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P. Dolan, D. Kahneman (2008)Interpretations of Utility and Their Implications for the Valuation of Health
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Douglas MacKay (2017)CALCULATING QALYS: LIBERALISM AND THE VALUE OF HEALTH STATES
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Abstract Policy-makers must allocate scarce resources to support constituents’ health needs. This requires policy-makers to be able to evaluate health states and allocate resources according to some principle of allocation. The most prominent approach to evaluating health states is to appeal to the strength of people’s preferences to avoid occupying them, which we refer to as decision utility metrics. Another approach, experienced utility metrics, evaluates health states based on their hedonic quality. In this article, we argue that although decision utility metrics face a number of significant problems, the appropriate response to these problems is not to replace them with experienced utility metrics. Rather, decision utility metrics should be employed in conjunction with experienced utility metrics. Specifically, respondents to decision utility surveys should be provided with the results of experienced utility metrics in an effort to make their decisions better informed and more robust to the deficiencies of decision utility metrics. Ultimately, this approach would improve quality-adjusted life year calculations and enhance the decision-making of policy-makers in allocating resources to support constituents’ health needs. Introduction Governments, international agencies and non-governmental organizations responsible for the health of their constituents must decide how to allocate scarce resources. To do so in a rational and effective manner, policy-makers require two things. First, they require a justifiable principle of allocation, for example, priority to the worse off or fair innings. Secondly, they require a health state metric—a principled way of determining the badness of particular states of being or functioning caused by the presence or absence of disease or disability. Such metrics allow policy-makers to assign cardinal values to different health states and so to calculate the number of quality-adjusted life years (QALYs) that particular treatments or public health interventions can be expected to yield (Weinstein et al., 2009), or the number of disability-adjusted life years (DALYs) that particular treatments or interventions can be expected to prevent (Murray, 1994). Policy-makers require a health state metric if they are to allocate scarce resources in a rational and effective manner, since they can only accurately apply principles of allocation if they have one; they can only give priority to the worse off if they know which individuals are worse off. The most prominent approach to evaluate health states is to appeal to the strength of people’s preferences to avoid occupying them. We shall refer to metrics that adopt this approach as decision utility metrics (Dolan and Kahneman, 2008). Widely used examples of these metrics include the Health Utilities Index (Horsman et al., 2003) and the EQ-5D (Williams, 2005). In recent years, however, some scholars have criticized these metrics, arguing that they suffer from a number of problems. Chief among these problems is that respondents’ preferences regarding particular health states are often based on mistaken beliefs about these states (Dolan and Kahneman, 2008; Hausman, 2015). Some scholars have argued further in favor of experienced utility metrics, which evaluate health states by appealing to the quality of experience of people experiencing those states (Dolan and Kahneman, 2008; Dolan, 2011).1 The choice of metrics matters since different metrics imply different health state valuations, and so different allocations of scarce resources (Wolff et al., 2012). In this article, we argue that although decision utility metrics face a number of significant problems, the appropriate response to these problems is not to replace them with experienced utility metrics; there are good reasons not to evaluate health states solely by appealing to the quality of experience of people inhabiting them. We argue instead that the appropriate response to these problems is to improve decision utility metrics by taking steps to ensure that respondents’ preferences regarding the health states under evaluation are as informed as possible. Most importantly, following Loewenstein and Ubel (2008), we argue that decision utility metrics should be used in conjunction with experienced utility metrics. Respondents to decision utility surveys should be provided with the results of experienced utility metrics to ensure that they are informed about the quality of experience of people inhabiting those states.2 Decision Utility Metrics and Their Problems Health states are states of being or functioning caused by the presence or absence of disease or disability. Health states therefore include normal functioning, i.e. full health, and departures from it, which may occur along a number of dimensions including mobility, sight, hearing, cognitive ability, pain and mood. In the calculation of QALYs, full health is given a value of 1, and death is given a value of 0. The role of health state metrics is to assign a value between 1 and 0 to health states that fall between full health and death. Scholars often justify particular health state metrics by arguing that they embody the correct account of well-being, thus allowing them to identify the extent to which different health states diminish the quality of people’s lives. By well-being here, we follow Fred Feldman (2004) in meaning the ‘life that is good in itself for the one who lives it’. Throughout the article, we shall use the terms well-being, the good life, quality of life and welfare interchangeably. Health states need not be evaluated only by appeal to their effect on the well-being of those inhabiting them. One might also think that the value of these state should depend on their effect on the well-being of others. In determining which treatments to prioritize, after all, policy-makers may wish to prioritize those treatments that address health states that are not just worse for patients but also patients’ families and the broader society.3 We take no position on the question of which additional factors—if any—should inform the evaluation of health states. We think that any reasonable evaluation of health states should include their impact on those inhabiting them. The question of how particular health states impact the well-being of those inhabiting them is thus a distinct and important question, and so our analysis has important implications for health state evaluation, regardless of whether other factors should matter as well. Decision utility metrics evaluate health states by measuring the strength of people’s preferences to avoid occupying them. These preferences can be elicited in a number of ways, including through the use of rating scale, time trade-off and standard gamble methods (Torrance, 1986). Rating scale methodologies simply ask subjects to place different health states on an interval scale (Torrance, 1986: 18–20). Standard gamble methodologies are more complex. They require individuals to choose between two alternatives, where alternative one is a particular health state, and alternative two is a treatment with two health outcomes—full health or death—where the probability of full health is P, and the probability of death is 1−P. The value of P is then adjusted until the subject is indifferent between the two alternatives, at which the value of the health state in question is then equivalent to P (Torrance, 1986: 20–22). Finally, time trade-off methodologies ask subjects to choose between being in health state x for time t and being fully healthy for time y < t (Torrance, 1986: 22–25). Time y is then varied until the subject is indifferent between the alternatives, and the value for state x is simply y/t. Decision utility metrics are often justified by appeal to a preference satisfaction account of well-being, according to which well-being consists in the satisfaction of preferences (Dolan and Kahneman, 2008; Hausman, 2015). Because decision utility metrics are justified in this way however, they inherit the problems with preference satisfaction as a theory of well-being, and these are precisely the objections critics of decision utility metrics raise against them. First, people’s preferences for particular outcomes or states of affairs are often based on false beliefs, and so the satisfaction of these preferences often does not promote their well-being.4 The strength of people’s preferences for or against a particular state of affairs, in these cases, is not a reliable indicator of the extent to which the realization of the state of affairs in question will affect their well-being. In the context of health state valuation, the strength of people’s preferences to avoid a particular health state may be based on false beliefs about the health state in question, thus providing a poor basis for estimating the extent to which such states impact their well-being. Even if respondents are provided with a description of a particular health state, they will not know what it is like to inhabit that state, and may have false beliefs about how the state would impact their lives (Ubel et al., 2003; Dolan and Kahneman, 2008; Dolan, 2011; Hausman, 2015). For example, people who become paraplegics may be pleasantly surprised by the fact that their paraplegia results in less of a drop in the quality of their experience than anticipated (Dolan and Kahneman, 2008). As such, respondents may not be in a position to make fine-grained judgments about exactly how much worse a particular heath state is for their well-being than full health. Secondly, people often have preferences for states of affairs that do not plausibly impact their well-being (Sumner, 1996). For example, an opponent of vaccinations who was vaccinated as a child might have a preference that their friends and neighbors not vaccinate their children. Yet, an unrevised preference satisfaction view would seem to be committed to the position that the satisfaction of preferences such as these impacts people’s well-being. In the health evaluation context, people may evaluate health states on the grounds of how these states would affect their ability to care for their elderly parents, i.e. how these states would affect the well-being of others, rather than how they would affect their own well-being strictly construed. Decision utility metrics therefore may yield health state valuations that do not reliably track the impact of these states on patients’ well-being, rather than the well-being of others. Why Not Experienced Utility Metrics? Given these problems with decision utility metrics, scholars have recently argued for the use of experienced utility metrics to evaluate health states (Dolan and Kahneman, 2008). Such metrics determine the value of health states by appealing to the hedonic quality of the experience of people inhabiting those states (Dolan and Kahneman, 2008). Experienced utility metrics might estimate the quality of such people’s experience by having them rate the quality of their feelings at random times of day (experience sampling method) or complete diary entries detailing the quality of the previous day’s experiences (daily reconstruction method) (Dolan and Kahneman, 2008). Experienced utility metrics are grounded in sensory hedonism (Dolan and Kahneman, 2008), the view that the good life is the pleasant life. On this view, pleasures are understood to be certain feelings or sensations, i.e. experiences, having positive affect (Feldman, 2004). The value of particular health states, according to sensory hedonism, is thus just a function of the quality of the experience of people inhabiting those states. Experienced utility metrics, their proponents argue, avoid the chief problems with decision utility metrics, since they measure health states by appeal to the direct experiences of people inhabiting those states. The problem with experienced utility metrics however, is that sensory hedonism faces a serious problem as an account of well-being. Although quality of experience surely matters for well-being, it is not the only thing that matters. More specifically, it is reasonable to think that the value of many activities and projects, i.e. the extent to which they contribute to people’s well-being, is not solely determined by the amount of pain or pleasure that they promise. For example, many people hold that projects and activities such as raising children, competitive running, rock climbing or backcountry camping contribute greatly to their well-being, even though they often promise little sensory pleasure and a good deal of pain, e.g. stress, anxiety and physical discomfort.5 This problem with sensory hedonism is highly relevant for health state evaluation. Because experienced utility metrics only consider the quality of experience associated with particular health states, they may not fully capture their disvalue. People may reasonably think that particular health states significantly impact their well-being insofar as they prevent them from engaging in their favorite activities, even though these states do not impact the quality of their experience to nearly the same degree. For example, in one study, colostomy patients reported experiencing similar moods as people without colostomies but were willing to give up 15 per cent of their projected remaining years to be free of this condition (Smith et al., 2006). In another study, patients receiving hemodialysis for end-stage renal disease exhibited no statistically significant difference in mood compared to healthy controls, despite requiring 3-hour treatments three times per week and having to follow a strict diet (Riis et al., 2005). In these and other cases, experienced utility metrics may underestimate the disvalue of these health states. Although people occupying these states exhibit similar moods as healthy controls, these states restrict the number and type of projects that sufferers can set and pursue (Hausman, 2015). Since well-being is not solely a function of the quality of one’s experience, the value of particular health states is not solely a function of the quality of experience of people inhabiting them. Paul Dolan (2011: 28), a chief proponent of experienced utility metrics, responds to this objection in the following way: I do not doubt that preferences should be accounted for when allocating resources and I also do not doubt that opportunities, capabilities, and ‘what people can do’ all matter…But all of these things only matter because they show up in better experiences—maybe not today, maybe not tomorrow, but at some point for someone, somewhere. As a ‘happiness economist’, I make no great claims for the significance of anything—being able to walk or having children—beyond its effect on happiness. The problem with Dolan’s response is that it begs the question. It assumes that the value of activities and projects is simply a function of the quality of experience of those engaging in them. The objection to sensory hedonism, by contrast, is precisely that there are projects and activities whose value is not solely dependent on the quality of experience that they offer.6 As we suggest below, we think this problem with experienced utility metrics, along with other considerations, is sufficient to show that policy-makers should not evaluate health states solely by appealing to experience utility metrics. But this does not mean that these metrics have no role in the evaluation of health states. Decision Utility or Experienced Utility Metrics? Both Both decision utility metrics and experienced utility metrics are therefore grounded in accounts of well-being that face significant problems, problems that these metrics inherit. Which metric should policy-makers use to evaluate health states? In our view, Loewenstein and Ubel (2008) offer a promising approach to this question. Discussing similar problems that arise with decision utility and experienced utility metrics in the broader context of public policy analysis, Loewenstein and Ubel (2008: 1807) suggest a hybrid approach involving ‘decision utility measures among people who are thoroughly and convincingly informed about the relevant research on experience utility’. Call this hybrid approach the Loewenstein–Ubel (LU) approach. In what follows, we explore what the LU approach might look like in the context of health state evaluation, and show that the resulting account is superior to other possible solutions to the above-mentioned problems with decision utility and experienced utility metrics. Loewenstein and Ubel (2008: 1807) develop their approach in the context of public policy evaluation. They suggest that government decisions regarding taxation, spending and regulation should be informed by consideration of residents’ well-being, and that to determine the effects of particular policy proposals on residents’ well-being, the LU approach should be used (Loewenstein and Ubel, 2008: 1807). In the context of public policy analysis, the LU approach involves the use of deliberative democracy methods, in which a random sample of the population would be informed by experts about the policy issue under discussion—including the likely effects of different proposals on residents’ experienced utility—and invited to deliberate about the policy proposals on offer (Loewenstein and Ubel, 2008: 1807). What might the LU approach look like in the context of health state evaluation? As we explain above, in this context, the relevant decision utility metrics are rating scale, time trade-off and standard gamble methodologies. To implement the LU approach in the context of health state evaluation would thus involve providing respondents with information regarding the objective features of health states, but also the results of experienced utility metrics regarding the health states under evaluation. On this proposal therefore, an experienced utility metric is housed within a decision utility metric. Respondents would be provided with information regarding the objective facts of particular health states, as well as the results of experienced utility metrics. They would then be asked to evaluate health states, considering how these states would affect the satisfaction of their preferences, using rating scale, time trade-off or standard gamble methodologies. The LU approach, we suggest, offers a defensible way to evaluate health states, given the problems we identify above with experienced utility and decision utility metrics. First, the LU approach goes some distance to address the problems with decision utility metrics. As we note above, preference satisfaction accounts of well-being face two problems that decision utility metrics inherit: (i) preferences based on mistaken beliefs and (ii) unrestricted preferences. To solve these problems, scholars have revised the preference satisfaction account of well-being, arguing that well-being consists not in the satisfaction of actual preferences but rather in the satisfaction of restricted and informed preferences. On this view, one’s well-being is determined by the satisfaction of the restricted preferences one would have if one occupied a privileged epistemic standpoint, i.e. had unconstrained access to all of the relevant information concerning the goods and lives one must choose among (Griffin, 1986; Railton, 1986; Rawls, 1971; Sobel, 1994). This position solves problem (i) since well-being consists in the satisfaction of fully informed preferences, and it solves problem (ii) since well-being consists in the satisfaction of restricted preferences, i.e. preferences that concern only one’s own interests and not the interests of others. While such a view largely addresses these problems in principle, it is of course not possible to occupy this privileged epistemic standpoint in actuality. Occupying this standpoint, after all, involves knowing all materially relevant facts and also knowing what it would be like to live out all possible lives. With respect to health state evaluation, since respondents cannot occupy this standpoint, no realistic decision utility metric could yield health state evaluations that reflect the impact of health states on people’s restricted and informed preferences. Still, the restricted and informed preference satisfaction view provides guidance for how policy-makers can improve existing decision utility metrics, and the LU approach goes some distance to implement this guidance. Addressing (ii), the LU approach requires that policy-makers direct respondents to evaluate health states based on the extent to which they impact the satisfaction of their restricted preferences and not their unrestricted preferences (Hausman, 2015: 77–78). With respect to (i), the LU approach requires policy-makers to provide respondents with materially relevant information about the health states they are asked to evaluate in an accessible form. By doing so, policy-makers can ensure that respondents’ preferences regarding health states are not based on false information or mistaken beliefs (Hausman, 2015: 77–78).7 Such information includes objective facts regarding the health states in question, e.g. the degree to which such states affect people’s bodies and limit their activities, and it also includes subjective facts, e.g. how the states in question affect the quality of people’s experience. Thus, according to the LU approach, respondents should be provided with the results of experienced utility metrics. The results of such metrics provide respondents with crucial information concerning central aspects of the health states to be evaluated: the quality of experience of people inhabiting those states. By providing respondents with the results of experienced utility metrics, they may be less likely to make mistakes in the evaluation of many health states. The LU approach also addresses the problem we identify above with experienced utility metrics. Recall that the chief problem with these metrics is that they only consider quality of experience in the evaluation of health states, a significant problem since quality of experience is not the sole determinant of well-being. The LU approach avoids this problem, since it allows respondents to evaluate health states by appeal to considerations other than the effect of these states on the quality of their experience. Moreover, by presenting respondents with the results of experienced utility metrics, this approach also retains the central insight motivating experienced utility metrics, namely, that quality of experience contributes to well-being. For these reasons therefore, we think that the LU approach, as we envision it in the context of health state evaluation, offers a promising way forward, given the problems with experienced utility and decision utility metrics. The LU approach is not the only possible way forward however, and some may find an alternative to be more defensible. First, one might argue in favor of the use of experienced utility metrics to evaluate health states. Since we identify problems with both decision utility and experienced utility metrics, one might argue our endorsement of the LU approach, i.e. a revised decision utility metric is arbitrary. It would be equally defensible, one might argue, to opt for an experienced utility metric. The LU approach does favor decision utility metrics, but we do not think this favoring is arbitrary or undue. First, the problems we identify above with decision utility metrics can be addressed—to some extent—by providing respondents with more information and directing them to consider how health states affect their own interests. The problem with experienced utility metrics, namely, that they take quality of experience to be the sole determinant of well-being, is impossible to solve without scrapping the basic features of experienced utility metrics. The defining feature of these metrics, after all, is that they evaluate health states solely by appeal to the quality of experience of people inhabiting them. In our view therefore, our proposed decision utility metric rests on what we take be a more defensible account of well-being than experienced utility metrics. Secondly, even if one is unconvinced that the satisfaction of restricted and informed preferences is constitutive of well-being, there is a good reason to think that the satisfaction of such preferences is evidence of well-being. As Daniel Hausman argues, even if we do not know what well-being is, it is reasonable to think that the satisfaction of people’s preferences tend to improve their well-being, when three conditions are satisfied: (i) people are self-interested; (ii) people’s preferences are complete and transitive and not influenced by biases; and (iii) people are fully informed (Hausman, 2015). Respondents to decision utility metrics will not of course completely satisfy these conditions, but the revisions to these metrics that we introduce above do ensure that respondents will come closer to satisfy them. As such, even if respondents’ health state valuations on our revised decision utility metric do not fully reflect the impact of particular health states on people’s well-being, these valuations can be understood to offer reasonably reliable evidence regarding this impact. Finally, there is a sense in which decision utility metrics offer a fairer way to evaluate health states compared to experienced utility metrics. Since citizens of contemporary liberal democracies reasonably disagree about well-being and the nature of the good life, it is unfair to evaluate health states by appeal to one particular conception of what makes people’s lives go well. Experienced utility metrics do precisely this, holding that the good life is the pleasant life. One might think that decision utility metrics do the same, since they hold that well-being consists in the satisfaction of restricted and informed preferences. But decision utility metrics can be understood in another way, as offering a fair procedure for evaluating health states under circumstances of reasonable disagreement concerning well-being (MacKay, 2017). Since decision utility metrics determine the value of health states by incorporating and averaging the health-related preferences of a representative sample of people, they constitute a procedure for evaluating health states that gives equal weight to each person’s conception of well-being. After all, such metrics allow individuals to evaluate health states according to their own conception of the good life: hedonists can evaluate health states by reference to the hedonic quality of experience of people inhabiting those states, and perfectionists can determine the value of health states by appeal to the substantive goals they deem to be valuable. One might also argue in favor of an alternative hybrid approach. Rather than have respondents incorporate the results of experienced utility metrics in their evaluation of health states by means of a decision utility metric, one might suggest that for particular health states, policy-makers should simply take the average value from representative experienced utility and decision utility metrics. This approach, after all, would appear to be a fair and neutral way to adjudicate the dispute among the proponents of experienced utility metrics on the one hand, and decision utility metrics on the other. We see two problems with this proposal. First, it does not address the problem of uninformed preferences in the way that the LU approach does. Secondly, while this proposal appeals to the value of fairness, we suggest that the LU approach offers a far fairer and more democratic solution to reasonable disagreement about the nature of well-being. As we note above, the LU approach leaves it up to members of the public to weigh considerations of quality of experience and preference satisfaction in the evaluation of wealth states, not policy-makers. The LU approach therefore gives equal weight to each respondents’ conception of well-being. Finally, one might grant that our proposal is superior to either unrevised decision utility or experienced utility metrics from a normative standpoint, but question whether it is feasible. Providing respondents with more information may sound like a promising idea, but respondents may struggle to understand, appreciate and process the information with which they are provided (Loewenstein and Ubel, 2008; Hausman, 2015).8 This is a legitimate concern, and unfortunately, we cannot fully address it here. Ultimately, to show that the LU approach is feasible, we would need to try it out with respondents and demonstrate that their responses were more informed than they would have been under an unrevised decision utility metric. One way to do this would be to randomly assign respondents to either the LU approach or an unrevised decision utility metric with the aim of determining whether and how the provision of information changed their responses. Investigators could intentionally provide respondents with health states for which the experienced utility scores are counter-intuitive as a way of determining whether respondents’ evaluations reflected an understanding and appreciation of these facts. Despite the challenges of successfully informing respondents, we are hopeful about the prospects of the LU approach. Social scientists are actively devising better and better ways to communicate technical information to members of the public with the aim of promoting informed decision-making, and this knowledge may be applicable to the context of health state evaluation (Fagerlin et al., 2011). In addition, even if the LU approach results in only marginally better informed health state evaluations, this would still be morally significant. The LU approach would clearly be superior to unrevised decision utility metrics, and given the problems with experienced utility metrics, namely, the way in which they exclude all contributors to well-being other than quality of experience, it is arguable it would be on balance superior to experienced utility metrics. With respect to feasibility therefore, the LU approach may only need to clear a very low bar to offer an approach to health state evaluation that is superior to existing alternatives. Conclusions We have outlined an approach to health state evaluation that combines the features of both decision utility and experienced utility metrics. With preference-based decision utility metrics as a framework, experienced utility metrics contribute to respondents’ decision-making processes as materially relevant information regarding the hedonic quality of experience. On this proposal, respondents to decision utility surveys are provided with the results of experienced utility metrics in an effort to make their decisions better informed. Adoption of this hybrid approach would allow policy-makers to allocate scarce resources using encompassing, defensible metrics. Ultimately, we argue such an approach promises to lead to more effective and just policy outcomes. Footnotes 1. For an excellent overview of the many value choices that must be made in the calculation of QALYs and DALYs, see Schroeder (2016). 2. While our article aims to show that a suitably reformed decision utility metric is morally superior to experienced utility metrics for the purposes of health state evaluation, we do not attempt to show that this reformed decision utility metric is morally superior to all possible health states metric. For recently developed, promising alternatives to both experienced utility and decision utility metrics, see Bleichrodt and Quiggin (2013) and Hausman (2015). 3. Thanks to an anonymous reviewer for raising this possibility. 4. For different versions of this objection, see Arneson (1999); Griffin (1986); Lauinger (2011); Sobel (1994); and Sumner (1996). 5. This problem with sensory hedonism is widely acknowledged in the philosophical literature. See Nozick (1975); Hausman (2010); and Dorsey (2011). 6. Roger Crisp offers the strongest defense of sensory hedonism against this line of argument, pressing non-hedonists to identify the features of activities and projects—other than pleasure—that contribute to people’s well-being. See Crisp (2006). In our view, Dale Dorsey argues convincingly that Crisp’s defense is not successful. See Dorsey (2011). 7. The use of informed consent forms in the context of medical practice and research is widespread. The literature on informed consent, particularly concerning what types of information should be presented in consent forms and how it should be presented, provides a good resource for reforming decision utility metrics. 8. Thanks to an anonymous reviewer for raising this worry. References Arneson R. ( 1999). Human Flourishing Versus Desire Satisfaction. Social Philosophy and Policy , 16, 113– 142. Google Scholar CrossRef Search ADS Bleichrodt H., Quiggin J. ( 2013). Capabilities as Menus: A Non-Welfarist Basis for QALY Evaluation. 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Public Health Ethics – Oxford University Press
Published: Apr 1, 2018
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