Separating gains and losses in health when calculating the minimum important difference for mapped utility measures.

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Title: Separating gains and losses in health when calculating the minimum important difference for mapped utility measures.
Authors: Nichol, Michael B., Epstein, Joshua D.
Source: Quality of Life Research. Aug2008, Vol. 17 Issue 6, p955-961. 7p. 3 Charts, 2 Graphs.
Subjects: Quality of life, Health status indicators, Health outcome assessment, Managed care programs, Patients, Medical research
Geographic Terms: California
Abstract: To estimate the minimum important difference (MID) for a variety of mapped utility measures and to determine whether patients perceiving gains and losses in health status should be treated equally when calculating the MID. A longitudinal study within a California managed care population of 6,932 patients was retrospectively analyzed. Utilities were derived from the SF-36 short-form health survey using multiple validated mapping methods. Absolute utility changes for patients who considered their current health as ‘somewhat better’ or ‘somewhat worse’ in the prior year were compared to determine if gains and losses in utility values could be combined. The MIDs were calculated and compared using anchor- and distribution-based methods. Two thousand one hundred patients reported ‘somewhat better’ or ‘somewhat worse’ health in the first year. When combining these patients, the average MID for all mapped utility measures was 0.03 (SD = 0.1), a magnitude similar to that identified by Walters. However, when separated, the mean MID utility change for those reporting ‘somewhat better’ and ‘somewhat worse’ health was 0.02 (SD = 0.1) and −0.06 (SD = 0.1), respectively ( P < 0.0001). Researchers should consider the effects of combining gains and losses when determining utility MID values. [ABSTRACT FROM AUTHOR]
Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: To estimate the minimum important difference (MID) for a variety of mapped utility measures and to determine whether patients perceiving gains and losses in health status should be treated equally when calculating the MID. A longitudinal study within a California managed care population of 6,932 patients was retrospectively analyzed. Utilities were derived from the SF-36 short-form health survey using multiple validated mapping methods. Absolute utility changes for patients who considered their current health as ‘somewhat better’ or ‘somewhat worse’ in the prior year were compared to determine if gains and losses in utility values could be combined. The MIDs were calculated and compared using anchor- and distribution-based methods. Two thousand one hundred patients reported ‘somewhat better’ or ‘somewhat worse’ health in the first year. When combining these patients, the average MID for all mapped utility measures was 0.03 (SD = 0.1), a magnitude similar to that identified by Walters. However, when separated, the mean MID utility change for those reporting ‘somewhat better’ and ‘somewhat worse’ health was 0.02 (SD = 0.1) and −0.06 (SD = 0.1), respectively ( P &lt; 0.0001). Researchers should consider the effects of combining gains and losses when determining utility MID values. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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              Text: Aug2008
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