BACKGROUND: Control of blood pressure, low-density lipoprotein cholesterol (LDL-c), and A1c can lower the risk for diabetes complications. These quality indicators often are examined separately and weighted equally in performance measurement, potentially discarding important information. OBJECTIVES: We sought to create a composite indicator of the clinical benefit, or value, of diabetes risk factor control that appropriately weights the clinical importance of A1c, LDL-c, and blood pressure, and to test its usability for quality measurement. METHODS: The combined value of control for 3 diabetes risk factors, measured by predicted quality-adjusted life years (QALYs), was compared in diabetes patients (n = 129,236 in 2001; n = 185,006 in 2003) in Kaiser Permanente Northern California across 16 medical center populations in 2001 and 2003 using hierarchical linear regression to adjust for case-mix differences. Patient-level QALYs, simulated from risk factor and case-mix variables in a Markov model, was the main outcome variable. RESULTS: There was significant cross-sectional variability in average case-mix adjusted QALYs for diabetes patients across centers in 2003. QALYs increased from 2001 to 2003 as the result of improved risk factor control; longitudinal improvements in QALYs also showed variation across centers. Regression analyses demonstrated the greater impact of blood pressure versus LDL-c or A1c control on QALYs, and the greater value of risk factor control in those with poor versus near or in-control blood pressure. CONCLUSION: Using predicted QALYs to measure value holds promise as a sensitive composite indicator for quality measurement. Complex, evidence-based quality indicators such as these can potentially provide accurate and useful information to health plans, providers, and consumers.
BACKGROUND: Control of blood pressure, low-density lipoprotein cholesterol (LDL-c), and A1c can lower the risk for diabetes complications. These quality indicators often are examined separately and weighted equally in performance measurement, potentially discarding important information. OBJECTIVES: We sought to create a composite indicator of the clinical benefit, or value, of diabetes risk factor control that appropriately weights the clinical importance of A1c, LDL-c, and blood pressure, and to test its usability for quality measurement. METHODS: The combined value of control for 3 diabetes risk factors, measured by predicted quality-adjusted life years (QALYs), was compared in diabetespatients (n = 129,236 in 2001; n = 185,006 in 2003) in Kaiser Permanente Northern California across 16 medical center populations in 2001 and 2003 using hierarchical linear regression to adjust for case-mix differences. Patient-level QALYs, simulated from risk factor and case-mix variables in a Markov model, was the main outcome variable. RESULTS: There was significant cross-sectional variability in average case-mix adjusted QALYs for diabetespatients across centers in 2003. QALYs increased from 2001 to 2003 as the result of improved risk factor control; longitudinal improvements in QALYs also showed variation across centers. Regression analyses demonstrated the greater impact of blood pressure versus LDL-c or A1c control on QALYs, and the greater value of risk factor control in those with poor versus near or in-control blood pressure. CONCLUSION: Using predicted QALYs to measure value holds promise as a sensitive composite indicator for quality measurement. Complex, evidence-based quality indicators such as these can potentially provide accurate and useful information to health plans, providers, and consumers.
Authors: Sergeui Pakhomov; Nilay Shah; Penny Hanson; Saranya Balasubramaniam; Steven A Smith; Steven Allan Smith Journal: AMIA Annu Symp Proc Date: 2008-11-06