Haomiao Jia1, Erica I Lubetkin. 1. Department of Biostatistics, School of Nursing, Columbia University, New York, NY 10032, USA. hj2198@columbia.edu
Abstract
BACKGROUND: We developed an estimation equation of EuroQol EQ-5D index scores from the Healthy Days measures of the Centers for Disease Control and Prevention for use in burden of disease and cost-effectiveness studies in population subgroups. This study estimated EQ-5D scores, quality-adjusted life years (QALYs) and quality-adjusted life expectancy (QALE) for the USA and the individual states. METHODS: We estimated the EQ-5D scores for respondents from the 2000-2003 Behavioral Risk Factor Surveillance System. We calculated QALYs and QALE lost to morbidity due to obesity/overweight, smoking, low income and chronic diseases. RESULTS: The mean EQ-5D score for US adults was 0.870. The mean scores ranged from 0.826 (West Virginia) to 0.902 (Hawaii). Smoking contributed from 5.6 (Utah) to 12.3 (Kentucky) percent, obesity/overweight 5.4 (South Dakota) to 13.8 (Louisiana) percent, low income 16.6 (Hawaii) to 39.9 (South Carolina) percent and chronic diseases 8.7 (Minnesota) to 22.9 (Tennessee) percent of explainable QALYs lost. These risks contributed the greatest proportion of explainable QALYs and QALE lost in Kentucky, Tennessee and South Carolina. CONCLUSIONS: We estimated the burden of disease contributed by selected risk factors. Currently, such data are unavailable but are needed to set targets for reducing modifiable health risks and eliminating health disparities among at-risk populations.
BACKGROUND: We developed an estimation equation of EuroQol EQ-5D index scores from the Healthy Days measures of the Centers for Disease Control and Prevention for use in burden of disease and cost-effectiveness studies in population subgroups. This study estimated EQ-5D scores, quality-adjusted life years (QALYs) and quality-adjusted life expectancy (QALE) for the USA and the individual states. METHODS: We estimated the EQ-5D scores for respondents from the 2000-2003 Behavioral Risk Factor Surveillance System. We calculated QALYs and QALE lost to morbidity due to obesity/overweight, smoking, low income and chronic diseases. RESULTS: The mean EQ-5D score for US adults was 0.870. The mean scores ranged from 0.826 (West Virginia) to 0.902 (Hawaii). Smoking contributed from 5.6 (Utah) to 12.3 (Kentucky) percent, obesity/overweight 5.4 (South Dakota) to 13.8 (Louisiana) percent, low income 16.6 (Hawaii) to 39.9 (South Carolina) percent and chronic diseases 8.7 (Minnesota) to 22.9 (Tennessee) percent of explainable QALYs lost. These risks contributed the greatest proportion of explainable QALYs and QALE lost in Kentucky, Tennessee and South Carolina. CONCLUSIONS: We estimated the burden of disease contributed by selected risk factors. Currently, such data are unavailable but are needed to set targets for reducing modifiable health risks and eliminating health disparities among at-risk populations.
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