Do Differences in Methods for Constructing SF-36 Physical and Mental Health Summary Measures Change Their Associations with Chronic Medical Conditions and Utilization?

Published in: Quality of Life Research, v. 12, no. 8, Dec. 2003, p. 1029-1035

Posted on RAND.org on January 01, 2003

by William Cunningham, Terry T. Nakazono, Kai Li Tsai, Ron D. Hays

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BACKGROUND AND OBJECTIVE: Various approaches have been employed to derive physical health and mental health summary scores for the SF-36 and the RAND-36, but head-to-head comparisons of alternative scoring algorithms are rare. The authors determined whether the associations of the physical and mental health summary scores with chronic medical conditions and utilization would differ depending on the scoring algorithm used. METHODS: The authors examined 5701 patients receiving medical care from an independent association of 48 physician groups located primarily in the western United States and compared SF-36 and RAND-36 scoring of physical health and mental health summary scores. Associations with the presence of diabetes, heart disease, and kidney disease, as well as with utilization of medical care and mental health care were compared using bivariate and multivariate analysis. To examine the relationship between SF-36 and RAND-36 scores, the authors regressed the SF-36 physical and mental health composite scores on the RAND-36 physical and mental health summary measures and vice versa. RESULTS: The authors found that the SF-36 and RAND-36 summary scores generally yielded results similar to one another across measures of heart disease, diabetes, and kidney disease, as well as measures of utilization. However, for each chronic medical condition, the RAND-36 showed a slightly larger decrement in mental health than did the SF-36. CONCLUSIONS: Differences between the two sets of summary scores were consistent with their respective conceptual and analytic approaches. Where comparisons of results between the SF-36 and RAND-36 summary scores are desirable in future studies, they can be estimated using the regression equations derived in this study.

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