Risk adjustment for Medicare beneficiaries with Alzheimer's disease and related dementias.

OBJECTIVE: To compare prospective risk adjustment measures on their ability to predict expenditures for Medicare beneficiaries with Alzheimer's disease and related dementias (ADRD). METHODS: Data were obtained from the 1999-2004 Medicare Current Beneficiary Survey linked with Medicare claims. Beneficiaries' base-year demographic and health characteristics were used to construct risk adjustment measures, comorbidity measures, functional status measures, and prior expenditures that were used to predict the subsequent year's total and drug expenditures. Adjusted R(2) values, predictive ratios, and receiver operating characteristic curves were used to compare overall predictive power, accuracy of subgroup prediction, and accuracy in identifying beneficiaries with the top 10% of expenditures, respectively. RESULTS: The Centers for Medicare & Medicaid Services-Hierarchical Condition Category (CMS-HCC) and the Chronic Illness and Disability Payment System-Medicare had higher overall and subgroup predictive power for total expenditures compared with other diagnosis-based measures. The Prescription Drug Hierarchical Condition Category (RxHCC) exhibited greater predictive power for drug expenditures than other measures and outperformed other measures in identifying ADRD beneficiaries with extremely high drug expenditures. Adding functional status to single-measure models generally improved predictive power (ie, R(2) value) for overall health expenditures by 2% to 4%, but not for drug expenditures. CONCLUSIONS: The CMS-HCC and the RxHCC measures currently used by CMS are more predictive and accurate than other risk adjustment measures for overall and drug expenditure prediction for beneficiaries with substantial disabilities and comorbidities. Prediction of overall expenditures may be modestly improved for these beneficiaries by using a combined model of these measures and functional status.

Duke Scholars

Author Matthew Leonard Maciejewski Population Health Sciences