Quality Of Life Assessment For Health Resource Allocation Robert Kaplan*
Health Policy: International Comparisons and Special Issues, Centre for Economic Policy Research, Australian National University, 1993 1-18. 1. Quality of Life Assessment Resource Allocation for Health Robert Kaplan* Diseases and disabilities are important for two reasons. First, illness may cause the life expectancy to be shortened. Second, illness may make life less desirable at times prior to death (health-related quality of life) (Kaplan & Bush, 1982; Kaplan & Anderson, 1988a, 1998b, 1990; Kaplan et al. 1989). Because of the important impacts of illness upon health-related quality of life, a wide variety of measures has !)een proposed to quantify these effects (Aaronson, Bullinger & Ahmedzai, 1988; Shipper et al., 1984; Ware & Sherbourne, 1992; Bergner et al, 1981; Cella & TulsM, 1990). These measures are similar in that each expresses the effects of medical care in terms that can be reported directly by a patient: However, the rationales for the methods differ considerably. Perhaps the most important distinction is between psychometric or 'profile' approaches and decision theory based methods. The psychometric tradition typically creates a profile of patient outcomes. The best known profile methods include the Medical Outcomes Study Short Form 36 (Ware & Sherbourne, 1992), the Sickness Impact Profile (Bergner et al, 19S1) and a variety of disease specific measures, such as the Quality of Life Questionnaire, C30 (QLQ C-30) of the European Organization for Research and Treatment of Cancer (EORTC) (Aaronson, Bullinger & Ahmedzai, 19S8) and the Functional Living Index-Cancer (FLIC) (Shipper et al., 1984). Briefly, profile approaches characterize patients on a series of outcomes. Most profile approaches are empirically driven. They begin with a large number of items and the item pool is eventually reduced through factor analysis or other data reduction methods. Although profile approaches are popular, they are very limited for economic analysis. In this paper, I emphasize alternative approaches that may have greater applications in policy studies and economic evaluations. These decision theory approaches are more Portionsofthispaperwere adaptedfrom previous work by the author, includingKaplan (1993a).
Robert Kaplan theoretically based. The theory attempts to characterize all of the different ways that diseases and disabilities affect the patients' lives. AIDS, for example, affects life expectancy, functioning, and symptoms. Further, duration of these problems must be considered. AIDS treatment, like the disease itself, has an impact upon life expectancy, functioning, symptoms, and prognosis. The goal of the decision theory approach is to develop a comprehensive expression of disease impact and an expression of the net benefits of treatment. For example, a patient with PCP pneumonia will have a limited life expectancy. This life expectancy may be extended through intensive therapy. However, in the short run, quality of life may be worse for treated than for untreated patients. The goal of the decision theory analysis is to quantify the expected course of the illness with and without treatment. Reducing these effects to a single number allows a quantitative expression of the net effects of treatment. The pieces that need to be quantified include life expectancy, functioning, symptoms, the qualitative evaluation of wellness, and prognosis or the duration of stay in health states. The goal of decision analysis is to express the outcomes in a term equivalent to years of life. Historically, outcome in most clinical studies has been expressed in terms of survival. In survival analysis, those who remain alive are statistically coded as 1.0, while those who die are coded 0.0 without considering stages between 0.0 and 1.0. Such coding treats a patient hospitalized with severe emesis as identical to one in total remission who is unaffected by the disease. Decision theory methods assign numbers between 0 and 1.0 to states of wellness so that years of survival can be adjusted for quality of life. For example, suppose that a man dies of throat cancer at age 50 and he was expected to live to age 75. Survival analysis suggests that he lost 25 years of life. However, assume that there was a successful surgery that allowed the man to complete his life expectancy but with very significant impact upon his functioning and ability to communicate. Traditional survival analysis would indicate that the disease was cured since the life expectancy was the same as someone without the disease. However, analysis that takes quality of life into consideration would yield different results. Let's assume that the life after the surgery is scored as 0.5 on 0.0 to 1.0 scale. The 25 years following surgery would be adjusted for this diminished quality to yield 12.5 quality adjusted years (25 years x 0.5 quality of life 12.5 Quality Adjusted Life Years). In the next section we will explore one method for estimating these Quality Adjusted Life Years (QALYs). The final section will discuss applications of decision theory approaches for cost/utility studies, and public policy analysis. 2
Quality of Life Assessment General theory Nearly all health-related quality of life measures have multiple dimensions. The exact dimensions vary from measure to measure. There is considerable debate in the field about which dimensions need to be included. For example, the most commonly included dimensions are physical functioning, role functioning, and mental health. The Medical Outcomes Study (MOS) includes eight health concepts (Ware & Sherbourne, 1992). Although many questionnaires include different dimensions, they still may be tapping the same constructs. For example, a measure without a mental health component does not necessarily neglect mental health. Mental health symptoms may be included and the impact of mental health, cognitive functioning, or mental retardation may be represented in questions about role functioning. Some measures have multiple dimensions for mental health symptoms while others include fewer items that ask about problems in general. It is not clear that multiple measures are more capable of detecting clinical differences. For example our studies show that dimensions in the SF-36 and other profile measures are highly intercorrelated and that most dimensions are correlated with a general summary score. This remains an empirical question that must be evaluated in systematic studies. Relative importance of dimensions Most treatments for serious illnesses have side effects as well as benefits. Typically, the frequencies of various side effects are tabulated. Thus, a medication to control tumor growth might be associated with tiredness, impotence, shortness of breath, or vomiting. The major challenge is in determining what it means when someone experiences a side effect. Should the patient who feels sleepy discontinue his or her medication? How do we determine whether or not observable side effects are important? Should a patient with acute leukemia discontinue therapy because it makes him tired? Clearly lethargy is a side effect of treatment. But without treatment the patient would face an increased probability of shortened survival. Often the issue is not whether treatment causes side effects, but how we should place these side effects within the perspective of total health. Ultimately, we must decide whether treatment produces a net benefit or a net deficit in health status. Many measures of health-related quality of life simply tabulate frequencies for different symptoms or represent health status using profiles of outcomes. Figure 1 is a representation of a series of two profiles from the SF-36. The first profile is for patients with depression (from Kaplan et al, 1989) while the second profile is from unpublished data on patients on renal dialysis. Depressed patients are higher on measures of pain, physical and role functioning while 3
Robert Kaplan dialysis patients score higher on measures of social and emotional functioning. The two groups are about equivalent on measures of energy and distress. Are depressed patients sicker than those on dialysis? Should we devote more resources to help them? Now suppose that a treatment for depression may improve scores on social and mental functioning, but will cause scores on energy and physical functioning to decline. Has the treatment worked? We must recognize that judgment about the relative importance of dimensions is common. Physicians may ignore a particular test result or a particular symptom because another one is more important to them. Typically, however, it is done implicitly, arbitrarily, and in an idiosyncratic way. We suggest that the process by which relative importance is evaluated can be studied explicitly and can be part of the overall model. Figure1. Comparisonof SF36profilesfor patientswithdepressionandthoseon renaldialysis. 9O !-- O 00 O to o3 U. a 80 A 60 ,- 40 Dialysis P f NG) "0 '" - - -e- - /\ 7o Depression -,p,, ",, ". /if" , ./ Xx xx / i 4 t.D 30 ! 0 ' I . " I " I -" , .c I o Scale 4 " I : ' I , .- . '" I o P 9
Quality of Life Assessment General health policy model Over the last two decades, a group of investigators at the University of California, San Diego, has developed a General Health Policy Model (GHPM). Central to the this model is a general conceptualization of health status. The model separates aspects of health status into distinct components. These are life expectancy (mortality), functioning and symptoms (morbidity), preference for observed functional states (utility) and duration of stay in health states (prognosis). Mortality A model of health outcomes necessarily includes a component for mortality. Indeed, many public health statistics focus exclusively on mortality through estimations of crude mortality rates, age-adjusted mortality rates, and infant mortality rates. Death is an important outcome that must be included in any comprehensive conceptualization of health. Morbidity In addition to death, behavioral dysfunction is also an important outcome. The GHPM considers functioning in three areas: mobility, physical activity, and social activity. Most public health indicators are relatively insensitive to variations toward the well end of the continuum. Measures of infant mortality, to give an extreme example, ignore all individuals capable of reading this article since they have lived beyond one year following their births (we assume that no infants are reading the article). Disability measures often ignore those in relatively well states. For example, the RAND Health Insurance Study reported that about 80 per cent of the general populations have no dysfunction. Thus, they would estimate that 80 per cent of the population is well. Our method asks about symptoms or problems in addition to behavioral dysfunction (Aaronson, Bullinger & Ahmedzai, 1988). In these studies, only about 12 per cent of the general population report no symptoms on a particular day. In other words, health symptoms or problems are a very common aspect of the human experience. Some might argue that symptoms are unimportant because they are subjective and unobservable. However, symptoms are highly correlated with the demand for medical services, expenditures on health care, and motivations to alter lifestyles. Thus, we feel that the quantification of symptoms is very important. Utility (relative importance) Given that various components of morbidity and mortality can be tabulated, it is important to consider their relative importance. For
Robert Kaplan example, it is possible to develop measures that detect very minor symptoms. Yet, because these symptoms are measurable does not necessarily mean they are important. A patient may experience side effects but be willing to tolerate them because the side effects are less important than the probable benefit of consuming the medication. Not all outcomes are equally important. A treatment in which 20 of 100 patients die is not equivalent to one in which 20 of 100 patients develop nausea. An important component of the GHPM attempts to scale the various health outcomes according to their relative importance. In the preceding example, the relative importance of dying would be weighted more than developing nausea. The weighting is accomplished by rating all states on a continuum ranging from 0 (for death) to 1.0 (for optimum functioning). These ratings are typically provided by independent judges who are representative of the general population. Using this system it is possible to express the relative importance of states in relation to the life-death continuum. A point halfway on the scale (0.5) is regarded as halfway between optimum function and death. The weighting system has been described in several different publications (see Kaplan & Bush, 1982; Kaplan & Anderson 1988a, 1988b, 1990; Kaplan, Anderson et al, 1989). There is considerable controversy about the appropriate model for estimating these utilities, and the debate often breaks down by disciplinary training. Economists require that the scaling method be true to the axioms of choice outlined in traditional gRme theory (von Neumen & Morganstern, 1944), while psychologists often prefer establishing scale properties (Anderson, 1991). These issues are addressed elsewhere (Kaplan, Feeny & Revicki, 1994) and will not be considered here. Suffice it to say that these are important technical and methodological debates, but that the basic conceptual model of a quality adjusted life year is similar in the minds of most advocates. Prognosis Another dimension of health status is the duration of a condition. A headache that lasts one hour is not equivalent to a headache that lasts one month. A cough that lasts three days is not equivalent to a cough that lasts .three years. In considering the severity of illness, duration of the problem is central. As basic as this concept is, most contemporary models of health outcome measurement completely disregard the duration component. In the GHPM, the term prognosis refers to the probability of transition among health states over the course of time. In addition to consideration of duration of problems, the model considers the point at which the problem begins. A person may have no symptoms or dysfunctions currently but may have a high probability of health problems in the future. The prognosis component of the model takes these transitions into 6 i
Quality of Life Assessment consideration future. The Quality and applies of Well-Being a discount rate for events that occur in the Scale The component of the GHPM that evaluates quality of life is known as the Quality of Well-being Scale. The roots of this general measure come from common surveys. For example, many of the concepts were borrowed from the US National Health Interview Survey, surveys created by the US Social Security Administration, and others. These instruments include three common dimensions of functional health status; Mobility, Physical Activity, and Social Activity. Mobility refers to the ability to get around the community. Can a person travel, use public transportation, drive a car, etc.? Or are they limited in mobility? Are they in a house? Axe they in a hospital? Are they in a special care unit within a hospital? Physical Activity considers whether a person walked without physical problems. Did they walk with limitations. Were they in a wheelchair? Were they in a bed or a chair for most of the assessment period? Social activity evaluates role performance. Did the person perform his or her major social role? Were they limited in these activities? Did they need help, etc.? The dimensions, summarized in Table 1, describe how a person might be affected by a disease or disability. Unlike functional limitations that might be verified by an observer, there are also phenomenological aspects of health status-symptom reporting. A person can be at the top level of all of the functional scales, but still experience symptoms or problems. To quantify these aspects of health outcome, we have created lists of symptoms/problems: Again, we have tried to simplify this and combine the symptoms/problems by preference, described in the section on relative importance. In addition to observable dysfunction, symptoms or problems might bother a person on a particular day. These include very bad events such as loss of consciousness, trouble thinking or learning, pain, stiffness, numbness of a limb. The continuum extends through a moderate level of symptoms such as general tiredness, weight loss, cough; relatively minor complaints such as using medications or a prescribed diet, wearing eyeglasses, breathing smog or unpleasant air, and so on (see Table 2). More recently we have added some mental health symptoms including anxiety, trouble falling asleep or staying asleep, problems with sexual interest or performance, and intoxication although we do not yet have specific utilities for these symptoms. 7
Robert Kaplan Table 1: Quality of Well-being/General Health Policy Model: Elements and Calculating Formulas (Function Scales, with Step Definitions and Calculating Weights) Step No. 5 StepDefinition Weight Mobility Scale (MOB) Nolimitations forhealthreasons -.000 4 Did not drive a car, health related; did not ride in a car as usual for age (younger than 15 yrs), health related, andor did not use public transportation, health related; or had or would have used more help than usual for age to use public transportation, health related -.062 2 In hospital, health related Physical Activity No limitations for health reasons -.090 4 3 1 Scale (PAC) -.000 In wheelchair, moved or controlled movement of wheel chair without help from someone else; or had trouble or did not try to lift, stoop, bend over, or use stairs or inclines, health related; andor had any other physical limitation in walking, or did not try to walk as far as or as fast as other the same age are able, health related In wheelchair, did not move or control the movement of wheelchair without help from someone else, or in bed, chair, or couch for most or all of the day, health related Social Activity for health reasons -.060 -.077 Scale (SAC) 5 No limitations 4 3 Limited in other (eg. recreational) role activity, health related Limited in major (primary) role activity, health related -.000 -.061 -.061 2 Performed no major role activity, health related, but did perform selfcare activities -.061 1 Performed no major role activity, health related, and did not perform or had more help than usual in performing one or more self-care activities, health related Formula 1: Point-in-time well-being score for an individual (W): W 1 (CPXwt) (MOBwt) (PACwt) (SACwt) -. 106 where 'wt' is the preference-weighted measure for each factor and CPX is Symptom/Problem complex. For example, the W score of a person with the following description profile may be calculated for one day as: CPX-11 -.257 MOB-5 Cough, wheezing or shortness of breath, with or without fever, chills, or aching all over No limitations PAC-1 In bed, chair, or couch for most or all of the day, health related -.077 SAC-2 Performed no major role activity, health related, but did perform selfcare -.061 W 1 (-.257) (-.000) (-.077) (-.061) .605 Formula 2: Well-years (WY) as an output measure: WY /No. of persons x (CPXwt MOBwt PACwt SACwt) x Time] -.000
Quality of Life Assessment Table 2" Quality Symptoms/Problem of Well-being/General Complexes (CPX) Health Policy with Calculating Model: Weights CPX No. CPX Description 1 Death (not on respondent's card) 2 " Loss of consciousness such as seizure (fits), fainting, or coma (out cold or knocked out) 3 Burn over large areas of face, body, arms, or legs 4 Pain, bleeding, itching, or discharge (drainage) from sexual organs does not include normal menstrual (monthly) bleeding 5 Trouble learning, remembering, or thinking dearly 6 Any combination of one or more hands, feet, arms or legs either missing, deformed (crooked), paralyzed (unable to move), or broken includes wearing artificial limbs or braces 7 Pain, stiffness, weakness, numbness, or other discomfort in chest, stomach (including hernia or rupture), side, neck, back, hips, or any joints or hands, feet, arms, or legs 8 Pain, burning, bleeding, itching, or other difficulty with rectum, bowel movements, or urination (passing water) 9 Sick or upset stomach, vomiting or loose bowel movement, with or without chills, or aching all over 10 General tiredness, weakness, or weight loss 11 Cough, wheezing, or shortness of breath, with or without fever, chills, or aching all over I2 Spells of feeling, upset, being depressed, or of crying 13 Headache, or dizziness, or ringing in ears, or spells of feeling hot, nervous or shaky 14 Burning or itching rash on large areas of face, body, arms, or legs 15 Trouble talking, such as lisp, stuttering, hoarseness, or being unable to speak 16 Pain or discomfort in one or both eyes (such as burning or itching) or any trouble seeing after correction 17 Overweight for age and height or skin defect of face, body, arms, or legs, such as scars, pimples, warts, bruises or changes in color 18 Pain in ear, tooth, jaw, throat, lips, tongue; several missing or crooked permanent teeth - includes wearing bridges or false teeth; stuffy, runny nose; or any trouble hearing - includes wearing a hearing aid 19 Taking medication or staying on a prescribed diet for health reasons 20 Wore eyeglasses or contact lenses 21 Breathing smog or unpleasant air 22 No symptoms or problem (not on respondent's card) 23 Standard symptom/problem X24 Trouble sleeping X25 Intoxication X26 Problems with sexual interest or performance X27 Excessive worry or anxiety 9 Weights -.727 -.407 -.387 -.349 -.340 .333 -.299 -.292 -.290 .259 .257 -.257 -.244 -.240 -.237 -.230 -.188 -. 170 -. 144 -. 101 -.101 -.000 -.257 -.257 -.257 -.257 -.257
Robert Kaplan Once a person is classified into an observable level of function on a particular day, and their symptom or problem is recorded, the quality dimension must be assessed. In addition to classification we need to determine the desirability of the states. This is done by selecting people randomly from the community to judge case descriptions. Hypothetical cases are rated on a scale from 0 (for dead) to 1.0 (for optimum function). Our studies have experimented with a variety of different scaling methods (Balaban et al., 1986; Kaplan, 1982; Kaplan, Bush & Berry, 1978, 1979). In summary, this system combines morbidity (the quality of life) and mortality (the duration of life) with prognosis ( duration of stay in state). A mathematical model integrates components of the model to express outcomes in a common measurement unit. Using information on current functioning and duration, it is possible to express the health outcomes in terms of equivalents of well-years of life, or as some have described them, Quality-Adjusted Life Years (QALYs). How this model differs from traditional conceptualizations The two major differences between the GHPM and other to health outcome measurement are: approaches the attempt to express benefits and consequences of health common unit known as the well-year or QALY, and emphasis on area measurement. under the curve rather than point in a in time We argue that the general approach to health outcome is, intuitively, what patients and consumers use as a guide. Their physicians may be more directed by a less comprehensive model that considers only a component of health outcome, such as mortality, or a measure of physiology. A basic objective for most people is to function without symptoms as long as possible. Clearly, early death contradicts this objective. Illness and disability during the interval between birth and death also reduces the total potential health status during a lifetime. Many approaches to health assessment consider only current functioning. We refer to these snapshots of health status as point-intime measures. The GHPM considers outcome throughout the life cycle. This is what we characterize as the 'area under the curve'. The more wellness a person experiences throughout the life span, the greater is the area under the curve. Success of interventions is marked by expanded area. Figure 2 illustrates some of these concepts. The X axis is time, or duration of life, while the Y axis is for quality of life. The top line summarizes change in quality of life group receiving treatment, 10
Quality of Life Assessment while the bottom line indicates the outcome without treatment. The area between the curves is the effect of program (treatment minus control) expressed in equivalents of well years of life or QALYs. Dividing the cost of the program by the QALY results in an estimate of the cost/utility of the program. The cost/utility ratio can be used to compare the relative value of different programs, thereby providing a common metric for comparison of programs with different specific objectives. Perfect Health 1.0 Outcome with treatment ! Area between curves 0.0 Death Death without treatment Initiation of Treatment End of Life Expectancy Time of Treatment Figure 2. Summary of outcome in QALY units The X axis is time, or duration of life. The Y axis is for quality of life. The top line summarizes change in quality of life group receiving treatment, while the bottom line indicates the outcome without treatment. The area between the curves is the effect of program (treatment minus control) expressed in equivalents of well years of life orQALYS. 11
Robert Kaplan Cost/utility versus cost/benefit In addition to effects, medical treatments also consume resources. Since resources are constrained in most westernized countries, there is increasing pressure to go beyond simple estimates of treatment effectiveness. Scientists, policy makers, and regulators are now asking for demonstrations of value for money. This means that we need evidence that investment in a treatment is a good use of resources when the same funds could have been used for another purpose. These decisions require applications of economic models. The terms cost / utility, cost / effectiveness, and cost / benefit are used inconsistently in the medical literature (Doubelet, Weinstein & McNeil, 1986). Some economists have favored the assessment of cost/benefit. These approaches measure both program costs and treatment outcomes in dollar units. For example, treatment outcomes are evaluated in relation to changes in use of medical services, economic productivity, etc. Treatments are cost/beneficial if the economic return exceeds treatment costs. Cancer patients with localized disease who are aggressively treated, for example, may need fewer medical services in the future. The savings associated with decreased services might exceed treatment costs. Russell (1986) argued that the requirement that health care treatments reduce costs may be unrealistic. Patients are willing to pay for improvements in health status just as they are willing to pay for other desirable goods and services. We do not treat cancer in order to save money. Instead, treatments are given in order to achieve better health outcomes. Cost/effectiveness is an alternative approach in which the unit of outcome is a reflection of treatment effect. In recent years, cost/effectiveness has gained considerable attention. Some approaches emphasize simple, treatment specific outcomes. For example cost per tumor detected might be used to evaluate a cancer screening program The major difficulty with cost/effectiveness methodologies is that they do not allow for comparison across very different treatment interventions. For example, health care administrators often need to choose between investments in very different alternatives. They may need to decide between supporting bone marrow transplantation for a few patients versus cancer screening for a large number of patients. For the same cost, they may achieve a large effect for a few people or a small effect for a large number of people. The treatment specific outcomes used in cost/effectiveness studies do not permit these comparisons. Cost/utility approaches use the expressed preference or utility of a treatment effect as the unit of outcome. This approach is characterized in the GHPM. As noted in World Health Organization documents, the goals of health care are to add years to life and to add 12
Quality of Life Assessment life to years. In other words, health care is designed to make people live longer (increase the life expectancy) and to have a higher quality of life in the years prior to death. Cost/utility studies use measures that combine mortality outcomes with quality of life measurements. The utilities are the expressed preferences for observable states of function on a continuum bounded by zero for death and 1.0 for optimum function as in the General Health Policy Model (Kaplan & Bush, 1982); Kaplan & Anderson, 1988a, 1988b, 1990; Kaplan & Anderson et al, 1989). In recent years, cost/utility approaches have gained increasing acceptance as methods for comparing many diverse options in health care (Weinstein & Stason, 1977; Kamlet, 1992). The key to cost/utility analysis is to have a unit of outcome that is equivalent across all diseases and health states. The unit must also be clearly linked with death so that mortality, morbidity, and prognostic information can be integrated. Clinical applications There are a growing number of applications of the model. Elsewhere, a variety of clinical trial applications have been summarized (Kaplan, 1993). In these trials patients are typically randomly assigned to treatment or placebo and then followed over time using the QWB. The outcome of the treatment is summarized in terms of QALYs, which take side effects and benefits of treatment into a comprehensive expression of tr
make life less desirable at times prior to death (health-related quality of life) (Kaplan & Bush, 1982; Kaplan & Anderson, 1988a, 1998b, 1990; Kaplan et al. 1989). Because of the important impacts of illness upon . evaluation of wellness, and prognosis or the duration of stay in health states. The goal of decision analysis is to express the .
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