Research report
The responsiveness of EQ-5D utility scores in patients with depression: A comparison with instruments measuring quality of life, psychopathology and social functioning

https://doi.org/10.1016/j.jad.2007.04.018Get rights and content

Abstract

Introduction

The EQ-5D provides preference weights (utilities) for health-related quality of life to be used for calculating quality-adjusted life years (QALYs) in cost-utility analysis. The aim of this study was to compare differences in EQ-5D utility scores with differences in quality of life, psychopathology, and social functioning scores.

Methods

In an observational longitudinal cohort study, EQ-5D utilities (EQ visual analogue scale (EQ VAS), EQ-5D indices of the United Kingdom (EQ-5D index-UK) and Germany (EQ-5D index-D)) were compared with scores of the WHOQOL-BREF, CGI, and GAF at baseline and at 18 months (N = 104). The patients' health status at follow-up was categorized as “worse”, “stable”, or “better” using the EQ-5D transition question (patient-based anchor) and the Bech–Rafaelsen melancholy scale (clinician-based anchor). Effect sizes (ES) were used to compare differences in scores within each group over time; regression analysis was used to derive meaningful difference scores in health status associated with a shift from “stable” to “better” health status.

Results

The most responsive instrument was the CGI (patient-based anchor: ES = |0.98|; clinician-based anchor: ES = |1.35|); responsiveness was large in EQ VAS (patient-based anchor: ES = |0.84|; clinician-based anchor: ES = |1.19|), but rather small to medium for EQ-5D index-UK (patient-based anchor: ES = |0.55|; clinician-based anchor: ES = |0.65|) and EQ-5D index-D (patient-based anchor: ES = |0.41|; clinician-based anchor: ES = |0.45|). Compared with the other instruments, the shift to a “better health status” was smaller if elicited by the EQ-5D indices.

Discussion

Both EQ-5D indices were less responsive and need larger patient samples to detect meaningful differences compared with EQ VAS and the other instruments.

Introduction

The EQ-5D is a short generic patient rated questionnaire measuring health-related quality of life (HRQOL). It is often applied as a measure of outcome in studies comparing different treatments (The EuroQol Group provides a detailed reference list [April 2007]: http://www.euroqol.org). The EQ-5D provides two important aspects: a descriptive profile of HRQOL based on five dimensions including mobility, self-care, usual activities, pain/discomfort, anxiety/depression, and a valuation of the profile by a visual analogue scale (EQ VAS), i.e. a single score reflecting patients’ preferences (Brooks, 1996, The EuroQol Group, 1990). In addition, for various countries (including the United Kingdom, Germany, and the United States) an index score is available assigned to all possible health states described by the EQ-5D according to a particular set of preference values derived from surveys of the general population (Dolan, 1997, Greiner et al., 2004, Shaw et al., 2005). The patients' scores and/or index scores derived from the general population might be used in evaluating change in health status, with the former reflecting the preferences of beneficiaries of care and the latter reflecting community preferences (Dolan, 1999). For the purpose of cost-utility analysis in economic evaluation, with the consequences of treatment being measured in terms of quality-adjusted life years (QALYs), these preference weights are typically used for calculating QALYs (Drummond et al., 1997, Gold et al., 1996).

Decisions about the suitability of the EQ-5D in economic evaluation, especially concerning the EQ VAS and the various country specific societal EQ-5D indices, need to be based on a clear conceptual framework; that means the EQ-5D has to demonstrate its psychometric validity and reliability (Revicki et al., 2000). In the field of depressive disorders, several studies evaluated the suitability of the EQ-5D index-UK (Hayhurst et al., 2006, Lamers et al., 2006, Sapin et al., 2004). In the study by Sapin et al., the authors showed that significant change in EQ-5D index-UK was found by disease severity level, with more severe patients having lower index scores (Sapin et al., 2004). Further studies demonstrated that the mean in EQ-5D index-UK decreased with deterioration in health status and that the EQ-5D index-UK discriminates between groups with varying levels of depression (Hayhurst et al., 2006, Lamers et al., 2006). Overall, these results could corroborate that the EQ-5D index-UK reflected psychopathology and mental aspects of the quality of life in patients with major depression.

In addition to aspects of validity and reliability, the EQ-5D has to show evidence demonstrating responsiveness. Responsiveness reflects the ability of an instrument to detect a change in health status. Responsiveness is determined by evaluating the relationship between changes in clinical endpoints and changes in an instrument’s outcome over time in either observational or clinical trials (Guyatt et al., 1987, Revicki et al., 2000). Recently, the Food and Drug Administration (FDA) in the Unites States has raised a draft on patient reported outcomes including methods to calculate responsiveness and to interpret the detected changes as meaningful (Food and Drug Administration, 2006). The recommended best practice in the evaluation of responsiveness is the calculation of various distribution-based estimates (i.e. effect size, standardized response mean, standard error of measurement) under several anchor-based criteria (i.e. patient or clinician ratings of global improvement) (Revicki et al., 2006): on this note, the anchor-based criterion is used as a external indicator to assign patients into groups reflecting “no change”, and a “(small) positive/negative change”. The distribution-based estimates describing responsiveness consist of a ratio in which the difference in mean baseline to endpoint score reflects the numerator, and different estimates of variability reflect the denominator. Each of these statistical measures acts as a quantitative description of change within the groups. Guidance on interpretation of the magnitude of a distribution-based estimate, for example whether differences in scores are viewed as meaningful, is provided (Cohen, 1988, Norman et al., 2003).

Nonetheless, there is no gold standard in terms of whether the difference in scores are meaningful either from the patient’s or clinician’s perspective, but there are some methods which one will find very useful in interpretation; for example, one definition of a meaningful difference is based on the “minimal important difference” (MID) in scores perceptible to patients as a beneficial change (Guyatt et al., 2002, Jaeschke et al., 1989). In practice, the MID is viewed as the difference in scores between the group with “no change” and the group with “small positive/negative change”. Actually, the MID is an interpretation of change across the groups.

The objective of this study was to compare and contrast the responsiveness in preference-based scores of the EQ-5D for patients with depression with the responsiveness in composite summary scores of instruments measuring quality of life, psychopathology and social functioning. To facilitate interpreting whether the differences in scores are meaningful, we combined measurement information with respect to a patient and clinician anchor-based criterion. We hypothesized the following: (1) since the preference-based scores of the EQ-5D as well as the other instruments used for comparison reflect aspects of HRQOL, the difference scores should show at least a moderate relationship with each other; (2) since the studies published on quality of life in depression showed that HRQOL is related to depressive symptoms, instruments measuring a specific “facet of depression” are expected to reflect a more responsive, meaningful change than the generic preference-based scores derived from the EQ-5D (Papakostas et al., 2004, Wiebe et al., 2003).

Section snippets

Subjects

Patients suffering from an affective disorder according to the International Statistical Classification of Diseases (ICD-10) (World Health Organization, 2004) were recruited consecutively by clinicians at three departments of psychiatry in the federal state “Schleswig Holstein”, Germany. Only patients with a depressive episode according to the ICD classification F32.1, F32.2, F33.1 and F33.2 participated in this observational longitudinal cohort study. Consequently, patients with psychotic

Demographic characteristics

Of the 141 patients initially participating in the study at baseline, 37 dropped out at follow-up, resulting in 104 patients completing both interviews. At baseline the mean age of patients who completed the study was 47.2 (SD 13.9). The age ranged from 20 to 86 years, and the majority (70.2%) were female. More than half of all patients lived with a partner (51.9%). Most patients were diagnosed with moderate to severe levels of depressive episodes (61.6%), followed by repeated depressive

Discussion

Consistent with the first hypothesis, there was a medium to large overlap of constructs measuring aspects of quality of life, psychopathology, and social functioning. More specifically, the EQ VAS seemed to measure similar constructs as the WHOQOL-BREF, whereas the two EQ-5D indices showed less overlap with the instruments used for comparison. The EQ-5D index-UK revealed almost perfect correlation with the EQ-5D index-D. Thus, correlation analysis could lead to the impression that the

Acknowledgement

This study was funded by the German Statutory Health Insurance (grant number 932000-050) and the German Federal Ministry of Education and Research (grant number 01ZZ0106).

References (38)

  • M.C. Angermeyer et al.

    Handbuch für die deutsche Version der WHO Instrumente zur Erfassung der Lebensqualität, Hogrefe Verlag

    (2000)
  • P. Bech et al.

    The Bech–Rafaelsen mania scale and the hamilton depression scale

    Acta Psychiatr. Scand.

    (1979)
  • J. Brazier et al.

    A comparison of the EQ-5D and SF-6D across seven patient groups

    Health Econ.

    (2004)
  • J. Cohen

    Statistical Power Analysis for Behavioral Science

    (1988)
  • P. Dolan

    Modeling valuations for EuroQol health states

    Med. Care

    (1997)
  • P. Dolan

    Whose preferences count?

    Med. Decis. Mak.

    (1999)
  • Food and Drug Administration

    Draft guidance for industry patient reported outcome measures: use in medical product development in support labeling claims

    Fed. Regist.

    (2006)
  • M. Gold et al.

    Cost-Effectiveness in Health and Medicine

    (1996)
  • H.H. Goldman et al.

    Revising axis V for DSM-IV: a review of measures of social functioning

    Am. J. Psychiatry

    (1992)
  • Cited by (0)

    Conflict of interest: All authors declare that they have no conflicts of interest.

    Contributors: All authors contributed to and have approved the final manuscript:

    Oliver H. Günther undertook the statistical analyze and wrote the first draft and the final version of manuscript. Christiane Roick designed the study and managed the process of data collection. Matthias C. Angermeyer planned the study and reviewed the final manuscript. Hans-Helmut König analyzed the data and reviewed the first draft.

    Role of funding source: Funding for this study was provided by German Statutory Health Insurance (grant number 932000-050) and the German Federal Ministry of Education and Research (grant number 01ZZ0106); both had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

    View full text