A welfare economic approach to measure outcomes in stuttering: Comparing willingness to pay and quality adjusted life years

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Abstract

Purpose

The purpose of this study was to compare two welfare outcome measures, willingness to pay (WTP) and quality adjusted life years (QALYs) gained, to measure outcomes in stuttering.

Method

Seventy-eight adult participants (74 nonstuttering and 4 persons with stuttering) completed one face-to-face structured interview regarding how much they would be willing to pay to alleviate severe stuttering in three interventions of varying impact. These data were compared with QALYs gained as calculated from time trade off (TTO) and standard gamble (SG) data.

Results

Mean (median) WTP bids ranged from US$ 16,875 (8000), for an intervention resulting in improvement from severe stuttering to mild stuttering, to US$ 41,844 (10,000) for an intervention resulting in a cure of severe stuttering. These data were consistent with mean changes in QALYs for the same stuttering interventions ranging from 2.19 (using SG) to 18.42 (using TTO).

Conclusions

This study presents the first published WTP and QALY data for stuttering. Results were consistent with previous cost-of-illness data for stuttering. Both WTP and QALY measures were able to quantify the reduction in quality of life that occurs in stuttering, and both can be used to compare the gains that might be achieved by different interventions. It is widely believed that stuttering can cause reduced quality of life for some speakers; the introduction into this field of standardized metrics for measuring quality of life is a necessary step for transparently weighing the costs and consequences of stuttering interventions in economic analyses.

Educational objectives: The reader will be able to (a) describe the underlying theoretical foundations for willingness to pay and quality adjusted life years, (b) describe the application of willingness to pay and quality adjusted life years for use in economic analyses, (c) compare and contrast the value of willingness to pay and quality adjusted life years in measuring the impact of stuttering treatment on quality of life, (d) interpret quality adjusted life years, and (e) interpret willingness to pay data.

Highlights

► Used welfare economic approaches to measure value of stuttering treatment. ► First use of willingness to pay and quality adjusted life years for stuttering. ► Stuttering impacts quality of life as measured using welfare economic metrics. ► Stuttering treatment shown to be valuable by standard welfare economic approaches.

Introduction

“Like everything else in life we are concerned with both what we pay for and what we get. It would make no sense to base decisions only on costs or only on consequences.” (Torrance, 1997, p. S9)

The decisions to which Torrance (1997) refers are health care decisions, and his point is relevant whether the decision to be made is by one person about his or her own health or by a group of people about other people's health. Most readers are familiar with the first of these two types of decisions: healthcare decisions made by individuals for themselves or on behalf of a family member. Arguably, however, it is decisions made by groups for other groups that are more influential, including decisions about healthcare priorities and resource allocation, public policy, regulations governing the legality of certain procedures, or determinations by private or public insurance programs as to which interventions they will cover for which conditions. Torrance (1997) and others recommend that these and related decisions be made on the basis of methods that combine outcome information (e.g., clinical information) with cost information for groups of people, such as cost-minimization, cost-effectiveness, cost-utility, and cost-benefit analyses (Drummond, O’Brien, Stoddart, & Torrance, 1997). All of these methods represent common, formalized approaches to weigh costs and outcomes. Only two of these methods, cost-benefit and cost-utility analyses are founded in welfare economic theory (Drummond et al., 1997, Gold et al., 1996). Welfare economics has been defined as the study of the “well-being, or welfare, of both individuals and society” (Messonier & Meltzer, 2003, p. 127). Thus, it is concerned with the ability to measure desirability, worth, preference, or what is termed the individual's utility of an intervention or program. The general purpose of this paper is to discuss and present an application of welfare economic principles, as applied to stuttering.

Cost-benefit analysis compares the cost of a good or service with its benefits, with both the costs and the benefits measured in monetary units. The comparison is most often achieved through the contingent valuation method, commonly referred to as willingness to pay (WTP) or willingness to accept (WTA) methods (Drummond et al., 1997). As the name implies, respondents are asked to value goods, as exemplified in this case by healthcare interventions, in a contingent or hypothetical market. They do so by providing what is referred to as a WTP or WTA bid, or a statement of the amount of money they would pay (or accept) to receive (or forgo) that intervention (Drummond et al., 1997). Put simply, the value that people place on any good or service, including health-related goods or services, can be expressed in monetary terms: how much they would be willing to pay to have it (i.e., what they would be willing to bid for it) or how much they would have to be paid to be willing to accept not getting it (i.e., the sum of money they would accept in lieu of the good or service being considered). Theoretically, preferences ascertained using WTA or WTP should be equivalent (Arrow et al., 1993, Green et al., 1998, Hanemann, 1991, Mansfield, 1999, Mitchell and Carson, 1989, Morrison, 1998). In health outcome studies, however, WTP predominates, in part because it provides more conservative estimates (i.e., lower WTP bids, possibly due to WTP being limited by income or ability to pay), and because respondents find this question easier or more realistic to answer than WTA (Diener, O’Brien, & Gafni, 1998).

Expressing the costs and the benefits of an intervention in the same (monetary) units has the advantage, as compared with other means of assessing outcomes, that it can provide the decision maker with a single metric, net benefit, to assess the value of the program from an efficiency perspective. Comparisons based on a single common metric are especially useful when the programs being compared have a wide range of possible outcomes and cannot be meaningfully compared without a common unit of output. Cost-benefit analysis also has the advantage that it does not require a comparator program; that is, the value or utility of the target intervention can be assessed without the complexities of introducing comparisons with an alternative intervention (Drummond et al., 1997).

It should be noted, however, that some authors and readers are uncomfortable with converting outcome data into monetary units or placing a dollar value on human life or suffering (Gafni, 1998). Other issues involve the implications or interpretation of willingness or ability to pay, as measured using WTP methods. In cost-benefit studies founded on welfare economic theory, the objective of the study is to determine whether the benefits of a program exceed its costs (Drummond et al., 1997). WTP bids founded in welfare economic theory are not meant to be paid out as dollar values, as they might be in market research, which uses assessments of consumers’ willingness to pay in different ways and for a distinctly different purpose (Gafni, 1998). It is also important to note that cost-benefit analysis is not a cost of illness (COI) assessment; COI studies are a type of economic evaluation frequently reported in specialty clinical publications (Byford, Torgerson, & Rafferty, 2000). The goal of COI studies is to measure the burden of a condition on society by assessing all costs of the condition, including both direct (e.g., medical costs) and indirect (e.g., productivity losses or lost income due to a condition) costs and intangibles (Byford et al., 2000, Messonier and Meltzer, 2003). Cost-benefit analyses, however, address broader questions: not only the cost, but also the benefits of various options, how different outcomes can be valued, and how options can be compared using monetary terms as the unit of measure (Byford et al., 2000, Drummond et al., 1997, Messonier and Meltzer, 2003).

Cost-benefit analysis has been used extensively in policy development by the U.S. federal government for decades; one of its first uses is typically cited as the U.S. Flood Control Act of 1936. It has also been widely used by the U.S. Environmental Protection Agency in studies of environmental effects and natural resource use, such as the impact of air or water pollution on population health, and its use is mandated by the Safe Drinking Water Act (Krupnick, 2003, Messonier and Meltzer, 2003). It is less common in analyses of healthcare interventions than other types of economic analyses, in part because healthcare practitioners are simply more familiar with viewing their work in clinical, not financial, terms. Nevertheless, many authors provide strong arguments for the use of cost-benefit analysis in healthcare, including Bala, Mauskopf, and Wood (1999), Gafni (1998), and O’Brien and Gafni (1996). Examples of completed and influential cost-benefit analyses in healthcare include, among others, studies of spinal surgery (Haefeli et al., 2008), caries prevention (Oscarson, Lindholm, & Kallestal, 2007), and hypertension (Johannesson, Aberg, Agreus, Borgquist, & Jonsson, 1991).

Cost-utility analysis is another form of economic evaluation. Like cost-benefit analysis, it measures costs in monetary terms, but the two approaches differ in how the outcomes are measured (Drummond et al., 1997, Gold et al., 1996). In general, cost-utility analyses are designed to compare the costs of a decision with what economic terminology refers to as the utility of the result, with utility defined as the desirability, preference, or worth to a person or group. As applied to healthcare, cost-utility analyses compare the costs of a management decision (e.g., to live with a certain condition, or to undergo a specific sort of assessment, treatment, or management procedure) with the overall desirability of that decision's outcome (Dolan, 1998, Gold et al., 1996).

Most commonly, outcomes for cost-utility analyses are measured as gains in quality-adjusted life years (QALYs), an index designed to incorporate both (health-related) quality of life and also quantity of life. Quality of life can be regarded as a multidimensional construct encompassing physical, emotional, social, and role (work or school) functioning, in addition to disease- and treatment-specific symptoms (Coons and Kaplan, 1993, Gold et al., 1996). Quantity of life refers in this context to the time that a person spends in a particular condition, referred to as a health state. Examples might range from a few hours with severe nausea and vomiting to the rest of a person's expected lifespan with mild arthritis in one knee, but quantity of life is typically measured in years (Gold et al., 1996). There is no single theoretical foundation attributed for QALYs, but classic welfare economics has been adopted as the theoretical framework by QALY advocates (Gold et al., 1996).

More specifically, QALYs are calculated by multiplying two terms. The first is utility (i.e., desirability, preference, worth, or Q weights) of a health state, usually measured on a scale ranging from 0.0 (the value assigned to death) to 1.0 (the value assigned to perfect or full health). These weights or values are estimated using one or more standard methods, most commonly either time trade off (TTO) or standard gamble (SG) procedures (Drummond et al., 1997, Gold et al., 1996). Both were explained in detail, in the general case and as applied to stuttering, by Bramlett, Bothe, and Franic (2006), to which interested readers are referred for additional detail. Briefly, TTO methods ask a series of questions about how much time in a less desirable health state one would be willing to give up to be allowed to live for a shorter time in a more desirable health state. For example, a respondent might choose 39 years of life with no back pain rather than 40 years of life with moderate back pain, and 40 years of life even with moderate back pain if the alternative were only 20 years of life without back pain. In such a series of questions, a respondent might be indifferent between living for 40 years with moderate back pain versus 34 years with no back pain. The preference value of this health state is then calculated as the ratio of the more preferred health state over the less preferred health state at the point of indecision (i.e., 34 divided by 40) and expressed as .85 on the 0–1 scale (Bramlett et al., 2006). This value, known as the TTO weight, can be used in cost-utility analysis (Drummond et al., 1997, Gold et al., 1996).

The SG method, in contrast, asks how much risk of a very negative outcome (usually, death) respondents would be willing to accept for an associated possibility of a very positive outcome (usually, perfect or full health), as compared with accepting the option of staying in an intermediate state with certainty, or with no risk involved. That is, the question is whether the respondent would accept, for example, a 4% chance of death and its associated 96% chance of cure (i.e., perfect heath) for a chronic condition such as mild arthritis, or whether he or she would prefer to stay in the condition of mild arthritis, forgoing the chance of the cure but taking no risk of death. The forms of the gamble (the relative possibilities of the two extreme states) that respondents are and are not willing to accept can be transformed into utilities, also known as preferences or SG weights. In the above example, if the respondent was indifferent between the 96% and 4% gamble and the certainty health state, but would accept higher risks of the cure and would reject higher risks of death, then the SG weight for mild arthritis would be .96.

Both SG and TTO procedures are considered acceptable for estimating QALY weights, although SG is considered the gold standard for utility estimation. Some authorities consider the TTO, a newer method, easier to administer (Drummond et al., 1997, Gold et al., 1996). TTO and SG procedures are also known to result in different preferences and must be compared carefully, with SG weights typically greater than TTO weights because the former incorporates risk while TTO does not.

The second term in a QALY calculation, as mentioned above, is the amount of time that will be spent in that health state. Living 2 years in good health, for example, if good health were rated as having a utility of 0.9, might then be expressed as having a value of 2.0 × 0.9, or 1.8 QALYs. One of the important features of QALYs is that an equivalent value could be assigned to living longer but with chronic pain (e.g., 3 years at a utility of 0.6) or to living for a slightly shorter time but in truly perfect health (1.8 years at a utility of 1.0). Another advantage and use of QALYs is that the relative gain in QALYs to be achieved by selecting one management or treatment approach over another, for a condition that affects quality but not quantity of life, can also easily be calculated. In this case, the difference between the QALY weights for the two approaches is multiplied by the time horizon, or the length of time that the intervention will impact. Assume, for example, that an individual rates the quality of life resulting from Treatment A as 0.90, and rates the quality of life resulting from Treatment B as 0.70. Assume further that the effects last for the rest of the respondent's life (a standard assumption for chronic conditions), the respondent is 20 years old and is expected to live until 77 years (an accepted actuarial standard), and that the treatment has no impact on mortality. The gain in QALYs can then be calculated as follows (Franic, Pathak, & Gafni, 2005):Gain in QALYs=[0.900.70][7720years]=[0.20][57]=11.4QALYs

This can be interpreted as meaning that for this particular person the selection of Treatment A will result in a gain of 11.4 quality-adjusted life years (QALYs), or the equivalent of 11.4 full-health years, as compared with Treatment B. In other words, if the respondent selected Treatment B instead of Treatment A, he/she would continue the remainder of his/her life (57 years) with poorer quality of life (at a utility of only .7, rather than .9). The choice of Treatment A results not only in an improvement in quality of life of .2, but, more importantly, in an improvement in quality of life of 0.2 that will extend over the course of 57 years. Another way of looking at achieving a quality of life of .9 for 57 years, therefore, is to think about the equivalent number of additional years in perfect health or full health which have been gained, which in this case is 11.4 QALYs (the difference, .2, multiplied by the number of years during which this difference will be enjoyed, 57).

Quality-adjusted life years also enables comparisons across very different interventions for the same condition and even across conditions, making QALYs a useful metric for resource allocation decisions and for any other comparison across disorders, conditions, or treatments (Gold et al., 1996). For this and other reasons, the QALY has become popular as a single index incorporating mortality and morbidity in health care (Gold et al., 1996). Drummond et al. (1997) recommend conducting a cost-utility analysis under certain criteria, including when (health related) quality of life is the single outcome or the most important outcome. Cost-utility analysis is appropriate, for example, when improvement in patients’ emotional well-being and physical and social functioning are the main foci, such as in evaluating the management of a chronic disease that affects quality but not quantity of life (e.g., arthritis or psoriasis, or, as we suggest below, stuttering). As was also the case for cost-benefit analysis, cost-utility analysis is also useful when the programs being compared have a wide range of possible outcomes and there is a need for a common unit of output. In such cases, QALYs gained from an intervention can serve as a summary measure encompassing quantity and quality of life that is common to the two different interventions (Gold et al., 1996).

QALYs require certain strict assumptions including constant proportionality and risk neutrality; readers interested in a more detailed discussion are referred to Gold et al. (1996). Philosophical and other objections have also been raised by some theoreticians and from some points of view, both by authors who ultimately support the use of QALYs and by those who do not (compare, for example, Ashmore et al., 1989, Cohen, 1997, Grosse et al., 2010). Despite these issues, QALYs remain one of the most widely accepted and widely used means of expressing the overall perceived value of the interactive relationships between quality and quantity of life, or of living different amounts of time in qualitatively different health states (Gold et al., 1996). More generally, cost-utility analysis has been used for decades to compare interventions involving pharmaceuticals (Gold et al., 1996, Tarn and Smith, 2004), medical devices (ISPOR HTA, 2011), and genetics (Ladabaum et al., 2011). QALYs were recommended by the consensus panel formed by the U.S. Public Health Service (USPHS) for use in cost-utility analysis (Bala et al., 1998, Franic et al., 2005, Gold et al., 1996). More recently, a U.S. Health Reform Bill passed in 2010 included provisions to develop the Patient Centered Outcomes Research Institute (PCORI). PCORI was devised to assist decision makers in outcomes and pharmacoeconomic research, including cost-utility analyses (Ramsey, 2011).

In short, both cost-benefit and cost-utility analyses are widely used, well established, and well accepted methods for measuring the combination that Torrance (1997) referred to as costs and consequences. Both approaches measure costs in monetary terms, but they differ in how outcomes are measured: cost-benefit analysis measures outcomes through questions that assess the value of different clinical outcomes in monetary terms, while cost-utility analysis most commonly measures outcomes in QALYs gained (Drummond et al., 1997, Gafni, 1997, Gold et al., 1996).

Balancing the costs and the consequences of management options is important to individuals who stutter and their families, and it is also important to group-level decision-making in stuttering, just as it is for any other condition. As Craig, Blumgart, and Tran (2009) noted, however, the procedures used to assess variables related to quality of life in stuttering have often been informal or focused on discipline-specific traditions, rather than reflecting explicit use of formal procedures developed in other disciplines. Multiple instruments have been designed and used to measure speakers’ reactions to their stuttering, for example, or to measure the influence of stuttering on aspects of living that are assumed to be affected [e.g., S-24 (Andrews & Cutler, 1974), Iowa Scale of Attitude Toward Stuttering (Ammons & Johnson, 1944), Perceptions of Stuttering Inventory (Woolf, 1967), Communication Attitude Test and its revisions (DeNil and Brutten, 1991, Vanryckeghem and Brutten, 1996), or Overall Assessment of the Speaker's Experience of Stuttering, OASES, and its Dutch translation (Koedoot et al., 2011, Yaruss, 2010, Yaruss and Quesal, 2006)]. As Franic and Bothe (2008) described in detail, however, most of these instruments have several psychometric and other weaknesses when they are assessed with respect to basic measurement standards, and none were designed for use in cost utility or cost benefit analyses.

More recently, specific terms such as quality of life and health-related quality of life have begun to be used in stuttering research, and some of this work has explicitly built on the strengths of existing models and instruments from other fields. Bramlett et al. (2006), for example, assessed the applicability to stuttering of three standardized holistic preference techniques commonly used in medicine and allied health fields: the rating scale (RS, a vertical thermometer scale) method and the two methods described briefly in Section 1.2, standard gamble (SG) and time trade off (TTO). Results of their study showed that these three preference methods were applicable to fluency disorders as they were in other chronic conditions, setting the groundwork for further explorations. Craig et al. (2009), in a more clinically oriented study with 200 persons who stuttered, used the Medical Outcomes Study Short Form-36 (SF-36; Ware & Gandek, 1998) to measure quality of life in adults who stutter. Craig et al.’s data showed that persons who stutter reported reduced quality of life in some aspects, as compared with persons who did not stutter. Koedoot et al. (2011), similarly, used well established instruments (Health Utilities Index Mark 3, HUI3, Furlong, Feeny, Torrance, & Barr, 2001; the EuroQoL's EQ-5D Index (EQ-5D Index) and EQ-5D Visual Analog Scale (EQ-5D VAS)) (Rabin & de Charro, 2001) and identified differences between adults who had sought treatment for their stuttering and adults who stuttered but were not seeking treatment. Perhaps most importantly, with respect to assessing the consequences of treatment, Bothe, Franic, and Ingham (2010) presented data from repeated measurement occasions from an ongoing clinical treatment study, also using the HUI3 and the EQ-5D VAS. Bothe et al.’s data showed reduced HUI3 scores and EQ-5D VAS preferences in pretreatment assessments, findings that were consistent with Craig et al.’s and Koedoot et al.’s results. Bothe et al.’s results also showed that both HUI3 scores and EQ-5D VAS scores improved after speech-related variables improved, for adults completing two different speech-focused stuttering treatment programs.

Overall, recent research has begun to establish that standard measures of quality of life from other disciplines may be useful in measuring the broader consequences of stuttering and of stuttering treatments. None of this previous work in stuttering used preferences to estimate QALYs, measured benefits of treatment with WTP methods, or directly compared QALYs and WTP methods using the societal perspective in stuttering, regarded as valuable for group level decision making in cost-utility and cost-benefit analyses (Gold et al., 1996).

The societal perspective views all members of society, not just those with the condition in question, as potential and relevant respondents in a study of utility values, willingness to pay, and related issues. This inclusion of general public values is also known as the Reference Case (Gold et al., 1996). This approach is standard and necessary in utility studies where preferences are elicited to determine allocation of resources, because the public values are less likely to be biased by any perceived personal gain. That is, the inclusion of healthy individuals or the general community is beneficial and necessary to the research, because no one knows the future or what possible future conditions await them. In fact, the input of persons without the condition in question is actually preferable to that of persons with the condition, because the former is regarded as impartial and under what is referred to as a “veil of ignorance” (Gold et al., 1996, p. 35). Even for a disorder such as stuttering for which the likelihood of an adult acquiring the disorder in the future is very low, all persons share the uncertainty associated with possible future diagnoses for children, grandchildren, friends, or other loved ones. Persons with any disorder, in contrast, may respond to research questions in ways that reflect a conscious or unconscious attempt to emphasize that their own condition should be prioritized in group decision making.

The societal approach is also consistent with many pharmacoeconomic guidelines that recommend the use of the societal perspective or of the related “health care sector perspective” (which views all enrollees of a managed care plan or members of a national health care plan as the relevant respondents; Gold et al., 1996, Hjelmegren et al., 2001, Minshall et al., 1999). WTP studies using the societal perspective, or general community preferences, have also been widely reported for other conditions in healthcare. Examples include assessing WTP for cancer treatments with the use of videos, using a random sample of an insurance company's enrollees (O’Brien et al., 1998); Gaucher's disease in healthy volunteers with the use of videos (Flowers, Garber, Bergen, & Lenert, 1997); breast cancer in healthy respondents using visual aids (Franic et al., 2005); and the common cold, retinal detachment, and myocardial infarction in a sample of the general population (Yasunaga, Ide, Imamura, & Ohe, 2006), just to name a few. In fact, the use of community preferences is also “less likely to discriminate against persons with illness or disability than the use of their own preference” (Gold et al., 1996, p. 101), and in some cases can show greater treatment benefit than that determined from patients’ responses (Gold et al., 1996). At first, this may seem counterintuitive, but it can be explained by patients’ true acceptance of their condition (responses consistent with a state of acceptance, or viewing their own objectively severe problems as “not so bad after all”) and/or by their cognitive dissonance about its influence (i.e., it may be difficult for respondents to accept or acknowledge the negative impact their condition has on their quality of life; Ubel, Richardson, & Menzel, 2000).

The issue of whose values to use, patient versus societal, may also be addressed empirically: Do patients report predictably higher (better) or lower (worse) quality of life scores than nonpatients? The short answer is no. A substantial body of research across multiple disorders shows that there are little data to support differences in preferences based on age, gender, income, ethnic background, religious beliefs, patient (or disorder) status, or health care training or expertise (i.e., professionals vs. the general public; see Kaplan and Bush, 1982, Sackett and Torrance, 1978, Torrance, 1986). There have been no reports of large differences. Given this empirical evidence, and given the concerns that may be introduced by limiting respondents to those with the disorder, utility and WTP research in healthcare is conducted using mixed samples of adult volunteers, often convenience samples, and most of whom do not have the disorders in question. Again, these samples are viewed within the societal perspective as the preferred and most appropriate samples for addressing questions of utility or willingness to pay.

The potential importance to stuttering treatment and research decisions of cost-benefit and cost-utility analyses is clear, when one considers the range of financial and other costs, and the range of speech-related, nonspeech, and other outcomes, that are possible from the many varied stuttering treatment options. It could be of great use to be able to compare treatments using existing, accepted, validated methods that are applicable across varied conditions and that use a common metric to compare various combinations of outcomes. The purpose of the present study, therefore, was to build on previous interdisciplinary work by continuing the process of adopting existing, common, and well-supported quality of life, preference, or utility measurement metrics from the healthcare outcomes and healthcare economics fields into stuttering. Specifically, this study was intended to accomplish three specific goals: to gather and assess willingness to pay (WTP) data for stuttering treatment; to gather and assess gains in quality adjusted life years (QALYs) for stuttering treatment; and to begin to compare the information provided by these and other economic assessments of stuttering and stuttering treatment.

Section snippets

Participants

In accordance with the societal perspective, initial respondents for this study were 80 adults who volunteered to participate. Of those 80, two were later determined by formal numeric methods to have provided outlier responses and were eliminated from further analyses (the two respondents claimed, for example, that they would have been willing to seek loans of 75–95% of very large sums of money (WTP bids) to cover stuttering treatments, using numbers that were well outside the range defined by

Descriptive data for WTP

Table 1 presents summary data for the three WTP interventions (in U.S. 2004 dollars). As anticipated, mean, median, minimum, and maximum bids were the greatest for the largest intervention (WTP S3, Severe to cure), supporting the test for the scope effect and suggesting that the WTP interventions were interpreted as intended by these respondents (p < .001, η2 = .40, power = 1.0; Arrow et al., 1993). Maximum WTP bids ranged from $250,000 to $400,000. The validity of these high values was supported by

WTP data and comparison with previous research

The results of this study add to the growing body of information that the impact of stuttering, and of stuttering treatments, can be measured using standard metrics from health outcomes analysis and healthcare economics. Respondents equated the improvements described in the WTP scenarios with amounts of money equal to two to four times their annual incomes. As mentioned in Section 1, WTP studies founded in welfare economics are not market research, and the best supported interpretations of WTP

Duska M. Franic, Pharm.D., Ph.D., is an associate professor in the Department of Clinical and Administrative Pharmacy at the University of Georgia. Author of 31 scholarly articles/book chapters. She focuses her research and teaching in quality of life measurement for multiple acute and chronic health conditions.

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    Duska M. Franic, Pharm.D., Ph.D., is an associate professor in the Department of Clinical and Administrative Pharmacy at the University of Georgia. Author of 31 scholarly articles/book chapters. She focuses her research and teaching in quality of life measurement for multiple acute and chronic health conditions.

    Anne K. Bothe, Ph.D., CCC-SLP, is a professor in Communication Sciences and Disorders at the University of Georgia. Author of approximately 70 scholarly works. She focuses her efforts on research and teaching related to stuttering and evidence-based practice.

    Robin E. Bramlett, M.Ed., CCC-SLP, is a speech-language pathologist and a doctoral student at the University of Georgia. She has worked in early intervention, coordinated family care, and stuttering research, among other areas.

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