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Purpose: Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated nonlinear relationships. This paper inquires into the degree of nonlinearity across the four most widely used HSU-instruments and proposes exchange rates that differ depending on the severity levels of the health state utility scale.Methods: Overall, 7933 respondents from six countries, 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of nonlinear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument.Results: Quantile regressions reveal significant nonlinear relationships across all four HSU-instruments. The degrees of nonlinearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.11, whilst at the upper end it is 0.38.Conclusion: Comparisons at different utility levels illustrate the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of nonlinear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument been used. Accounting for nonlinearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY gains have been based on different HSU-instruments. [ABSTRACT FROM AUTHOR]

The study asked how well Achenbach's 8-factor cross-informant model for the Child Behavior Check-list (Achenbach, 1991a, 1991b, 1991c) fits clinic data in the USA, Holland, and Australia. DeGroot et al.'s Dutch 8-factor model (DeGroot, Koot, & Verhulst 1994) was also tested for its cross-cultural generalizability. Achenbach's matched clinical sample data (N = 2110) were analyzed and contrasted with the previously reported Dutch findings (N = 2335), as well as a new data set collected on clinic referred children and adolescents in Australia (N = 2237). Confirmatory factor analyses supported the Dutch as much as the American model in the USA, Holland, and Australia. Although about 90% of items showed convergent validity across models and countries, the attention and especially the social problems factor found least support. Most double loadings in the current models were not upheld. Instead, additional analyses discovered a number of unmodelled loadings including many cross-loadings. This led to the redefinition of the social problems factor as a mean aggression factor (with associated social problems) whereas the original aggression factor focuses on emotional acting out and the delinquent factor describes an evasive, covert type of antisocial behavior. Overall most support was obtained for the withdrawn, somatic, anxious/depressed, thought problems, and aggressive factors. [ABSTRACT FROM AUTHOR]