Performance of Comorbidity, Risk Adjustment, and Functional Status Measures in Expenditure Prediction for Patients With Diabetes
- Matthew L. Maciejewski, PHD12,
- Chuan-Fen Liu, PHD34 and
- Stephan D. Fihn, MD345
- 1Health Services Research and Development, Durham VA Medical Center, Department of Veterans Affairs, Durham, North Carolina
- 2Division of Pharmaceutical Outcomes and Policy, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- 3Health Services Research and Development, VA Puget Sound Health Care System, Department of Veterans Affairs, Puget Sound, Washington
- 4Department of Health Services, University of Washington, Seattle, Washington
- 5Department of Medicine, University of Washington, Seattle, Washington
- Corresponding author: Matthew L. Maciejewski, matthew.maciejewski{at}va.gov
Abstract
OBJECTIVE—To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes.
RESEARCH DESIGN AND METHODS—This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R2 statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups.
RESULTS—Administrative data–based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power.
CONCLUSIONS—Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.
Footnotes
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Published ahead of print at http://care.diabetesjournals.org on 22 October 2008.
The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the U.S. Government, the University of North Carolina at Chapel Hill, or the University of Washington.
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
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- Accepted October 14, 2008.
- Received June 18, 2008.
- DIABETES CARE
