Background
There is a growing evidence base that taking a specific treatment, particularly one requiring long-term use for a chronic condition, can cause inconvenience or ‘disutility’ to a patient that is distinct from the unwanted harms, adverse outcomes or specific effects of the treatment. DTD is a type of process disutility102 related to the inconvenience of obtaining prescriptions and medicines, needing to modify lifestyles to take medicines and attending healthcare visits for monitoring treatment.40
DTD may have particular relevance for long-term medication use, such as statins for the primary prevention of CVD and bisphosphonates for osteoporosis, as the benefits of treatment are typically small and accrue over long periods, while any inconvenience, however small, is likely to start with treatment initiation and may be persistent.
Existing empirical studies have estimated a range of values of DTD, with the general size of the disutility being around 0.01 on average, which is equivalent to a loss of ≈ 3.6 days of perfect health over 1 year.47,48,103 With these sorts of DTD values, several primary preventative treatments for CVD have switched from meeting acceptable levels of cost-effectiveness to not being cost-effective.2,44,46,104–109 The mechanism underpinning this change is a simple one, whereby the DTD value is much larger than the expected health benefits of the medicine over the longer term. However, DTD input values have been elicited empirically in only a few studies, several of which have either adopted small study sizes47 or sampling frames which are not representative of either patients48 or the general population.103 Consequently, there is considerable uncertainty as to the actual size and distribution of DTD values, whether or not DTD changes with the experience of taking medicines and, consequently, whether or not these values could, and should, be used to inform decision-making.
Methods
This study used two types of preference elicitation methods: (1) TTO and (2) BWS.
Selection of exemplar medicines
We quantified the DTD of statins for the primary prevention of heart disease and bisphosphonates for the primary prevention of fractures using two medicine-taking case studies. Statins were chosen as an example of a class of medicines that are perceived by professionals to be benign, but which some people perceive as harmful. Bisphosphonates were selected because they are medicines we thought had an obvious influence on daily life (i.e. people who take a bisphosphonate are required to drink a large glass of water before taking the medication, remain upright for 30 minutes after taking it and avoid food and drink for 2 hours thereafter).
Selection of elicitation methods
We sought to get an accurate description of the medicine-taking health states within our survey so that our respondents could appropriately value them. In addition, we wanted to check that the methods we adopted were appropriate and robust enough to estimate small disutilities associated with the ongoing use of a medicine. Therefore, we reviewed methods from previous studies47,48,103 that had attempted to estimate DTD values and integrated those methods with the experience and views of our clinical research team, as well as our two patient representatives who were members of the research team. This review suggested that TTO should be the predominant method, but that there is emerging interest in using BWS to elicit DTD.
The context
The elicitation surveys were designed to take account of the specific context in which a respondent is taking a medicine for primary prevention. Two distinct surveys for each method (i.e. TTO and BWS) were designed to focus on each of the two exemplars of statins and bisphosphonates.
For each exemplar, respondents were asked to consider taking medicine A, which was a ‘one-off pill’ assumed to have no ongoing inconvenience, or medicine B, which was a daily pill for 10 years. In the TTO exercise, we also decided to include four scenarios to understand whether or not DTD values differed based on how the benefits and harms of the medication were framed. Therefore, we asked our respondents to consider medicines A and B in the context of the pills having (1) no side effects, (2) minor side effects (MSEs), (3) severe side effects (SSEs) and (4) reduced effectiveness. Finally, we also wanted to explore if there was any systematic difference between how different groups might value DTDs. In particular, we wanted to understand whether or not patients with experience of taking pills valued DTD differently from people with little or no experience. In addition, we thought it was important to explore other factors, such as age and sex.
Time trade-off exercise
The first method we selected to value the disutility of medicine-taking health states was the TTO method. TTOs are a widely used approach for eliciting utility values and were used to generate the valuations for the EuroQol-5 Dimensions, three-level version (EQ-5D-3L) health states, which are part of the current NICE reference case. In general, the TTO method involves asking respondents to consider the relative amounts of time (e.g. number of life-years) they would be willing to sacrifice to avoid a poorer health state.110 In this study, the TTO method followed the approach taken by Hutchins et al.48,103 and asked respondents the maximum amount of time they are willing to give up at the end of their life to avoid having to take a medicine. Per respondent, for each of the four questions, the estimated utility was calculated as the ratio x/t, where x is the final selected time period for the medicine A option (i.e. one pill taken once) and t is the full life-years assumed for the medicine B option (i.e. a pill taken every day for 10 years).111
Best–worst scaling experiment
Best–worst scaling experiments are an extension of discrete choice experiments.112 There are three types of BWS: case 1 (object case), case two (profile case) and case three (multiprofile case).113 This study used a case 2 (profile case) BWS experiment. A profile case BWS experiments ask respondents to select their most preferred and least preferred items (defined by attributes and levels) in a question.
An argued advantage of profile case BWS over standard discrete choice experiments is that the choices made reveal more information about the relative strength of people’s preferences for each attribute in the design, using fewer questions, which could, in turn, reduce the response error. Importantly, using a BWS allows a rank ordering of the attributes in the experiment together with utility weights to be estimated. In this study, the BWS experiment was framed around the choice question ‘we want you to indicate which of the listed features of medicine A you think are the most and least likely to make you want to avoid taking the tablet’. The BWS experiment contained three attributes: (1) inconvenience (levels: no inconvenience and inconvenience), (2) probability of a MSE (levels: 1%, 5%, 9% and 13%) and (3) probability of a SSE (levels: 0.1%, 0.3%, 0.5% and 0.7%). Rapid reviews of the relevant published literature and input from the research team, including patient involvement, were used to generate a list of potential attributes and assigned levels. The BWS exercise was created using a full factorial design of 32 choice sets, which were split into four randomised blocks. Each respondent was assigned to one of the four blocks, comprising eight choice sets, with each choice set displaying three features of medicine A (i.e. inconvenience, MSE and SSE). The levels for these three features varied across choice sets.
Design of training materials
Consistent with emerging good practice in the design of a stated-preference study, training materials, that introduced the background for each case study (i.e. statin or bisphosphonate) and the attributes and levels used in the BWS exercise were created using a storyboard approach.114 The same training materials were used for the TTO and BWS experiment. In addition, we used visual arrays to communicate absolute risk consistent with best practice.115
Survey format and content
Online surveys were designed for each method (i.e. TTO and BWS), using the same approach and format for each exemplar medicine (i.e. statins and bisphosphonates). The surveys were formatted and administered online using Sawtooth software (Sawtooth Software, Inc., Provo, UT, USA). Respondents were sent a secure link to complete one of the surveys (no reminders were used). There were three parts to each survey. The first part of the survey consisted of the EQ-5D-3L questions, training materials and questions about the respondent’s attitude towards taking a medicine for the first time. The second part of the survey consisted of the main elicitation exercise (i.e. TTO or BWS). The third part of the survey included questions about whether respondents were currently taking or had ever taken one of the branded bisphosphonates (or statins), respondents’ perceived benefits and harms of bisphosphonates (or statins), whether or not respondents had experienced any side effects from bisphosphonates (or statins) and whether or not respondents found it inconvenient to take the medicine. Participants were also asked about their opinion on taking the medicine (i.e. whether they mind/dislike taking it), whether or not they took any other medicines, the number of medicines taken on a regular basis, the number of times medicines are taken daily and sociodemographic questions, such as age, gender, qualifications, employment status, ethnicity and religion. There were also non-compulsory probability questions to understand the respondents’ understanding of risk. Feedback questions towards the end of the survey included asking respondents about their confidence in making similar choices in real life, their perceived difficulty in making choices between alternatives and in understanding the survey, as well as general feedback comments to improve the clarity of the survey.
Piloting of experiments
Two pilot phases were conducted for the design of each experiment. For the TTO and BWS surveys, early piloting involved ‘think-aloud’ interviews with a sample of 19 patients recruited from a general practice in Greater Manchester. The intention of the early pilot was to understand whether or not the draft surveys, training materials and valuation exercises were sufficiently clear for respondents. We also wanted to understand how our respondents were interacting with the material presented. Following on from this, a few minor changes were made to the training materials and valuation exercise. The survey was then tested again in quantitative pilot studies involving members of the public to assess whether or not the data could be analysed from the survey design. No changes were made following the quantitative pilot study. The final elicitation studies were then launched and involved a sample of members of the public and a sample of people with experience of taking a statin or bisphosphonate.
Data samples
People with experience of taking a statin or bisphosphonate were recruited from general practices via the NHS Research Scotland Primary Care Network and the Scottish Health Research Register (SHARE which is a register of people living in Scotland, allowing recruitment after a search of their medical records). To be included, patients needed to have been prescribed a statin or bisphosphonate in the previous year, needed to be aged ≥ 30 years and should not have been diagnosed for dementia or be taking a drug for dementia. Members of the public for the valuation study were recruited online using the panel company Dynata (Shelton, CT, USA). Within this sample, we also identified members of the public with experience of taking a statin or bisphosphonate. Respondents to the online survey needed to be aged over ≥ 30 years, but otherwise the sample should be a demographically balanced representation of the general public willing to take an online survey.
Results
The results are presented in two distinct sections for the TTO and BWS experiments.
Time trade-off
Analysis characteristics for respondents to the TTO for statins (n = 514) and respondents to the TTO for bisphosphonates (n = 365) are reported in . Statin patients tended to report marginally higher mean TTO values than public respondents [difference 0.007 (SE 0.003); i.e. the amount of life expectancy respondents were willing to sacrifice to avoid taking statins was 0.7 percentage points greater in the public respondents than in people with experience of statins], although this finding was not statistically significant. Bisphosphonate patients reported much higher TTO values – indicating less DTD – than public respondents [difference 0.024 (SE 0.006)], with this difference being statistically significant. For both statins and bisphosphonates, changing the question context did not alter the mean TTO scores by more than 0.01. Irrespective of the type of question or respondent, there was a clear difference between statins and bisphosphonates survey results (). Mean TTO values for the entire statin sample (0.967) were higher than TTO scores for the bisphosphonate sample (0.933), meaning that the average respondent was willing to trade twice as much life expectancy to avoid bisphosphonates as they would to avoid statins [0.033 vs. 0.067; absolute difference 0.034 (SE 0.004)] (). Respondents who had experience of taking medications more than three times a day provided a much lower DTD value than respondents who had no experience of daily medicine use. None of the other explanatory variables had a statistically significant association with DTD size, including the number of pills taken per day.
Description of the sample characteristics completing the TTO
Kernel density plots showing distribution of TTO responses stratified by medicine, question context and respondent type. (a) Statin questions 1–4 utility values in patients; (b) statin questions 1–4 utility values in the public; (c) bisphosphonates (more...)
Mean values and other summary statistics of DTD elicited using TTO
Best–worst scaling experiment
Analysis characteristics for respondents completing the BWS for statins (n = 319) and respondents completing the BWS for bisphosphonates (n = 312) are reported in . Appendix 5, Table 46, shows the count data [normalised based on the number of levels for each attribute: in/convenience (two levels), MSEs (four levels), SSEs (four levels)] for the number of times a respondent chose the attribute level as ‘best’ and ‘worst’, and the difference between ‘best and worst’. Appendix 5, and , show the distribution of the count data for statins and bisphosphonates, respectively.
Description of the sample characteristics for BWS
The data show that all the respondents had the strongest preference for taking a medicine with no inconvenience, which is the result expected a priori. This result was consistent between respondents with and without previous experience of taking a statin or bisphosphonate. The results from the pooled sample indicated that respondents had a strong dislike for experiencing the highest level of risk of a SSE. Overall, the respondents with experience of taking a statin or bisphosphonate indicated a greater dislike for a SSE and stronger preference for no inconvenience.
There was significant preference heterogeneity in the results. A fully correlated mixed logit model was, therefore, used to estimate preference weights for each attribute level relative to the reference level of 0.7% risk of a SSE (). The signs for all estimated coefficients in the fully correlated mixed logit model were consistent with a priori expectations based on the normalised best–worst scores. The estimated coefficients from the fully correlated mixed logit model were ‘re-scaled’ by setting no inconvenience at a value of 1 and 0.7% risk of SSEs at zero to calculate an indicative utility score for no inconvenience. Using this approach, the DTD for a statin was 0.2 and 0.46 for a statin and bisphosphonate, respectively (i.e. compared with a life free of the medications, life lived with medications should be seen as 80% or 54% as desirable, respectively).
Correlated mixed logit results for statin sample
Discussion
In this study, using the TTO method, we find that long-term statin use is associated with a DTD of 0.034 among people willing to take statins. We find that bisphosphonate use is associated with a DTD of 0.067 among people willing to take bisphosphonates. These values imply that, even if medicines have no specific adverse effects, the act of taking medicines can have a non-trivial effect on people’s quality of life. We found no difference between patient and public disutilities for statins, but we did find that bisphosphonate patients generated smaller disutility values than the values coming from the general public.
Although we find that DTD values do not tend to differ based on the reported characteristics of the respondents in our survey, we do still find large variations across individuals. In line with previous empirical studies,47,48,103 we find strong evidence for three different groups or types of respondent: (1) some never trading, suggesting zero disutility associated with treatments, (2) some always selecting the lowest possible value, suggesting that they would be unlikely to initiate treatment and (3) some willing to trade length of life for no ongoing treatment, suggesting a DTD. In our survey, the groups willing to trade and generate a DTD made up the majority of respondents surveyed, with approximately 72% and 84% for statins and bisphosphonates respondents, respectively.
The findings from the BWS experiment had face validity in that no inconvenience was the most preferred attribute. Inconvenience was a stronger driver of what to avoid when taking a medicine, but avoiding side effects were more important. The estimated values for DTD did not, however, have face validity. The DTD values were very large. This result is probably because the design of a BWS experiment that includes only the attributes of interest would tend to inflate the importance of inconvenience. Further analysis is planned to explore the effect of adjusting the observed DTD from the BWS with results from a TTO experiment that was embedded in the BWS survey.
Our research shows that people will trade life expectancy to avoid treatment characteristics in exactly the same way they will trade like expectancy to avoid undesirable health states. However, even though the preferences can be measured on the same scale, whether or not these two types of preferences should be treated as tradeable with each other is a normative question (i.e. should the NHS be paying for patients’ convenience?) In England, at least, NICE appears to answer that question in the affirmative, stating that ‘If characteristics of healthcare technologies have a value to people independent of any direct effect on health, the nature of these characteristics should be clearly explained and if possible the value of the additional benefit should be quantified. These characteristics may include convenience and the level of information available for patients’ (emphasis added in bold; © NICE 2013 Guide to the Methods of Technology Appraisal 2013. Available from http://web.archive.org/web/20230117104032/https://www.nice.org.uk/process/pmg9/chapter/the-reference-case. All rights reserved. Subject to Notice of rights).117 As far as we are aware, NICE has taken account of such factors only when it helps to differentiate one mode of treatment from another. For example, NICE Technology Appraisal 606,118 considered subcutaneous compared with intravenous administration of medicines for hereditary angioedema, and the economic modelling in NICE Guideline 17119 incorporated the benefit of continuous blood glucose monitoring compared with fingerprick tests. In both cases, the evidence relied on used methods that are comparable with our approach (i.e. TTO in members of the UK general population).120,121 We see no reason why the process characteristics of some treatment compared with none should not be considered in the same way.
Nevertheless, the implications of our findings for future cost–utility analyses evaluating treatment pathways featuring statins or bisphosphonates (and potentially other oral medicines) are not straightforward. On the one hand, CEAs should ideally capture the impact of all relevant costs and consequences associated with alternative forms of treatment,122 and so it must be relevant that we have demonstrated that the average person anticipates the act of taking statins or bisphosphonates will have a non-trivial impact on their quality of life. Accounting for this disutility is likely to reduce the desirability of treatments that are currently considered very cost-effective, and estimates of cost-effectiveness for long-term preventative interventions have been shown to be particularly sensitive to the inclusion of DTD.40,44,47,48,103,107,108,123,124 Indeed, we have previously shown that for some people for whom guidelines currently recommend statins (e.g. people at a 10% risk of a cardiovascular event over 10 years), a DTD that appears moderate in the light of the current study (0.015) would result in treatment doing more harm than good.41
On the other hand, the apparent existence of distinct preference groups among our respondents requires careful consideration. A substantial minority of participants repeatedly indicated that they would be unwilling to trade any life expectancy to avoid taking these medicines, suggesting that participants consider any inconvenience with which they are associated negligible. It would be difficult to deny access to a treatment on the grounds that the average person would be bothered by its process characteristics, which is a danger if population-level cost-effectiveness estimates routinely incorporate average DTD.
In view of these conflicting considerations, we recommend that decision-makers review scenarios with and without DTD. If evidence suggests that including DTD would materially alter the balance of benefits, harms and costs associated with treatment, then this should be highlighted in population-level guidance, enabling prescribers at an individual level to engage in shared decision-making that gives appropriate weight to the person’s preferences for avoiding the treatment’s process characteristics. Such an approach fits well with the guideline development methods for NICE,125 which encourage the explicit identification of ‘preference-sensitive decision points’, taking the practicalities of possible treatments into account.
An alternative approach – broadly reflecting current practice – is to prescribe regardless of DTD and allow each person’s emergent level of inconvenience define whether or not they adhere to the therapy. We believe that incorporating anticipated preferences in prospective shared decision-making is a superior approach, as it allows for an informed discussion of pros and cons and enables prescribers to tailor the strength of their advice accordingly. This should have the consequence that people whose expected benefits easily outweigh their short-term inconvenience will be less likely to discontinue treatments, whereas others who can expect only marginal gain will be less likely slavishly to adhere to a course of action that impairs their quality of life just because their doctor appeared to recommend it. Similarly, given that the emphasis of treatment decision-making now emphasises ‘concordance’ (i.e. agreement) rather than ‘compliance’ by the patient with clinical recommendations, better understanding that an individual’s preferences around medicine-taking can influence whether or not net benefit is expected may lead to less potential conflict between prescribers and patients.
Limitations
There are some limitations to our study that need to be considered. Although we endeavoured to communicate complex concepts clearly within the study, this had a set of clear trade-offs for participants. The length of the survey, the cognitive burden and the time required to complete the survey were noted as challenges. Some of our respondents reported that they had difficulty understanding the TTO methods, whereas others provided inconsistent values across the survey questions. In addition, participants who took part in our survey ultimately self-selected and this could be related to their ability to complete the survey, as well as their self-reported health. Applicability for a different patient or general population should be made based on a careful judgement of the self-reported characteristics summarised for this study cohort.
It is not immediately clear which values elicited for DTD should be used. In terms of face validity, the DTD elicited from the TTO seemed ‘better’. For the decision-analytic modelling reported in Chapters 6 and 7, we used the DTD values from the TTO study. This decision was made because the TTO method is consistent with how utility values are typically elicited to attach to health states in CEA.117
Conclusion
This study indicated that DTD does exist. It is not clear whether or not the DTD elicited from the TTO or BWS should be used; however, using face validity would suggest that the TTO method produced more realistic DTD values. We suggest that DTD should be included in model-based CEA of long-term preventative medicines, and Chapters 6 and 7 explore the impact of using the elicited DTD values from the TTO method.
