David Feeny

Measuring Health-Related Quality of Life

What Is Health-Related Quality of Life? Health status, functional status, and quality of life are three concepts often used interchangeably to refer to the same domain of health [1]. The health domain ranges from negatively valued aspects of life, including death, to the more positively valued aspects such as role function or happiness. The boundaries of definition usually depend on why one is assessing health as well as the particular concerns of patients, clinicians, and researchers. We use the term health-related quality of life (HRQL) because widely valued aspects of life exist that are not generally considered as health, including income, freedom, and quality of the environment. Although low or unstable income, the lack of freedom, or a low-quality environment may adversely affect health, these problems are often distant from a health or medical concern. Clinicians focus on HRQL, although when a patient is ill or diseased, almost all aspects of life can become health related. Why Measure HRQL? HRQL is important for measuring the impact of chronic disease [2]. Physiologic measures provide information to clinicians but are of limited interest to patients; they often correlate poorly with functional capacity and well-being, the areas in which patients are most interested and familiar. In patients with chronic heart and lung disease, exercise capacity in the laboratory is only weakly related to exercise capacity in daily life [3]. Another reason to measure HRQL is the commonly observed phenomena that two patients with the same clinical criteria often have dramatically different responses. For example, two patients with the same range of motion and even similar ratings of back pain may have different role function and emotional well-being. Although some patients may continue to work without major depression, others may quit their jobs and have major depression. These considerations explain why patients, clinicians, and health care administrators are all keenly interested in the effects of medical interventions on HRQL [4]. Administrators are particularly interested in HRQL because the case mix of patients affects use and expenditure patterns, because increasing efforts exist to incorporate HRQLs as measures of the quality of care and of clinical effectiveness, and because payers are beginning to use HRQL information in reimbursement decisions. The Structure of HRQL Measures Some HRQL measures consist of a single question that essentially asks How is your quality of life? [5] This question may be asked in a simple or a sophisticated fashion, but either way it yields limited information. More commonly, HRQL instruments are questionnaires made up of a number of items or questions. These items are added up in a number of domains (also sometimes called dimensions). A domain or dimension refers to the area of behavior or experience that we are trying to measure. Domains might include mobility and self-care (which could be further aggregated into physical function), or depression, anxiety, and well-being (which could be aggregated to form an emotional-function domain). For some instruments, investigators do rigorous valuation exercises in which the importance of each item is rated in relation to the others. More often, items are equally weighted, which assumes that their value is equal. Modes of Administration The strengths and weaknesses of the different modes of HRQL administration are summarized in Table 1. Health-related quality-of-life questionnaires are either administered by trained interviewers or self-administered. The former method is resource intensive but ensures compliance, decreases errors, and decreases missing items. The latter approach is much less expensive but increases the number of missing subjects and increases missing responses. A compromise between the two approaches is to have instruments completed with supervision. Another compromise is the phone interview, which decreases errors and decreases missing data but dictates a relatively simple questionnaire structure. Investigators have done initial experiments with computer-administration of HRQL measures, but this is not yet a common method of questionnaire administration. Table 1. Modes of Administration of HRQL Measures Investigators sometimes use a surrogate respondent to predict results that would be obtained from the patient. For instance, McKusker and Stoddard [6] were interested in what patients might score on a general, comprehensive measure of HRQLthe Sickness Impact Profilewhen they were too ill to complete the questionnaire. The investigators used a surrogate to respond on behalf of the patient but wanted assurance that surrogate responses would correspond to what patients would have said had they been capable of answering. They administered the Sickness Impact Profile to terminally ill patients who were still capable of completing the questionnaire and to close relatives of the respondents. The correlation between the two sets of responses was 0.55, and the difference between the two pairs of responses was greater than 6 on a 100-point scale for 50% of the patients. The results provide only moderate support for the validity of surrogate responses to the Sickness Impact Profile. These results are consistent with other evaluations of ratings by patients and proxies. In general, the correspondence between respondent and proxy response to HRQL measures varies depending on the domain assessed and the choice of proxy. Proxy reports of more observable domains, such as physical functioning and cognition, are more highly correlated with reports from the patients themselves. For functional limitations, proxy respondents tend to consider patients more impaired (they overestimate patient dysfunction relative to the patients themselves). This is particularly characteristic of those proxies with the greatest contact with the respondent [7]. For other sorts of morbidity, patients tend to report the most problems, followed by close relatives, and clinicians report the least. These findings have important clinical implications because they suggest that clinicians should concentrate on careful ascertainment of the reported behaviors and perceptions of patients themselves, and they should limit the inferences they make on the basis of the perceptions of the caregivers. What Makes a Good HRQL Instrument? Measuring at a Point in Time versus Measuring Change The goals of HRQL measures include differentiating between people who have a better HRQL and those who have a worse HRQL (a discriminative instrument) as well as measuring how much the HRQL has changed (an evaluative instrument) [8]. The construction of instruments for these two purposes is different. If we want to discriminate between those with and without thyroid disease, we would be unlikely to include fatigue as an item because fatigue is too common among people who do not have thyroid disease. On the other hand, in measuring improvement in HRQL with treatment, fatigue, because of its importance in the daily lives of people with thyroid disease, would be a key item. In the next sections, we list key measurement properties separately for discriminative and evaluative instruments. The properties that make useful discriminative and evaluative instruments are presented in Table 2. Table 2. What Makes a Good HRQL Measure? Signal and Noise Investigators examining physiologic end points know that reproducibility and accuracy are the necessary attributes of a good test. For HRQL instruments, reproducibility means having a high signal-to-noise ratio, and accuracy translates into whether they are really measuring what they intended to measure. For discriminative instruments, the way of quantitating the signal-to-noise ratio is called reliability. If the variability in scores between patients (the signal) is much greater than the variability within patients (the noise), an instrument will be deemed reliable. Reliable instruments will generally show that stable patients have more or less the same results after repeated administration. For evaluative instruments, those designed to measure changes within patients during a period of time, the method of determining the signal-to-noise ratio is called responsiveness. Responsiveness refers to an instruments ability to detect change. If a treatment results in an important difference in HRQL, investigators want to be confident that they will detect that difference, even if it is small. Responsiveness will be directly related to the magnitude of the difference in score in patients who have improved or deteriorated (the signal) and the extent to which patients who have not changed provide more or less the same scores (the noise). Validity When a Gold Standard Exists Although no gold standard for HRQL exists, instances occur in which a specific target for an HRQL measure exists that can be treated as a criterion or gold standard. Under these circumstances, one determines whether an instrument is measuring what is intended using criterion validity (an instrument is valid if its results correspond to those of the criterion standard). Criterion validity is applicable when a shorter version of an instrument (the test) is used to predict the results of the full-length index (the gold standard). Another example is using an HRQL instrument to predict death. In this instance, the instrument will be valid if variability in survival between patients (the gold standard) is explained by the questionnaire results (the test). Self-ratings of health, like more comprehensive and lengthy measures of general health perceptions, include a patients evaluation of physiologic, physical, psychological, and social well-being. Perceived health, measured through self-ratings, is an important predictor of death [9]. Validity When No Gold Standard Exists Validity examines whether the instrument is measuring what it is intended to measure. When no gold or criterion standard exists, HRQL invest

Multiattribute and single-attribute utility functions for the Health Utilities index mark 3 system

Background. The Health Utilities Index Mark 3 (HUI3) is a generic multiattribute preference‐based measure of health status and health‐related quality of life that is widely used as an outcome measure in clinical studies, in population health surveys, in the estimation of quality‐adjusted life years, and in economic evaluations. HUI3 consists of eight attributes (or dimensions) of health status: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain with 5 or 6 levels per attribute, varying from highly impaired to normal. Objectives. The objectives are to present a multiattribute utility function and eight single‐attribute utility functions for the HUI3 system based on community preferences. Study Design. Two preference surveys were conducted. One, the modeling survey, collected preference scores for the estimation of the utility functions. The other, the direct survey, provided independent scores to assess the predictive validity of the utility functions. Measures. Preference measures included value scores obtained on the Feeling Thermometer and standard gamble utility scores obtained using the Chance Board. Respondents. A random sample of the general population (≥16 years of age) in Hamilton, Ontario, Canada. Results. Estimates were obtained for eight single‐attribute utility functions and an overall multiattribute utility function. The intraclass correlation coefficient between directly measured utility scores and scores generated by the multiattribute function for 73 health states was 0.88. Conclusions. The HUI3 scoring function has strong theoretical and empirical foundations. It performs well in predicting directly measured scores. The HUI3 system provides a practical way to obtain utility scores based on community preferences.

The Tragedy of the Commons: twenty-two years later

Hardins Tragedy of the Commons model predicts the eventual overexploitation or degradation of all resources used in common. Given this unambiguous prediction, a surprising number of cases exist in which users have been able to restrict access to the resource and establish rules among themselves for its sustainable use. To assess the evidence, we first define common-property resources and present a taxonomy of property-rights regimes in which such resources may be held. Evidence accumulated over the last twenty-two years indicates that private, state, andcommunal property are all potentially viable resource management options. A more complete theory than Hardins should incorporate institutional arrangements and cultural factors to provide for better analysis and prediction.

Multiattribute Utility Function for a Comprehensive Health Status Classification System Health Utilities Index Mark 2

The Health Utilities Index Mark 2 (HUI:2) is a generic multiattribute, preference-based system for assessing health-related quality of life. Health Utilities Index Mark 2 consists of two components: a seven-attribute health status classification system and a scoring formula. The seven attributes are sensation, mobility, emotion, cognition, self-care, pain, and fertility. A random sample of general population parents were interviewed to determine cardinal preferences for the health states in the system. The health states were defined as lasting for a 60-year lifetime, starting at age 10. Values were measured using visual analogue scaling. Utilities were measured using a standard gamble technique. A scoring formula is provided, based on a multiplicative multiattribute utility function from the responses of 194 subjects. The utility scores are death-anchored (death = 0.0) and form an interval scale. Health Utilities Index Mark 2 and its utility scores can be useful to other researchers in a wide variety of settings who wish to document health status and assign preference scores.

The Health Utilities Index (HUI®): concepts, measurement properties and applications

This is a review of the Health Utilities Index (HUI®) multi-attribute health-status classification systems, and single- and multi-attribute utility scoring systems. HUI refers to both HUI Mark 2 (HUI2) and HUI Mark 3 (HUI3) instruments. The classification systems provide compact but comprehensive frameworks within which to describe health status. The multi-attribute utility functions provide all the information required to calculate single-summary scores of health-related quality of life (HRQL) for each health state defined by the classification systems. The use of HUI in clinical studies for a wide variety of conditions in a large number of countries is illustrated. HUI provides comprehensive, reliable, responsive and valid measures of health status and HRQL for subjects in clinical studies. Utility scores of overall HRQL for patients are also used in cost-utility and cost-effectiveness analyses. Population norm data are available from numerous large general population surveys. The widespread use of HUI facilitates the interpretation of results and permits comparisons of disease and treatment outcomes, and comparisons of long-term sequelae at the local, national and international levels.

Utilities and Quality-Adjusted Life Years

Utilities and quality-adjusted life years (QALYs) are reviewed, with particular focus on their use in technology assessment. This article provides a broad overview and perspective on these two techniques and their interrelationship, with reference to other sources for details of implementation. The historical development, assumptions, strengths/weaknesses, and applications of each are summarized. Utilities are specifically designed for individual decision-making under uncertainty, but, with additional assumptions, utilities can be aggregated across individuals to provide a group utility function. QALYs are designed to aggregate in a single summary measure the total health improvement for a group of individuals, capturing improvements from impacts on both quantity of life and quality of life--with quality of life broadly defined. Utilities can be used as the quality-adjustment weights for QALYs; they are particularly appropriate for that purpose, and this combination provides a powerful and highly useful variation on cost-effectiveness analysis known as cost-utility analysis.

The Health Utilities Index (HUI®) system for assessing health-related quality of life in clinical studies

This paper reviews the Health Utilities Index (HUI? systems as means to describe health status and obtain utility scores reflecting health-related quality of life (HRQoL). The HUI Mark 2 (HUI2) and Mark 3 (HUI3) classification and scoring systems are described. The methods used to estimate multiattribute utility functions for HUI2 and HUI3 are reviewed. The use of HUI in clinical studies for a wide variety of conditions in a large number of countries is illustrated. HUI provides a comprehensive description of the health status of subjects in clinical studies. HUI has been shown to be a reliable, responsive and valid measure in a wide variety of clinical studies. Utility scores provide an overall assessment of the HRQoL of patients. Utility scores are also useful in cost-utility analyses and related studies. General population norm data are available. The widespread use of HUI facilitates the interpretation of results and permits comparisons. HUI is a useful tool for assessing health status and HRQoL in clinical studies.

Multi-Attribute Preference Functions

SummaryMulti-attribute utility theory. an extension of conventional utility theory, can be applied to model preference scores for health slates defined by multi-attribute health status classification systems. The type of preference independence among the attributes determines the type of preference function required: additive, multiplicative or multilinear. In addition, the type of measurement instrument used determines the type of preference score obtained: value or utility.Multi-attribute utility theory has been applied to 2 recently developed multi-attribute health status classification systems the Health Utilities Index (HUI) Mark II and Mark III systems. Results are presented for the Mark system, and ongoing research is described for the Mark system. The theory is also discussed in the context of ocher well known multi-attribute systems.The HUI system is an efficient method of determining a general public-based utility score for a specified health outcome or for the health status of an individual. In clinical populations, the scores can be used 10 provide a single summary measure of health-related quality of life. In cost-utility analyses, the scores can be used as quality weights for calculating quality-adjusted life years. In general populations, the measure can be used as quality weights for determining population health expectancy.

Effects on quality of life with comprehensive rehabilitation after acute myocardial infarction

Abstract This investigation was designed to determine the impact of a brief period of cardiac rehabilitation, initiated within 6 weeks of acute myocardial infarction (AMI), on both disease-specific and generic health-related quality of life, exercise tolerance and return to work after AMI. With a stratified, parallel group design, 201 low-risk patients with evidence of depression or anxiety, or both, after AMI, were randomized to either an 8-week program of exercise conditioning and behavioral counseling or to conventional care. Although the differences were small, significantly greater improvement was seen in rehabilitation group patients at 8 weeks in the emotions dimension of a new disease-specific, health-related Quality of Life Questionnaire, in their state of anxiety and in exercise tolerance. All measures of health-related quality of life in both groups improved significantly over the 12-month followup period. However, the 95% confidence intervals around differences between groups at the 12-month follow-up effectively excluded sustained, clinically important benefits of rehabilitation in disease-specific (limitations, −2.70, 1.40; emotions, −4.86, 1.10, where negative values favor conventional care and positive values favor rehabilitation) and generic health-related quality of life (time trade-off, −0.062, 0.052; quality of well-being, −0.042, 0.035) or in exercise tolerance (−38.5, 52.1 kpm/min); also, return to work was similar in the 2 groups (relative risk, 0.93; confidence interval, 0.71, 1.64). It is concluded that in patients with evidence of depression or anxiety, or both, exercise conditioning and behavioral counseling after AMI was associated with an accelerated recovery in some outcome measures at 8 weeks, but by 12 months similar improvements were seen in both diseasespecific and generic health-related quality of life and in other outcome measures when compared with conventional care in this community.

Visual Analog Scales: Do They Have a Role in the Measurement of Preferences for Health States?

Visual analog scales (VASs) have long been used as a method of measuring preferences for health outcomes. They are easy and inexpensive to implement, can be administered quickly, and lend themselves to self-completion. Over time, however, disturbing questions have emerged concerning the validity of the VAS approach. This article reviews briefly the history, theory, practice, problems, and advantages of VASs; presents some suggestions to improve the validity of VASs; and recommends a limited but useful role for VASs in the process of measuring preferences for health states.