A comparison of comorbidity measurements to predict healthcare expenditures.

Abstract

To compare the performance of the Elixhauser, Charlson, and RxRisk-V comorbidity indices and several simple count measurements, including counts of prescriptions, physician visits, hospital claims, unique prescription classes, and diagnosis clusters.Each measurement was calculated using claims data during a 1-year period before the initial filling of an antihypertensive medication among 20 378 members of a managed care organization. The primary outcome variable was the log-transformed sum of prescription, physician, and hospital expenditures in the year following the prescription encounter.In addition to descriptive statistics and Spearman rank correlations between measurements, the predictive performance was determined using linear regression models and corresponding adjusted R(2) statistics.The Charlson index and the Elixhauser index performed similarly (adjusted R(2) = 0.1172 and 0.1148, respectively), while the prescription claims-based RxRisk-V (adjusted R(2) = 0.1573) outperformed both. An age- and gender-adjusted regression model that included a count of diagnosis clusters was the best individual predictor of payments (adjusted R(2) = 0.1814). This outperformed age- and gender-adjusted models of the number of unique prescriptions filled (adjusted R(2) = 0.1669), number of prescriptions filled (R(2) = 0.1573), number of physician visits (adjusted R(2) = 0.1546), logtransformed prior healthcare payments (adjusted R(2) = 0.1359), and number of hospital claims (adjusted R(2) = 0.1115).Simple count measurements appear to be better predictors of future expenditures than the comorbidity indices, with a count of diagnosis clusters being the single best predictor of future expenditures among the measurements examined.