INTRODUCTION: The continued success of the Medicare Part D program is contingent on appropriate Medicare payment adjustments for the projected drug costs of Part D plan enrollees. This article describes a major revision of these "risk adjustments," intended to more accurately match payments to costs, especially for high-cost, disadvantaged populations. METHODS: For the first time actual Part D data are used to calibrate risk adjustment. The sample is Medicare beneficiaries with fee-for-service enrollment in 2007 and Part D standalone prescription drug plan enrollment in 2008 (N = 14,224,301). Part D plan liability expenditures are predicted using demographic and diagnostic factors in a weighted least squares regression. Models for Medicare subpopulations are analyzed. The predictive accuracy of risk adjustment models is evaluated using R and predictive ratio statistics. RESULTS: Based on differences in both mean expenditures and incremental expenditures by diagnosis, separate Part D risk adjustment models are calibrated for 5 Medicare subpopulations: aged not low income; aged low income; nonaged not low income; nonaged low income; and institutionalized. The variation in plan liability drug expenditures (R) explained by these models ranges from 13% to 29%. The 5 separate models accurately predict mean plan liability expenditures ranging from $967 to $1762 across subpopulations and account for differences in incremental disease coefficients by subpopulation. CONCLUSIONS: The refined Part D risk adjustment model represents a significant improvement in the accuracy and fairness of payment to Part D plans. The new model provides greater incentives for drug plans to compete for low-income and institutionalized enrollees.
INTRODUCTION: The continued success of the Medicare Part D program is contingent on appropriate Medicare payment adjustments for the projected drug costs of Part D plan enrollees. This article describes a major revision of these "risk adjustments," intended to more accurately match payments to costs, especially for high-cost, disadvantaged populations. METHODS: For the first time actual Part D data are used to calibrate risk adjustment. The sample is Medicare beneficiaries with fee-for-service enrollment in 2007 and Part D standalone prescription drug plan enrollment in 2008 (N = 14,224,301). Part D plan liability expenditures are predicted using demographic and diagnostic factors in a weighted least squares regression. Models for Medicare subpopulations are analyzed. The predictive accuracy of risk adjustment models is evaluated using R and predictive ratio statistics. RESULTS: Based on differences in both mean expenditures and incremental expenditures by diagnosis, separate Part D risk adjustment models are calibrated for 5 Medicare subpopulations: aged not low income; aged low income; nonaged not low income; nonaged low income; and institutionalized. The variation in plan liability drug expenditures (R) explained by these models ranges from 13% to 29%. The 5 separate models accurately predict mean plan liability expenditures ranging from $967 to $1762 across subpopulations and account for differences in incremental disease coefficients by subpopulation. CONCLUSIONS: The refined Part D risk adjustment model represents a significant improvement in the accuracy and fairness of payment to Part D plans. The new model provides greater incentives for drug plans to compete for low-income and institutionalized enrollees.
Authors: John Kautter; Gregory C Pope; Melvin Ingber; Sara Freeman; Lindsey Patterson; Michael Cohen; Patricia Keenan Journal: Medicare Medicaid Res Rev Date: 2014-05-09
Authors: Bruce Friedman; Peter J Veazie; Benjamin P Chapman; Willard G Manning; Paul R Duberstein Journal: Milbank Q Date: 2013-09 Impact factor: 4.911