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Quality-of-Life Assessment When There Is a Loss of Income

Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut, Department of Bioinformatics and Biostatistics, University of Louisville, Kentucky, john.myers{at}louisville.edu

Steven McCabe, MD, MSc

Department of Bioinformatics and Biostatistics, University of Louisville, Kentucky

Stephan Gohmann, PhD

School of Business, Department of Economics, University of Louisville, Kentucky

Purpose. The current study aims to provide the first empirical evidence demonstrating whether people automatically consider morbidity costs when assessing the quality of life for a health state.

Methods. One hundred eighty-one undergraduate students were randomly assigned to 1 of 2 groups: 1) those participants who were not informed of morbidity costs and 2) those participants who were informed of morbidity costs. Students were asked to read a description of a health state and to assign an assessment of quality of life for the health state described by the use of the paper standard gamble.

Results. The overall mean quality of life for the informed group was significantly lower than that of the uninformed group (P < 0.0001, F = 24.2, df = 1, 179). Similarly, there is a significant difference between illness severity levels in mean quality of life (P < 0.0001, F = 29.5, df = 2, 178). No statistically significant interaction between level of illness severity and prior knowledge was observed (P = 0.5904, F = 0.53, df = 2, 178). Therefore, the authors fit a model removing the interaction term.

Conclusion. This study demonstrates that those subjects informed of morbidity costs score quality of life lower than do subjects unin-formed of morbidity costs. To accurately represent the effectiveness of an intervention, the authors argue that morbidity costs should be included in the description of health states.

Key Words: cost-effectiveness • decision analysis • productivity costs

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