis and potentially affect resource allocation decisions.
