Mapping the COPD Assessment Test onto EQ-5D.

OBJECTIVES: To develop an algorithm for estimating EuroQol five-dimensional questionnaire (EQ-5D)-equivalent utilities from the chronic obstructive pulmonary disease (COPD) assessment test (CAT) and evaluate its use in economic evaluations as part of health technology assessments for COPD. METHODS: Data for the three-level EQ-5D (EQ-5D-3L) and the CAT were obtained from two multinational, phase III clinical trials. Three approaches were explored for estimating EQ-5D-equivalent utilities from the CAT: ordinary least-squares (OLS) regression, multinomial logistic regression, and a combination of the two. Estimated utilities were compared with actual EQ-5D-3L utilities, including treatment effect and the impact of disease severity on health, to evaluate the predictive performance of each algorithm. RESULTS: Root mean squared error and mean absolute error analyses showed that an OLS algorithm performed as well as algorithms developed using more complex modeling structures. The OLS regression included EQ-5D-3L utility weights as dependent variables and CAT items as independent variables. Within-sample validation showed systematic overestimation and underestimation within the range 0.5 ≤ EQ-5D-3L ≤ 0.9 (although all mean absolute errors were ≤0.100), with the smallest difference between estimated and actual values within 0.7 < EQ-5D-3L ≤ 0.9. The algorithm underestimated utility near full health and overestimated utility less than 0.5. As a consequence, the change from baseline was lower and the confidence intervals were narrower than those observed with actual EQ-5D-3L data. CONCLUSIONS: In the absence of EQ-5D data, the OLS regression algorithm may provide an estimate of utility for treatment models, but it is likely to underestimate treatment effects. Therefore, it is recommended that utilities be derived directly from the EQ-5D for the purposes of health technology assessments for COPD treatments in the United Kingdom.

Creating scenarios of the impact of COPD and their relationship to COPD Assessment Test (CAT™) scores.

BACKGROUND: The COPD Assessment Test (CAT™) is a new short health status measure for routine use. New questionnaires require reference points so that users can understand the scores; descriptive scenarios are one way of doing this. A novel method of creating scenarios is described. METHODS: A Bland and Altman plot showed a consistent relationship between CAT scores and scores obtained with the St George's Respiratory Questionnaire for COPD (SGRQ-C) permitting a direct mapping process between CAT and SGRQ items. The severity associated with each CAT item was calculated using a probabilistic model and expressed in logits (log odds of a patient of given severity affirming that item 50% of the time). Severity estimates for SGRQ-C items in logits were also available, allowing direct comparisons with CAT items. CAT scores were categorised into Low, Medium, High and Very High Impact. SGRQ items of corresponding severity were used to create scenarios associated with each category. RESULTS: Each CAT category was associated with a scenario comprising 12 to 16 SGRQ-C items. A severity 'ladder' associating CAT scores with exemplar health status effects was also created. Items associated with 'Low' and 'Medium' Impact appeared to be subjectively quite severe in terms of their effect on daily life. CONCLUSIONS: These scenarios provide users of the CAT with a good sense of the health impact associated with different scores. More generally they provide a surprising insight into the severity of the effects of COPD, even in patients with apparently mild-moderate health status impact.

Economic evaluations of treatments for systemic fungal infections: a systematic review of the literature.

We systematically reviewed published economic evaluations of systemic antifungal therapies and assessed their strengths and weaknesses. The study identified all economic evaluations published before May 2002 and critically appraised their methods using a published checklist. Over 1000 papers on antifungal treatments and costs were retrieved, 40 of which were economic evaluations. Fifteen of these were evaluations of systemic antifungal therapy. The majority of studies examined prophylaxis (11 out of 15), two examined empiric treatment and two examined the treatment of confirmed infections. Methods varied quite dramatically and the quality of the studies was, in general, very poor. Consequently, we were unable to find a coherent body of economic evidence for any particular patient group and/or indication. Three methodological issues were identified as requiring improvement in future economic evaluations of the prevention and treatment of systemic mucoses: cost estimates, outcome measures and evidence of effectiveness. Costs of both antifungal infection and treatment are important, and despite this, seven of the studies only collected cost information on drug acquisition costs. This is such a fundamental flaw that these studies should not be used to inform decision making on the use of antifungal medication. The estimation of costs in the other studies appeared appropriate, although limited reporting obscured other important issues, such as the appropriate use of per diem costs. Outcome measures were necessarily produced in all evaluations, yet only two studies produced cost-effectiveness ratios. This makes comparisons across studies very difficult. Also, small samples sizes meant that 'hard' outcome measures such as life-years saved could not be used, as deaths were rare within the studies. Both studies that produced incremental cost-effectiveness ratios were model-based studies. These allow simulations to be run on large cohorts of hypothetical patients, thus allowing differences in mortality between treatments to be evaluated. Evidence of effectiveness was limited, with many of the studies based on non-randomised clinical studies and consequently open to bias. Future clinical trials of the effectiveness of antifungal prophylaxis and treatment need to incorporate the collection of costs and economic outcome measures. Even quite simple analyses can be quite powerful, particularly when combined with modelling work to allow other data sources to be added.