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Background frailty is a dynamic condition for which a range of interventions are available. Health state utilities are values that represent the strength of an individual's preference for specific health states, and are used in economic evaluation. This is a topic yet to be examined in detail for frailty. Likewise, little has been reported on minimally important difference (MID), the extent of change in frailty status that individuals consider to be important. Objectives to examine the relationship between frailty status, for both the frailty phenotype (FP) and frailty index (FI), and utility (preference-based health state), and to determine a MID for both frailty measures. Design and setting population-based cohort of community-dwelling Australians. Participant in total, 874 adults aged ≥65 years (54% female), mean age 74.4 (6.2) years. Measurements frailty was measured using the FP and FI. Utilities were calculated using the short-form 6D health survey, with Australian and UK weighting applied. MID was calculated cross-sectionally. Results for both the FP and FI, frailty was significantly statistically associated (P  < 0.001) with lower utility in an adjusted analysis using both Australian and UK weighting. Between-person MID for the FP was identified as 0.59 [standard deviation (SD) 0.31] (anchor-based) and 0.59 (distribution-based), whereas for the FI, MID was 0.11 (SD 0.05) (anchor-based) and 0.07 (distribution-based). Conclusions frailty is significantly associated with lower preference-based health state utility. Frailty MID can be used to inform design of clinical trials and economic evaluations, as well as providing useful clinical information on frailty differences that patients consider important. [ABSTRACT FROM AUTHOR]

Purpose: To compare the relationship of the eight SF-36 v1 subscale scores to the summary scores of the PCS and MCS derived from two different scoring algorithms: one based on the original scoring method (Ware, Kosinski and Keller, SF-36 physical and mental health summary scales: a users manual. The Health Institute, New England Medical Centre, Boston, MA, 1994); and the other based on scoring algorithms that use parameters derived from structural equation modelling. Further, to provide SF-12 scoring algorithms similarly based on structural equation modelling. Methods: The Australian Bureau of Statistics 1995 Australian National Health Survey dataset was used as the basis for the production of coefficients. There were 18,141 observations with no missing data for all eight SF-36 subscales following imputation of data items, and 17,479 observations with no missing data for the SF-12 data items. Data were analysed in LISREL V8.71. Structural equation models were fit to the data in confirmatory factor analyses producing weighted least squares estimates, which overcame anomalies found in the traditional orthogonal scoring methods. Results: Models with acceptable fits to the hypothesised factor structure were produced, generating factor score weighting coefficients for use with the SF-36 and SF-12 data items, to produce PCS and MCS summary scores consistent with their underlying subscale scores. Conclusions: The coefficients generated will score the SF-36 summary PCS and MCS in a manner consistent with their subscales. Previous Australian studies using version 1 of SF-36 or SF-12 can re-score their summary scores using these coefficients. [ABSTRACT FROM AUTHOR]