Purpose: There is evidence that patient preferences are systematically higher than societal preferences for a patient’s self-reported health state, possibly due to adaption to chronic illness by patients. It is less clear whether stated preferences for hypothetical health states differ between persons with and without specific conditions. The aim of this study was to determine if presence of specific chronic conditions affected the values estimated for hypothetical EQ-5D health states.
Methods: Data were taken from the US Valuation of EQ-5D Health States. Study participants (N = 3,773) comprised a probability sample of the US adult population in 2002. Each participant valued 12 of a subset of 45 of the 243 EQ-5D health states in a TTO exercise and reported on the presence or absence of 18 chronic conditions. A novel conceptual model was developed to explain the direct and indirect effects of illness experience on values for hypothetical health states. The analyses focused on six conditions: arthritis, diabetes, depression, congestive heart failure, cancer, and allergic rhinitis. Multivariable linear regression was used to estimate differences in health state preferences among persons with a given condition alone, that condition plus one or more other conditions, one or more other conditions, or no chronic conditions while controlling for the satisfaction attributed to own health, other interpersonal differences, and the perceived severity of the valued states. All analyses accounted for the complex sampling design of the US EQ-5D valuation study.
Results: There were no statistically significant differences in mean health state preferences among the four condition-related strata for any of the six chronic conditions. No trend towards adaptation was suggested among those with specific conditions as the direction of the relationship was inconsistent. The strongest predictors of health state preferences were race/ethnicity, age, and marital status.
Conclusions: Results suggest self-reported chronic conditions have a trivial impact on preferences for hypothetical health states while race/ethnicity has a strong effect, consistent with results of a previous study. These results have important implications for researchers who seek to use patient preferences to generate preference-weighting algorithms for condition-specific health state classifiers. However, due to data limitations, including reliance on self-reported data and lack of data on severity/treatment of disease, further investigation is needed.