Correspondence between EQ-5D health state classifications and EQ VAS scores.
BACKGROUND
The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS). The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified.


METHODS
A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA) provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress.


RESULTS
In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress.


CONCLUSIONS
In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although there also existed other group variables which contributed systematically and independently towards determining such scores. These variables comprised psychological disposition, socio-demographic factors such as age and education, clinically-important distress, and the clinical intervention itself.


BACKGROUND
ISRCTN34841617.
Whynes
David K
DK
School of Economics, University of Nottingham, Nottingham, UK. david.whynes@nottingham.ac.uk
D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001007:Anxiety; D003710:Demography; D004522:Educational Status; D005260:Female; D006801:Humans; D007389:Internal-External Control; D016015:Logistic Models; D008403:Mass Screening / Q000379:methods / Q000706:statistics & numerical data; D008875:Middle Aged; D010147:Pain Measurement; D011230:Precancerous Conditions / Q000473:pathology* / Q000523:psychology; D011594:Psychometrics / Q000295:instrumentation*; D011788:Quality of Life; D012309:Risk-Taking; D015983:Selection Bias; D018434:Sickness Impact Profile; D012907:Smoking; D006113:United Kingdom; D002583:Uterine Cervical Neoplasms / Q000473:pathology*; D014626:Vaginal Smears