A comparison of comorbidity measurements to predict healthcare expenditures

Am J Manag Care. 2006 Feb;12(2):110-9.


Objective: 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.

Study design: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Comorbidity*
  • Female
  • Forecasting
  • Health Expenditures*
  • Humans
  • Male
  • Middle Aged
  • United States