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The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients
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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“0 38 (1) 30 (1) 8 (1) 1–2 902 (22) 779 (22) 123 (20) 3–4 940 (26) 802 (23) 138 (22) 5–6 985 (24) 820 (23) 165 (27) 7–8 499 (12) 414 (12) 85 (14) 9 292 (7) 268 (8) 24 (4) Unknown 500 (12) 425 (12) 75 (12) Insurance status, n (%) .”
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“- The age-adjusted Charlson comorbidity index was used to estimate the 10-year life expectancy of the patients as a function of their age and the presence of comorbidities at admission for the infectious episode [27].”
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Esterberg, E. J., Iyer, S. S., Nagar, S. P., Davis, K. L., & Tannir, N. M. (2023) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2023.09.009 · Cited by 15
Levels and Variation in Overuse of Fecal Occult Blood Testing in the Veterans Health Administration
Partin, M. R., Powell, A. A., Bangerter, A. K., Halek, K., Burgess, J. F., Fisher, D. A., & Nelson, D. B. (2012) · Journal of General Internal Medicine · doi:10.1007/s11606-012-2163-9 · Cited by 14
Ricardo Rabagliati, B., Bertín, P., Cerón, I., Rojas, H., Domínguez, I., Vera, Á., Sir¡, L., Flores, J., Fernández, P., Pérez, M., et al. (2014) · Revista chilena de infectología · doi:10.4067/s0716-10182014000600013 · Cited by 14
Cho, S. I., Lee, D. H., & Park, Y. M. (2019) · The Journal of Dermatology · doi:10.1111/1346-8138.15115 · Cited by 14
Geng, J., Deng, L., Qiu, S., Bian, H., Cai, B., Li, Y., Li, J., Qin, Z., Yang, Q., Dong, B., et al. (2021) · Aging Clinical and Experimental Research · doi:10.1007/s40520-021-01835-w · Cited by 14
High Use of SABAs is Associated with Higher Exacerbation Rate in Dutch Patients with Asthma
Baron, A. J., Blok, B. M. J. F.-D., Kerstjens, H. A. M., Koopmans-Klein, G., Price, D. B., Sellink, A. A., Tsiligianni, I., & Kocks, J. W. H. (2021) · Journal of Asthma and Allergy · doi:10.2147/jaa.s292943 · Cited by 14
A Comparison of Falls and Dizziness Handicap by Vestibular Diagnosis
Huang, R. J., Smith, S. L., Brezina, L., & Riska, K. M. (2021) · American Journal of Audiology · doi:10.1044/2021_aja-21-00086 · Cited by 14
Factors associated with frailty in kidney transplant recipients: A cross‐sectional study
Zhang, B., Zhao, P., Wang, H., Wang, S., Wei, C., Gao, F., & Liu, H. (2021) · Journal of Renal Care · doi:10.1111/jorc.12407 · Cited by 14
Cafagna, G., Seghieri, C., Vainieri, M., & Nuti, S. (2018) · International Journal for Equity in Health · doi:10.1186/s12939-018-0878-x · Cited by 14
Oh, J. H., Yang, W. W., Moore, T., Dushaj, K., Cooper, H. J., & Hepinstall, M. S. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.01.035 · Cited by 14
Antidepressants and the Risk of Cardiovascular Events in Elderly Affected by Cardiovascular Disease
Biffi, A., Rea, F., Scotti, L., Lucenteforte, E., Vannacci, A., Lombardi, N., Chinellato, A., Onder, G., Vitale, C., Cascini, S., et al. (2020) · Journal of Clinical Psychopharmacology · doi:10.1097/jcp.0000000000001189 · Cited by 14
Determinants of participation in a post-hospitalization physical exercise program for older adults
Urquiza, M., Echeverria, I., Besga, A., Amasene, M., Labayen, I., Rodriguez‐Larrad, A., Barroso, J., Aldámiz, M., & Irazusta, J. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-01821-3 · Cited by 14
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Shcherbakova, N., Rascati, K. L., Brown, C. M., Lawson, K. A., Novak, S., Richards, K. M., & Yoder, L. H. (2014) · CNS Drugs · doi:10.1007/s40263-014-0191-1 · Cited by 14
Psychological processes in adapting to dementia: Illness representations among the IDEAL cohort.
Clare, L., Gamble, L. D., Martyr, A., Quinn, C., Litherland, R., Morris, R. G., Jones, I. R., & Matthews, F. E. (2021) · Psychology and Aging · doi:10.1037/pag0000650 · Cited by 13
“Comorbidity was assessed with the Charlson Comorbidity Index age-adjusted score (CCI; Charlson et al., 1987, 2008); this measure records the presence of any of 23 chronic conditions (e.g., hypertension), each of which is assigned a weighted score indicating the associated mortality risk, with the…”
MethodWebber, T. A., Woods, S. P., Lorkiewicz, S. A., Yazbeck, H. W., Schultz, E. R., & Kiselica, A. M. (2024) · Neuropsychology · doi:10.1037/neu0000969 · Cited by 13
Corrao, G., Compagnoni, M. M., Franchi, M., Cantarutti, A., Pugni, P., Merlino, L. G., Catapano, A. L., & Mancia, G. (2017) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2017.09.023 · Cited by 13
Asthana, S., & Gibson, A. (2018) · Health Economics Policy and Law · doi:10.1017/s1744133118000361 · Cited by 13
“due to late presentation); and differences in co-morbidity, resulting in additional treatment costs through poorer functional impairment or presenting contra-indications to standard therapies (Charlson et al., 2008; Cunic et al., 2014).”
BackgroundData mining techniques utilizing latent class models to evaluate emergency department revisits
Ben‐Assuli, O., & Vest, J. R. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103341 · Cited by 14
Patterns of antiepileptic drug use among elderly patients with epilepsy: 2004-2015
Fox, J., Ajinkya, S., & Lekoubou, A. (2020) · Epilepsy Research · doi:10.1016/j.eplepsyres.2020.106297 · Cited by 13
Gon, Y., Ohyanagi, N., & Kobayashi, A. (2021) · Respiratory Investigation · doi:10.1016/j.resinv.2021.02.003 · Cited by 15
Hayes, C. J., Gressler, L. E., Hu, B., Jones, B. L., Williams, J., & Martin, B. C. (2021) · Journal of Pain Research · doi:10.2147/jpr.s308196 · Cited by 13
Factors influencing mobility among people post-surgery for hip fractures: A cross-sectional study
Yoryuenyong, C., Jitpanya, C., & Sasat, S. (2023) · Belitung Nursing Journal · doi:10.33546/bnj.2759 · Cited by 15
Comparing Adherence to Two Different HIV Antiretroviral Regimens: An Instrumental Variable Analysis
Nelson, R. E., Nebeker, J. R., Hayden, C. L., Reimer, L. G., Kone, K., & LaFleur, J. (2012) · AIDS and Behavior · doi:10.1007/s10461-012-0266-2 · Cited by 13
“a Charlson comorbidity index is a validated instrument that applies weights for different levels of vascular diseases, (myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease); endocrine disorders (diabetes with or without end-organ damage including retinopathy, neuropath…”
BackgroundDevelopment and Validation of a Score for Adjusting Health Care Costs in General Practice
Lapi, F., Bianchini, E., Cricelli, I., Trifirò, G., Mazzaglia, G., & Cricelli, C. A. (2015) · Value in Health · doi:10.1016/j.jval.2015.05.004 · Cited by 13
“The Charlson index was also demonstrated to be effective in predicting costs in primary care [5], and 16 other multimorbidity classification systems are currently available [16].”
BackgroundMethodResultInfluentialArfè, A., Nicotra, F., Cerveri, I., de Marco, R., Vaghi, A., Merlino, L. G., & Corrao, G. (2016) · COPD Journal of Chronic Obstructive Pulmonary Disease · doi:10.3109/15412555.2016.1141877 · Cited by 13
“, gender, age at cohort entry, type of inhaled long-acting bronchodilator received at therapy start, and Charlson’s co-morbidity index (16) calculated from inpa-”
MethodMehta, H. B., Sura, S. D., Sharma, M., Johnson, M. L., & Riall, T. S. (2016) · Medical Care · doi:10.1097/mlr.0000000000000517 · Cited by 13
“Different weights are available to predict 1-year mortality, health care expenditure, and HRQoL.(5,8,23,32,39)”
BackgroundRathbun, A. M., Shardell, M. D., Orwig, D., Hebel, J. R., Hicks, G. E., Beck, T., Magaziner, J. S., & Hochberg, M. C. (2016) · Bone · doi:10.1016/j.bone.2016.08.020 · Cited by 13
“Clinical characteristics included comorbidity, assessed using a modified version of the Charlson Comorbidity Index that omitted liver disease [27]; depressive symptoms, measured with the Center for Epidemiological Studies Depression Scale [28]; functional limitations, evaluated with the Instrumental Activities of Dail…”
MethodMediators of Weight Loss Maintenance in the Keep It Off Trial
Crain, A. L., Sherwood, N. E., Martinson, B. C., & Jeffery, R. W. (2017) · Annals of Behavioral Medicine · doi:10.1007/s12160-017-9917-x · Cited by 13
“Exclusion criteria included anorexia nervosa history, bariatric surgery, modified Charlson [31] score ≥3, non-skin cancer, congestive heart failure, use of a phone-based weight loss program to achieve their recent weight loss, or current participation in another weight management study.”
BackgroundHoopes, M. J., Angier, H. E., Raynor, L. A., Suchocki, A., Muench, J., Marino, M., Rivera, P., & Huguet, N. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy095 · Cited by 14
“The Charlson comorbidity index was used as a measure of clinical complexity.(23) Encounter-level factors included reason for visit, provider type, visit with assigned PCP, and whether medications prescribed at visit were e-prescribed and primary vs.”
MethodBenzodiazepine and unhealthy alcohol use among adult outpatients.
Hirschtritt, M. E., Palzes, V. A., Kline‐Simon, A. H., Kroenke, K., Campbell, C. I., & Sterling, S. (2019) · PubMed · Cited by 13
Thörling, J., Ljungqvist, O. H., Sköldenberg, O. G., & Hammarqvist, F. (2020) · Clinical Nutrition · doi:10.1016/j.clnu.2020.08.023 · Cited by 13
First insights on value-based healthcare of elders using ICHOM older person standard set reporting
Lee, W., Peng, L., Lin, C.-H., Lin, S., Loh, C., Kao, S., Hung, T.-S., Chang, C.-Y., Huang, C.-F., Tang, T., et al. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-01734-1 · Cited by 13
Metadata
- 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.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2008-12
- Volume
- 61
- Issue
- 12
- Pages
- 1234-1240
- ISSN
- 0895-4356 (ISSN-L)
- 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
- 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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