اثرات آموزش در زمان تجارت کردن ارزش گذاری بر اساس EQ-5D ایالات بهداشت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24632||2012||6 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Value in Health, Volume 15, Issue 2, March–April 2012, Pages 340–345
Objectives In EuroQol five-dimensional questionnaire valuation studies, each participant typically assesses more than 10 hypothetical health states by using the time trade-off (TTO) method. We wanted to explore potential learning effects when using the TTO method, that is, whether the valuations were affected by the number of previously rated health states (the sequence number). Methods We included 3773 respondents from the US EQ-5D valuation study, each of whom valued 12 health states (plus unconscious) in random order. With linear regression, we used sequence number to predict mean and standard deviations across all health states. We repeated the analysis separately for TTO responses indicating a state better than death and a state worse than death. Each TTO value requires a specific number of choice iterations. To test whether respondents used fewer iterations with experience, we used linear regression with sequence number as the independent variable and number of iterations as the dependent variable. Results Mean TTO values were fairly stable across the sequence number, but analyzing state better than death and state worse than death values separately revealed a tendency toward more extreme values: state better than death values increased by 0.02, while state worse than death values decreased by 0.21 (P < 0.0001) over the full sequence. The standard deviations increased slightly, while the number of choice iterations was the same over the sequence number. The findings were stable across the levels of health state severity, age, and sex. Conclusions TTO values become more extreme with increasing experience. Because of the randomized valuation order, these effects do not bias specific health states; however, they reduce the overall validity and reliability of TTO values.
Several methods are available to measure preferences for health states. Ideally, the elicitation method should not affect the responses, but there is ample evidence that it does  and . Gaining experience with a specific elicitation method may also influence responses. A study examining willingness to pay reported lower willingness to pay and reduced variance as respondents gained experience with the valuation method . The time trade-off (TTO) method is frequently used to elicit health state values  and . It is used to identify the point of indifference between a fixed length of life in an impaired health state and a shorter life span in perfect health. Utilities are calculated as time in perfect health divided by time in the target health state. The TTO method is a challenging cognitive task, and it is conceivable that gaining experience with the method may influence the resulting values. The EuroQol five-dimensional (EQ-5D) questionnaire is one of the most frequently used multiattribute utility instruments, and it describes composite health states along five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has three levels: no health problems (level 1), moderate health problems (level 2), and extreme health problems (level 3). The EQ-5D thus describes 35 (=243) health states . The TTO method is dominant in EQ-5D valuation studies, in which respondents typically value 13 to 17 health states . In a Polish EQ-5D TTO valuation study, each participant valued 23 health states but there were no differences between population means or variances for early valuations (6th–17th) and late valuations (18th–23rd) . The first valuations (1st–5th) were considered “warm-up exercises” and did not include the same sample of health state profiles as the rest of the TTO tasks. To our knowledge, this is the only study that has examined the effect of increasing experience with the TTO exercise on the valuations. It is unknown whether there are effects earlier in the valuation process (1st–5th valuations) or whether experience with the TTO method affects the distribution of the responses in other ways than the mean. In the present study, we use the term learning effect for all systematic differences in responses as a function of increasing experience with the TTO method. Learning effect thus refers to the TTO method, and not learning from valuing specific health states. This is not only restricted to an improved understanding of the method but may also include strategies enabling the respondent to finish the task quickly and avoid discomfort, exhaustion, boredom, and so on. The primary objective of the study was to identify potential learning effects by analyzing the distribution of respondents' values as a function of the number of previously valued health states with the TTO task (sequence number). Because the TTO procedure is a complex task, one might expect that a part of the variance in TTO responses is attributable to respondents not understanding the task (noise) and that this noise would be reduced with increasing experience with the valuation task. Hence, an expected learning effect was a reduction in SD for single health states. The secondary objective was to test whether potential learning effects were stable for health states of different severity, as described by the EQ-5D system, and whether they were stable for respondents across age and sex.
نتیجه گیری انگلیسی
Health state values are usually elicited in face-to-face interviews, and costs are largely driven by the number of respondents. Therefore, there is an interest in having each respondent value a large number of health states. We have demonstrated the presence of learning effects in the assessments of health states with the TTO method, in particular for SWD, indicating that the number of states valued by each respondent may influence the resulting EQ-5D tariffs. The causes of these effects and implications for the validity of TTO responses remain uncertain. An investigation of potential learning effects from other valuation methods could help gain an understanding of the processes involved.