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

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<.001). At lower comorbidity, the costs were similar across different chronic diseases. Using regression to predict total costs, top 5th and 10th percentile of costs, the comorbidity index, prior costs and DCG achieved almost identical explained variance in both adults and children. Conclusions and Relevance The comorbidity index predicted health costs in the subsequent year, performing as well as prior cost and DCG in identifying those in the top 5% or 10%. The comorbidity index can be used prospectively to identify patients who are likely to incur high costs. Trial Registration ClinicalTrials.gov NCT01761253

[1]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[2]  Lessons from Medicare’s Demonstration Projects on Disease Management, Care Coordination, and Value-Based Payment , 2011 .

[3]  A. Soni The Five Most Costly Children's Conditions, 2011: Estimates for U.S. Civilian Noninstitutionalized Children, Ages 0-17 , 2001 .

[4]  Willard G. Manning,et al.  Choosing Between the Sample-Selection Model and the Multi-Part Model , 1984 .

[5]  T M Morgan,et al.  Functional outcomes of acute medical illness and hospitalization in older persons. , 1996, Archives of internal medicine.

[6]  Chuan-Fen Liu,et al.  The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures. , 2005, Health services research.

[7]  D. Atkins,et al.  Designing and Implementing Medicaid Disease and Care Management Programs: A User's Guide , 2008 .

[8]  M. Inzitari,et al.  Predictive Validity of Measures of Comorbidity in Older Community Dwellers: The Insufficienza Cardiaca negli Anziani Residenti a Dicomano Study , 2006, Journal of the American Geriatrics Society.

[9]  C. Forrest,et al.  Medication, diagnostic, and cost information as predictors of high-risk patients in need of care management. , 2009, The American journal of managed care.

[10]  D. Chang,et al.  Pediatric injury outcomes in racial/ethnic minorities in California: diversity may reduce disparity. , 2013, JAMA surgery.

[11]  Spyridon S Marinopoulos,et al.  The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. , 2008, Journal of clinical epidemiology.

[12]  S. Ramachandran,et al.  An employer perspective on annual employee and dependent costs for pediatric asthma. , 2009, Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology.

[13]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[14]  Jayasree Basu,et al.  Research on multiple chronic conditions: where we are and where we need to go. , 2014, Medical care.

[15]  L. Berkman,et al.  Social support and physical disability in older people after hospitalization: a prospective study. , 1994, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[16]  High-Cost Medicare Beneficiaries , 2005 .

[17]  Jill Sage,et al.  The Patient-Centered Outcomes Research Institute. , 2014, Bulletin of the American College of Surgeons.

[18]  V. Hasselblad,et al.  Does disease management improve clinical and economic outcomes in patients with chronic diseases? A systematic review. , 2004, The American journal of medicine.

[19]  Matthew L Maciejewski,et al.  Costs Associated With Multimorbidity Among VA Patients , 2014, Medical care.

[20]  L. Bouter,et al.  How to measure comorbidity. a critical review of available methods. , 2003, Journal of clinical epidemiology.

[21]  E. Bayliss,et al.  The Agency for Healthcare Research and Quality Multiple Chronic Conditions Research Network: Overview of Research Contributions and Future Priorities , 2014, Medical care.

[22]  Robert A Silverman,et al.  Effect of asthma exacerbations on health care costs among asthmatic patients with moderate and severe persistent asthma. , 2012, The Journal of allergy and clinical immunology.

[23]  J. Hollenberg,et al.  Improvement of outcomes after coronary artery bypass. A randomized trial comparing intraoperative high versus low mean arterial pressure. , 1995, The Journal of thoracic and cardiovascular surgery.

[24]  J. Fleishman,et al.  Using information on clinical conditions to predict high-cost patients. , 2010, Health services research.

[25]  S. Downs,et al.  Indiana Chronic Disease Management Program Risk Stratification Analysis , 2005, Medical care.

[26]  R. Goodman,et al.  Toward a More Cogent Approach to the Challenges of Multimorbidity , 2012, The Annals of Family Medicine.

[27]  R. Goetzel,et al.  Return on Investment in Disease Management: A Review , 2005, Health care financing review.

[28]  Lisa I. Iezzoni,et al.  Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model , 2004, Health care financing review.

[29]  C. Salisbury,et al.  Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide , 2012, The Annals of Family Medicine.

[30]  Robert J. Stroebel,et al.  Risk-stratification methods for identifying patients for care coordination. , 2013, The American journal of managed care.

[31]  M. McHugh,et al.  Health benefits in 2010: premiums rise modestly, workers pay more toward coverage. , 2010, Health affairs.

[32]  Richard A. Goodman,et al.  Managing Multiple Chronic Conditions: A Strategic Framework for Improving Health Outcomes and Quality of Life , 2011, Public health reports.

[33]  William D. Marder,et al.  Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management, and Costs , 2007, Journal of General Internal Medicine.

[34]  A. Parekh,et al.  The challenge of multiple comorbidity for the US health care system. , 2010, JAMA.

[35]  P. Allison,et al.  Mortality after the hospitalization of a spouse. , 2006, The New England journal of medicine.

[36]  D W Bates,et al.  Can comorbidity be measured by questionnaire rather than medical record review? , 1996, Medical care.

[37]  D. Bates,et al.  Using Diagnoses to Describe Populations and Predict Costs , 2000, Health care financing review.

[38]  Nancy Lenfestey,et al.  Evaluation of the extended Medicare Care Management for High Cost Beneficiaries (CMHCB) demonstration: Health Buddy® program at Montefiore. Final report , 2013 .

[39]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[40]  C. Winograd,et al.  The Natural History of Functional Morbidity in Hospitalized Older Patients , 1990, Journal of the American Geriatrics Society.