The Impact of Co-Morbidity Burden on Preference-Based Health-Related Quality of Life in the United States
Background:Background: Many statistical analyses, clinical trials and cost-utility analyses designed to measure the impact of a particular disease on utility scores often overlook the important influence of co-morbidity burden. Abstract: Objectives:Objectives: This study aims to examine the impact of co-morbidity burden on EQ-5D index scores in a nationally representative sample of the US. Abstract: Methods:Methods: The pooled 2001 and 2003 Medical Expenditure Panel Survey was used. The total number of chronic conditions for each individual was calculated based on Clinical Classification Categories codes. Spline regression was used to identify nonlinear age effects: individuals were separated into four quartiles based on age. Censored least absolute deviation was used to regress EQ-5D index scores on age and chronic co-morbidity, controlling for income, gender, race, ethnicity, education, physical activity and smoking status. Interactions between age and chronic conditions were also explored. Abstract: Results:Results: The coefficients for chronic co-morbidities were highly statistically significant with large magnitudes for those with two or more chronic conditions (coefficient two chronic conditions - -0.16; coefficient nine chronic conditions - -0.28). After controlling for chronic co-morbidities and other confounders, age was not statistically significant except for those aged >58 years and the magnitude of this coefficient was very small (coefficient aged >58 years - -0.0006). The interactions between age and chronic co-morbidity were significant, but the deleterious impact of their interaction was largely dominated by the existence and number of chronic conditions. Abstract: Conclusions:Conclusions: Chronic conditions have a significant deleterious impact on EQ-5D index scores that is much more pronounced than age and other sociodemographic and behavioural characteristics. Future analyses and cost-utility models should incorporate the impact of multiple morbidity.
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