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Valuing Health for Regulatory Cost-Effectiveness Analysis
various combinations; and our benefits transfer from the CEA Registry studies used one estimate for chronic bronchitis and one estimate for all post-AMI conditions.
In the expert assignment, we found that the results did not always vary across the severity categories. The EQ-5D allows a choice of three attribute levels within each domain. In some cases, individual experts assigned the same attribute levels to cases of differing severities. The assignments also indicated that the experts disagreed about whether certain conditions would impose no, moderate, or severe problems in a particular domain. Where the estimates varied across endpoints, they generally followed the expected pattern, showing increasing problems for cases with increasing severity. Mild cases resulted in median HRQL values close to 1.0, indicating a negligible effect on the quality of life. In contrast, the most severe form of congestive heart failure led to HRQL values close to zero, with median estimates of 0.05 or less. In general, the median values were identical for the two age groups specified in the AMI scenarios (those above and below 65 years).
The QALY estimates varied across the three approaches. In Table A-14, we provide the results for the average age at incidence under each approach, in comparison to both average and perfect health. (The adjustments made in these comparisons are described in the “General Approach” section, above.) While these adjustments seem sensible within the context of each approach, they lead to inconsistencies in the relationships across the results.
As illustrated by the table, for the expert assessment, the “with condition” values (and the decrement from normal health) are consistently lower under the average health scenario than under the perfect health scenario; we applied the same percentage reduction to a lower value (average “without condition” HRQL is less than perfect HRQL). For the MEPS-based EQ-5D catalogue, the “with condition” values are the same under both scenarios, but the decrement is larger under the perfect health scenario and increases with age (because we add the difference between average and optimal health, which grows with age). For the values taken from the CEA Registry studies, which scenario results in larger estimates depended on age, because we anchored the percentage reduction from average population health at the average age of the underlying study samples. The average age in the chronic bronchitis study is 55 years, slightly higher than the average age at incidence used in our analysis (Torrance et al., 1999). For the AMI study, the average age of the study sample is 69 years (Oostenbrink et al., 2001).
We multiplied the estimates of decrements from “without condition” health by duration (taking life expectancy into account) to determine the QALY losses associated with each nonfatal endpoint as well as with pre-