Health-related quality of life (HRQoL) is a multidimensional concept that covers physical health, psychological state, and social relationship (Schipper et al., 1996), thereby describing a comprehensive picture of the individual's overall well-being. Another commonly used measure, quality-adjusted life year (QALY) is a composite metric that integrates HRQoL with the duration of life to provide a single comprehensive expression of health outcome. More specifically, QALY incorporates both quality and quantity of life into one score, thereby enabling the comparisons across diseases and populations. As such, QALY has become a standard measure of HRQoL in cost-effectiveness research in clinical medicine (Gold, 1996).
When assessing HRQoL of interested subjects, health care providers have the choice of using a generic or disease-specific instrument. Disease-specific measures are often more sensitive to subtle changes in the disease of interest, but may ignore changes in other areas of health or functioning. Given the unpredictability of interventions/medications on multiple body systems, it is essential to ascertain health in ways that can capture a subject's overall functioning and wellbeing (Gold, 1996). Hence, in practice, a generic instrument is usually applied together with a disease-specific instrument.
Epilepsy, as a chronic disorder, has considerable negative effect on people's day-to-day functioning (Baker, 1995). Apart from experiencing seizures and their detrimental impact on cognitive function (particularly memory), those with epilepsy may also experience adverse reactions to antiepileptic drugs (AEDs). In addition, epilepsy is also associated with psychological burden, including anxiety and depression (Wong & Lhatoo, 2000; Vingerhoets, 2006; Ramaratnam et al., 2008). In view of these factors, the traditionally assessed clinical outcomes that measure the treatment effect such as seizure frequency, seizure-free days might not be sufficiently comprehensive to reflect the total impact on the patient's well-being and perception about treatment effect. To capture the patient's own perception of treatment effect, a variety of validated HRQoL measures are available. For epilepsy, the three most commonly reported epilepsy-specific measures were Quality of Life Epilepsy Inventory (QOLIE-10, QOLIE-31, and QOLIE-89), and the two most commonly used generic measures were the Short-Form Questionnaire (SF-18 and SF-36) and World Health Organization Quality of life questionnaire (WHOQOL-BREF and WHOQOL-100; Taylor et al., 2011). Nevertheless, none of the aforementioned instruments could provide a utility score, thus hampering their subsequent uses in the cost-effectiveness/utility research.
Unlike the aforementioned generic instruments, Quality of Well-being Scale (Seiber et al., 2008) was the first instrument specifically designed to measure the quality of life for the estimation of QALYs. It is a preference-weighted instrument combing the three scales of functioning with a measure of symptoms and problems to produce a point-in-time expression of wellbeing that runs from 0 (for death) to 1.0 (for symptomatic full function). With the preference weights derived from a community sample, a unique aspect of QWB-SA version is that a person's utility score reflects a societal perspective on the value of that person's level of functioning and wellbeing (Seiber et al., 2008). The information obtained via QWB-SA would therefore be extremely beneficial for conducting cost-effectiveness/utility research.
Several generic preference-based HRQoL instruments are available in the Chinese versions. For instance, EuroQol (EQ-5D) and Short-form 6D (SF-6D) have been validated in certain Chinese populations (Zhao et al., 2010). However, both EQ-5D and SF-6D, focus only on the functioning aspects, whereas in contrast, QWB-SA has a functioning component complemented by a strong symptom component. Prior work by developers of QWB has demonstrated that on any particular day, nearly 80% of the general population is optimally functional, but less than half of the population experiences no symptoms (Seiber et al., 2008). Consequently, administration of QWB-SA could provide important supporting information that is not captured by EQ-5D or SF-6D.
Our research, therefore, intended to translate and validate the QWB-SA and investigate the psychometric properties of this Chinese version in Chinese patients with epilepsy. At the same time, the performance of QWB-SA was compared with another widely utilized generic preference-based HRQoL instrument: EQ-5D.
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- Supporting Information
Given that QALYs have been widely adopted as the effectiveness outcome in cost-effectiveness/utility analysis studies, the utility generated from generic preference-based HRQoL instruments is an important determinant in making clinical as well as health care allocation decisions. In the case of epilepsy, which is the most common neurologic disorders affecting people of all ages (Hauser et al., 1991; Forsgren et al., 2005; Preux & Druet-Cabanac, 2005), no generic preference-based HRQoL measure has yet been validated in epileptic patients in China. With the increasing numbers of new antiepileptic drugs/devices/technologies being invented and introduced, cost-effectiveness/utility analysis will be needed to assess their cost-effectiveness. Hence, a validated HRQoL instrument that could calculate QALYs would be the most useful and greatly in demand. Our study is the first to translate and validate such an instrument (QWB-SA) in Chinese epilepsy patients.
Studies have been conducted previously to investigate the psychometric properties of generic preference-based HRQoL instrument in English-speaking patients with epilepsy. In general, EQ-5D/UK/US, 15D, SF-6D, HUI-2, and HUI-3 were shown to be reliable utility instruments in an epilepsy population (Stavem et al., 2001; Langfitt et al., 2006). In addition, compared to EQ-5D/VAS, the following instruments seemed to be more capable of discriminating between patients with different seizure controls and seizure severity: HUI-2 and HUI-3, SF-6D. This would suggest better psychometric advantages of the SF-6D over the other preference instruments for epilepsy patients. Although 15D and the assessment of Quality of life (AqoL) were sensitive to variability at the upper end of the HRQoL continuum as well, the studies were not targeted at epilepsy patients (Langfitt et al., 2006).
The construct (convergent, discriminative, sensitivity) validity of QWB-SA has been successfully demonstrated in our study. Most importantly, the sensitivity of the QWB-SA was demonstrated by its ability to discriminate between different seizure frequencies and antiepileptic treatment (mono vs. poly) groups, which is of clinical importance. In addition, seizure frequency and antiepileptic treatment were found to be predictors of HRQoL as measured by the QWB-SA rather than the EQ-5D. Lastly 77 (16.5%) versus 275 (58.9%) subjects on the QWB-SA and the EQ-5D scored 1.0 (perfect health), respectively, which suggested that the QWB-SA has fewer ceiling effects.
The utility of the QWB-SA was substantially lower than that of the EQ-5D in both epilepsy and control groups. It was worth noting that the disagreement on utility scores for these two instruments was not uncommon and had been observed by previous large sample studies (N = 3,844; Fryback et al., 2007; Bentley et al., 2011; Khanna et al., 2011). The means for EQ-5D and QWB-SA were reported to be 0.89 and 0.67, respectively (Fryback et al., 2007), whereas subjects with arthritis reported utilities ranging from 0.77 to 0.80 on EQ-5D, and from 0.56 to 0.59 on QWB-SA. The same difference was also observed in the utility scores of subjects without arthritis (Khanna et al., 2011). Furthermore, when the participants were categorized according to body mass index (BMI), the utility score for the EQ-5D was also higher than the QWB-SA among normal, overweight, and obese subjects (Bentley et al., 2011). There might be two explanations for this observation: first, unlike the EQ-5D, which utilizes the time trade-off to elicit the preference-weight for each health state, the QWB-SA adopts VAS, and the utility scores derived from VAS tend to be inherently lower than the TTO or Standard Gamble (Fryback et al., 2007). Second, the large acute and chronic symptom weight in the QWB-SA may cause the utility to be lower than the EQ-5D, as the latter does not include detailed symptoms. The difference in utility between EQ-5D and QWB-SA would raise a huge concern in future cost-effectiveness analysis, because the variation in utilities will definitely cause differences in the calculation of QALYs, and subsequently the incremental cost-effectiveness ratio (ICER). For example, in an analysis evaluating an antirheumatoid agent, it was reported that four kinds of HRQoL instruments (EQ-5D, HUI2, HUI3, and SF-6D) provided different QALYs and hence different ICERs (Marra et al., 2007). Hence, even if one AED generated obviously desirable ICER in indirect comparison with another AED, a decision could not be easily made because distinctive HRQoL measures with different sensitivities might have been utilized. Therefore, when conducting a cost-effectiveness analysis, the decision in choosing the ideal generic HRQoL measure has to balance the sensitivity and the generalizability of the instrument.
In our study, age- (Fig. 2) and education-by-group effects were observed on both the QWB-SA and the EQ-5D for the epilepsy or control populations. Generally, there was a downward trend in utility with increasing age (in both patient and control groups) and decreasing education level (in control population). However, our current results of the associations with age and education level observed in the epilepsy cohort were not in line with those of previous studies. According to a review of HRQoL determinants, age was not associated with HRQoL, whereas education level might be correlated although the conclusion was not consistent (Taylor et al., 2011). Nevertheless, the normative data of the QWB-SA reported a descending trend of utility with increasing age (Seiber et al., 2008). Therefore, the inherent attributes of the QWB-SA might be sensitive to identify changes in HRQoL affected by age, as the acute and chronic symptoms might occur more often in aged subjects, whereas other HRQoL measures such as the EQ-5D do not take the specific symptoms into account.
Furthermore, working status was another contributing factor of HRQoL for the epilepsy group. For both the QWB-SA and the EQ-5D, employed patients got higher scores even after age and level of education were controlled (e.g., the estimated QWB-SA utilities were 0.686 and 0.632 for employed and unemployed epilepsy patients, respectively). Still, the impact of employment status on the HRQoL of epilepsy patients was inconsistent across studies. Several studies showed unemployment associated with poorer HRQoL (Buck et al., 1999; Gilliam et al., 1999; Mollaoglu et al., 2004; Liou et al., 2005; Elsharkawy et al., 2009; Tlusta et al., 2009), whereas others reported no correlations (Choi-Kwon et al., 2003; Djibuti & Shakarishvili, 2003; Alanis-Guevara et al., 2005; Thomas et al., 2005; Mosaku et al., 2006; Tracy et al., 2007; Zhao et al., 2008; Giovagnoli et al., 2009). Even so, it should be noted that the sample size of three studies was <115, which indicated low statistical power (Thomas et al., 2005; Mosaku et al., 2006; Zhao et al., 2008). A recent study also reported that fully employed epileptic patients might have worse HRQoLs, owing primarily to the discrimination of and misconception about epilepsy in the work place (Mahrer-Imhof et al., 2012). So accordingly, the inconsistency in this result would necessitate future study to confirm.
As to the epilepsy-specific variables, in our multivariate analysis, seizure frequency was shown to be a predictor of HRQoL as measured by the QWB-SA. In addition to suggesting the better sensitivity of the QWB-SA over the EQ-5D, this is of clinical importance when evaluating the therapeutic effects of AEDs. If the HRQoL instrument is insensitive to changes in seizure frequency, the generated QALY and other clinical merits might be underestimated resulting in rejection of valuable therapy. Although numbers of AEDs were shown to be another predictor of utility by the QWB-SA in the present study, again, this association was not consistent across studies (Gilliam et al., 1999; Choi-Kwon et al., 2003; Johnson et al., 2004; Thomas et al., 2005; Tracy et al., 2007). Actually, it is well acknowledged that antiepileptic monotherapy may have several advantages compared to polytherapy in terms of better tolerability, improved adherence, fewer interactions, and lower cost (Guberman, 1998). In addition, adverse effects of AEDs have been shown to be positively associated with decreased HRQoL (Luoni et al., 2011). Therefore, it is reasonable to expect patients who are taking more than one AED to experience more toxic effects of medication, and consequently have poorer HRQoL. Yet, the correlation between numbers of AEDs and HRQoL requires future study to confirm.
The QWB-SA normative data (mean ± SD) reported that the utilities for clinical and control (general outpatient medical sample) cohorts were 0.599 ± 0.1629 to 0.648 ± 0.1257 and 0.602 ± 0.1323 to 0.67 ± 0.1286 for various age groups (range from 18 to >71 years) (Seiber et al., 2008). Studies were also conducted utilizing the QWB-SA to investigate HRQoL for different disease cohorts. For instance, a study that recruited inpatients and outpatients with depression found that the QWB-SA scores for inpatients were substantially lower than those for outpatients (0.383 ± 0.118 vs. 0.479 ± 0.115) (Pyne et al., 2003). Other reported QWB-SA utilities included family medicine controls (0.6427 ± 0.1349) and subjects with arthritis (0.4966 ± 0.1542) (Frosch et al., 2004); as well as presurgery cataract subjects (0.595 ± 0.134) (Rosen et al., 2005). The epilepsy data from our data set were comparable to the QWB-SA normative data as well as those from the general medical controls, although the utilities in our controls seemed to be higher than the controls from aforementioned studies. There might be several reasons underlying this. First of all, the controls from our data set were substantially younger (36.15 ± 16.406) as HRQoL would decline with increasing age (Seiber et al., 2008). Second, the participants were generally relatives/caregivers of patient group, medical school students, and hospital general staff, most may enjoy better health than subjects from outpatient medical samples or family medicine controls as included in the QWB-SA normative sample.
Nevertheless, several limitations should be noted. First of all, interrater reliability and responsiveness were not tested due to the cross-sectional design of our study. Admittedly, responsiveness is an important psychometric property of an HRQoL instrument, especially for epilepsy due to its chronic nature and unpredictability of seizures, thus requiring treatment adjustment from time to time. Second, heterogeneity existed between our two groups in terms of age, gender, level of education, and employment status. As identified by our study, the factors age, education, and employment might have associations with quality of life; the variation in these demographic data would somewhat introduce bias to the result. Nonetheless, even when age and education level were adjusted, utilities of the QWB-SA and the EQ-5D still showed differences between two groups. Third, the preference weights utilized to estimate the utilities of the QWB-SA and the EQ-5D were not originated from Chinese subjects (one from America, the other from United Kingdom). However, it was found that the preference scoring does not vary significantly, and the results are similar across different countries (Drummond et al., 2005). Nevertheless, future study to address the responsiveness of the Chinese QWB-SA and to ascertain the preference weights from societal perspective of China is still needed.
In conclusion, from the present study, the QWB-SA was shown to cover more dimensions of HRQoL, have better sensitivity, fewer ceiling effects, and less skewed distribution than the EQ-5D. Hence, it is potentially a more suitable HRQoL measure for patients with epilepsy in China.