Validity of a quality of well-being scale as an outcome measure in chronic obstructive pulmonary disease

J Chron Dis Vol. 37. No. 2. pp. 85-95. 1984 Printed in Great Britain. All rights reserved

Copyright

t

0021-9681184 $3.00 + 0.00 1984 Pergamon Press Ltd

VALIDITY OF A QUALITY OF WELL-BEING SCALE AS AN OUTCOME MEASURE IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE ROBERTM. KAPLANI, CATHERINEJ. ATKINS’, and RICHARD TIMMS’ ‘San Diego State University, San Diego, CA 92182 and University of California, San Diego, La Jolla, CA 92093 and *Scripps Clinic and Research Foundation. La Jolla. CA 92037, U.S.A. (Receioed

in rrrised form

18 July

1983)

Abstract-This paper evaluates the validity of the Quality of Well-being Scale (QWB) as an outcome measure for research on Chronic Obstructive Pulmonary Disease (COPD). The Quality of Well-being Scale was originally designed for use as a general health outcome measure. One criticism of this approach has been that it may not be valid in studies limited to a specific disease or condition. We report correlations between the QWB and a variety of other outcome measures obtained in an experimental trial evaluating the benefits of behavioral programs for COPD patients. The data from the trial suggest that the QWB is substantially correlated with both performance and physiological variables relevant to the health status of COPD patients. An advantage of the QWB is that it can be transformed into well-year units for cost-effectiveness studies. It is concluded that the QWB has many advantages as an outcome measure for specitic disease groups.

INTRODUCTION THE course of the last decade, Bush and colleagues have developed a comprehensive health decision model [l-6]. The best known component in the model is a general Health Status Index. The model and associated methods of measurement were originally developed in response to the need of health service researchers for a comprehensive health outcome measure that could be used for evaluation research, policy analysis, and the comparison of groups suffering from different diseases and conditions. One of the most important aspects of general health outcome measures is that they allow comparison between heterogeneous patient groups. For example, using these models, it is possible to evaluate the cost-effectiveness of a screening program for thyroid abnormalities [7] with the cost-effectiveness of a treatment such as estrogen replacement for post-menopausal women [8]. This method is in contrast to approaches that utilize a specific outcome measure for each disease entity [9]. Despite its advantages, the general healfh index approach has been criticized as not useful as an outcome measure in disease specific programs. In this paper we argue that the General Health Status Index, and in particular a subcomponent known as the Quality of Well-being Scale, has validity as an outcome measure for evaluating interventions for Chronic Obstructive Pulmonary Disease (COPD) patients. In medical research, program or treatment effectiveness is often measured using simple indicators. In chronic lung disease, those indicators may be pulmonary function, ventilatory capacity, pulmonary hypertension, etc. These measures are essential for monitoring the course of a specific disease. Yet, disease specific indicators cannot permit the assessment of cost-effectiveness across different treatments with different specific goals. OVER

All correspondence should be addressed to: Robert M. Kaplan. Center for Behavioral Medicine, State University. San Diego, CA 92182, U.S.A. Supported by Grants ROl HL 25109 and K04 HL 00809 from the National Heart, Lung, Institute. 85

San Diego and

Blood

86

ROBERT M. KAPLAN et 01.

For instance, it would be difficult to evaluate the relative value of programs for lung vs diabetic patients when the benefits are measured in terms of pulmonary function and plasma glucose, respectively. Further, specific indicators may allow side effects of treatments to be overlooked [ 11,121. Steroid drugs can modify many symptoms for COPD patients, but may also be associated with decreased immunity and increased susceptibility to many problems in the long run. A measure focusing only on ventilatory capacity may miss the overall impact of the treatment upon function and symptoms. That overall assessment requires a comprehensive measure of health status [I]. Obtaining scores on a general health status measure requires several steps. During the early phases of the Health Index Project, Bush and a group of colleagues reviewed multiple sources to determine all the ways that diseases and disabilities can impact function. They organized items from multiple sources into three scales that represent different dimensions of daily functioning: mobility, physical activity, and social activity [lO,l 11. Levels of these scales are shown in Table 1. The reader is cautioned that Table 1 is not the scale, but a listing of labels representing the scale steps. Survey instruments have been developed to classify individuals into one step on each of the three scales. The reliability and validity of these instruments is discussed elsewhere [13]. Combinations of steps from the three scales are referred to as Funcfion Levels [14-161. Thus, for any particular point in time, an individual can be classified into a Function Level which is a unique combination of the steps from the three scales. In summary, the Function Level classification describes the health functioning of a person at a particular point in time. Classification of Function Level alone is insufficient as a health outcome measure. As many as 80% of those interviewed in population surveys are not dysfunctional. So, in addition to function states, symptoms and problems that produce dysfunction are also noted. The Health Index system includes a classification of symptoms and problems for each patient on each day he/she is interviewed. Examples of complexes of symptoms and problems for the system are shown in Table 2. Combinations of Function Levels and symptom/problem complexes might describe different levels of wellness. An example of a level of wellness for a particular day might be: In house (mobility 3) In bed or chair (physical activity 1) Performed self-care but not work, school, or housework (social activity Cough, wheezing or shortness of breath (symptom/problem 11)

2)

Survey instruments are available to place individuals into defined states of wellness. However, assigning numbers to these levels of wellness is a matter of preference, value or utility [17]. Human judgment studies have been conducted to place the observable

Mobility

Physical

activity

Walked without problems (4)

Drove car and used bus or train without help (5)

physical

Walked with physical limitations (3)

Did not drive, or had help to use bus or train (4)

Moved own wheelchaIr without help (2)

Social activity Did work, school, or housework and other activities (5) Did work, school. or housework but other activities limited (4)

In house (3) In bed or chair (1) In hospital

(2)

In special care unit (1)

Limited in amount or kind of work. school, or housework (3) Performed self-care but not work, school or housework (2) Had help with selfcare (I)

Adapted

from Kaplan

and Bush,

1982 [6].

x7

Quality of Well-being in COPD TABLE 2. TEE SAMPUZSYMPTOMOR PROBLEMCOMPLEXESAND ADIUSTMENX (W,) FOR Complex number (1)

Cl C9 Cl I Cl3 Cl5 Cl9 C13 (32 (‘33 c3s Adapted

problemcomplex seeing-includes wearingglassesor contact lenses Symptom

Levt~

OF

WELL-BEING SCORES

or

Any trouble Pain in chest, stomach, side, back, or hips Cough, wheezing, or shortness of breath Fever or chills with aching all over and vomiting or diarrhea Pamful, burning or frequent urination Pain, stiffness. numbness, or discomfort of neck, hands. feet, arms. legs. ankles, or several joints together Two legs deformed (crooked), paralyzed (unable to move), or broken -mcludes wearing art~iiual limbs or braces Loss of consciousness such as seizures (fits), fainting. or coma (out cold or knocked out) Taking medication or staying on a prescribed diet for health reasons No symptom or problem from Kaplan

Adjustment (WJ 0.0190 -0.0382 -0.0075 - 0.0722 -0.0327 -0.0344

~0.1507 0.1 124 0.2567

of al.. 1976 [l&3].

functional states onto a preference continuum with the anchor 0 for death and 1.0 for completely well. In several studies, random samples of citizens from a metropolitan community evaluated the desirability of over 400 case descriptions. Using these ratings, a model of preference structure has been developed that assigns weights to each function level scale step and symptom/problem complex [15, 181. Cross-validation studies have shown that the model can be used to assign weights to all possible states of functioning with a high degree of accuracy (R2 = 0.96). In fact, the model can accurately forecast ratings of the same case by different judges at a different point in time with an R2 of 0.94 [5]. The preferences are used as weights or weighting factors for Function Levels. In addition, there is an adjustment for the most undesirable symptom or problem. The resultant preference weighted state is called a Quality of Well-being score. The preference for the function level described above has been measured as 0.5715 [ 181, and adjustment for the symptom or problem was -0.0075. Therefore, the Quality of Well-being score is 0.5640. In previous publications, this has also been referred to as the Index of Well-being. In summary, the Quality of Well-being score is the preference, or value, members of the community associate with a particular combination of a Function level and a Symptom/problem complex at a specific point in time. Typically. the Quality of Well-being score is obtained on four consecutive days, and the mean Quality of Well-being score across these days is taken to enhance reliability [5]. For each day, the score for a group can be stated symbolically as

where N is the total number of persons in the population, NJ is the number of persons at each function level, Q, is the social preference weight for each function level j = 1 . J, J is the total number of function levels. Another component of the general health measurement system considers the transition among states over the course of time. The fact that different individuals are in the same state for different reasons is reflected in different expected transitions to other states over the course of time. These transition probabilities are prognoses reflecting the probability of staying in the same state, getting better, or getting worse. Consider two persons in the state described earlier: one because he had the flu, and the other because he had chronic obstructive pulmonary disease. The person with the flu may be acutely ill today, but may be in a more desirable state of functioning within a few days. The COPD patient, however, may continue at a low level of functioning. A health status measure would be incomplete if it included only the current state. To be comprehensive, it must include the expected transitions to other states of wellness over the course of time. A cigarette smoker may be functioning well at present, but in comparison to a nonsmoker, may have a higher probability of transition to poorer functioning or to death in the future than a nonsmoker. Cancer would not be a concern if the disease did not affect current functioning or the probability that functioning would

88

ROBERTM. KAPLAN et al.

be limited at some point in the future. The General Health Decision model considers the lifetime expected utility or Well-Life Expectancy. The Well-Life Expectancy is the product of the Quality of Well-being score times the expected duration of stay in each function level for a standard life period. The expected duration of the stay in each state is determined by the transition rates [3, 191. The Well-Life Expectancy is expressed as

where E is the Well(quality-adjusted)-Life Expectancy for a cohort or population in well-year equivalents, Y, is the expected duration in each function level j, computed from transition probability j = 1, . __.I, Qj is the Quality of Well-being score or social preference weight associated with each function level, J is the total number of function levels in a given analysis. The Well-Life Expectancy gives the current life expectancy adjusted for diminished well-being associated with disability states and the duration of stay in each state. The diminished well-being is given in the preference scores (see equation 1). Using this system, it is possible to simultaneously consider mortality, morbidity, and the social desirability of function states. When the proper steps have been followed, the model quantifies the health output of a treatment in terms of the equivalents of years of life that it produces or saves. Thus, a “well-year” can be defined conceptually as the equivalent of a year of completely well life, or a year of life free of dysfunction, symptoms, and health-related problems. A disease that reduces the health-related quality of life by l/2, for example, will take away 0.500 well-years over the course of 1 year. If it affects two people, it will take away 1.0 well-year (2 x 0.500). A medical treatment that improves the Quality of Well-being by 0.100 for each of 10 individuals produces 1 well-year if this benefit is maintained over the course of 1 year. The effectiveness of programs and treatments can be compared with each other by the number of well-years that they produce. Dividing the cost of a program by the number of well-years gives the relative efficiency or cost/effectiveness. Using this system, it is possible to make comparisons between very different types of programs. The rationale for this system is given in a variety of publications [l-6]. In particular, [6] provides an overview and gives examples. As suggested above, the General Health Decision Model has many advantages for health policy and health program evaluation. In particular, it has the advantage of allowing cost/effectiveness comparisons across very different types of interventions. However, many biomedical researchers desire outcome measures relevant to specific disease groups. In order for the general model to be used for any specific problem the Quality of Well-being portion must be meaningful. Although the validity of the Quality of Well-being scale has been reviewed in previous publications [6, 181,most of the supporting data were taken from general population surveys. This left unanswered the question of validity for use with any specific disease group. In this paper we will report data on the validity of the Quality of Well-being scale for the evaluation of programs focusing on patients with Chronic Obstructive Pulmonary Disease. CHRONIC

OBSTRUCTIVE

PULMONARY

DISEASE

Chronic Obstructive Pulmonary Disease (COPD) is a chronic condition characterized by a persistent slowing of airflow during forced exhalation [20]. The diseases most often categorized as COPD include chronic bronchitis, emphysema, and asthma. COPD is the leading cause of bed disability and the fourth leading cause of limitation of major activity [21-241. The economic costs of COPD are staggering-and may go as high as $15 billion per year for health care costs, time lost from work, and lost wages [24]. Although COPD patients can benefit from exercise [25], motivating them to participate in exercise programs is difficult. Increasing adherence to exercise may improve oxygen

Quality of Well-beingin COPD

89

consumption and utilization and may promote improvements in the quality of life [26]. However, no behavioral or medical intervention can repair damaged lung tissue and restore pulmonary function [26]. In this paper, we report data gathered in an experimental study designed to improve compliance to exercise among COPD patients. The central focus of the paper is the validity of the Quality of Well-being scale that served as the major outcome measure. Validity

Validity indicates the range of inferences that are appropriate when interpreting a measurement, score, or the results of a test [27]. In other words, validity defines the meaning of a score. Validity is not absolute; it is relative to the domain about which statements are made. Thus, if we want to validate a Quality of Well-being Scale for use in studies of COPD patients, we must show that the measure is associated with other variables believed to be indicators of function. Criterion validity is not appropriate for studies of health status because there is no clearly defined and directly observable measure of health that can serve as our criterion. Instead, we must employ the strategy of construct validity as developed by Cronbach and Meehl [28]. This process is required when, “. . no criterion or universal content is accepted as entirely adequate to define the quality to be measured” [28]. Construct validation involves assembling empirical evidence to support the inferences that a particular measure has meaning. It is an ongoing process, akin to gathering support for a complex scientific theory for which no single set of observations provides crucial or critical evidence [28]. Evidence on the validity of the QWB scale as a health-related quality of life measure has been published in several earlier papers [3-6, 181. In this paper, we present further evidence for the construct validity of the QWB scale by showing its association with performance, psychological, and physiologic measures. One purpose of this exercise is to demonstrate the value of this approach as an outcome measure for programs designed to help COPD patients. SUBJECTS

The subjects were 28 male and 47 female moderate to severe COPD patients who had experienced progressive loss of pulmonary function. The specific criteria for inclusion in the program were: (1) diagnosis of emphysema, chronic bronchitis, and/or asthma, (2) absence of other significant pulmonary disease (i.e. TB, fibrosis, or neoplasm), (3) freedom from chronic disabling nonpulmonary disease which would hinder participation (i.e. arthritis, retardation, etc.), (4) absence of an acute cardiac disorder (myocardial infarction) within the past three months, and (5) ability to stand and walk unaided for at least 100 yards without complaint of severe dypsnea. The mean age was 64.79 years (SD = 7.86 years). General assessment

All patients were invited to the Scripps Clinic and Research Foundation (La Jolla, California) for an initial assessment. During this assessment, the patient was interviewed to complete a Quality of Well-being Scale of a Health Decision Model (also known as the Health Status Index). The Index was designed to express diminished quality of life attributable to illness or disability. The reliability and validity of the scale is given in earlier papers [5, 181. In addition, the patient was given a spirometric examination to determine Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV,). The FVC is the total amount of air that can be exhaled after a maximum inhalation. We express FVC as the percentage of the expected for each height, sex and age. FEV, is the amount of air that can be expired in the first second. We express FEVl as the percentage of that predicted for age, sex, and height from the Standard tables [29]. FVC and FEV, were obtained using a 570 Med Science Wedge Spirometer. The procedure required the patient to take a deep breath and blow it into the spirometer as rapidly as possible. Exhalation was to continue until the patient was unable to move

90

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et al.

air. This procedure was repeated three times, and the most favorable test was chosen for analysis. Following the spirometric test, the patient was placed on a treadmill with bipolar ECG leads to monitor cardiac frequency and rhythm during exercise. Exercise tests began at 0.6 mph, at 0% grade. The patient was encouraged to exercise as long as he or she could and still recover. The endpoint of the exercise test was reached when any of the following occurred: (1) the patient reached 85% of a predicted maximum heart rate, (2) the patient reported chest pain or dizziness, (3) the ECG diplayed heart arrythmias, (4) the patient reported being exhausted or severely short of breath. Shortness of breath was the most common cause for stopping the treadmill. Arterial

saturation

Arterial saturation of oxygen in blood (SaO,) was continuously monitored before and during the exercise test using a Hewlett-Packard ear oximeter with continuous digital read out. The values reported here were obtained prior to the exercise stress test while the patient rested quietly. Walking Each patient was assigned an exercise prescription based upon the maximum mph he or she obtained during the treadmill test. Each patient recorded his or her own daily exercise in a walking log. Patients were asked to record the time, total number of minutes spent walking, the approximate distance, and a resting exercise pulse rate for each of the two prescribed daily walks. Eficacy

expectation

Bandura and other advocates of social learning theory argue that expectations that a particular behavior can be successfully executed are important mediators of behavior change. These cognitive constructs are called self-efficacy expectations. Self-efficacy expectations were assessed after each patient had been given his or her walking prescription and again at the three-month follow-up. A set of six self-efficacy scales were adapted from those used by Ewart et al. [31]. Within each scale, the patient was presented with a series of progressively more difficult performance requirements within a specified domain of activity. In this report, we present only data for walking efficacy. The scale for walking included: walk one block (approximately 5 min), walk two blocks (10 min), walk three blocks (15 min) . . . walk miles (90 min). In sum, the walking scale had nine items representing increasing gradations (in nonequal intervals) of difficulty. For each item, the patient indicated the expectation that the behavior could be performed. Then, for the items selected, the patient was asked to rate the strength of their expectation to perform the activity on a loo-point probability scale, ranging in IO-point intervals from high uncertainty, through moderate certainty, to complete certainty [31]. The numbers 1 through 9 were assigned to the nine walking-distance levels. The score for walking efficacy equalled the longest distance which the patient indicted 100% confidence he or she could perform [30, 3 11. Follow -up After the assessment session, the patients were randomly assigned to different behavior modification or control groups designed to increase their adherence to an exercise program. As a result of these interventions, we observed change on several of the variables and differences between experimental groups increased the variability on the measures. The results of the experimental portion of the study are reported elsewhere [32]. After 3 months, 6 months, 1 year, and 18 months, each patient was re-tested using the same procedures. This paper will focus on data obtained at the first two assessment sessions. However, some longer term data will also be discussed.

Quality

of Well-being

91

in COPD

Initial

Follow-up

Variable

N;-

x

SD

N

x

SD

QW-i Self

66

0.608

0.0X

67

0.603

0.09

&kKy

66

4.02

2.40

68

3.82

2.50

74

431.53

242.66

73

543.59

315.29

64

36.22

23.84

63

37.23

24.36

64 70

60.11 91.53

19.86 4.00

63 66

62.21 91.42

20.69 6.61

Exercise tolerance (set) FEV, (“, pred.) FVC (:, pred.) sao,

*N’s are less than 75 because some patients refused to take portions or follow-up tests or because of equipment problems. TABLE 4.

VALIDITY

CORRELATIONS

BtlWEEN

QWB

of the initial

/,lul> PER-

FORMANCEMEASURBS Assessment session

Measure

QWh Initial

Efficacy Exercise tolerance

QWB, Follow-up Critical

Efficacy Exercise tolerance

value of I for 60 df=

QWB, QWB, IT00 0.49 0.41

0.65 0.35 0.32

0.65 0.41 0.44

0.49 0.54

0.25 at 0.05 significance

I .oo level

RESULTS

The means and standard deviations for the variables used in the analyses are presented in Table 3. These values are presented separately for the initial and the first follow-up visits. Table 4 shows the validity (Pearson Product Moment) correlations between the Quality of Well-being Scale and the efficacy and performance measures. The columns labeled QWB, and QWB, represent Quality of Well-being scores for the initial and 3-month follow-up sessions respectively. Examination of Table 4 suggests that all correlations between the QWB and measures of self-efficacy and exercise tolerance are statistically significant. Further, the correlations between the QWB, and other variables are higher at the initial assessment than with the same variables measured at the follow-up assessment period (upper portion of table). Similarly, QWB scores at the 3-month follow-up (QWB,) are more highly correlated with efficacy and tolerance measures also obtained at the 3-month follow-up (lower portion of table). Table 5 presents the correlations between the QWB and physiologic measures. FEV, is considered the best single indicator of obstructive lung impairment. Of particular interest is the high correlation between QWB at the initial assessment and FEV, (r = 0.51, p < 0.001) and the similar high correlation between QWB at the 3-month follow-up and the corresponding FEV, value (r = 0.54, p < 0.001). FVC is regarded as a less important indicator of lung abnormalities. As Table 5 suggests, measures of FVC are substantially correlated with the QWB but not to the same extent as were the FEV, assessments. Measures of SaOz tended not to be highly associated with QWB scores except for at the 3-month follow-up. However, SaO, may not be a strong indicator of dysfunction because of a variety of compensatory reactions, shifts in the oxygen disassociation curve, and because of statistical problems such as restricted range in variability. Another test of the validity of the Quality of Well-being score is gained through the examination of dynamic correlations. Dynamic correlations consider changes in the dependent variables as a function of changes in the independent variable. Table 6 presents the dynamic correlations between the Quality of Well-being Scale and the other measures. Changes in Quality of Well-being were significantly correlated with changes in exercise tolerance. self-efficacy, walking compliance, and SaO,. Changes in the Quality of Wellbeing Scale were not significantly correlated with changes in FVC or FEV,. However,

92

ROBERTM. KAPLAN et al.

TABLE

5.

VALHXTY

QWB AND

CORRELATIONS

PHYSlOLOGICAL

TABLE 6. DYNAMIC CORRELATIONS.

SETwtaN

WELL-BEING

MEASURES

RELATED

Assessment session

Measure

QWB,

QWB,

Initial

FVC FEV, SaO,

0.34 0.51 0.12

0.35 0.33 0.09

Follow-up

FVC FEV, SaO,

0.44 0.40 0.09

0.35 0.54 0.29

OVER WITH

A THREE-MONTH

CHANGES

OVER

THE

IN THE

SAME

CHANGES INTERYAL

OTHER

IN COR-

VARlABLES

INTERVAL

AQWB A Exercise tolerance A Self efficacy Walking compliance A FVC A FEV, A SaO,

0.40 0.31 0.42 0.03 0.1 I 0.28

Excludes dead cases-including deaths analysis boosts the correlations.

p< 0.001 0.02 0.001 NS NS 0.02 in the

evidence suggests that FVC and FEV, do not change significantly over a short inte even with treatment. Thus, variability in changes for these two measures was almost A variable that does not vary cannot significantly correlate with another measure. T the two nonsignificant correlations in Table 6 are attributed to the lack of variability changes on the two pulmonary function indicators. It is worth noting that deaths have I eliminated from the analysis presented in Table 6. Inclusion of the deaths does boost correlation for the FVC and FEV, by providing points at the extreme of each scale. T inclusion of the dead cases would artificially inflate the correlations. It might be argued that a time interval longer than 3 months is required to evaluate correlation between FEV, and the QWB. This is because FEV, changes slowly. In o to analyze these longer term changes, we examined the changes in FEV, over the el 18 months duration of the study with changes in the QWB over this same period. correlation was observed to be very successful (r = 0.63, p < 0.001). Well - Years Data on the Quality of Well-being Scale were obtained at the initial interview an the 3, 6, 12 and 18 month follow-up periods. A one-way analysis of variance demonstr that experimental and control groups did not differ prior to the interventions. For all o analyses, differences in Quality of Well-being scores from the initial visit were L Differences in QWB scores between the experimental and control groups were statistic significant by the first follow-up period (F,,65= 9.98, p < 0.002). Following the 3-ml assessment, the experimental and control groups continued to differ at each assessr period. However, by the last follow-up, the differences were only marginally statistic significant. The reduction in statistical significance results primarily from the incre variability in both groups across follow-up sessions. Table 7 summarizes the obse well-year benefits from the experiment. The first column shows the follow-up periods. second column shows the change in well-being score for the treated groups while the t column shows the mean change in well-being for the control subjects. The fourth co1 shows the differences between the treated and control group defined as mean treated m mean control. The next column displays the number of patients available at that folio\ period. The second column from the right of the table shows the duration for whick assessment was based. For example, 0.25 means that the data represent assumed well-b difference over a three month or 0.25 year period. The final column shows the wellTABLE

7. OBSERVED

CONTROL

GROUPS

WELL-YEAR

BY NUMBER

BENtFITS

Om,uNtD

OF SUBJECTS AND

BY MULTLPLYINC

ADJUSTlNG

FOR THE

DlFFERENCES DURATlON

OVER

BETWLEN WHlcH

T&ATE”

THE

EFFECT

OBSERVED

Foliow-up 3 months 6 months 12 months 18 months

X treated 0.02 1 0.036 -0.012 -0.032

X control

-

0.055 -0.021 - 0. I34 -0.131

Difference

N

0.076 0.056 0.114 0.099

70 55 50 48

Total well-year production Toevs CI al.. 1983 [43].

Duration 0.25 0.25 0.50 0.50

over project period

Well-years 1.33 0.77 2.85 2.38 7.33

AND WAS

Quality

of Well-being in COPD

93

yield for that period. It is calculated by obtaining the product of the difference in QWB scores between treated and control groups times the number of patients available for observation. Then, that product is multiplied by the proportion of the year the assessment represents. For example, at the 3 month follow-up, the treated and control groups differed by 0.076 units of well-being. Multiplying this value times the 70 patients included in the analysis yields 5.32. However, this value is obtained only after one-quarter (0.25) of the year. We multiply 5.32 by 0.25 to estimate the well year production of 1.33 years. Similar assessments were made for the 6-, 12- and 18-month follow-ups. The total well year production is the sum of the well years produced at each assessment interval. According to this analysis, 7.33 well-years were produced. This figure can be used in cost/effectiveness analysis. The total costs for the project were S 174,000. Thus, the cost/well-year was approximately $23.740.00. This analysis makes many assumptions. In another paper, we provide details of the analysis and explore a variety of different assumptions including discount rates, sensitivity, and expected duration of experimental effects [43]. DISCUSSION

Data presented in this paper suggest that a Quality of Well-being Scale, which is a point-in-time component of a general health decision model, correlates with performance and physiological measures used to evaluate COPD patients. Each of these dimensions affects the quality of life for COPD patients. Although the measures are substantially intercorrelated, the QWB may be the most useful as an outcome measure in rehabilitation research. FEV, is highly correlated with performance measures, but does not change as a function of the rehabilitation process [26]. Thus, it is impossible to demonstrate program effectiveness with FEV, as an outcome. Exercise tolerance and efficacy judgments reflect aspects of the quality of life, but are limited to a specific aspect of functioning. Efficacy assessments for walking are only weakly correlated with efficacy judgments relevant to other activities [34]. Some investigators have argued that the important variables to measure in COPD include dyspnea, decreased ventilatory capacity, respiratory muscle fatigue, and pulmonary hypertension leading to right ventricular dysfunction. Certainly these are the major characteristics of obstructive lung disease. However, these factors are important because they limit function or infhrence the probability of reduced function (including death) in the future. The objective of health care should be to extend life and provide the highest obtainable quality of life for the longest duration. Pulmonary hypertension should be treated because it may cause limitations in functioning and shortened life expectancy. Thus, pulmonary hypertension can be thought of as a mediator of health status. If pulmonary function did not affect health status, physicians and patients would be unconcerned about it. Our point is that producing the highest qualify of life for the longest duration should be the objective of health care and that various methods should be used to achieve this objective. These methods may or may not influence important mediating variables such as ventilatory capacity, respiratory muscle fatigue, etc. The Quality of Well-being Scale is not the only quality of life measure available for quantifying outcomes in chronic disease studies. Bergner et al. [35] have developed a similar method known as the Sickness Impact Profile or SIP [36]. The SIP has been an outcome measure in several major studies on COPD patients. In the Nocturnal Oxygen Therapy Trial (NOTT), the SIP was shown to have substantial validity. The SIP total score was significantly correlated with PaO, (r = -0.16) and maximum work load during exercise (r = -0.37) [37]. In the Intermittant Positive Pressure Breathing (IPPB) clinical trial, the SIP was significantly correlated with measures of breathlessness but not with measures of cough, phlegm, or wheezing from the Respiratory Disease Questionnaire. These latter three measures were found to have poor validities as outcome measures [38]. The SIP was not significantly correlated with FEV,. However, our sample included nine people with asthma and higher FEV, values. It is possible that there was a more restricted

94

ROBERTM. KAPLAN

et a(.

range of FEV, values for studies validating the SIP. Our sample was heterogenous on several of the measures, and the higher correlations may owe to the richer variability. The SIP may have some advantages over the QWB since it separately quantifies physical, psychosocial, and life quality impacts of illness upon daily function. Despite its many advantages, the SIP may not be as appropriate for cost-effectiveness studies as the QWB. For instance, SIP scores (observed over time) are not in scale units that are easily transformed into well-years. Further, the SIP does not allow the integration of morbidity and mortality data. Unfortunately, mortality is a frequent outcome in studies of COPD patients. The mean QWB score reflects both death and disability in the same index number. It is likely that the SIP and QWB tap the same sources of variance since several analyses have shown a high correspondence between the two approaches [39]. Read [40] is currently comparing both measures against indicators of pulmonary function. When available, the results should be most informative. Among the many quality of life measures, only the QWB is directly linked to a health policy model. In other publications, Bush and colleagues [l-6] have demonstrated how the Quality of Well-being Scale and associated decision models can be used for policy analysis. Recent papers by Epstein [7] and Amberg et al. [41] have shown the value of the Index for evaluating clinical lab tests. Other more general analyses have shown how similar methods can be used to compare programs that have different specific objectives [42]. We believe the model can be used to evaluate interventions relevant to a specific disease, or for cost-effectiveness comparisons of interventions for very different problems. In summary, chronic diseases such as Chronic Obstructive Pulmonary Disease have a profound impact upon the quality of life. A quality of life measure derived from Bush’s General Health Decision Model may be a valid outcome measure for evaluating the impact of medical and psychosocial interventions. Although the measure does not directly assess physiological processes, it allows the investigator to express the benefit of treatment in a generalized unit. This unit can be used for cost-effectiveness studies, and data from such studies can be placed in context of other cost-effectiveness evaluations. Ultimately, interventions in very different areas can be compared in common units of cost per well-year. Acknowledgemen_rs-The authors gratefully acknowledge the assistance of the Scripps Clinic General Clinical Research Center (GCRC Grant RR 00833) and the following individuals: Connie Toevs, Michele Chadwick, Leslie Schimmel, Ken Lofback, Sybille Reinsch, Joe Vaughn, and Sandy Casmer. Critical comments by J. W. Bush are also appreciated.

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