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2006 HSR&D National Meeting Abstract

1054 — Using Quality-Adjusted Life-Years (QALYs) to Measure Quality of Care in Diabetes

Author List:
Vijan S (Ann Arbor VA HSR&D)
Schmittdiel J (Kaiser Permanente Northern California)
Fireman B (Kaiser Permanente Northern California)
Elston-Lafata J (Henry Ford Health System)
Oestreicher N (Kaiser Permanente Northern California)
Selby JV (Kaiser Permanente Northern California)

Profiling of intermediate outcomes such as blood pressure, cholesterol, and blood glucose is used to measure quality of care in diabetes. However, these measures lack means of aggregation or weighting of importance based on clinical benefit. We therefore sought to evaluate the utility of QALYs as an aggregate quality of care indicator, and to develop a decision tool that could be used to predict QALYs, to profile facilities using a single outcome measure, and to estimate potential improvements in QALYs based upon improving intermediate outcomes.

We used a previously developed Markov model to simulate expected QALYs using 2003 data from a registry of 185,006 patients with diabetes. The Markov model incorporated age, blood pressure (BP), LDL-C, A1C, and diabetes comorbidities as inputs, and considered simultaneous endpoints and combinations of risk factors. These simulated QALYs were used as the dependent variable in multivariate linear regressions to compare 16 facilities and examine the relative impact of improvements in BP, LDL-C, and A1C after adjusting for patient characteristics.

Average adjusted QALYs differed by up to 15% (p<.0001) among facilities. This variation was entirely explained by differences in BP, LDL-C, and A1C control. BP improvements were associated with the greatest increase in predicted QALYs, followed by improvements in LDL-C and A1C. Rankings based on each intermediate outcome were inconsistent and did not fully agree with QALY measures due to differences in comorbidities and the patterns of risk factors.

QALYs may be a useful way for expressing differences in quality of diabetes care among populations. Improvements in blood pressure control yield greater expected QALY gains than improvements in LDL-C or A1C levels. Use of QALYs provides a single metric that can account for baseline comorbidities across populations and provides a measure of quality of care that is appropriately weighted to the degree of benefit achieved by different interventions.

Decision tools developed from this regression model can assist health plan policy makers in determining the effects of clinical marker improvements in target populations, and allow them to take into account variances in patient baseline characteristics and QALY expectations across groups.

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