Qual Life Res (2008) 17:453–462 DOI 10.1007/s11136-008-9309-6
The validity of generic and condition-specific preference-based instruments: the ability to discriminate asthma control status Helen M. McTaggart-Cowan Æ Carlo A. Marra Æ Yaling Yang Æ John E. Brazier Æ Jacek A. Kopec Æ J. Mark FitzGerald Æ Aslam H. Anis Æ Larry D. Lynd
Accepted: 11 January 2008 / Published online: 15 February 2008 Ó Springer Science+Business Media B.V. 2008
Abstract Objective A goal of asthma management is to improve the patient’s health-related quality of life (HRQL). However, it is unclear whether HRQL instruments can discriminate across asthma control measures. The objective of this study was to evaluate the validity of generic and condition-specific preference-based instruments, in terms of their ability to distinguish asthma control. Methods Asthma patients (n = 157) completed three generic preference-based instruments: the Health Utility Index Mark 3 (HUI-3), the EuroQol (EQ-5D), and the Short Form 6D (SF-6D) and two condition-specific questionnaires: the standardized Asthma Quality of Life Questionnaire (AQLQ(S)) and the Asthma Control H. M. McTaggart-Cowan C. A. Marra J. M. FitzGerald L. D. Lynd Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, 7th Floor, 828 West 10th Avenue, Vancouver, BC, Canada V5Z 1L8 H. M. McTaggart-Cowan J. A. Kopec A. H. Anis Department of Health Care and Epidemiology, Faculty of Medicine, University of British Columbia, 5804 Fairview Avenue, Vancouver, BC, Canada V6T 1Z3 C. A. Marra L. D. Lynd (&) Faculty of Pharmaceutical Sciences, University of British Columbia, 2146 East Mall, University of British Columbia, Vancouver, BC, Canada V6T 1Z3 e-mail:
[email protected] Y. Yang J. E. Brazier School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK J. M. FitzGerald Faculty of Medicine, University of British Columbia, 317-2194 Health Sciences Mall, Vancouver, BC, Canada V6T 1Z3
Questionnaire (ACQ). The AQLQ(S) scores were converted into the condition-specific preference-based scores: the Asthma Quality of Life Utility Index (AQL-5D). Results The preference-based instruments were generally able to discriminate across control measures, such as ACQ scores and magnitude of asthma medication, but were not able to discriminate for self-reported control and severity levels. These instruments also correlated with most control measures (r = 0.32–0.37). Significant relationships between AQL-5D scores and all control variables were observed. Conclusions Overall, the AQL-5D discriminated across all levels of asthma control. The HUI-3, the EQ-5D, and the SF-6D differentiated between the highest and lowest levels of control but could not discriminate between the moderate levels. Keywords Utility Health-related quality of life Asthma Preference-based instruments Disease-specific instruments Abbreviations ACQ Asthma control questionnaire ANOVA Analysis of variance AQL-5D Asthma quality of life utility index AQLQ Asthma quality of life questionnaire AQLQ(S) Standardized version of the asthma quality of life questionnaire BC British Columbia EQ-5D EuroQol FEV1 Forced expired volume in the first second HRQL Health-related quality of life HUI Health utility index QALY Quality-adjusted life year
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QOL SA SF-6D SG SGRQ TTO VAS
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Quality of life Short-acting Short Form 6D Standard gamble St. George’s respiratory questionnaire Time trade-off Visual analogue scale
Introduction Asthma is a prevalent and chronic respiratory disease that affects approximately one in 12 Canadian adults [1]. Although it is a controllable disease, asthma poses a burden on its patients, often impairing the individual’s overall quality of life (QOL). To improve asthma management strategies, it is vital to have a valid instrument which can assess these impacts on an individual [2]. Given that the primary goal of asthma management is to achieve optimal control and ultimately to improve the individual’s healthrelated quality of life (HRQL) [3, 4], the instrument must be capable of discriminating across all levels of asthma control rather than levels of disease severity. This must be achieved independent of the treatment used to improve asthma control. Asthma control describes the adequacy of treatment strategies, whereas asthma severity reflects the untreated status of the disease for an individual [5]. Currently, there is a lack of empirical evidence identifying which HRQL instruments have the sensitivity to provide a valid representation of asthma control status. HRQL can be evaluated using either condition-specific or generic instruments. An example of a condition-specific instrument that estimates asthma-specific QOL is the Standardized Asthma Quality of Life Questionnaire (AQLQ(S)) [6, 7]. While the advantage of using conditionspecific measures is their capacity to detect minimal changes in a disease, these instruments are not suitable for comparisons across different disease states. Generic measures, on the other hand, can be used to compare HRQL between diseases. A strength of using generic instruments that generate utility values is that they integrate different aspects of health into a single index anchored by a value of 1 for perfect health and 0 for death. Some health states can attract negative values, which identify, from a societal perspective, states as being worse than death. Using the calculated utility value for a particular health state and the length of time in that state, the quality-adjusted life year (QALY), a metric used in economic evaluations, can be identified. Determining utility scores normally involves valuing individuals’ preferences, using either direct approaches such as the standard gamble (SG) and time trade-off (TTO), or indirect techniques such as the
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multiattribute health classification systems. The Health Utility Index Mark 3 (HUI-3) [8], the EuroQol (EQ-5D) [9], and the Short Form 6D (SF-6D) [10] are frequently used indirect measures. Such generic preference-based instruments, capable of quantifying HRQL, have significant applications in health policy and resource allocation decision-making [11, 12]. The validity of these instruments, defined as the extent to which an instrument measures the property that it is intended to measure [2, 13], has yet to be demonstrated for distinguishing between different levels of asthma control. It has been shown that, for certain medical conditions, the set of dimensions covered by generic instruments may not be relevant [14]; thus, another method for evaluating HRQL involves the development of a condition-specific preference-based measure of health utility. By obtaining preference weights for a condition-specific descriptive system, the sensitivity of generic preference-based measures can be improved by broadening their coverage to include additional dimensions relevant to the condition under investigation. Several studies have been undertaken to obtain health state utility values for condition-specific instruments, such as the multiattribute Rhinitis Symptom Utility Index [15] and The Asthma-Symptom 4 Utility Index [16]. While most previous economic evaluations of asthma therapies have used either the cost per symptom day avoided or the cost per 0.5 unit change on the AQLQ(S) as a measure of cost effectiveness [17], only a limited number of studies have measured effectiveness in terms of QALYs [18–23]. A recent study, exploring the relationship between asthma control and HRQL, found that the EQ-5D was able to distinguish across levels of control [23]. The authors classified asthma control status using international guidelines [24] in terms of frequency of symptoms and asthma medication use, and the St. George’s Respiratory Questionnaire (SGRQ). While the SGRQ commonly assesses chronic obstructive pulmonary disease outcomes, studies have also shown it to be reliable in asthmatic populations [25, 26]. However, the impact of a validated measure of control, such as the score from the Asthma Control Questionnaire (ACQ) and the frequency of short-acting (SA) b-agonist use [23, 27–29], on the HRQL has not been evaluated. The validity of preference-based instruments to discriminate between different levels of control in asthma has not yet been adequately evaluated. Therefore, the objective of this study was to evaluate the validity of the HUI-3, the EQ-5D, the SF-6D, and the Asthma Quality of Life Utility Index (AQL-5D, a condition-specific preference-based measure [30]), in terms of their ability to distinguish between different levels of asthma control. Asthma control was quantified using the ACQ, as well as self-reported use of SA b-agonist and self-perceived control status.
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Methods Study population English-speaking patients with self-reported, physiciandiagnosed asthma residing in the Vancouver metropolitan area in British Columbia (BC), Canada participated in this cross-sectional study. The patients were between 19 and 49 years of age and had no other concurrent respiratory conditions. While we are aware of the potential misclassification that may arise from using self-reported disease identification, previous studies have successfully recruited participants in the same manner [31, 32]. Each subject, recruited by poster advertisement, was assessed in a pulmonary research clinic and received $20 (in Canadian dollars) to defray time and travel costs. The research ethics boards of the University of BC, Providence Health, and Fraser Health approved the study protocol, and each participant provided informed consent. Based on results from other preference-based instruments, a difference of 0.05 in mean utility measures for health states is considered important and meaningful [33]. Using 80% power to detect a difference in mean health state of 0.05 between different control groups and assuming that the common standard deviation is 0.10 using a two-tailed t-test at the 5% significant level indicates that a minimum sample size of 31 in each group is needed.
Data collection Clinical measures Data pertaining to demographic information and asthma status were obtained using a self-administered questionnaire: annual utilization of all asthma-related drugs prior to the evaluation, self-perception of their overall QOL measured using a 10-cm horizontal visual analogue scale (VAS) anchored by one for perfect health and zero for death, and self-assessment of their asthma control and their asthma severity, both measured using a five-point Likert scale [31]. In addition, subjects performed three spirometric tests, with the best measurement recorded. The forced expiratory volume in the first second (FEV1) is expressed as a percent of predicted FEV1 based on the individual’s age, height, and sex [34, 35].
Asthma quality of life and asthma control The standardized version of the Asthma Quality of Life Questionnaire (AQLQ(S)) measures the individual’s asthma-specific QOL [6, 7, 36]. The instrument consists of
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32 questions each scored on a seven-point response scale ranging from one (worst possible QOL) to seven (best possible QOL). It encompasses four domains of asthmaspecific QOL: symptoms, activity limitation, emotional function, and environmental stimuli. Averaging the scores for all the questions yields an overall QOL score. Juniper et al. also developed the ACQ to assess asthma control status [27]. Comprised of seven questions (five relating to asthma symptoms and one each assessing the magnitude of SA b-agonist use and FEV1), the ACQ has been shown to be both valid and reliable, and possesses strong evaluative and discriminative properties [37]. Scaling each question on a seven-point response scale, from zero (best) to six (worst), provides an ordinal characterization of asthma control status. As done previously, the results are rescaled to coincide with the AQLQ(S) to ensure that higher scores from both instruments indicate better control and better QOL [31].
Preference-based instruments HUI-3. The HUI-3 includes a health status classification system and a utility scoring formula, derived based on multiattribute utility theory [8]. The scoring formula is based on the SG utilities measured in the general populace. The self-administered HUI-3 survey includes eight domains: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. As the number of levels for each domain varies from four to six, the total number of possible health states is 972,000. The baseline preferences for the HUI-3 were measured on a random sample of adults residing in Hamilton, Canada using both a VAS and an SG instrument. Using a multiplicative model, states worse than death can be measured on the zero (dead) to one (perfect health) scale, with a lower bound of -0.36. EQ-5D. The EQ-5D is designed as a cardinal index of health for describing and valuing HRQL [9]. This selfadministered survey consists of a descriptive health state classification system with five domains (mobility, self care, usual activity, pain/discomfort, and anxiety/depression) and a VAS ‘health thermometer’. The ‘health thermometer’ represents a subjective, global evaluation of the respondent’s health status on a scale between zero and 100, where zero represents the worst imaginable health state and 100 represents the best imaginable health. Each attribute in the classification system has three levels (no problem, some problems, and major problems) for a total of 243 possible health states. Preferences for the scoring function used in this study were measured with the TTO technique from a random sample of the adult population in the United Kingdom [38]. The utility scores fall on the zero (dead) to one (perfect health) scale, with a lower bound of -0.59.
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SF-6D. The SF-6D is derived from another popular HRQL questionnaire, the Short Form 36 [10]. The SF-6D consists of a multiattribute health status classification system with six domains and a scoring table that was derived using econometric modeling, similar to the EQ-5D. The attributes in this survey are physical functioning, role limitations, social functioning, pain, mental health, and vitality. The classification system ranges from four to six levels for each of the six domains for a total of 18,000 unique health states. The scoring model for the SF-6D is based on the SG utilities of 836 members of the general population in the United Kingdom. The obtained utilities fall on the standard zero to one (death to perfect health) scale; the worst state in the SF-6D system has a utility of 0.30. AQL-5D. The results from the AQLQ(S) were converted to the condition-specific preference-based instrument, AQL-5D, using the methodology that successfully transformed the results from the Short Form 36 to the SF-6D [10]. The AQLQ(S) was initially reduced to a fivedimension classification system, consisting of the following domains: concern about asthma, shortness of breath, weather and pollution stimuli, sleep impact, and activity limitation [39]; this forms the basis of the unvalidated AQL-5D instrument. A sample of the general population in the United Kingdom then used a TTO to value a selection of the states defined by this reduced classification system. Finally, an econometric model for predicting the health state values for all states defined by the new classification system was estimated, which enabled the calculation of health state utility values for calculating QALYs based on AQLQ(S) data. The overall aim of the modeling was to predict values for all health states described by the AQL5D [30].
Analysis Descriptive statistics were used to characterize the sample in terms of age, sex, pulmonary function, HRQL scores, perceived asthma severity and control, asthma medication use, and concomitant chronic illnesses. Continuous variables are presented as means and standard deviations while categorical variables are presented as the proportion of the sample within each group. The validity of each specific instrument was assessed based on its ability to discriminate between patients with different levels of control as represented by the ACQ score, magnitude of SA b-agonist use, and self-reported control status. Current management guidelines characterize good asthma control as requiring four or fewer SA b-agonist canisters per year [24, 28, 29]. Therefore, it is expected that patients who use excessive doses of SA b-agonist as their
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mainstay of therapy will probably have poorer asthma control and subsequently lower QOL [31]. Patients with poor asthma control were hypothesized to have lower scores for all measures. Other variables used in assessing validity included the presence of other chronic diseases and self-reported asthma severity. Analysis of variance (ANOVA) evaluated the differences among the utility scores when stratified by magnitude of SA b-agonist use, number of chronic illnesses, ACQ scores, and self-reported asthma severity and control. As a further test of validity, the relationships between FEV1, preference-based utilities, and the condition-specific and VAS scores were compared. FEV1 was used because it is a commonly used clinical measurement of asthma control. Both parametric and nonparametric tests were conducted; however, as the results were statistically similar, only the Pearson’s correlation coefficients are reported. A correlation coefficient of greater than 0.5 or less than -0.5 is considered to be strong, 0.30 to 0.49 or -0.49 to -0.30 is considered to be moderate, and values between 0.30 and -0.30 are considered to be weak [40]. All analyses were performed using the SAS statistical software package, version 8.2 (SAS Institute, Cary, NC). Significance was defined a priori as P B 0.05.
Results Sample One hundred and fifty-seven respondents (mean age of 35.0 years, 110 females (70%)) participated in the study. The recruitment methodology resulted in a heterogeneous sample of patients with a full spectrum of HRQL and included all levels of disease severity, disease control, and magnitude of drug use (Table 1). The majority of the patients considered their asthma to be at least adequately controlled (87%) and reported their asthma severity as either mild (38%) or moderate (33%). The mean AQLQ(S) and ACQ scores were 5.4 (±1.1) and 5.9 (±0.9), respectively, which suggested that the study sample had a moderately high overall asthma-related QOL and good asthma control.
Description of global utilities Table 2 displays a summary of the results from all instruments utilized in this study. For the HUI-3, EQ-5D, SF-6D, and AQL-5D, a maximum score of 1.0 was achieved but the minimum values varied. The HUI-3, SF-6D, and AQL5D had similar interquartile ranges: 0.17, 0.13, and 0.12, respectively; conversely, the interquartile range of the EQ-
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Table 1 Characteristics of the study population
Table 2 Utility scores of the instrumentsa
Parametera
Frequency (%) or mean (±SD)b
Instrument
Mean
SD
Median
IQR
Min.
Max.
EQ-5D
0.84
0.23
0.92
0.28
-0.06
1.00
Age
35.0 (±7.9)
HUI-3
0.84
0.20
0.92
0.17
0.12
1.00
Female
110 (70%)
SF-6D
0.79
0.10
0.80
0.13
0.48
1.00
FEV1
90.9 (±19.9)
AQL-5D
0.85
0.12
0.87
0.12
0.45
1.00
EQ-5D VAS
0.74
0.18
0.80
0.21
0.15
1.00
Self-reported asthma severity Very mild
21 (13%)
VAS
0.73
0.20
0.76
0.27
0.16
1.00
Mild
59 (38%)
AQLQ(S)
5.4
1.1
5.53
1.43
2.03
7.00
Moderate Severe
51 (33%) 20 (13%)
ACQ
5.7
0.9
5.86
1.29
2.29
7.00
Very severe
5 (3%)
Self-reported asthma control Very well controlled
37 (24%)
Well controlled
43 (28%)
Adequately controlled
54 (35%)
Not well controlled
19 (12%)
Not controlled at all
3 (2%)
Number of short-acting b-agonist canisters used in the past year None 4 or fewer
15 (10%) 110 (71%)
5–12
35 (22%)
13 or more
12 (8%)
Number of ICS canisters used in the past year None 4 or fewer
63 (41%) 56 (36%)
5–12
28 (18%)
13 or more
8 (5%)
Current use of oral steroids Yes
8 (5%)
No
147 (95%)
Concomitant chronic illnesses Yes
57 (37%)
No
99 (63%)
a
FEV1: forced expired volume in the first second; ICS: inhaled corticosteroids b
SD: standard deviation
5D was wider (0.28). Higher scores were obtained with the EQ-5D and the HUI-3, with 79 (50%) and 86 (55%) respondents, respectively, reporting a utility of greater than 0.9, whereas only 56 (36%) and 19 (12%) respondents reported utilities of greater than 0.9 on the AQL-5D and the SF-6D, respectively.
Validity Table 3 illustrates the relationships between the scores obtained from the instrument and all investigated measures
a
ACQ: Asthma Control Questionnaire; AQL-5D: Asthma Quality of Life Utility Index; AQLQ(S): standardized version of the Asthma Quality of Life Questionnaire; EQ-5D: EuroQol; EQ-5D VAS: ‘health thermometer’ from EuroQol; HUI-3: Health Utility Index Mark 3; IQR: interquartile range; SD: standard deviation; SF-6D: Short Form 6D; VAS: visual analogue scale
of asthma control. The relationships between the magnitude of SA b-agonist use and all instruments generally demonstrated a monotonic gradient, such that a lower HRQL was associated with greater medication use. This expressed the ability of the instruments to discriminate between different levels of asthma control, thereby supporting validity for all instruments for this specific population. However, although a consistent gradient was seen, the ANOVA results revealed that the SF-6D was not able to differentiate between levels of SA b-agonist use (P = 0.7). All instruments were able to discriminate between the presence of other chronic diseases (P \ 0.03). As the lowest levels of self-perceived control and severity contained only three and five respondents, respectively, these small subgroups were combined with the adjacent groups. The assessment of the validity of the instruments using the patients’ perception of their asthma severity and control levels varied, in that only the AQL-5D discriminated across these variables (P \ 0.0001). Conversely, both the AQLQ(S) and the ACQ (P \ 0.0001) and both VASs (P \ 0.0008) were able to differentiate between levels of these variables. Furthermore, the comparison of the stratified ACQ scores with the one condition-specific and three generic preference-based instruments generated an overall positive gradient, such that higher utilities were associated with better asthma control (Fig. 1). However, the absence of a linear gradient with the EQ-5D and the SF6D at the moderate control levels suggests that these instruments are insensitive to changes in HRQL for these levels of control. The ANOVA results revealed that all instruments were able to discriminate between different ACQ scores. The correlation between the FEV1 and the majority of the instruments was positive but weak (r = 0.06–0.26),
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Table 3 Relationship between asthma control variables and the HRQL instrumentsa Score, mean (SD)b EQ-5D
HUI-3
SF-6D
AQL-5D
ACQ
AQLQ(S)
EQ-5D VAS VAS
Number of short-acting b-agonist canisters used in the past year 4 or less
0.87 (0.21)**
0.86 (0.19)**
0.79 (0.10)*** 0.87 (0.10)*
5.95 (0.82)* 5.64 (0.98)* 0.78 (0.18)* 0.75 (0.20)*
5–12
0.85 (0.14)**
0.81 (0.20)**
0.80 (0.10)*** 0.83 (0.09)*
5.51 (0.71)* 5.15 (1.04)* 0.76 (0.10)* 0.77 (0.17)**
12 or more
0.69 (0.38)**
0.74 (0.23)**
0.76 (0.11)*** 0.76 (0.11)*
4.67 (1.01)* 4.21 (1.10)* 0.61 (0.22)* 0.57 (0.20)**
0.90 (0.11)*
0.90 (0.11)*
0.81 (0.08)*
0.86 (0.10)** 5.82 (0.88)* 5.55 (1.03)* 0.80 (0.15)* 0.78 (0.17)*
1 or more 0.73 (0.28)* Self-reported asthma severity
0.72 (0.25)*
0.74 (0.12)*
0.82 (0.11)** 5.48 (1.02)* 5.05 (1.20)* 0.67 (0.21)* 0.64 (0.22)*
Other chronic diseases None
Very mild
0.84 (0.29)*** 0.82 (0.22)*** 0.80 (0.22)*** 0.92 (0.08)*
6.34 (0.76)* 6.20 (0.81)* 0.79 (0.18)* 0.80 (0.22)*
Mild
0.89 (0.18)*** 0.88 (0.18)*** 0.80 (0.09)*** 0.87 (0.08)*
6.01 (0.74)* 5.68 (0.79)* 0.80 (0.15)* 0.78 (0.15)*
Moderate
0.81 (0.21)*** 0.84 (0.15)*** 0.78 (0.08)*** 0.83 (0.09)*
5.56 (0.72)* 5.10 (1.06)* 0.76 (0.15)* 0.73 (0.16)*
Severe
0.76 (0.27)*** 0.75 (0.27)*** 0.75 (0.12)*** 0.74 (0.15)*
4.72 (1.11)* 4.43 (1.35)* 0.60 (0.25)* 0.53 (0.24)*
Self-reported asthma control Very well controlled
0.90 (0.22)*** 0.88 (0.18)*** 0.82 (0.11)*** 0.92 (0.06)*
6.32 (0.66)* 6.16 (0.72)* 0.81 (0.15)* 0.81 (0.18)*
Well controlled
0.84 (0.20)*** 0.83 (0.20)*** 0.79 (0.09)*** 0.88 (0.10)*
5.95 (0.78)* 5.67 (0.86)* 0.78 (0.15)* 0.77 (0.17)*
Adequately controlled 0.81 (0.22)*** 0.84 (0.15)*** 0.78 (0.08)*** 0.81 (0.12)*
5.29 (0.90)* 4.92 (1.15)* 0.73 (0.19)* 0.69 (0.20)*
Not controlled
5.25 (0.98)* 4.60 (1.00)* 0.67 (0.23)* 0.60 (0.23)*
0.80 (0.21)*** 0.84 (0.16)*** 0.77 (0.10)*** 0.78 (0.12)*
a
ACQ: Asthma Control Questionnaire; AQL-5D: Asthma Quality of Life Utility Index; AQLQ(S): standardized version of the Asthma Quality of Life Questionnaire; EQ-5D: EuroQol; EQ-5D VAS: ‘health thermometer’ from EuroQol; HRQL: health-related quality of life; HUI-3: Health Utility Index Mark 3; SF-6D: Short Form 6D; VAS: visual analogue scale.
b
SD: standard deviation. Comparison (using ANOVA) of mean values, * P \ 0.001, ** P \ 0.05, *** P [ 0.09
although ACQ strongly correlated with this physiological measurement (r = 0.55) (Table 4). The scores from the three generic preference-based instruments were strongly correlated (r = 0.67–0.73) with each other, but the correlations between these instruments and the VASs, the AQL5D, and the condition-specific instruments were considerably weaker. The AQL-5D scores were moderately correlated with the all generic instruments and the ACQ and strongly correlated with the AQLQ(S) scores. The condition-specific scores were moderately to strongly correlated with the results from both VASs.
Discussion This study evaluated the validity of the HRQL instruments in asthma, specifically in their ability to distinguish between different levels of asthma control. As expected, the condition-specific instruments and VASs were able to discriminate between levels of SA b-agonist use and selfreported asthma control and severity, which suggests the face validity of the study results. The generic preferencebased instruments, in general, were able to discriminate between the extreme levels of control and severity but did
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not differentiate between different levels of control in the moderate ranges of these outcomes. This does not, however, suggest that these instruments are invalid over a moderate range. This difference may not represent a genuine difference in QOL for patients: future work is needed to determine the source of this discrepancy. The FEV1 was only weakly correlated with the preference-based instruments and the AQLQ(S), which is consistent with previous studies [21, 22]. Since this pulmonary measurement is a component of the ACQ, it was not surprising that it correlated strongly with this specific instrument (r = 0.55). In addition, the results revealed that these instruments were generally able to discriminate across the ACQ scores, providing evidence that the preference-based instruments could detect differences in asthma control, especially at the lowest and highest levels of control. However, when using the magnitude of SA b-agonist use as an objective measure of asthma control, all the instruments except for the SF6D were able to differentiate between different levels of this medication use. The preference-based instruments were moderately correlated with most measures of control; however, the HUI-3 was only weakly correlated with the ACQ (r = 0.20).
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Fig. 1 Relationship between Asthma Control Questionnaire (ACQ) score and preference-based instruments. *AQL-5D: Asthma Quality of Life Utility Index; EQ-5D: EuroQol; HUI-3: Health Utility Index Mark 3; SF-6D: Short Form 6D. Comparison (using ANOVA) of mean values stratified by ACQ score (P \ 0.001)
Table 4 Correlations for the instrumentsa EQ- HUI- SF5D 3 6D
AQL- EQ5D VAS
VAS ACQ AQLQ(S)
EQ-5D HUI-3
0.73
SF-6D
0.67 0.69
AQL-5D
0.41 0.39
0.43
EQ-VAS
0.59 0.58
0.51 0.60
VAS ACQ
0.53 0.45 0.37 0.32
0.48 0.56 0.34 0.82
0.81 0.59
AQLQ(S) 0.41 0.40
0.43 0.91
0.63
0.60 0.83
FEV1
0.15 0.26
0.21
0.23 0.55
0.14 0.06
0.54 0.26
a
ACQ: Asthma Control Questionnaire; AQL-5D: Asthma Quality of Life Utility Index; AQLQ(S): standardized version of the Asthma Quality of Life Questionnaire; EQ-5D: EuroQol; EQ-5D VAS: ‘health thermometer’ from EuroQol; FEV1: forced expired volume in the first second; HUI-3: Health Utility Index Mark 3; SF-6D: Short Form 6D; VAS: visual analogue scale
Unlike the preference-based instruments, the AQLQ(S), the ACQ, and the AQL-5D were able to differentiate across all measures of asthma control. Although the results from the two condition-specific instruments cannot be directly compared across different disease states, these results suggest that information obtained from a condition-specific instrument can be successfully converted into a preferencebased single-index equivalent. This was shown by the significant relationships between the scores from the condition-specific preference-based instrument, AQL-5D, and all measures of asthma control. As this instrument was derived from the AQLQ(S), it should be noted that the
AQL-5D also correlated with the ACQ, which was used as a validated measure of control in this study. The inability of the generic preference-based instruments to discriminate across some measures of selfreported asthma control may imply that patients interpret this concept differently. Self-reported asthma control may have latent or differing dimensions that are not captured by the ACQ or the self-reported use of b-agonist. Further, from the study results, poorly controlled or severe asthma patients seem to perceive their asthma as more well controlled or less severe. These individuals may gradually learn to cope and adapt to their limitations in a number of ways such that, over time, the perception of the impact of their disease may be reduced [41]. Future work should investigate potential psychosocial factors that contribute to this adaptation, as well as the mechanisms by which individuals’ cope with their health condition. Understanding the degree to which patients adapt to their condition could provide useful information in obtaining more valid health state valuations by modifying societal valuations to incorporate these effects. In addition to these potential behavioural effects, the valuation methods and the psychometric properties of the preference-based instruments may explain the differences between utility scores [42]. Both the SF-6D and the HUI-3 use the SG technique for valuation, while the EQ-5D and the AQL-5D use the TTO approach. The HUI-3 uses multiattribute utility theory and multiplicative scoring models, while the SF-6D and EQ-5D use empiric and additive scoring methods [43]. In addition, the scoring functions for the EQ-5D and SF-6D are derived based on responses from the United Kingdom population; as a result, they may differ from the HUI-3 preferences, which are derived from Canadian respondents [10, 44]. There is also concern that population valuations tend to differ from the values of patients actually in that specific health state [41]. Both the EQ-5D and HUI-3 are known to have strong ceiling effects, such that they are limited in their ability to discriminate between patients in nearly perfect health [43]. A probable reason for the ceiling effect of the EQ-5D is the presence of only three response levels. This may result in a lack of sensitivity in detecting differences in health states, preventing patients from expressing only minor health problems [13]. For this study, over half of the study population reported utilities of greater than 0.9. This ceiling effect from the preference-based instruments explicitly showed that they may not be capable of discriminating across the entire range of asthma control levels. A further limitation of generic instruments is that they incorporate broad domains covering all aspects of HRQL. Thus, these instruments may not be able to discriminate between the severity levels related to the specific symptoms associated with a chronic health condition. For example, the HUI-3
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domains encompass walking, dexterity, pain, happiness, cognitive function, vision, hearing, and speech, none of which are directly affected by asthma. As such, there is a need for more studies which examine the measurement properties of generic instruments in different settings, diseases, and populations. While this study is cross-sectional in nature, the responsiveness of the preference-based instruments should also be evaluated longitudinally. If a treatment strategy results in an important change in HRQL, the instruments will need to be able to detect that change. The evaluative nature of these instruments needs to be assessed, as it would be beneficial not only to measure improvements in HRQL with treatments of asthma but also to compare asthma-based HRQL scores with those for other chronic diseases over a longer term. The study sample covered the entire range of asthma control levels, which is of critical importance when evaluating the validity and measurement properties of various instruments. For the work reported here, this requirement is more important than ensuring that the study population is demographically representative of the general asthmatic population. In this study, participants were asked to provide self-reported data on medication use, comorbidities, and control and severity status. While this could potentially have resulted in misclassification, the results indicate that the relationship between HRQL and all markers of asthma control are in the anticipated direction. This suggests that any misclassification was likely minimal and that the potential for bias was unlikely. As health is a function of both quality and length of life, the QALY is used to measure health outcomes in economic evaluations; the quality weight is often derived from one of the three generic preference-based utility instruments evaluated in this study. The QALY is often used in a general health policy model to compare the efficiency of different programmes or treatment strategies in the healthcare system. For utilities to be of value, the scores obtained from these preference-based instruments need to be incorporated into a QALY measure for resource allocation decision-making. However, conducting a cost-utility analysis using the HRQL estimates obtained in this study, only small changes will be observed in the utility outcome measures. Combined with the poor sensitivity of these instruments to detect subtle changes in asthma-related QOL, particularly in patients with moderately well-controlled asthma, these results indicate that the generic instruments studied may not be appropriate for comparing asthma treatment options. In conclusion, for this study population, the HRQL scores derived from the AQL-5D, condition-specific instruments, and VASs had greater capacity for distinguishing between differing degrees of asthma control than
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generic preference-based instruments. The results demonstrate the need to assess the responsiveness of the AQL-5D, as this measure could have considerable implications in the economic assessment of asthma management strategies, especially in clinical situations where only condition-specific instruments are administered. Although the preference-based instruments generally discriminate between ACQ scores, these instruments were unable to distinguish across levels of subjective control measures, such as the individual’s self-perception of their control status. Overall, the HUI-3, the EQ-5D, and the SF-6D discriminated across the highest and lowest levels of asthma control but not for the moderate levels. Acknowledgements We would like to acknowledge the British Columbia Lung Association and the Michael Smith Foundation for Health Research for their financial support of this study. Presently, Drs. Lynd and Marra are both recipients of a Michael Smith Foundation for Health Research scholar award. In addition, Dr. Lynd is a Canadian Institute for Health Research new investigator award recipient and Dr. Marra holds a Government of Canada Research Chair in Pharmaceutical Outcomes. Drs. Kopec and FitzGerald hold a Senior Scholar Award and a Distinguished Scholar Award, both from the Michael Smith Foundation for Health Research, respectively. At the time of the study, Ms. McTaggart-Cowan was a recipient of a Michael Smith Foundation for Health Research trainee award. We would also like to thank the participation of the asthma patients in this study and the clinical support by the following respiratory therapists and technicians: Bev Beaudin, Linda Hui, Louella Markortoff, and Tanja Teofilovic. Finally, we acknowledge the reviewers for their constructive comments.
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