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A comparison of three measures
Journal of Clinical Epidemiology 54 (2001) 565–570
A comparison of three measures: the time trade-off technique, global health-related quality of life and the SF-36 in dialysis patients Yasmin Maor*, Miri King, Liraz Olmer, Benjamin Mozes The Center for the Study of Clinical Reasoning, The Gertner Institute for Epidemiology and Health Policy Research, Sackler School of Medicine, Tel Aviv University, Sheba Medical Center, Tel Hashomer 52621, Israel Received 3 August 1999; received in revised form 23 August 2000; accepted 21 September 2000
Abstract We investigated the correlation between descriptive and valuational measures of health-related quality of life (HRQL) and assessed determinants affecting these measures. Our suspicion was that there is little similarity in the content of descriptive and valuational measures of HRQL. We thus conducted a cross-sectional observational study of 56 hemodialysis patients. All underwent structured interviews. Dependent variables were patients utilities [time trade-off (TTO)], global rating of HRQL and generic HRQL (SF-36). Independent variables were socioeconomic details, disease severity, comorbidity, symptoms, depression, social support, and laboratory data. The correlation between TTO and global HRQL was ⫺0.33 (P ⫽ .0178) and between TTO and the SF-36 physical and mental summary scores ⫺0.16 (P ⫽ .2383) and ⫺0.20 (P ⫽ .1443), respectively. The regression models for the SF-36 physical and mental summary scores explained 75% and 64% of the variance, and for global HRQL 29% of the variance. The independent variables had no effect on the TTO. This confirmed our suspicion that a qualitative difference exists between TTO and descriptive quality of life tools. The TTO content could not be explained by the variables that entail the content of HRQL instruments. © 2001 Elsevier Science Inc. All rights reserved. Keywords: Utility; Time trade-off; Quality of life; SF-36; Global measure; End stage renal disease
1. Introduction Quality-adjusted life years (QALYs) are widely used in health economics and in medical decision-making as a useful measure that reflects both life expectancy and quality of life (QOL). The calculation of QALYs requires a single number (or index) that reliably reflects QOL. Descriptive QOL measurements describe functional status and wellbeing in various dimensions such as physical functioning, mental health, social functioning, health perception, vitality, and pain. The most commonly used generic QOL tools include the Sickness Impact Profile (SIP) [1], the Notingham Health Profile (NHP) [2], and the Short Form-36 (SF-36) [3]. These tools are multidimensional and have high reliability and construct validity [4]. Valuational measures (utilities) capture patients’ preferences for health states, encompassing the positive and negative aspects of a particular health state into a single number. The attractiveness of these methods lies in their ability to describe patients’ preferences and values in addition to their
* Corresponding author. Tel: 972-3-5303272; fax: 972-3-5303277. E-mail address: [email protected] (Y. Maor)
QOL. These measurements were introduced into the medical community during the early 1970s [5,6] and have since gained a wide acceptance. Methods used to elicit preferences include the rating scale, standard gamble, and timeoff (TTO) techniques. In this study we focused on the TTO, in which patients are given a series of choices between living in perfect health for a certain amount of time and living in a particular condition worse than perfect health for a longer period of time [7]. The amount of time for the perfect health state is varied until the point where the patient is indifferent between the two alternatives. Patients’ preferences regarding health states are expected to correlate with health-related quality of life (HRQL), as well as with other attributes, such as risk-attitude, framing, and past experience [5,8]. That is to say that patients experiencing a lower QOL will be willing to trade more time to improve their QOL compared with patients with a better subjective QOL. However, several studies assessing the relationship between descriptive QOL tools and utility measures demonstrate only low to moderate associations [9– 12]. These studies call into question the theoretical framework of utility measures. In this study we undertook a different approach by adding a direct measure of the relations between utilities and disease severity parameters. We chose dialysis patients since this population suffers from a debili-
0895-4356/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S0895-4356(00)00 3 3 8 - 3
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tating chronic disease affecting many of the domains constructing QOL [13–15]. Our hypothesis was that no correlation exists between the TTO the SF-36 and global HRQL. 2. Methods 2.1. Patients The study involved a convenience sample of patients undergoing hemodialysis in a tertiary care center in Israel. To be included in the study, patients had to be on dialysis for 3 months or more, understand Hebrew and agree to complete the TTO question and the SF-36 questionnaire. Of 96 patients that were approached, 22 patients did not understand the questionnaire due to cognitive problems or inadequate understanding of Hebrew and 18 patients refused to participate. The remaining 56 patients were interviewed during hemodialysis treatment. Information was gathered by a structured interview and chart abstraction performed by a qualified nurse and one of us. Informed consent was obtained from all patients. The study was approved by the hospital’s IRB. 2.2. Questionnaire The questionnaire included the following items. A TTO question that was formulated according to the method described by Torrance et al. [5] (see the Appendix). The question assessing global health-related quality of life (HRQL) was formulated as follows: please rate your HRQL in the past year. Patients rated their HRQL on a Likert scale ranging from 1 (worst possible HRQL) to 7 (best possible HRQL). The Short Form-36 (SF-36) generic HRQL tool describes patients’ current HRQL using 36 items divided into 8 domains: Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health. Two summary scores are also computed: physical component score (PCS) and mental component score (MCS). The SF-36 has good reliability and construct validity [16–18]. Scores range from 0 to 100. Higher scores reflect better QOL. Socioeconomic details included age, sex, marital status, education (years of schooling), work status, and religion. All questions were close-ended. The End-State Renal Disease Severity Index (ESRD-SI) is a measure designed to assess hemodialysis patients’ severity of illness. The scale addresses specific hemodialysis problems such as access, blood pressure, bone problems, and comorbidity. Different weights are assigned according to problem type and severity, and then summed into an index. The index score ranges from 0 to 95. A higher score indicates a more severe state. The scale has adequate reliability and construct validity [19]. A symptom checklist modified from Parfrey’s specific health questionnaire for end-stage renal disease patients was used to assess patients’ severity of symptoms [20]. Parfrey’s
original questionnaire contains 8 subscales, one of which is a symptom checklist. For the purpose of this study we chose to use the symptom checklist, as it provides a comprehensive measure for assessing the presence of symptoms related to hemodialysis. To increase sensitivity, we added 4 symptoms instead of the “other” category: weakness and dizziness, thirst, lack of appetite, and parasthesia. Patients were asked to rate the severity of each symptom using 5 categories ranging from absent to very severe. The overall score was computed by summing the individual symptom scores. Scores range from 15 to 75. Higher scores indicate more severe symptoms. The Center for Epidemiologic Studies Depression Scale (CES-D) [21] contains 20 items assessing patients’ frequency of various feelings during the past week. Feelings are rated by frequency on a 4-category scale ranging from rarely or none of the time to most or all of the time. The overall score was computed by summing the scores of all items. Scores range from 0 to 60. Higher scores indicate a more depressed state. The social support scale (Apgar) [22] contains 17 items that measure social support in 3 settings: family, friends, and work. Items are rated on a 5-category scale ranging from hardly ever to almost always. Each setting receives an independent score by summing relevant items. A higher score indicates less support. In this study we omitted the work domain since most of the patients interviewed did not work. Laboratory variables known to be associated with mortality or decreases in QOL such as cholesterol [23], albumin [24], hematocrit [25], and the delivered dose of dialysis as measured by Kt/V (K⫽clearance, t⫽dialysis time, and V⫽volume of distribution of the patient) [26] were abstracted from patients’ charts. All questionnaires were translated into Hebrew by a twoway translation process: the questionnaires were translated into Hebrew by a bilingual translator and then translated back into English by another translator who was not familiar with the wording of the original English versions. Ambiguities were discussed and resolved with the consensus of bilingual experts. To assess reliability of the TTO and the global HRQL, a convenience sample of 34 patients were approached 2 to 4 weeks after the primary interview and were asked to respond once again to these questions. 2.3. Data analysis As the distribution of the TTO was skewed (11 patients were not willing to give up any time to improve their health), we performed a logarithmic transformation of the TTO variable [log(x⫹1)] [27]. All analyses were performed on the transformed values. Spearman’s correlations were used to assess associations between the TTO, SF-36 domains and summary scores, and the global HRQL question.
Y. Maor et al. / Journal of Clinical Epidemiology 54 (2001) 565–570
To identify variables associated with different tools’ scores we performed bivariate analysis and then multiple linear regression. The level of significance to participate in the models was set at P⬍.05. Several analyses were done: a) The dependent variable was the TTO. The independent variables were the eight domains of the SF-36. b) The dependent variable was the TTO. The independent variables were socioeconomic details, ESRD-SI, the symptom checklist, CES-D scores, Apgar scores, and laboratory data (cholesterol, albumin, hematocrit and Kt/V). c) The dependent variable was the TTO. In this analysis we excluded patients who were not willing to trade any time to improve their health in the TTO question. The independent variables were the same as in analysis b. d) The dependent variable was the PCS score of the SF36. The independent variables were the same as used in analysis b. e) The dependent variable was the MCS score of the SF36. The independent variables were the same as used in analysis b. f) The dependent variables were each of the 8 domains of the SF-36 (Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health). The independent variables in each of these analyses were the same as in analysis b. g) The dependent variable was the global HRQL score. The independent variables were the same as used in analysis b. Reliability of the TTO (test–retest) was assessed using Pearson’s correlation coefficient. Reliability of the global HRQL was assessed using Spearman’s correlation coefficient [5,28]. 3. Results The study included 56 hemodialysis patients. Sixty-four percent were males. The mean age was 58.2 years (S.D. 14.6 years), 73% were married, and mean schooling was 11.3 years (S.D. 3.6 years). Time traded for perfect health in the TTO ranged from 0 to 120 months, median 18 months. The correlation between the TTO and the domains of the SF-36 ranged from 0.05 to 0.22. The correlation between the TTO and the PCS was ⫺0.16 and between the TTO and MCS was ⫺0.20 (Table 1). None of these results was significant. The correlation between TTO and the global HRQL question was ⫺0.33 (P⫽.0178). None of the independent variables was significantly correlated with the TTO measure. These included socioeconomic details, ESRD-SI, the symptom checklist, depression, social support and laboratory data. Similar results were obtained when omitting patients who were not willing to trade any time in the TTO
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question. Correlation between SF-36 summary scores and the global HRQL and between these independent variables are presented in Table 2. In contrast to the low correlation between these variables and the TTO, the correlation with the SF-36 summary scores and the global HRQL rating scale were high to moderate. The domains of the SF-36 used as independent variables in the multiple linear regression had no effect on the TTO. Also, socioeconomic details, ESRD-SI, the symptom checklist, depression, social support, and laboratory data used as explanatory variables had no effect on the TTO. Using the same variables, models for the domains and summary scores of the SF-36 explained a large degree of the variance: 54% for Physical Functioning domain, 56% for Role Physical domain, 62% for Bodily Pain domain, 48% for General Health domain, 46% for Vitality domain, 45% for Social Functioning domain, 28% for Role Emotional domain, and 63% for Mental Health domain. The most contributing variables were the ESRD-SI reflecting severity of disease, the symptom checklist, and the CES-D score. The model for the PCS and MCS explained 75% and 64% of the variance, respectively (Table 3). The regression model of the global HRQL question explained 29% of the variance. Explanatory variables that entered this model were the CES-D score and hematocrit (Table 3). Pearson’s correlation coefficient between the two measurements of the TTO was 0.85 (P⫽.0001) and the Spearman correlation coefficient between the two measurements of the global HRQL question was 0.69 (P⫽.0001). 4. Discussion The results demonstrated that no significant correlation exists between the TTO and the SF-36. Furthermore, although high correlations existed between the SF-36 and a set of variables known to affect QOL, we could not demonstrate significant correlations between these variables and the TTO. In the same line, none of the independent variables affected the TTO, although we used the same candidate variables that had a high explanatory power when the dependent variable was the SF-36. Thus, our suspicion that descriptive and valuational measures differ in the content they entail was confirmed.
Table 1 Spearman’s correlation and significance level off the time trade-off, SF-36 summary scores, and global health related quality of life SF-36 summary scores
TTO Global HRQL
PCS (P)
MCS (P)
Global HRQL (P)
⫺0.16 (.2383) 0.38 (.0049)
⫺0.20 (.1443) 0.40 (.0029)
⫺0.33 (.0178) —
TTO—time trade-off, PCS—SF-36 physical component score, MCS— SF-36 mental component score, HRQL—health-related quality of life.
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Table 2 Spearman’s correlation and significance level of the time trade-off, global health-related quality of life, SF-36 summary scores and possible determinants
Age Education ESRD-SI Symptom checklist CES-D Apgar family Apgar friends Cholesterol Albumin Hematocrit Kt/V
TTO (P)
Global HRQL (P)
⫺0.07 (.6328) 0.01 (.9406) 0.15 (.2735) 0.10 (.4846) 0.14 (.3385) ⫺0.08 (.5885) 0.10 (.4710) ⫺0.07 (.6311) 0.08 (.5592) ⫺0.04 (.7469) 0.12 (.3988)
⫺0.04 (.7861) 0.27 (.0549) ⫺0.26 (.0586) ⫺0.32 (.0236) ⫺0.46 (.0011) ⫺0.06 (.6598) ⫺0.36 (.0120) ⫺0.02 (.8879) 0.13 (.3472) 0.27 (.0551) 0.21 (.1426)
SF-36 summary scores PCS (P)
MCS (P)
⫺0.20 (.1385) 0.34 (.0118) ⫺0.60 (.0001) ⫺0.82 (.0001) ⫺0.67 (.0001) ⫺0.22 (.1202) ⫺0.52 (.0001) ⫺0.04 (.7867) ⫺0.07 (.6082) 0.00 (.9863) 0.06 (.6848)
⫺0.14 (.3037) 0.30 (.0257) ⫺0.39 (.0033) ⫺0.72 (.0001) ⫺0.69 (.0001) ⫺0.31 (.0267) ⫺0.49 (.0002) 0.29 (.8322) ⫺0.17 (.2194) ⫺0.07 (.6105) 0.05 (.7082)
TTO—time trade-off, HRQL—health-related quality of life, PCS—SF36 physical component score, MCS—SF-36 mental component score, ESRD-SI—end-stage renal disease severity index, CES-D—depression scale, Kt/V—the delivered dose of dialysis (K⫽clearance, t⫽dialysis time, and V⫽volume of distribution of the patient).
Elsewhere, several studies performed in diverse populations have created models for utility measures using QOL tools, thus trying to investigate the degree of similarity in content between utility measures and QOL. In asthma patients using SF-36 domains as explanatory variables, the model for the standard gamble technique explained only 10% of the variance [9]. The model for the TTO in intermittent claudication patients explained 28% of the variance with social function and mental health domains of the RAND questionnaire being the explanatory variables. In contrast, physical functioning domain from the RAND
questionnaire was the main explanatory variable of the TTO in dialysis patients [10]. Correlation of utility measures with QOL tools ranged from 0.21 to 0.46 [9–12]. These studies call into question the theoretical framework of utility measures, which emphasize the importance of the impact of HRQL on patients’ utility values. In these studies, direct comparisons between descriptive HRQL measures and utilities were performed. This approach may underestimate the relations between utility measures and disease severity parameters. Generic HRQL tools are influenced by biological derangement and symptom severity, but also by other factors such as adjustment, personality, mental state, and socioeconomic factors. These other factors may mask the effect of disease on utilities. The merit of our study is that we directly measured the relations between utilities and disease severity parameters [severity of disease (ESRD-SI), severity of symptoms common in dialysis patients and laboratory data representing the severity of biological derangement]. The determinants we examined had a significant effect on QOL as measured by a generic descriptive tool. Utility measures are not expected to be identical to descriptive QOL measures as they address patients’ preferences, a concept that isn’t directly rooted in descriptive QOL tools. Yet, the comprehensive understanding of health today presumes that QOL and disease severity parameters should influence patients’ preferences. Another aspect of this study is the results regarding the global question assessing HRQL. Global measures are known to be suboptimal measures. Because they contain a single item, the content entailed in the question is not always clear, varying between different subjects, thus decreasing their reliability and validity compared to more elaborate tools [29,30]. Another possible problem related to global measures is that they may be affected by mood changes, portraying subjects’ current mood rather than a true assessment of QOL [31]. Despite these limitations, a moderate correlation was demonstrated between the global question and both the SF-36 domains and summary scores and other variables we tested. We demonstrated that the global measure is influenced mainly by mental health parameters. The global HRQL question was the only variable significantly correlated with TTO. We can thus assume that
Table 3 Multiple linear regression models for the time trade-off, global health-related quality of life, and summary scores of SF-36 SF-36 summary scores
Symptom checklist CES-D Apgar friends Hematocrit Models R2
TTO Partial R2
Global HRQL Partial R2
PCS Partial R2
MCS Partial R2
— — — — —
— 0.1838 — 0.1014 0.2851
0.6609 — 0.0934 — 0.7543
0.5470 0.0952 — — 0.6422
TTO—time trade-off, HRQL—health-related quality of life, PCS—SF-36 physical component score, MCS—SF-36 mental component score, CES-D— depression scale. The cited R2 values are for those independent variables that entered the model under the condition of P⬍.05.
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they share some type of content, or influencing factors, although this study was unable to identify them. Although we did not use visual probes to convey the TTO question, we took several measures to ensure that patients understood the TTO task. Thus, the TTO questions were administered either by a nurse specifically trained for this purpose or by one of us. The questions were elaborately explained to patients, and the times traded were “ping-ponged” several times to ensure patients’ understanding. Special efforts were made in the population that refused to trade any time at all. As far as we can tell the nil responses represent true and reliable answers and do not reflect misunderstanding of the questions. This is further supported by the reliability study results that ensured that patients did not respond randomly. Many potential pitfalls may compromise the results of utility measures and these have been reviewed extensively [32,33]. Attitudes towards length of life may change as life expectancy changes; thus, by presenting patients with a fixed life expectancy of 10 years we may have biased the time traded. The life expectancy of patients undergoing dialysis is markedly reduced. Quantitatively, this reduction is greater among younger age groups. At age 49 life expectancy is 7.1 years and at age 59, it is 4.3 years [34]. Therefore, a 10-year life span in our particular population (mean age 58.2, S.D. 14.6 years) was a fair optimistic approach relevant to the patients’ medical status. Stiggelbout et al. [35] have noted in testicular and colorectal cancer patients that patients tend to give valid answers to TTO questions when using a time frame patients consider realistic. In this respect the time frame we selected seems reasonable. Adjusting the time frame given in the TTO instrument to the known mean in a particular population studied is common practice [36–38]. Furthermore, in a recent study published by Sherbourne et al. [39], it was demonstrated that using a constant time frame of 10 years, after adjusting for patients’ health state, preferences of older people did not differ from those of younger people. Another possible bias is framing [32,33]. When constructing a TTO question, it is possible to frame the question as loss or gain in reference to the first health state. In this study, it was easier to ask patients to give up time as they were already at the less than perfect health state and the health state chosen was their present state. Researchers have raised the issue whether different framing affects results. In general, framing can alter results obtained but it seems that the TTO is insensitive to this type of framing, as opposed to the standard gamble [40]. The inability to find variables significantly affecting utility measures may arise from different reasons. It is possible that other determinants may be correlated with TTO. These may include personality traits, religious beliefs, attitude towards risk, and health values. It is also possible that nonlinear or heterogenous correlations exists between utility measures and QOL tools [41]. Another possible problem is that the concept of perfect health may change during prolonged illness. For example, HIV-infected patients whose disease progressed over time showed marked deterioration in many
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domains of the SF-36 but in contrast the time trade-off scores of these patients remained remarkably constant [12]. The stability of utility values was also demonstrated in patients with benign prostatic hypertrophy who underwent prostatectomy. In these patients the utility scores as measured by the EuroQoL remained constant over time while the SF-36 scores indicated statistically significant improvement in these patients [42]. Robinson et al. [43] demonstrated that a threshold of tolerability exists below which health states have to decrease before some respondents would be willing to give up any time at all on the TTO. Further research is needed in these directions. To conclude, we think that a better understanding of the content entailed in the TTO measure and its relation with QOL is required. Acknowledgments We would like to thank the Nephrology Unit in Sheba Medical Center for the cooperation and assistance in gathering data from patients and files. Appendix This is the TTO questionnaire presented to responders. Try to remember a period in the past, a few years ago, when you felt perfectly healthy. Suppose we had a magic drug that could restore you to perfect health, as you felt then. I emphasize that this is an imaginary question and that such a medication does not really exist. This magic drug has no side effects but it has a price. Using the drug will shorten your life in a few months or years. I would like to know how much time you are willing to trade to gain perfect health. I will begin with a simple question. Would you be willing to take the drug if your life would not be shortened at all? If the respondent was not willing to take the drug at this stage the idea was explained again and if s/he persisted they were excluded from the study. Would you be willing to take the drug if your life would be shortened in 1 year? At this stage times were varied according to the response to discover the indifference point. The intervals chosen were ping-ponged upwards or downwards according to the response. Patients willing to trade 1 year were offered trading 10 years to verify understanding and the times were further halved according to responses.
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A comparison of three measures: the time trade-off technique, global health-related quality of life and the SF-36 in dialysis patients Yasmin Maor*, Miri King, Liraz Olmer, Benjamin Mozes The Center for the Study of Clinical Reasoning, The Gertner Institute for Epidemiology and Health Policy Research, Sackler School of Medicine, Tel Aviv University, Sheba Medical Center, Tel Hashomer 52621, Israel Received 3 August 1999; received in revised form 23 August 2000; accepted 21 September 2000
Abstract We investigated the correlation between descriptive and valuational measures of health-related quality of life (HRQL) and assessed determinants affecting these measures. Our suspicion was that there is little similarity in the content of descriptive and valuational measures of HRQL. We thus conducted a cross-sectional observational study of 56 hemodialysis patients. All underwent structured interviews. Dependent variables were patients utilities [time trade-off (TTO)], global rating of HRQL and generic HRQL (SF-36). Independent variables were socioeconomic details, disease severity, comorbidity, symptoms, depression, social support, and laboratory data. The correlation between TTO and global HRQL was ⫺0.33 (P ⫽ .0178) and between TTO and the SF-36 physical and mental summary scores ⫺0.16 (P ⫽ .2383) and ⫺0.20 (P ⫽ .1443), respectively. The regression models for the SF-36 physical and mental summary scores explained 75% and 64% of the variance, and for global HRQL 29% of the variance. The independent variables had no effect on the TTO. This confirmed our suspicion that a qualitative difference exists between TTO and descriptive quality of life tools. The TTO content could not be explained by the variables that entail the content of HRQL instruments. © 2001 Elsevier Science Inc. All rights reserved. Keywords: Utility; Time trade-off; Quality of life; SF-36; Global measure; End stage renal disease
1. Introduction Quality-adjusted life years (QALYs) are widely used in health economics and in medical decision-making as a useful measure that reflects both life expectancy and quality of life (QOL). The calculation of QALYs requires a single number (or index) that reliably reflects QOL. Descriptive QOL measurements describe functional status and wellbeing in various dimensions such as physical functioning, mental health, social functioning, health perception, vitality, and pain. The most commonly used generic QOL tools include the Sickness Impact Profile (SIP) [1], the Notingham Health Profile (NHP) [2], and the Short Form-36 (SF-36) [3]. These tools are multidimensional and have high reliability and construct validity [4]. Valuational measures (utilities) capture patients’ preferences for health states, encompassing the positive and negative aspects of a particular health state into a single number. The attractiveness of these methods lies in their ability to describe patients’ preferences and values in addition to their
* Corresponding author. Tel: 972-3-5303272; fax: 972-3-5303277. E-mail address: [email protected] (Y. Maor)
QOL. These measurements were introduced into the medical community during the early 1970s [5,6] and have since gained a wide acceptance. Methods used to elicit preferences include the rating scale, standard gamble, and timeoff (TTO) techniques. In this study we focused on the TTO, in which patients are given a series of choices between living in perfect health for a certain amount of time and living in a particular condition worse than perfect health for a longer period of time [7]. The amount of time for the perfect health state is varied until the point where the patient is indifferent between the two alternatives. Patients’ preferences regarding health states are expected to correlate with health-related quality of life (HRQL), as well as with other attributes, such as risk-attitude, framing, and past experience [5,8]. That is to say that patients experiencing a lower QOL will be willing to trade more time to improve their QOL compared with patients with a better subjective QOL. However, several studies assessing the relationship between descriptive QOL tools and utility measures demonstrate only low to moderate associations [9– 12]. These studies call into question the theoretical framework of utility measures. In this study we undertook a different approach by adding a direct measure of the relations between utilities and disease severity parameters. We chose dialysis patients since this population suffers from a debili-
0895-4356/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S0895-4356(00)00 3 3 8 - 3
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tating chronic disease affecting many of the domains constructing QOL [13–15]. Our hypothesis was that no correlation exists between the TTO the SF-36 and global HRQL. 2. Methods 2.1. Patients The study involved a convenience sample of patients undergoing hemodialysis in a tertiary care center in Israel. To be included in the study, patients had to be on dialysis for 3 months or more, understand Hebrew and agree to complete the TTO question and the SF-36 questionnaire. Of 96 patients that were approached, 22 patients did not understand the questionnaire due to cognitive problems or inadequate understanding of Hebrew and 18 patients refused to participate. The remaining 56 patients were interviewed during hemodialysis treatment. Information was gathered by a structured interview and chart abstraction performed by a qualified nurse and one of us. Informed consent was obtained from all patients. The study was approved by the hospital’s IRB. 2.2. Questionnaire The questionnaire included the following items. A TTO question that was formulated according to the method described by Torrance et al. [5] (see the Appendix). The question assessing global health-related quality of life (HRQL) was formulated as follows: please rate your HRQL in the past year. Patients rated their HRQL on a Likert scale ranging from 1 (worst possible HRQL) to 7 (best possible HRQL). The Short Form-36 (SF-36) generic HRQL tool describes patients’ current HRQL using 36 items divided into 8 domains: Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health. Two summary scores are also computed: physical component score (PCS) and mental component score (MCS). The SF-36 has good reliability and construct validity [16–18]. Scores range from 0 to 100. Higher scores reflect better QOL. Socioeconomic details included age, sex, marital status, education (years of schooling), work status, and religion. All questions were close-ended. The End-State Renal Disease Severity Index (ESRD-SI) is a measure designed to assess hemodialysis patients’ severity of illness. The scale addresses specific hemodialysis problems such as access, blood pressure, bone problems, and comorbidity. Different weights are assigned according to problem type and severity, and then summed into an index. The index score ranges from 0 to 95. A higher score indicates a more severe state. The scale has adequate reliability and construct validity [19]. A symptom checklist modified from Parfrey’s specific health questionnaire for end-stage renal disease patients was used to assess patients’ severity of symptoms [20]. Parfrey’s
original questionnaire contains 8 subscales, one of which is a symptom checklist. For the purpose of this study we chose to use the symptom checklist, as it provides a comprehensive measure for assessing the presence of symptoms related to hemodialysis. To increase sensitivity, we added 4 symptoms instead of the “other” category: weakness and dizziness, thirst, lack of appetite, and parasthesia. Patients were asked to rate the severity of each symptom using 5 categories ranging from absent to very severe. The overall score was computed by summing the individual symptom scores. Scores range from 15 to 75. Higher scores indicate more severe symptoms. The Center for Epidemiologic Studies Depression Scale (CES-D) [21] contains 20 items assessing patients’ frequency of various feelings during the past week. Feelings are rated by frequency on a 4-category scale ranging from rarely or none of the time to most or all of the time. The overall score was computed by summing the scores of all items. Scores range from 0 to 60. Higher scores indicate a more depressed state. The social support scale (Apgar) [22] contains 17 items that measure social support in 3 settings: family, friends, and work. Items are rated on a 5-category scale ranging from hardly ever to almost always. Each setting receives an independent score by summing relevant items. A higher score indicates less support. In this study we omitted the work domain since most of the patients interviewed did not work. Laboratory variables known to be associated with mortality or decreases in QOL such as cholesterol [23], albumin [24], hematocrit [25], and the delivered dose of dialysis as measured by Kt/V (K⫽clearance, t⫽dialysis time, and V⫽volume of distribution of the patient) [26] were abstracted from patients’ charts. All questionnaires were translated into Hebrew by a twoway translation process: the questionnaires were translated into Hebrew by a bilingual translator and then translated back into English by another translator who was not familiar with the wording of the original English versions. Ambiguities were discussed and resolved with the consensus of bilingual experts. To assess reliability of the TTO and the global HRQL, a convenience sample of 34 patients were approached 2 to 4 weeks after the primary interview and were asked to respond once again to these questions. 2.3. Data analysis As the distribution of the TTO was skewed (11 patients were not willing to give up any time to improve their health), we performed a logarithmic transformation of the TTO variable [log(x⫹1)] [27]. All analyses were performed on the transformed values. Spearman’s correlations were used to assess associations between the TTO, SF-36 domains and summary scores, and the global HRQL question.
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To identify variables associated with different tools’ scores we performed bivariate analysis and then multiple linear regression. The level of significance to participate in the models was set at P⬍.05. Several analyses were done: a) The dependent variable was the TTO. The independent variables were the eight domains of the SF-36. b) The dependent variable was the TTO. The independent variables were socioeconomic details, ESRD-SI, the symptom checklist, CES-D scores, Apgar scores, and laboratory data (cholesterol, albumin, hematocrit and Kt/V). c) The dependent variable was the TTO. In this analysis we excluded patients who were not willing to trade any time to improve their health in the TTO question. The independent variables were the same as in analysis b. d) The dependent variable was the PCS score of the SF36. The independent variables were the same as used in analysis b. e) The dependent variable was the MCS score of the SF36. The independent variables were the same as used in analysis b. f) The dependent variables were each of the 8 domains of the SF-36 (Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health). The independent variables in each of these analyses were the same as in analysis b. g) The dependent variable was the global HRQL score. The independent variables were the same as used in analysis b. Reliability of the TTO (test–retest) was assessed using Pearson’s correlation coefficient. Reliability of the global HRQL was assessed using Spearman’s correlation coefficient [5,28]. 3. Results The study included 56 hemodialysis patients. Sixty-four percent were males. The mean age was 58.2 years (S.D. 14.6 years), 73% were married, and mean schooling was 11.3 years (S.D. 3.6 years). Time traded for perfect health in the TTO ranged from 0 to 120 months, median 18 months. The correlation between the TTO and the domains of the SF-36 ranged from 0.05 to 0.22. The correlation between the TTO and the PCS was ⫺0.16 and between the TTO and MCS was ⫺0.20 (Table 1). None of these results was significant. The correlation between TTO and the global HRQL question was ⫺0.33 (P⫽.0178). None of the independent variables was significantly correlated with the TTO measure. These included socioeconomic details, ESRD-SI, the symptom checklist, depression, social support and laboratory data. Similar results were obtained when omitting patients who were not willing to trade any time in the TTO
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question. Correlation between SF-36 summary scores and the global HRQL and between these independent variables are presented in Table 2. In contrast to the low correlation between these variables and the TTO, the correlation with the SF-36 summary scores and the global HRQL rating scale were high to moderate. The domains of the SF-36 used as independent variables in the multiple linear regression had no effect on the TTO. Also, socioeconomic details, ESRD-SI, the symptom checklist, depression, social support, and laboratory data used as explanatory variables had no effect on the TTO. Using the same variables, models for the domains and summary scores of the SF-36 explained a large degree of the variance: 54% for Physical Functioning domain, 56% for Role Physical domain, 62% for Bodily Pain domain, 48% for General Health domain, 46% for Vitality domain, 45% for Social Functioning domain, 28% for Role Emotional domain, and 63% for Mental Health domain. The most contributing variables were the ESRD-SI reflecting severity of disease, the symptom checklist, and the CES-D score. The model for the PCS and MCS explained 75% and 64% of the variance, respectively (Table 3). The regression model of the global HRQL question explained 29% of the variance. Explanatory variables that entered this model were the CES-D score and hematocrit (Table 3). Pearson’s correlation coefficient between the two measurements of the TTO was 0.85 (P⫽.0001) and the Spearman correlation coefficient between the two measurements of the global HRQL question was 0.69 (P⫽.0001). 4. Discussion The results demonstrated that no significant correlation exists between the TTO and the SF-36. Furthermore, although high correlations existed between the SF-36 and a set of variables known to affect QOL, we could not demonstrate significant correlations between these variables and the TTO. In the same line, none of the independent variables affected the TTO, although we used the same candidate variables that had a high explanatory power when the dependent variable was the SF-36. Thus, our suspicion that descriptive and valuational measures differ in the content they entail was confirmed.
Table 1 Spearman’s correlation and significance level off the time trade-off, SF-36 summary scores, and global health related quality of life SF-36 summary scores
TTO Global HRQL
PCS (P)
MCS (P)
Global HRQL (P)
⫺0.16 (.2383) 0.38 (.0049)
⫺0.20 (.1443) 0.40 (.0029)
⫺0.33 (.0178) —
TTO—time trade-off, PCS—SF-36 physical component score, MCS— SF-36 mental component score, HRQL—health-related quality of life.
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Table 2 Spearman’s correlation and significance level of the time trade-off, global health-related quality of life, SF-36 summary scores and possible determinants
Age Education ESRD-SI Symptom checklist CES-D Apgar family Apgar friends Cholesterol Albumin Hematocrit Kt/V
TTO (P)
Global HRQL (P)
⫺0.07 (.6328) 0.01 (.9406) 0.15 (.2735) 0.10 (.4846) 0.14 (.3385) ⫺0.08 (.5885) 0.10 (.4710) ⫺0.07 (.6311) 0.08 (.5592) ⫺0.04 (.7469) 0.12 (.3988)
⫺0.04 (.7861) 0.27 (.0549) ⫺0.26 (.0586) ⫺0.32 (.0236) ⫺0.46 (.0011) ⫺0.06 (.6598) ⫺0.36 (.0120) ⫺0.02 (.8879) 0.13 (.3472) 0.27 (.0551) 0.21 (.1426)
SF-36 summary scores PCS (P)
MCS (P)
⫺0.20 (.1385) 0.34 (.0118) ⫺0.60 (.0001) ⫺0.82 (.0001) ⫺0.67 (.0001) ⫺0.22 (.1202) ⫺0.52 (.0001) ⫺0.04 (.7867) ⫺0.07 (.6082) 0.00 (.9863) 0.06 (.6848)
⫺0.14 (.3037) 0.30 (.0257) ⫺0.39 (.0033) ⫺0.72 (.0001) ⫺0.69 (.0001) ⫺0.31 (.0267) ⫺0.49 (.0002) 0.29 (.8322) ⫺0.17 (.2194) ⫺0.07 (.6105) 0.05 (.7082)
TTO—time trade-off, HRQL—health-related quality of life, PCS—SF36 physical component score, MCS—SF-36 mental component score, ESRD-SI—end-stage renal disease severity index, CES-D—depression scale, Kt/V—the delivered dose of dialysis (K⫽clearance, t⫽dialysis time, and V⫽volume of distribution of the patient).
Elsewhere, several studies performed in diverse populations have created models for utility measures using QOL tools, thus trying to investigate the degree of similarity in content between utility measures and QOL. In asthma patients using SF-36 domains as explanatory variables, the model for the standard gamble technique explained only 10% of the variance [9]. The model for the TTO in intermittent claudication patients explained 28% of the variance with social function and mental health domains of the RAND questionnaire being the explanatory variables. In contrast, physical functioning domain from the RAND
questionnaire was the main explanatory variable of the TTO in dialysis patients [10]. Correlation of utility measures with QOL tools ranged from 0.21 to 0.46 [9–12]. These studies call into question the theoretical framework of utility measures, which emphasize the importance of the impact of HRQL on patients’ utility values. In these studies, direct comparisons between descriptive HRQL measures and utilities were performed. This approach may underestimate the relations between utility measures and disease severity parameters. Generic HRQL tools are influenced by biological derangement and symptom severity, but also by other factors such as adjustment, personality, mental state, and socioeconomic factors. These other factors may mask the effect of disease on utilities. The merit of our study is that we directly measured the relations between utilities and disease severity parameters [severity of disease (ESRD-SI), severity of symptoms common in dialysis patients and laboratory data representing the severity of biological derangement]. The determinants we examined had a significant effect on QOL as measured by a generic descriptive tool. Utility measures are not expected to be identical to descriptive QOL measures as they address patients’ preferences, a concept that isn’t directly rooted in descriptive QOL tools. Yet, the comprehensive understanding of health today presumes that QOL and disease severity parameters should influence patients’ preferences. Another aspect of this study is the results regarding the global question assessing HRQL. Global measures are known to be suboptimal measures. Because they contain a single item, the content entailed in the question is not always clear, varying between different subjects, thus decreasing their reliability and validity compared to more elaborate tools [29,30]. Another possible problem related to global measures is that they may be affected by mood changes, portraying subjects’ current mood rather than a true assessment of QOL [31]. Despite these limitations, a moderate correlation was demonstrated between the global question and both the SF-36 domains and summary scores and other variables we tested. We demonstrated that the global measure is influenced mainly by mental health parameters. The global HRQL question was the only variable significantly correlated with TTO. We can thus assume that
Table 3 Multiple linear regression models for the time trade-off, global health-related quality of life, and summary scores of SF-36 SF-36 summary scores
Symptom checklist CES-D Apgar friends Hematocrit Models R2
TTO Partial R2
Global HRQL Partial R2
PCS Partial R2
MCS Partial R2
— — — — —
— 0.1838 — 0.1014 0.2851
0.6609 — 0.0934 — 0.7543
0.5470 0.0952 — — 0.6422
TTO—time trade-off, HRQL—health-related quality of life, PCS—SF-36 physical component score, MCS—SF-36 mental component score, CES-D— depression scale. The cited R2 values are for those independent variables that entered the model under the condition of P⬍.05.
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they share some type of content, or influencing factors, although this study was unable to identify them. Although we did not use visual probes to convey the TTO question, we took several measures to ensure that patients understood the TTO task. Thus, the TTO questions were administered either by a nurse specifically trained for this purpose or by one of us. The questions were elaborately explained to patients, and the times traded were “ping-ponged” several times to ensure patients’ understanding. Special efforts were made in the population that refused to trade any time at all. As far as we can tell the nil responses represent true and reliable answers and do not reflect misunderstanding of the questions. This is further supported by the reliability study results that ensured that patients did not respond randomly. Many potential pitfalls may compromise the results of utility measures and these have been reviewed extensively [32,33]. Attitudes towards length of life may change as life expectancy changes; thus, by presenting patients with a fixed life expectancy of 10 years we may have biased the time traded. The life expectancy of patients undergoing dialysis is markedly reduced. Quantitatively, this reduction is greater among younger age groups. At age 49 life expectancy is 7.1 years and at age 59, it is 4.3 years [34]. Therefore, a 10-year life span in our particular population (mean age 58.2, S.D. 14.6 years) was a fair optimistic approach relevant to the patients’ medical status. Stiggelbout et al. [35] have noted in testicular and colorectal cancer patients that patients tend to give valid answers to TTO questions when using a time frame patients consider realistic. In this respect the time frame we selected seems reasonable. Adjusting the time frame given in the TTO instrument to the known mean in a particular population studied is common practice [36–38]. Furthermore, in a recent study published by Sherbourne et al. [39], it was demonstrated that using a constant time frame of 10 years, after adjusting for patients’ health state, preferences of older people did not differ from those of younger people. Another possible bias is framing [32,33]. When constructing a TTO question, it is possible to frame the question as loss or gain in reference to the first health state. In this study, it was easier to ask patients to give up time as they were already at the less than perfect health state and the health state chosen was their present state. Researchers have raised the issue whether different framing affects results. In general, framing can alter results obtained but it seems that the TTO is insensitive to this type of framing, as opposed to the standard gamble [40]. The inability to find variables significantly affecting utility measures may arise from different reasons. It is possible that other determinants may be correlated with TTO. These may include personality traits, religious beliefs, attitude towards risk, and health values. It is also possible that nonlinear or heterogenous correlations exists between utility measures and QOL tools [41]. Another possible problem is that the concept of perfect health may change during prolonged illness. For example, HIV-infected patients whose disease progressed over time showed marked deterioration in many
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domains of the SF-36 but in contrast the time trade-off scores of these patients remained remarkably constant [12]. The stability of utility values was also demonstrated in patients with benign prostatic hypertrophy who underwent prostatectomy. In these patients the utility scores as measured by the EuroQoL remained constant over time while the SF-36 scores indicated statistically significant improvement in these patients [42]. Robinson et al. [43] demonstrated that a threshold of tolerability exists below which health states have to decrease before some respondents would be willing to give up any time at all on the TTO. Further research is needed in these directions. To conclude, we think that a better understanding of the content entailed in the TTO measure and its relation with QOL is required. Acknowledgments We would like to thank the Nephrology Unit in Sheba Medical Center for the cooperation and assistance in gathering data from patients and files. Appendix This is the TTO questionnaire presented to responders. Try to remember a period in the past, a few years ago, when you felt perfectly healthy. Suppose we had a magic drug that could restore you to perfect health, as you felt then. I emphasize that this is an imaginary question and that such a medication does not really exist. This magic drug has no side effects but it has a price. Using the drug will shorten your life in a few months or years. I would like to know how much time you are willing to trade to gain perfect health. I will begin with a simple question. Would you be willing to take the drug if your life would not be shortened at all? If the respondent was not willing to take the drug at this stage the idea was explained again and if s/he persisted they were excluded from the study. Would you be willing to take the drug if your life would be shortened in 1 year? At this stage times were varied according to the response to discover the indifference point. The intervals chosen were ping-ponged upwards or downwards according to the response. Patients willing to trade 1 year were offered trading 10 years to verify understanding and the times were further halved according to responses.
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