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The Charlson Comorbidity Index Can Be Used Prospectively to Identify Patients Who Will Incur High Future Costs

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  • Mary Charlson
  • Martin T Wells
  • Ralph Ullman
  • Fionnuala King
  • Celia Shmukler

Abstract

Background: Reducing health care costs requires the ability to identify patients most likely to incur high costs. Our objective was to evaluate the ability of the Charlson comorbidity score to predict the individuals who would incur high costs in the subsequent year and to contrast its predictive ability with other commonly used predictors. Methods: We contrasted the prior year Charlson comorbidity index, costs, Diagnostic Cost Group (DCG) and hospitalization as predictors of subsequent year costs from claims data of fund that provides comprehensive health benefits to a large union of health care workers. Total costs in the subsequent year was the principal outcome. Results: Of the 181,764 predominantly Black and Latino beneficiaries, 70% were adults (mean age 45.7 years; 62% women). As the comorbidity index increased, total yearly costs increased significantly (P

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  • Mary Charlson & Martin T Wells & Ralph Ullman & Fionnuala King & Celia Shmukler, 2014. "The Charlson Comorbidity Index Can Be Used Prospectively to Identify Patients Who Will Incur High Future Costs," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0112479
    DOI: 10.1371/journal.pone.0112479
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    2. Geir Fagerjord Lorem & Henrik Schirmer & Nina Emaus, 2016. "Health Impact Index. Development and Validation of a Method for Classifying Comorbid Disease Measured against Self-Reported Health," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-14, February.
    3. Mengna Luan & Wenjing Shi & Zhigang Tao & Hongjie Yuan, 2023. "When patients have better insurance coverage in China: Provider incentives, costs, and quality of care," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(4), pages 1073-1106, October.
    4. Piotr Swierkowski & Adrian Barnett, 2018. "Identification of hospital cost drivers using sparse group lasso," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    5. Lena Harries & Jill Gwiasda & Zhi Qu & Harald Schrem & Christian Krauth & Volker Eric Amelung, 2019. "Potential savings in the treatment pathway of liver transplantation: an inter-sectorial analysis of cost-rising factors," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(2), pages 281-301, March.

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