Cited by 894Chronic Disease Management StrategiesHealth Systems, Economic Evaluations, Quality of LifeHealthcare Policy and Management

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The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients

Charlson, Mary E., Charlson, Robert E., Peterson, Janey C., Marinopoulos, Spyridon S., Briggs, William M., Hollenberg, James P.·2008·Journal of Clinical Epidemiology

DOI 10.1016/j.jclinepi.2008.01.006

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Abstract

Objective (1) To determine chronic illness costs for large cohort of primary care patients, (2) to develop prospective model predicting total costs over one year, using demographic and clinical information including widely used comorbidity index.Study design and setting Data including diagnostic, medication, and resource utilization were obtained for 5,861 patients from practice-based computer system over a 1-year period beginning December 1, 1993, for retrospective analysis. Hospital cost data were obtained from hospital cost accounting system.Results Average annual per patient cost was $2,655. Older patients and those with Medicare or Medicaid had higher costs. Hospital costs were $1,558, accounting for 58.7% of total costs. In the predictive model, individuals with higher comorbidity incurred exponentially higher annual costs, from $4,317 with comorbidity score of two, to $5,986 with score of three, to $13,326 with scores greater than seven. To use an adapted comorbidity index to predict total yearly costs, four conditions should be added to the index: hypertension, depression, and use of warfarin with a weight of one, skin ulcers/cellulitis, a weight of two.Conclusion The adapted comorbidity index can be used to predict resource utilization. Predictive models may help to identify targets for reducing high costs, by prospectively identifying those at high risk.

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Springer, R. J., Marino, M., Bailey, S. R., Angier, H. E., O'Malley, J. P., Hoopes, M. J., Lindner, S., DeVoe, J. E., & Huguet, N. (2019) · Addiction · doi:10.1111/add.14667 · Cited by 5
Medications for opioid use disorder: Predictors of early discontinuation and reduction of overdose risk in US military veterans by medication type
Hayes, C. J., Raciborski, R. A., Nowak, M., Acharya, M., Nunes, E. V., & Winhusen, T. M. (2024) · Addiction · doi:10.1111/add.16659 · Cited by 5
“Additional covariates for patient characteristics were selected based on existing literature establishing their association with overdose or MOUD retention [44 – 52].”
Health Costs and Outcomes Associated with Medicare Part D Prescription Drug Cost-Sharing in Beneficiaries on Dialysis
Park, H., Rascati, K. L., Lawson, K. A., Barner, J. C., Richards, K. M., & Malone, D. C. (2015) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2015.21.10.956 · Cited by 5
Cost of palliative external beam radiotherapy (EBRT) use for bone metastases secondary to prostate cancer
Nickman, N. A., Ye, X., Gaffney, D. K., Barney, R. B., BISKUPIAK, J. E., Okano, G. J., Lee, V. C., & Arellano, J. (2015) · The Journal of Community and Supportive Oncology · doi:10.12788/jcso.0115 · Cited by 5
Seguridad y supervivencia de las terapias biológicas: primer informe del registro paraguayo-uruguayo de acontecimientos adversos con terapias biológicas Biobadaguay
de Abreu, P., Avila-Pedretti, G., Morel, Z., Acosta, M. I., Cabrera-Villalba, S., Melgarejo, P., Franco, M., Delgadillo, P., Román, L., Elizaur, G., et al. (2018) · Reumatología Clínica · doi:10.1016/j.reuma.2018.08.009 · Cited by 5
Evidence beyond the digital medication pill
Hadidi, S. E., & Rosano, G. M. C. (2019) · European Heart Journal - Cardiovascular Pharmacotherapy · doi:10.1093/ehjcvp/pvz055 · Cited by 5
Factors associated with humanistic burden and indirect cost among patients with cancer in Japan
Ohno, S., Chen, Y., Sakamaki, H., Matsumaru, N., & Tsukamoto, K. (2020) · Journal of Medical Economics · doi:10.1080/13696998.2020.1839234 · Cited by 5
Factors Associated With Prolonged Length of Stay in Patients Hospitalized With Generalized Convulsive Status Epilepticus in the United States
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“In addition, medical comorbidities were evaluated using the Charleson comorbidities index (CCI), a composite score of 17 medical comorbidities.(14) Also, a variable was included based on whether patients underwent continuous video electroencephalographic (EEG) monitoring, which was identified using the Procedure code…”
Method
Healthcare Costs Associated with Potentially Inappropriate Medication Prescribing Detected by Computer Algorithm Among Older Patients
Pagès, A., Costa, N., Mounié, M., Cestac, P., Barreto, P. D. S., Rolland, Y., Vellas, B. J., Molinier, L., Juillard‐Condat, B., MAPT/DSA Group, et al. (2022) · Drugs & Aging · doi:10.1007/s40266-022-00938-x · Cited by 5
“This observation is well-supported in the literature, especially by the Charlson Comorbidity Index [32].”
Method
Are we there yet? Increasing use of cardioprotective antihyperglycemic agents in patients with T2D and CVD or CV risk in the United States
Mody, R., Meyers, J., Yu, M. B., Davis, K. L., & Levine, J. A. (2022) · Current Medical Research and Opinion · doi:10.1080/03007995.2022.2085962 · Cited by 5
The Future Evolution of the Mortality Acceleration Due to the COVID-19: The Charlson Comorbidity Index in Stochastic Setting
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“The relevance of comorbidity, as expressed by the total burden of chronic diseases, has received surprisingly little attention in the literature, despite its proven ability to predict mortality (10).”
Background
Attitudes toward own aging and cognition among individuals living with and without dementia: findings from the IDEAL programme and the PROTECT study
Sabatini, S., Martyr, A., Ukoumunne, O. C., Ballard, C. G., Collins, R., Pentecost, C., Rusted, J. M., Quinn, C., Anstey, K. J., Kim, S., et al. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03336-5 · Cited by 5
“The Charlson Comorbidity Index [33, 34] was used to enumerate co-morbid conditions and was administered as a joint interview between the PwD and family caregiver where available.”
Method
Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning
Blankemeier, L., Gallegos, I., Chaves, J. M. Z., Maron, D. J., Sandhu, A. T., Rodríguez, F., Rubin, D. L., Patel, B. N., Willis, M. H., Boutin, R. D., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_30 · Cited by 5
Mortality of patients with metastatic colorectal cancer who received elective or emergent operation after exposure to bevacizumab: A nationwide database study
Chen, Y., Su, Y., Hsu, C., Hsieh, M., Wang, Y.-C., Lee, M.-H., Lin, C.-Y. E., Chang, Y., & Wu, C. (2022) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2022.09.018 · Cited by 5
The use of electroconvulsive therapy (ECT) en bloc in Denmark: a nationwide register-based study
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“The CCI is a widely used metric to quantify the burden of medical comorbidity.”
Method
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“The Charlson Comorbidity Index (Charlson et al. 2008) was > 3 in both patient groups.”
Background
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Alexander, C. M., Martyr, A., Gamble, L. D., Quinn, C., Pentecost, C., Morris, R. G., & Clare, L. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1277336 · Cited by 5
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Nakon, O., Utriyaprasit, K., Wanitkun, N., Sindhu, S., Viwatwongkasem, C., & Tankumpuan, T. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s472860 · Cited by 5
“20 This instrument uses a weighted sum of secondary diagnoses (comorbid conditions) to reflect the functional burden of the illness conditions.”
Method
Predictive modeling of health care costs: do cardiovascular risk markers improve prediction?
Baumeister, S. E., Dörr, M., Radke, D., Nauck, M. A., John, U., Marschall, P., Fleßa, S., Schmidt, C. O., Alte, D., & Völzke, H. (2009) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e328333a0b7 · Cited by 5
Screening for osteoporosis following non-vertebral fractures in patients aged 50 and older independently of gender or level of trauma energy—a Swiss trauma center approach
Hemmeler, C., Morell, S., Amsler, F., & Groß, T. (2017) · Archives of Osteoporosis · doi:10.1007/s11657-017-0334-3 · Cited by 5
Evaluación de los cuidados recibidos por pacientes con enfermedades crónicas
Adrián-Arrieta, L., & de Tejerina, J. M. C. F. (2017) · Atención Primaria · doi:10.1016/j.aprim.2017.03.014 · Cited by 5
The burden of frailty in older people visiting GPs in Veneto and Sicily, Italy
Battaggia, A., Scalisi, A., Magliozzo, F., Novelletto, B. F., Fusello, M., Michieli, R., & Cancian, M. (2019) · Journal of Drug Assessment · doi:10.1080/21556660.2019.1612409 · Cited by 5
In-hospital dietary intake and the course of mobilization among older patients with hip fracture in the post-surgical period
Rodríguez-Bocanegra, J. C., Cañavate-Solano, C., Cruz‐Cobo, C., Bibiloni, M. D. M., Tur, J. A., & Santi-Cano, M. J. (2020) · European Geriatric Medicine · doi:10.1007/s41999-019-00282-1 · Cited by 5
Association Between Pain Intensity and Discontinuing Opioid Therapy or Transitioning to Intermittent Opioid Therapy After Initial Long-Term Opioid Therapy: A Retrospective Cohort Study
Hayes, C. J., Krebs, E. E., Brown, J. D., Li, C., Hudson, T. J., & Martin, B. C. (2021) · Journal of Pain · doi:10.1016/j.jpain.2021.05.008 · Cited by 5
Impact of transitioning from long-term to intermittent opioid therapy on the development of opioid-related adverse outcomes: A retrospective cohort study
Hayes, C. J., Krebs, E. E., Brown, J. D., Li, C., Hudson, T. J., & Martin, B. C. (2021) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2021.109236 · Cited by 5
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Baralić, M., Pažitná, L., Brković, V., Laušević, M., Gligorijević, N., Katrlı́k, J., Nedić, O., & Robajac, D. (2023) · Cells · doi:10.3390/cells12030351 · Cited by 5
“Charlson comorbidity index is commonly used as a predictive factor of mortality in a 10-year period [21,22].”
Method
Warfarin Discontinuation in Patients With Unprovoked Venous Thromboembolism: A Large US Insurance Database Analysis
Xie, L., Liu, X., Phatak, H. M., Mardekian, J., Tan, W., Başer, O., & Ramacciotti, E. (2015) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000000167 · Cited by 5
Safety of endoscopic sphincterotomy in patients undergoing antithrombotic treatment: a retrospective study
Yamamiya, A., Kitamura, K., Ishii, Y., Mitsui, Y., & Yoshida, H. (2019) · Therapeutic Advances in Gastrointestinal Endoscopy · doi:10.1177/2631774519846327 · Cited by 5
“Acute biliary pancreatitis, n (%) 1 (2) 1 (3) 0.”
Assoziation zwischen Multimorbidität und Krankheitskosten — Eine systematische Übersichtsarbeit
Hodek, J.-M., Ruhe, A., & Greiner, W. (2010) · PharmacoEconomics German Research Articles · doi:10.1007/bf03320764 · Cited by 5
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Sicras‐Mainar, A., Velasco‐Velasco, S., Navarro‐Artieda, R., Tamayo, M. B., Aguado-Jodar, A., Ruiz-Torrejón, A., Prados‐Torres, A., & Violán, C. (2011) · Atención Primaria · doi:10.1016/j.aprim.2011.05.010 · Cited by 5
The Impact of Mental Health Conditions on Public Insurance Costs of Treating HIV/AIDS
Leibowitz, A. A., & Desmond, K. A. (2019) · AIDS and Behavior · doi:10.1007/s10461-019-02663-w · Cited by 5
High Risk Medication Regimens and Medication Related Predictors of Hospital Readmission in Elderly Home Care Patients
Dierich, M. (2010) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 5
Anti‐seizure medications in patients with post‐stroke epilepsy: A survival analysis study
Antonazzo, I. C., Fornari, C., Buongarzone, G., Ferrara, P., Crotti, G., Zucchi, A., Cortesi, P. A., Rozza, D., Mantovani, L. G., & Mazzaglia, G. (2025) · Epilepsia · doi:10.1111/epi.18706 · Cited by 4
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Tang, H., Li, M., Ji, X., Zhao, Q., Wang, Y., Zhao, Y., Wáng, Q., Xue, F., & Liu, X. (2025) · Frontiers in Health Services · doi:10.3389/frhs.2025.1699513 · Cited by 5
Longitudinal insights into comorbidity patterns and burden among middle-aged and older adults with diabetes in China: a nine-year cohort study using CHARLS
Tang, H., Li, M., Zheng, H., Zhou, Y., & Liu, X. (2025) · Journal of Global Health · doi:10.7189/jogh.15.04353 · Cited by 4
“The weighting system was based on the original CCI framework (Table S1 in the Online Supplementary Document ), informed by subsequent updates from Charlson (2008) and Quan (2011), and refined in accordance with Glasheen and colleagues (2019), which incorporated explicit weights for additional…”
Relationship Between ICU Delirium and Change in Quality of Life, Mood, and Cognition Over 12 Months in Survivors of Acute Respiratory Failure
Ferguson, H. N., SANJUAN, A., Perkins, A. J., Elias, M. N., Wang, S., Mekala, S. N., Devine, B., Koehler, Z., Chi, R., Gao, S., et al. (2026) · CHEST Critical Care · doi:10.1016/j.chstcc.2026.100242 · Cited by 5
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Authors: Charlson, M. E., Charlson, R. E., Peterson, J. C., Marinopoulos, S. S., Briggs, W. M., & Hollenberg, J. P.
Affiliations: Weill Cornell Medicine (US)
Yale New Haven Hospital (US)
Johns Hopkins University (US)
Central Michigan University (US)
Corresponding author: Charlson, Mary E.
ORCID: Charlson, Mary E.
Marinopoulos, Spyridon S.
Briggs, William M.
Venue: Journal of Clinical Epidemiology
Publisher: Elsevier BV
Published: 2008-12
Volume: 61
Issue: 12
Pages: 1234-1240
ISSN: 0895-4356 (ISSN-L)
DOI: 10.1016/j.jclinepi.2008.01.006
OpenAlex: W2110548428
Cited by: 894
References: 38
Citations by year: 2026: 35 · 2025: 52 · 2024: 79 · 2023: 74 · 2022: 80 · 2021: 77 · 2020: 81 · 2019: 66 · 2018: 83 · 2017: 56 · 2016: 42 · 2015: 51 · 2014: 37 · 2013: 21 · 2012: 22
FWCI: 9.15
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Clinical Epidemiology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
Keywords: Charlson Comorbidity Index, chronic disease costs, health care costs, primary care patients, cost prediction model, hospital costs, resource utilization (health care), hypertension, depression, warfarin, skin ulcers, cellulitis, Medicare and Medicaid, elderly patients
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Chronic Disease, Epidemiologic Methods, Female, Health Resources, Health Services Research, Hospitalization, Humans, Male, Middle Aged, New York City, Primary Health Care, Prognosis, Drug Costs, Health Care Costs, Hospital Costs, Young Adult

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The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients

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