Audiologic Rehabilitation for Adults published by the American Speech-Language-Hearing Association (ASHA, 2006) and, for simplicity, we are focusing on the adult population. ASHA (2006) defines AR as a “facilitative process that provides intervention to address the impairments, activity limitations, participation restrictions, and possible environmental and personal factors that may affect communication, functional health, and well-being of persons with hearing impairment or by others who participate with them in those activities.” The clinical processes involved in AR include, but are not limited to, counseling; selection and fitting of hearing instruments and other hearing-assistive technologies; individual and group training; and other forms of follow-up care. One of the expected outcomes of AR is the enhancement of well-being and QoL for those individuals with hearing impairment, as well as for their family members and/or caregivers. The challenge to the clinician is measuring the extent to which an improvement in HRQoL has been achieved. In order to make such a measurement, we first need to determine what we mean by HRQoL.
Defining Health-Related Quality of Life
QoL, in its simplest form, can be thought of as “how good or bad you feel your life to be” (Bradley et al., 1999). Of course, many factors can influence QoL. According to the National Institutes of Health (NIH), factors influencing QoL involve the cultural, psychological, interpersonal, spiritual, financial, political, temporal, and philosophical domains, in addition to one’s health status (NIH, 1993). Although health status may or may not affect other aspects of QoL, nonhealth domains also can influence the impact of a disease or disorder and a person’s response to treatment. As early as 1948, the World Health Organization (WHO) defined health as a “state of complete physical, mental and social well-being and not merely the absence of infirmity and disease” (WHO, 1948, p. 100). More recently, the WHO Quality of Life Group (WHOQOL) offered a definition of QoL as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (WHOQOL Group, 1995, p. 1405). It should also be noted that the Healthy People 2020 initiative (U.S. Department of Health and Human Services, 2010) recognizes the importance of measuring and improving HRQoL among United States citizens and extends the definition of HRQoL to include measures of well-being; specifically, those aspects of a person’s life associated with positive emotions and life satisfaction.
Although numerous definitions of HRQoL can be found, there is general agreement that HRQoL is a multidimensional concept that focuses on the impact of diseases and disorders, as well as their treatments, on the well-being of individuals (Fairclough, 2002). It’s important to note that not only the immediate impact of disease or disorder must be considered but also the length of time an individual must deal with the consequences of the health condition. A definition of HRQoL that considers quantity as well as quality of life is suggested by Patrick (1993, p. 82): “Health-related quality of life is the value assigned to duration of life as modified by the impairments, functional states, perceptions, and social opportunities influenced by disease, injury, treatment or policy.”
Measuring HRQoL
HRQoL assessment involves examining the extent to which one’s usual or expected physical, emotional, and social well-being is affected by a health condition and its treatments. HRQoL cannot be measured objectively, but rather reflects the subjective assessment of an individual’s self-perception of their health status and well-being. As illustrated in Figure 7–1, there are two general types of HRQoL measures: health status, and health decision or preference assessment measures. Health status assessment is based on well-established psychometric principles and involves the posing of a question or questions about a patient’s well-being, with scores being derived from their responses. Patient preference assessment is derived from econometrics and is influenced by the concept of utility, or the making of decisions under known or assumed probabilities of outcomes (i.e., under risk or uncertainty). Scores can reflect trade-offs between quality and quantity of life.
As shown in Figure 7–1, both health status and preference-based HRQoL assessments can be classified as being either disease-specific or generic (NIH, 1993). Disease-specific measurement focuses on the effects of a single disease or disorder (e.g., heart disease, depression, hearing loss) and its treatments (e.g., surgery, medication, hearing aids). Disease-specific instruments serve an important function for the clinician because they are responsive to intervention (i.e., pre- versus post-treatment). Although audiologists can use a variety of disease-specific instruments to measure the impact of hearing loss and validate the benefits of AR, the results do not allow for comparison with other diseases and disorders and their treatments. For example, with a disease-specific instrument, we cannot compare the impact of depression to the impact of hearing loss on HRQoL. Likewise, the effects of hearing aids on the HRQoL of an adult with hearing impairment cannot be compared to the effects of surgery for a patient with heart disease or medication for a patient suffering from depression. The ability to compare the effects of different diseases and disorders and their treatments is taking on increasing importance as society struggles with the question of which health conditions and interventions are worthy of third-party reimbursement and public funding for research.
Figure 7–1. Schematic of types of health-related quality of life measurements.
To determine if depression is worse than hearing loss or whether we should be spending more money to find a cure for hearing loss versus a cure for atherosclerosis, generic HRQoL measures are required. Generic measures do not focus on any particular disorder or treatment, but rather on the self-perceived overall health of the individual. Although generic measures have demonstrated negative impacts of hearing loss on HRQoL (e.g., Bess, Lichtenstein, & Logan, 1991; Chia et al., 2007; Mulrow et al., 1990), most generic measures are not sensitive to the effects of treatment for hearing loss (Abrams, Chisolm, McArdle, & Wilson, 2005; Arnoldner et al., 2014; Bess, 2000). The reason is simple: Whereas most generic profiles include items related to major health domains—such as physical, social, and mental health—generic measurement instruments, with few exceptions, do not include questions that specifically address problems with communication or hearing.
As noted above, HRQoL also can be assessed by measuring the preference an individual or community expresses for a particular health state. These measures are referred to as utilities and can be considered a cardinal measure of the strength of one’s preference, expressed as a value ranging from 0.0 (least desirable health state) to 1.0 (most desirable health state). The techniques associated with utilities emerged from von Neumann and Morgenstern’s (1944) decision-making theory and are increasingly being used by health care researchers to compare HRQoL across and within conditions and interventions.
What follows is a brief description of measuring instruments that have been used within the adult population to assess the impact of hearing loss and audiologic interventions for hearing loss on self-perceived HRQoL. The measures are categorized according to the model presented in Figure 7–1.
Health Status Instruments
Disease-Specific Profiles
Many self-reported measures are available to the field of audiology. To determine if a given measure truly assesses the HRQoL construct, it is important to consider its content rather than simply the name of the instrument. Thus, although the term handicap may be used rather than HRQoL, it is clear from a review of the items in the Hearing Handicap Inventory for the Elderly (HHIE) (Ventry & Weinstein, 1982) that the instrument was designed to assess the impact of a hearing loss on an individual’s emotional and social well-being, which are key components in the definition of HRQoL. Indeed, the HHIE is perhaps the most widely known disease-specific HRQoL instrument used in the assessment of the effects of AR. The 25-item HHIE assesses the self-perceived psychosocial effects of a hearing loss on the older individual. Thirteen of the HHIE items measure the emotional impact of the hearing loss and 12 items measure the effect of hearing loss on social/situational functioning. The question, “Does a hearing problem cause you to feel embarrassed when meeting new people?” is an example of an emotional-domain item; on the other hand, the question, “Does a hearing problem cause you difficulty when listening to radio or TV?” is a social/situational domain item. For each item, the respondent answers yes (4 points), sometimes (2 points), or no (0 points). The scores for each item response are summed to provide a total score (ranging from 0 to 100), as well as emotional (0 to 52) and social/situational (0 to 48) subscale scores. The higher the score for each of these values, the greater the self-perception of hearing handicap. The questionnaire can be administered pre- and post-AR to assess the change in HRQoL. When administered in a face-to-face format, a reduction in score of 18.7 points, post-intervention, is needed for the clinician to conclude that real benefit has been attained. If a paper-and-pencil format is used, however, test-retest reliability diminishes and the 95% confidence interval for a true change in score becomes 36 points (Weinstein, Spitzer, & Ventry, 1986). Variations of the HHIE include a 10-item screening version, the HHIE-S (Ventry & Weinstein, 1983); a 25-item version for younger, working adults, the Hearing Handicap Inventory for Adults (HHIA and its shortened version, HHIA-S) (Newman, Weinstein, Jacobson, & Hug, 1991); and a Spanish version (Lopez-Vazquez, Orozco, Jimenez, & Berruecos, 2002).
Another example of a disease-specific profile that targets the impact of hearing loss and subsequent audiologic intervention on HRQoL is the Hearing Handicap Questionnaire (HHQ) (Gatehouse & Noble, 2004). The HHQ consists of 12 questions scored on a 5-point Likert scale ranging from never (0 points) to almost always (5 points). Items address such QoL issues as avoiding social situations, feelings of being cut off from things, loss of self-confidence, and feelings of self-consciousness.
While some instruments assess only HRQoL, other audiology-specific instruments include one or more items to assess HRQoL, along with constructs such as impairment, activity limitations, and participation restrictions. A prime example is the International Outcomes Inventory-Hearing Aids (IOI-HA) (Cox & Alexander, 2002). One of the seven items on the inventory asks individuals how much the use of hearing aids has changed “enjoyment of life.” Individuals respond on a 5-point Likert scale, with 1 indicating worse and 5 indicating very much better. Originally designed as an assessment of a variety of outcome domains for hearing aid intervention (i.e., number of hours per day of hearing aid use; improvement in hearing-related activities; residual activity limitations; satisfaction; residual participation restrictions; impact on others; quality of life), variations of the IOI-HA have been developed for assessing other aspects of AR intervention and the perception of intervention effectiveness by significant others (Noble, 2002).
The Audiological Disabilities Presence Index (ADPI) (Joore et al., 2002) was developed to measure HRQoL and differs from other disease-specific instruments by inclusion of a preference-based item. The ADPI was adapted from the Amsterdam Inventory for Auditory Disability and Handicap scale (AIADH) (Kramer, Kapteyn, Festen, & Tobi, 1995). Although the AIADH is a 30-item questionnaire designed to assess the impact of hearing loss in daily life, the ADPI includes only five items. The questions in both the AIADH and ADPI assess sound distinction, intelligibility in quiet, intelligibility in noise, auditory localization, and sound detection. For the ADPI, each item has three possible answers: no problems, moderate problems, and severe problems. In the case of ADPI, higher numeric scores indicate fewer problems. In addition to the five descriptive questions, the ADPI includes a preference measure, a horizontal visual analog scale (VAS) of 10 centimeters in length, with 0 equal to deaf and 100 equal to perfect hearing (Joore et al., 2002). The inclusion of the hearing-specific VAS is an important innovation, as the scores can be compared to those obtained with the generic visual analog scales described below.
A disease-specific profile designed specifically to measure self-assessed HRQoL among cochlear implant recipients is the Nijmegen Cochlear Implant Questionnaire (NCIQ) (Hinderink, Krabbe, & van den Broek, 2000). The 60 items on this questionnaire are answered on a 5-response Likert scale ranging from never to always. The items are categorized into three general domains (physical functioning, psychological functioning, and social functioning) and six subdomains (sound perception basic, sound perception advanced, speech production, self-esteem, activity, and social interaction). The NCIQ has been found to be a sensitive and reliable measure of self-perceived changes in HRQoL resulting from cochlear implants (Damen, Beynon, Krabbe, Mulder, & Mylanus, 2007; Loeffler et al., 2010; Hinderink et al., 2000).
Generic Profiles
As previously noted, although generic instruments can be sensitive to the effects of hearing loss in adults, they tend to be relatively insensitive to the effects of AR. Nonetheless, several generic measures have been used to study the impact of hearing loss and audiologic interventions on self-perceived HRQoL.
The Sickness Impact Profile (SIP) (Bergner, Bobbitt, Carter, & Gilson, 1981) is a 136-item, self-administered questionnaire designed to access the impact of a disease or disorder on an individual’s HRQoL across 12 activities of daily living categories: emotional behavior, body and movement, social behavior, sleep and rest, home management, mobility, work, recreation, ambulation, alertness behavior, communication, and eating. In addition to providing 12 subscale scores, scores can be combined to provide a physical score, a psychosocial score, and an overall or total score. The higher the SIP scores, the greater the negative impact is on HRQoL. Bess and colleagues (1989) demonstrated that SIP scores increased as hearing loss progressed in older adults. In addition, Crandall (1998) demonstrated statistically significant improvements in SIP total, physical, and psychosocial scores as a function of hearing aid intervention in 20 older adults. Although Crandall’s data suggest that SIP might be a useful generic instrument for AR, the SIP’s length makes it time consuming and difficult to administer, thus limiting its clinical utility.
At only 36 self-recorded items, the MOS SF-36 Health Survey (SF-36) (Ware & Sherbourne, 1992) is much shorter and easier to administer than the SIP; it is popular among HRQoL researchers. The SF-36 measures several areas related to overall general health-related well-being, including physical functioning, role limitations due to physical health problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. In addition to providing eight subscale scores, the SF-36 yields two component scale scores: a Physical Component Summary (PCS) scale score and a Mental Component Summary (MCS) scale score. For the subscale and component scales, scores are standardized to a mean of 50 and a standard deviation of 10, with higher scores indicating more positive health status. Variations of the original SF-36 have been developed to include a screening version (SF-12), a health survey for children (SF-10), and a health survey designed for use in very large population studies (SF-8) (Ware, Kosinski, Dewey, & Gandek, 2001).
Interestingly, in the aforementioned study by Crandall (1998), hearing aid intervention did not have a significant effect on the eight SF-36 subscale scores. Crandall’s finding, however, may be due to a lack of statistical power, since there were only 20 participants. Support for a conclusion that the Crandall study was underpowered, comes from Abrams et al. (2002) and Hyams et al. (2018) with larger numbers of participants (n = 105 and n = 100, respectively). Abrams et al. (2002) demonstrated significant improvements for the SF-36 MCS scale score and Hyams et al. demonstrated significant improvements on the SF-36 general health subscale score as a function of hearing aid intervention. As generic instruments tend to include few, if any, questions directly related to hearing and communication, it is likely that any treatment effects will be relatively small and the demonstration of statistical significance will require a relatively large number of subjects.
A generic health-status instrument that does include questions about communication is the WHO’s Disability Assessment Schedule 2.0 (WHODAS 2.0) (Üstün et al., 2010). The WHODAS 2.0 is a 36-item questionnaire that assesses self-perceived functioning over an individual’s most recent 30 days in six different life domains: communication (i.e., understanding and communicating with the world), mobility (i.e., moving and getting around), self-care (i.e., attending to one’s hygiene, dressing, eating, and staying alone), interpersonal (i.e., getting along with people), life activities (i.e., domestic responsibilities, leisure, and work), and participation in society (i.e., joining in community activities). These six subscale scores map directly onto activity and participation domains of the WHO’s International Classification of Functioning, Disability, and Health (WHO-ICF) (WHO, 2001). In addition to providing six subscale scores, the WHODAS 2.0 provides a total score. Scoring for the WHODAS 2.0 is accomplished by averaging responses and then normalizing scores into a standard scale, with higher scores indicating better HRQoL. Chisolm and colleagues (2005) evaluated the psychometric properties of the WHODAS 2.0 for the measurement of functional health status in adults with acquired hearing loss. The results indicated that the communication and participation domains, as well as the total score, could be considered psychometrically sound assessments of HRQoL in an adult hearing-impaired population in convergent validity, internal consistency, and test-retest stability.
Health Decision and Preference Instruments
The value added by a given treatment is often of significant interest to patients, clinicians, insurers, and policy makers. With rising health care costs and shrinking reimbursement rates, applied health economics are helpful in demonstrating improvements as a result of the intervention, which can in turn influence resource allocations. HRQoL measures are commonly used in health economics to evaluate and compare intervention gains in relation to a monetary value or cost. Two methods are commonly used in AR: cost-effectiveness analysis (CEA) and cost-benefit analysis (CBA). CEA relates monetary costs with effectiveness in health outcomes (e.g., reduced mortality, pain reduction, activities of daily living) and is measured in monetary units (e.g., U.S. dollars) per quality-adjusted life year (QALY). A QALY is a generic measurement of the quality and the quantity of life lived with a disease, is used to assess the health-related value of an expenditure in medical intervention, and is equal to 1 year in perfect health. Cost-utility analysis (CUA) is a subtype of CEA that applies measures of utility, or health preferences under uncertainty. While both are theoretically similar, CUA relies on a cardinal measure of utility, or a health preference index (i.e., 0.0 = death; 1.0 = perfect health) whereas CEA compares observed effects of different treatments. CBA, on the other hand, compares costs with outcomes or benefits, both measured in monetary terms. Table 7–1 summarizes the characteristics for each of these decision- and preference-based techniques.
Cost-Effectiveness Analysis
CEA and CUA are best suited for assessing technical efficiency in achieving a predetermined goal within a given budget and cannot be performed on one intervention alone, but rather require comparisons of two or more interventions for the same disorder (e.g., HA versus HA + AR, or pre-HA versus post-HA). CEAs are performed under the assumption that each patient and health care system has a specific monetary amount they are willing to pay (i.e., willingness to pay [WTP] or cost-effectiveness threshold) per QALY. WTP thresholds are specific to a given health care system and are estimated to be $50,000/QALY in North America, compared to £20,000 to £30,000/QALY in the United Kingdom and €30,000/QALY in Spain (Shiroiwa, Igarashi, Fukuda, & Ikeda, 2013; Bobinac, van Exel, Rutten, & Brouwer, 2010; Pérez-Martín, Artaso, & Díez, 2017). CEA can often provide information on allocative efficiency via an incremental cost-efficiency analysis (ICER), which demonstrates the amount of extra benefit that is obtained as a result of extra cost (National Information Center on Health Services Research and Health Care Technology [NICHSR], 2016; Pérez-Martin et al., 2017; Bond et al., 2009). A treatment is deemed to be cost-effective if the results for the treatments of interest are below the WTP threshold and can identify the most cost-effective intervention among the two options. CEA employs probabilistic decision models in which data are simulated through a flowchart of probabilities for items such as test sensitivity, specificity, and disease prevalence, and provide simulated data for outcomes. Commonly utilized CEA modeling tools include decision trees, Markov processes, and stochastic trees. A detailed description of these tools is beyond the scope of this chapter; however, for an introduction to these methods, the reader is directed to Chapman and Sonnenberg’s text (2000). The basic formula for cost-effectiveness analysis is expressed in a ratio: Cost Benefit Ratio = (Cost A – Cost B) / (Effect A – Effect B).
CEA has historically been the most common method of economic evaluation in AR. Hearing aids have been found to be a cost-effective treatment for adults aged 50 to 80 when compared to nonuse of hearing aids at an estimated $13,615/QALY for men and $9,702/QALY for women, both of which fall below a WTP threshold of $20,000/QALY (Chao & Chen, 2008). Favorable findings for the cost-effectiveness of hearing aids were reported for fittings in the Netherlands with an estimate of €15,807/QALY, which was below the €16,000 threshold for consideration of insurance coverage and reimbursement in the Netherlands (Joore, van der Stel, Peters, Boas, & Anteunis, 2003). Newman and Sandridge (1998) explored the cost-effectiveness of various hearing aids that were available at the time. Unilateral and bilateral cochlear implantation have been found to be cost-effective solutions for hearing loss (Bond et al., 2009; Pérez-Martín et al., 2017). Unilateral cochlear implantation in adults and children was found to be cost-effective for adults and children with severe to profound hearing loss in the United Kingdom when compared to no intervention and hearing aids (Bond et al., 2009). Bilateral cochlear implantation, both simultaneous and sequential, was found to be cost-effective when compared to unilateral implantation (Pérez-Martín et al., 2017). Other applications of CEA in audiology include tinnitus management (Maes et al., 2014), magnetic resonance imaging studies for asymmetric hearing loss (Hojjat et al., 2016), adult hearing screening (Baltussen & Smith, 2009; Rob et al., 2009), and newborn hearing screening (Chiou et al., 2017).
Cost-Utility Analysis
Generic Utilities
As previously described, CUA is a type of CEA that measures utility. A utility is defined as a cardinal measure of the strength of one’s preference and ranges from 0.0 (death) to 1.0 (perfect health). A cardinal utility is an economic concept that assumes people are able to assign a meaningful number (i.e., util) to their satisfaction in one situation as compared to their satisfaction in an alternative situation. Utils are assumed to be similar to, for example, temperature or distance—that is, the incremental units are reasonably constant and objective. Although it is generally accepted that, in practice, the measurement of utilities is only a rough approximation of value, utility assessment has taken on an important role in health care economics. Utilities are particularly useful in making comparisons across diseases and interventions and are often used to calculate the cost of treatment outcomes in terms of improved HRQoL. Such economic evaluations are known as CUAs, the result of which is expressed as a cost per QALY gained (Drummond, Sculpher, Torrance, O’Brien, & Stoddart, 2005). The basic formula for CUA is expressed in a ratio (NICHSR, 2017): Cost-Utility Ratio = (Cost A – Cost B) / (Utile A – Utile B).
The most common direct methods used to measure utility include the standard gamble (SG), time tradeoff (TTO), and visual analog scale (VAS) techniques (Bennett & Torrance, 1996). Each is described briefly below.
Standard Gamble
The classic method of measuring health preference is the SG approach. In a SG, a person is offered a choice between two alternatives: living with Health State B with certainty (which, in a clinical population, is presumably a patient’s present health state) or gambling on Treatment X. The result of Treatment X is either perfect health (Health State A) or immediate death (Health State C). The probabilities of achieving Health State A or Health State C as a result of Treatment X are manipulated until a person is indifferent between his present Health State B and Treatment X. An assumption of the technique is that the higher the probability of death a person is willing to consider, the poorer the health state (or HRQoL) perceived in remaining with Health State B. Utility is calculated as 1.0 (perfect health) minus p (the probability of death associated with Treatment X) when a person is unable to choose between “A” and “B”).
Time Trade-Off
Another approach commonly used to measure health state preferences is the TTO technique. In TTO, a person is offered a choice between living a normal life span in the present health state or a shortened life span in perfect health. The number of years spent in perfect health is manipulated until a person is indifferent to the shorter period of perfect health and the longer period in the less desirable state. An individual who is willing to trade years of life for a shorter life in perfect health (or perfect hearing) is assumed to be revealing a great deal about his or her perceived HRQoL as imposed by a disease or a disorder.
Visual Analog Scale
The use of a VAS was briefly described earlier in the introduction of the ADPI (Joore et al., 2002). Measuring utility with a VAS or a “feeling thermometer” has the advantage of simplicity. A person is simply required to rate his or her perceived health state on a scale marked with 0 at one end, representing death or some least desirable state, and 100 at the other end, representing perfect health or a most desirable state.
Disease-Specific Utilities
Disease-specific utilities are a relatively new approach to measuring HRQoL (Drummond et al., 2005). They combine the benefits of a generic utility measurement—the ability to generate a single preference rating—with the advantage of a disease-specific measure that is more sensitive to the disorder or intervention under study. The techniques used to measure utilities for specific diseases are no different from those previously described (SG, TTO, VAS) with the exception that the choices or “anchors” describe the extremes of a specific disorder, rather than perfect health or death. For example, instead of asking the patient to choose between living a normal life span in his or her present health state or a shortened life span in perfect health, the TTO may be worded in such a way as to require the patient to choose between living a normal life span with his or her current hearing loss or living a shortened life span with perfect hearing. Examples of disease-specific utility studies include those examining the HRQoL impact of prostate cancer (Saigal, Gornbein, Reid, & Litwin, 2002), schizophrenia (Lenert, Rupnow, & Elnitsky, 2005), and hearing aid use (Kenworthy, 2002; Piccirillo, Merritt, Valente, Littenberg, & Nease, 1997).
In our own work, we have developed a software program, Utility Measures for Audiological Applications (UMAA) and have obtained preliminary disease-specific utilities for tinnitus, benign paroxysmal positional vertigo, and hearing loss (Abrams, Roberts, Lister, & Hnath Chisolm, 2006). Figures 7–2, 7–3, and 7–4 demonstrate the UMAA screenshots for hearing-specific utility assessments using SG, TTO, and VAS techniques, respectively. Significant correlations were obtained between the utilities as measured with all three techniques and responses to the HHIE from 48 adults with hearing loss (Condill, 2006), as well as between utilities measured with all three techniques and responses to the Dizziness Handicap Inventory (DHI) (Jacobson & Newman, 1990) from 52 patients with benign paroxysmal positional vertigo (BPPV) (Roberts et al., 2009).
As described by Drummond et al. (2005), the use of disease-specific and generic utilities in a two-stage approach may allow for more appropriate resource allocation within disorders (because disease-specific utilities will be sensitive to the effects of different interventions within the same disorder) and across disorders (because generic utilities provide data for making CUA comparisons across different disorders). An alternative to performing a two-stage approach is using a multi-attribute health status classification system that evaluates both health profiles and utilities.
Multi-Attribute Preferences
Direct measurement of utilities for health outcomes using the SG, TTO, and VAS approaches can be a time-consuming and complex task. To overcome some of the difficulties with direct assessment of utilities in clinical populations, researchers have developed prescored health status classification systems. Within each system, several dimensions or attributes of health are assessed with a number of levels of functioning defined for each dimension. For example, the Health Utilities Index Mark 3 (HUI3) (Feeny, Furlong, Boyle, & Torrance, 1995; Furlong et al., 1998) measures six health attributes: sensation, mobility, emotion, cognition, self-care, and pain. The sensation attribute is subdivided into vision, hearing, and speech. Each of these subattributes contains a list of level descriptors where Level 1 (single attribute utility factor = 1.0) describes the highest health state or preference and Level 6 (single attribute utility factor = 0.0) describes the lowest health state or preference. With regard to hearing, for example, Level 1 is associated with the statement “Able to hear what is said in a group conversation with at least three other people without a hearing aid” whereas the Level 6 statement indicates that the person is “Unable to hear at all.” In the HUI3, the scores of the six attributes are weighted and combined in a complex formula to create a single utility estimate. The multiplicative formulas used to determine the utility estimates for the HUI3 and for a related instrument, the Health Utilities Index Mark II (HUI2), are based on the direct assessments of utility using SG and VAS techniques among Canadian adults.
Figure 7–2. Screenshot from the Utility Measures for Audiological Applications (UMAA) showing an example of the time tradeoff technique for hearing.
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