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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Method
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Method
Safety and survival associated with biologic therapies: first report of the Biobadaguay on the Paraguayan-Uruguayan registry of adverse events with biologic therapies
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. (2020) · Reumatología Clínica (English Edition) · doi:10.1016/j.reumae.2018.08.010 · Cited by 3
Medical comorbidity and projected survival in patients admitted to a specialist addictions in-patient unit
Mogford, D. V., & Lawrence, R. J. (2016) · BJPsych Bulletin · doi:10.1192/pb.bp.115.051987 · Cited by 3
Health care resource use in preschool children with cerebral palsy
Easton, T., Boyd, R. N., Scuffham, P., & Downes, M. (2024) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15964 · Cited by 2
“26 There are few existing studies on the health resource requirements and costs of children with CP in Australia.”
The use of the surgical pleth index to guide anaesthesia in gastroenterological surgery: a randomised controlled study
Bergman, T., Kalliomäki, M., Särkelä, M. O. K., & Harju, J. (2025) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-025-01262-6 · Cited by 2
“Our find - ings are also supported by a recent study with patient undergoing colorectal surgery under sevoflurane-remifentanil illnesses was described using the Charlson Comorbidity Score [18].”
Analysis of the dynamic correlation between chronic comorbidities and health shocks among middle-aged and older adults people in rural mountainous areas of southern Ningxia—from 14 years of follow-up panel data
Yang, J., Liu, S., Li, F., Meng, H., Qiao, H., & Xie, Y. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1599656 · Cited by 2
“By consulting relevant domestic and foreign literatures (17–19), the Charlson Comorbidity Index (CCI) is currently the most widely used comorbidity assessment tool.”
Effects of integrated care on the subjective quality of life and social relationships of older adults in South Korea
Lee, H. J., Yoo, A. J., Bang, H. J., Shin, J., & Choi, J. W. (2025) · BMC Geriatrics · doi:10.1186/s12877-025-06191-2 · Cited by 3
“The CCI is the summation of weighted scores assigned to many major health conditions [30].”
The Impact of Integrated Home-Based Primary Care on Continuity and Outcomes of Care Among the Community Homebound Older Adults in Taiwan
Li, M.-T., Tung, Y., & Kuo, K. (2025) · Medical Care · doi:10.1097/mlr.0000000000002183 · Cited by 2
Assessment of body fat distribution by ultrasound as a risk factor for mortality and readmission in patients admitted for sepsis
Rabasa, J. T. A., Ribes, I., Ronda, E., & Algado-Sellés, N. (2025) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2025.502369 · Cited by 2
The use of centralized waiting lists for attachment to family physicians in Quebec, Canada: predictive factors and association with non-urgent emergency department use
Adi, S., Bosson-Rieutort, D., Breton, M., Smithman, M. A., Laberge, M., & Sourial, N. (2026) · BMC Health Services Research · doi:10.1186/s12913-025-13941-w · Cited by 2
Incident and predictors of 30-day mortality in critically ill patients after admission to the emergency department in the central region of Thailand: a prospective observational study
Sangsongrit, N., Utriyaprasit, K., Tankumpuan, T., & Khruekarnchana, P. (2026) · BMC Health Services Research · doi:10.1186/s12913-025-13994-x · Cited by 2
“Comor-bidities were assessed using the Charlson Comorbidity Index (CCI) [39].”
Influential
The Influence of the Common Cold on Self-Rated Health: A Population-Based Study
Pu, C. (2015) · AIMS Public Health · doi:10.3934/publichealth.2015.3.247 · Cited by 2
“It has been shown that this index is a significant and independent predictor of patient survival [16–18], and is associated with treatment decisions, clinical outcomes, and resource utilization [19,20].”
Background
Kairotropy: Discovering Critical Illness Trajectories Using Clinical Phenotypes with Big Data
Weissman, G. E., & Halpern, S. D. (2016) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-319-27349-5_39 · Cited by 2
Incident delirium in acute geriatric medicine: Are iatrogenic causes really important?
Beuscart, J., Convain, J., Lemaître, M., Charpentier, A., Périchon, R., Gaxatte, C., Boumbar, Y., Boulanger, É., & Puisieux, F. (2016) · European Geriatric Medicine · doi:10.1016/j.eurger.2016.06.004 · Cited by 2
Fractura de cadera en el paciente añoso. ¿Es necesario esperar el ecocardiograma?
Cosini, F., Lulkin, E., Pereira, S. P., Vindver, G. I., & Bidolegui, F. (2019) · Revista de la Asociación Argentina de Ortopedia y Traumatología · doi:10.15417/issn.1852-7434.2020.85.1.988 · Cited by 2
Thickness of rectus abdominis measured by ultrasound in critically ill patients after abdominal surgery
Yang, M.-C., Wang, Y.-C., Chen, I.-S., & Huang, W. (2021) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000001407 · Cited by 3
Assessing the Added Value of Blood Pressure Information Derived from Electronic Health Records in Predicting Health Care Cost and Utilization
Kharrazi, H. H. K., Chang, H., Weiner, J. P., & Gudzune, K. A. (2021) · Population Health Management · doi:10.1089/pop.2021.0250 · Cited by 2
Determinants of continuing mental health service use among older persons diagnosed with depressive disorders in general hospitals: latent class analysis and GEE
Mulalint, T., Seeherunwong, A., Wanitkun, N., & Tongsai, S. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08250-5 · Cited by 2
“Comorbidity was measured using the Charlson Comorbidity Index (CCI) [30], a measure of the incidence of 23 common diseases.”
Method
Evaluating the effect of digital primary care on antibiotic prescription: Evidence using Swedish register data
Wilkens, J., Thulesius, H. O., Arvidsson, E., & Ekman, B. (2023) · Digital Health · doi:10.1177/20552076231156213 · Cited by 2
“25 We selected the infection diagnoses which have recommendations on antibiotic use by the Swedish strategic programme against antibiotic resistance (STRAMA) 26 to focus the study on conditions that are speci ﬁ cally sensitive to careful antibiotic prescription.”
Method
Predicting Kidney Transplant Evaluation Non-attendance
Ford, C. G., Leyva, Y. L., Kruger, E. S., Zhu, Y., Croswell, E., Kendall, K., Puttarajappa, C. M., Dew, M. A., Ng, Y., Unruh, M. L., et al. (2023) · Journal of Clinical Psychology in Medical Settings · doi:10.1007/s10880-023-09953-5 · Cited by 2
“…Scale, ranging on a scale from 1 (e.g., Service Workers) to 9 (e.g., Executives, CEOs) The cutoff between “high” and “low” was a score of 4 (e.g., (Charlson et al., 2008) The score was obtained using patient data obtained from the medical record reflecting the number and severity of…”
Evaluating ‘living well’ with mild-to-moderate dementia: Co-production and validation of the IDEAL My Life Questionnaire
Clare, L., Pentecost, C., Collins, R., Martyr, A., Litherland, R., Morris, R. G., Quinn, C., Gamble, L. D., Sabatini, S., & Victor, C. R. (2023) · Dementia · doi:10.1177/14713012231188502 · Cited by 2
“Information about demographic characteristics, dementia diagnosis and co-morbid conditions (Charlson et al., 2008) was taken from study records.”
Method
Características clínicas y evolución del paciente anciano mayor de 80 años en una Unidad de Cuidados Intensivos
Rodríguez-García, R., Sanchís, L. G.-L., Santullano, M., Martín-Carro, B., Martín, J. L. F., Basanta, M. D. C. C., Forcelledo, L., & Antequera, C. P. (2024) · Medicina Intensiva · doi:10.1016/j.medin.2024.01.009 · Cited by 2
Clinical features and disease progression of elderly patients at the ICU setting
Rodríguez-García, R., Sanchís, L. G.-L., Santullano, M., Martín-Carro, B., Martín, J. L. F., Basanta, M. D. C. C., Forcelledo, L., & Antequera, C. P. (2024) · Medicina Intensiva (English Edition) · doi:10.1016/j.medine.2024.02.009 · Cited by 2
Comparative effectiveness of primary tumor resection versus chemotherapy in patients with asymptomatic unresectable metastatic colorectal cancer: a retrospective cohort study using the target trial emulation framework
Wu, C. (2024) · American Journal of Cancer Research · doi:10.62347/ugxa9515 · Cited by 2
“…therapy, age, sex, histological grade, primary tumor location, stage (4A, B, and C), tumor size, lymph node status, radiotherapy, surgical procedures before the index date (Table S2), Charlson comorbidity index score [11, 12] (Table S3), and co-medication at 1 year before the index date (Table S4).”
Background
Impact of a Pilot Project for Integrated Care on Hospitalization Rate among Older Adults in South Korea
Choi, J. W., Bang, H. J., Yoo, A. J., & Lee, H. J. (2024) · International Journal of Integrated Care · doi:10.5334/ijic.7665 · Cited by 2
Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
Hayes, C. J., Noor, N. B., Raciborski, R. A., Martin, B. C., Gordon, A. J., Hoggatt, K. J., Hudson, T. J., & Cucciare, M. A. (2024) · Journal of Addictive Diseases · doi:10.1080/10550887.2024.2363035 · Cited by 2
The impact of comorbidities on outcomes of concomitant mitral valve intervention with ascending aortic surgery
Rahouma, M., Khairallah, S. M., Lau, C., Alzghari, T., Girardi, L. N., & Mick, S. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132398 · Cited by 2
Burden of Mitral Regurgitation in Spain from 2016–2021: An Analysis by Aetiology and Sex
Zamorano, J. L., Álvarez-Bartolomé, M., Arzamendi, D., Carnero, M., Cruz‐González, I., Li, C., Sanz, A. P., Martínez-Pérez, Ó., Cerezales, M., Cuervo, J., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13216372 · Cited by 2
“Comorbidities were analysed by calculating the Charlson Comorbidity Index adjusted for age (a-CCI) [14,15], utilizing an algorithm designed for ICD-10 codes and relying on available patient-on-admission (POA) data [16,17].”
Method
WHO/ISH total risk approach for primary prevention of cardiovascular disease shows greater decrease in costs for women but not the elderly in Jamaica
Abdulkadri, A. O., Tulloch-Reid, M. K., Francis, D., Gordon-Strachan, G. M., Younger-Coleman, N. O., Rocke, K. D., McFarlane, S., Cunningham-Myrie, C. A., Ferguson, T. S., Wilks, R., et al. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.11.027 · Cited by 2
A comparison of the utility of variants of the charlson comorbidity index (CCI) in predicting patient-reported health outcomes
Witt, E. A., & Goren, A. (2014) · Value in Health · doi:10.1016/j.jval.2014.03.1134 · Cited by 2
Perspective: Autonomic Care Systems for Hospitalized Patients
Goldschmidt‐Clermont, P. J., Dong, C., Rhodes, N. M., McNeill, D., Adams, M. B., Gilliss, C. L., Cuffe, M. S., Califf, R. M., Peterson, E. D., & Lubarsky, D. A. (2009) · Academic Medicine · doi:10.1097/acm.0b013e3181bf9bfd · Cited by 2
Cancer and Aging; Preparing for Silver Tsunami
Shahrokni, A. (2015) · Journal of Geriatrics and Palliative Care · doi:10.13188/2373-1133.1000013 · Cited by 2
“The cost of managing patients with multiple comorbid conditions is more than those with less comorbidities [65].”
Background
For Diabetes Shared Savings Programs, 1 Year of Data Is Not Enough
VanArsdale, L., Curran‐Everett, D., Haugen, H. A., Smith, N. S., & Atherly, A. J. (2016) · Population Health Management · doi:10.1089/pop.2016.0015 · Cited by 2
Palliative prognostic index and Charlson comorbidity index as predictors of mortality in acute lymphoblastic leukaemia patients who are candidates for palliative care
Trejo-Ayala, R. A., Peñafiel, C. O. R., Santoyo-Sánchez, A., Rozen‐Fuller, E., Olarte-Carrillo, I., Collazo-Jaloma, J., & Martínez‐Tovar, A. (2016) · Revista Médica Del Hospital General De México · doi:10.1016/j.hgmx.2016.09.008 · Cited by 2
Effect of statins in preventing hospitalizations for infections: A population study
Policardo, L., Seghieri, G., Gualdani, E., & Franconi, F. (2018) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4557 · Cited by 2
Specialist Advice Does Not Modify the Risk of Death of Diabetic 2 Patients
Battaggia, A., Scalisi, A., Bruno, F. N., Fusello, M., Michieli, R., & Cancian, M. (2019) · Journal of Integrative Cardiology Open Access · doi:10.31487/j.jicoa.2019.04.02 · Cited by 2
Effect of referral systems on costs and outcomes after hip fracture surgery in Taiwan
Chiou, B.-L., Chen, Y.-F., Chen, H.-Y., Chen, C., Yeh, S. J., & Shi, H. (2020) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzaa115 · Cited by 2
A Cross-Sectional Study to Predict Mortality for Medicare Patients Based on the Combined Use of HCUP Tools
Zikos, D., Shrestha, A., & Fegaras, L. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00091-x · Cited by 2
[Nutritional screening in hospitalized patients with vascular disease - The relationship of nutritional risk with clinical and economic outcomes in a surgery department].
Novo-Martínez, G., Ballesteros-Pomar, M., Sierra-Vega, M., & García-Gallego, A. (2021) · Nutrición Hospitalaria · doi:10.20960/nh.03386 · Cited by 2
“0,372 Edad (años) 68,68 72,25 0,326 Peso al alta (kg) 73,79 67,19 0,042 IMC al alta 26,10 23,99 0,032 ICC 4,85 4,69 0,948 Polimedicación 42 (63,6”
BackgroundInfluential
Comorbidities and Percutaneous Coronary Intervention in Elderly Patients with Acute Coronary Syndrome
Gilyarov, M. Y., Константинова, Е. В., Atabegashvili, M. R., Solntseva, T. D., Аничков, Д. А., Kostina, А. N., Polybin, R. V., Удовиченко, А. Е., & Svet, A. V. (2021) · Rational Pharmacotherapy in Cardiology · doi:10.20996/1819-6446-2021-04-10 · Cited by 2
Patients affected by squamous cell carcinoma of the head and neck: A population particularly prone to developing severe forms of COVID‑19
Perri, F., Crispo, A., F, I., Muto, P., Caponigro, F., Longo, F., Montagnese, C., Franco, P., Pavone, E., Aversa, C., et al. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.10733 · Cited by 2

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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