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A comparison of comorbidity measurements to predict healthcare expenditures
Joel F Farley1, Carolyn R Harley, Joshua W Devine
1Graduate Program in Social, Administrative and Clinical Pharmacy, University of Minnesota, 7-174 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN 55455, USA. farl0032@umn.edu
Insights
Simple count measurements, like diagnosis clusters, better predict future healthcare spending than traditional comorbidity indices (Charlson, Elixhauser, RxRisk-V). This finding aids in resource allocation and risk stratification for healthcare management.
Area of Science:
- Health Services Research
- Medical Informatics
- Healthcare Economics
Background:
- Comorbidity indices (Elixhauser, Charlson, RxRisk-V) are used to assess patient health status.
- Predicting future healthcare expenditures is crucial for resource allocation and risk management.
Purpose of the Study:
- To compare the predictive performance of established comorbidity indices against simple count measurements.
- To identify the most effective predictor of future healthcare expenditures.
Main Methods:
- Utilized claims data from 20,378 managed care organization members over a 1-year period.
- Calculated Elixhauser, Charlson, RxRisk-V indices, and various count measures (prescriptions, visits, claims, diagnosis clusters).
- Employed linear regression models with adjusted R-squared statistics to assess predictive performance.
Main Results:
- RxRisk-V (adjusted R² = 0.1573) outperformed Elixhauser (0.1148) and Charlson (0.1172) indices.
- A count of diagnosis clusters, in an age- and gender-adjusted model, was the best individual predictor (adjusted R² = 0.1814).
- This diagnosis cluster model surpassed other count-based predictors and prior healthcare payment models.
Conclusions:
- Simple count measurements demonstrate superior predictive ability for future healthcare expenditures compared to comorbidity indices.
- A count of diagnosis clusters emerges as the most potent single predictor of future healthcare spending among the evaluated metrics.
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.
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