Volume 14, Issue 1 (1-2025)
JCHR 2025, 14(1): 134-139 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ajmal Dina M, Madidadizadeh F, Arshed A. Tutorial on Comorbidity Indices in Medical Research. JCHR 2025; 14 (1) :134-139
URL: http://jhr.ssu.ac.ir/article-1-1109-en.html
URL: http://jhr.ssu.ac.ir/article-1-1109-en.html
1- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,statlearningeasy@gmail.com
3- Rahbar Medical and Dental College, Lahore Pakistan
2- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,
3- Rahbar Medical and Dental College, Lahore Pakistan
Keywords: CCI (Comorbidity by Charlson Index), ECI (Elixhauser comorbidity index, AML (Acute myeloid leukemia), CDI (Chronic disease index), Indices, Research
Full-Text [PDF 379 kb]
(190 Downloads)
| Abstract (HTML) (698 Views)
1. Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2. Rahbar Medical and Dental College, Lahore Pakistan
How to cite this paper:
Ajmal Dina M, Madadizadeh F, Arshed A. Tutorial on Comorbidity Indices in Medical Research. J Community Health Research 2025; 14(1): 134-139.
Introduction
Table 2. Table of the Elixhauser comorbidities, their descriptions, and van Walraven weights
Source: “Development and validation of a structured query language implementation of the Elixhauser comorbidity index”(18).
Table 3. The advantages and disadvantages of different comorbidity indices
References
1. Trüeb RM, Trüeb RM. Patient expectation management. The Difficult Hair Loss Patient: Guide to Successful Management of Alopecia and Related Conditions. 2015: 31-48.
2. Svicher A. PAINMIG: Cohort Study for the Evaluation of Mental Pain in Migraine Patients; 2020.
3. Zulman DM, Asch SM, Martins SB, et al. Quality of care for patients with multiple chronic conditions: the role of comorbidity interrelatedness. Journal of general internal medicine. 2014; 29: 529-37.
4. Nanayakkara N, Curtis AJ, Heritier S, et al. Impact of age at type 2 diabetes mellitus diagnosis on mortality and vascular complications: systematic review and meta-analyses. Diabetologia. 2021; 64: 275-87.
5. Ssentongo P, Ssentongo AE, Heilbrunn ES, et al. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PloS one. 2020; 15(8): e0238215.
6. Austin SR, Wong Y-N, Uzzo RG, et al. Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work. Medical care. 2015; 53(9): e65-e72.
7. Sinvani L, Kuriakose R, Tariq S, et al. Using Charlson comorbidity index to predict short-term clinical outcomes in hospitalized older adults. The Journal for Healthcare Quality (JHQ). 2019; 41(3): 146-53.
8. Drosdowsky A, Gough K. The Charlson Comorbidity Index: problems with use in epidemiological research. Journal of clinical epidemiology. 2022; 148: 174-7.
9. Charlson ME, Carrozzino D, Guidi J, et al. Charlson comorbidity index: a critical review of clinimetric properties. Psychotherapy and psychosomatics. 2022; 91(1): 8-35.
10. Asai N, Ohashi W, Sakanashi D, et al. Combination of Sequential Organ Failure Assessment (SOFA) score and Charlson Comorbidity Index (CCI) could predict the severity and prognosis of candidemia more accurately than the Acute Physiology, Age, Chronic Health Evaluation II (APACHE II) score. BMC infectious diseases. 2021; 21: 1-11.
11. Grady PA, Gough LL. Self-management: a comprehensive approach to management of chronic conditions. American journal of public health. 2014; 104(8): e25-e31.
12. Fernando DT, Berecki-Gisolf J, Newstead S, et al. Effect of comorbidity on injury outcomes: a review of existing indices. Annals of epidemiology. 2019; 36: 5-14.
13. Zhang X-M, Wu X-J, Cao J, et al. Effect of the age-adjusted Charlson comorbidity index on all-cause mortality and readmission in older surgical patients: a national multicenter, prospective cohort study. Frontiers in Medicine. 2022; 9: 896451.
14. Whitney DG, Kamdar NS. Development of a new comorbidity index for adults with cerebral palsy and comparative assessment with common comorbidity indices. Developmental Medicine & Child Neurology. 2021; 63(3): 313-9.
15. Kirby JJ, Shaikh S, Bryant DP, et al. A simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019. Journal of Clinical Medicine Research. 2021; 13(4): 237.
16. Oterino-Moreira I, Lorenzo-Martínez S, López-Delgado Á, et al. Comparison of three comorbidity measures for predicting in-hospital death through a clinical administrative nacional database. International journal of environmental research and public health. 2022; 19(18): 11262.
17. Cai M, Liu E, Zhang R, et al. Comparing the performance of Charlson and Elixhauser comorbidity indices to predict in-hospital mortality among a Chinese population. Clinical Epidemiology. 2020: 307-16.
18. Epstein RH, Dexter F. Development and validation of a structured query language implementation of the Elixhauser comorbidity index. Journal of the American Medical Informatics Association. 2017; 24(4): 845-50.
19. Soroa G, Gorostiaga A, Aritzeta A, et al. A shortened Spanish version of the Emotional Creativity Inventory (the ECI-S). Creativity Research Journal. 2015; 27(2): 232-9.
20. Schneeweiss S, Seeger JD, Maclure M, et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. American journal of epidemiology. 2001; 154(9): 854-64.
21. Pot GK, Richards M, Prynne CJ, et al. Development of the Eating Choices Index (ECI): a four-item index to measure healthiness of diet. Public health nutrition. 2014; 17(12): 2660-6.
22. Huang Y-J, Chen J-S, Luo S-F, et al. Comparison of indexes to measure comorbidity burden and predict all-cause mortality in rheumatoid arthritis. Journal of Clinical Medicine. 2021; 10(22): 5460.
23. Döhner H, Estey EH, Amadori S, et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood, The Journal of the American Society of Hematology. 2010; 115(3): 453-74.
24. Roman E, Smith A, Appleton S, et al. Myeloid malignancies in the real-world: Occurrence, progression and survival in the UK’s population-based Haematological Malignancy Research Network 2004–15. Cancer epidemiology. 2016; 42: 186-98.
25. Delie A. Impact of new treatment guidelines pertaining to the indication for allogeneic stem cell transplantation in acute myeloid leukemia at the: Ghent University; 2018.
26. Porter CC, Druley TE, Erez A, et al. Recommendations for surveillance for children with leukemia-predisposing conditions. Clinical Cancer Research. 2017; 23(11): e14-e22.
27. Airlangga G. Comparative Analysis of Machine Learning Models for Chronic Disease Indicator Classification Using US Chronic Disease Indicators Dataset. MALCOM: Indonesian Journal of Machine Learning and Computer Science. 2024; 4(3): 1034-42.
28. Betancourt M, Roberts K, Bennett T-L, et al. Monitoring chronic diseases in Canada: the chronic disease indicator framework. Chronic Diseases & Injuries in Canada. 2014; 34.
29. Kuntz JL, Chrischilles EA, Pendergast JF, et al. Incidence of and risk factors for community-associated Clostridium difficile infection: a nested case-control study. BMC infectious diseases. 2011; 11: 1-7.
30. Scott RD, Slayton RB, Lessa FC, et al. Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals. Antimicrobial Resistance & Infection Control. 2019; 8: 1-17.
Full-Text: (15 Views)
| Tutorial on Comorbidity Indices in Medical Research |
1. Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2. Rahbar Medical and Dental College, Lahore Pakistan
| ARTICLE INFO | ABSTRACT | |
| Review Article Received: 13 Jan 2025 Accepted: 30 Apr 2025 |
Background: Comorbidity indices, such :as char:lson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM), are essential tools for assessing the influence of comorbid health conditions on patient outcomes. This study aims to review comorbidity indices by assigning weighted scores to a spectrum of comorbidities; these instruments play a critical role in predicting mortality rates and evaluating healthcare resource utilization. Methods: In this narrative review study, we reviewed all comorbidity indices and important points were mentioned. Results: CCIand ECIhave proven to be highly effective tools for predicting mortality and healthcare outcomes across a wide range of patient populations. This review examines four comorbidity indices: CCI, ECI, Acute Myeloid Leukemia (AML), and the Chronic Disease Index (CDI). Conclusion: Among four types, CCI and ECM were the most applied, and the researcher should know different types of comorbidity indices to use in research. Keywords: CCI (Comorbidity by Charlson Index); ECI (Elixhauser comorbidity index; AML (Acute myeloid leukemia); CDI (Chronic disease index); Indices; Research |
|
 |
||
Corresponding Author: Farzan Madadizadeh madadizadehfarzan@gmail.com |
Ajmal Dina M, Madadizadeh F, Arshed A. Tutorial on Comorbidity Indices in Medical Research. J Community Health Research 2025; 14(1): 134-139.
Introduction
Comorbidity is a term of Latin origins introduced by Alvan R. Feinstein (1925-2001), an American researcher and epidemiologist (1). According to him, it is a distinct additional clinical entity that exists or can be apparent during the clinical course of a disease that is under investigation (2). Condition of comorbidity has a potential impact on a patient's cause and alters the treatment plans with outcome. The condition of comorbidities coexists with the condition of disease interest (3), it leads to delayed diagnosis, and confounders in the analysis of course and clinical status raise the mortality and morbidity (4). Researchers investigate their statistical models to identify associations with comorbidities such as heart disease and diabetes. (5). It is widely used by researchers to summarize comorbidity measurement with adjusting outcomes via the use of health data (6). To the best of the authors’ knowledge, there are four indices for comorbidity including Elixhauser comorbidity index (ECI), Charlson Comorbidity Index (CCI)), acute myeloid leukemia (AML) comorbidity index and chronic disease indicators (CDI). In the next session, the authors will review all of them.
According to the protocol, CCI has assigned a numerical score for single comorbidity, and the sum of the score provides a quantitative measure of the patient’s overall health (10). Before checking the CCI score, first we should identify medical professionals and document the comorbidity that the patient has (9). Chronic diseases have mostly had conditions in general, and many health issues can affect the patient’s prognosis, treatment, and outcomes (11).
Table 1. CCI and its weight
Source: “Charlson Comorbidity Index: A Critical Review of Clinimetric Properties”(9)
A specific eight number for each comorbidity is assigned and scored depending on the severity and impact on his / her mortality(6). Once scores of comorbidities are identified, they are summed to calculate the CCI score, the score ranges from 0 to 37 or more (12), depending on comorbidities on the number and severity. If CCI score is higher, it indicates a high burden of comorbidities, and a high risk of complications with mortality, or adverse outcomes (13).
- Comorbidity indices
-
- CCI
According to the protocol, CCI has assigned a numerical score for single comorbidity, and the sum of the score provides a quantitative measure of the patient’s overall health (10). Before checking the CCI score, first we should identify medical professionals and document the comorbidity that the patient has (9). Chronic diseases have mostly had conditions in general, and many health issues can affect the patient’s prognosis, treatment, and outcomes (11).
Table 1. CCI and its weight
| Condition | Weight |
| Myocardial infarction | 1 |
| Congestive heart failure | 1 |
| Peripheral vascular disease | 1 |
| Cerebrovascular disease | 1 |
| Dementia | 1 |
| Chronic pulmonary disease | 1 |
| Connective tissue disease | 1 |
| Ulcer disease | 1 |
| Mild liver disease | 1 |
| Diabetes without end-organ damage | 1 |
| Hemiplegia | 2 |
| Moderate or severe renal disease | 2 |
| Diabetes with end-organ damage | 2 |
| Any tumor | 2 |
| Leukemia | 2 |
| Lymphoma | 2 |
| Moderate or severe liver disease | 3 |
| Metastatic solid tumor | 6 |
| AIDS | 6 |
A specific eight number for each comorbidity is assigned and scored depending on the severity and impact on his / her mortality(6). Once scores of comorbidities are identified, they are summed to calculate the CCI score, the score ranges from 0 to 37 or more (12), depending on comorbidities on the number and severity. If CCI score is higher, it indicates a high burden of comorbidities, and a high risk of complications with mortality, or adverse outcomes (13).
-
- ECI
Table 2. Table of the Elixhauser comorbidities, their descriptions, and van Walraven weights
| Comorbidity | Description/Example | Van Walraven Weight |
| Congestive heart failure | Heart failure | 7 |
| Cardiac arrhythmias | Atrial fibrillation, ventricular tachycardia | 5 |
| Valvular disease | Mitral valve disease, aortic stenosis | -1 |
| Pulmonary circulation disorders | Pulmonary hypertension, pulmonary embolism | 4 |
| Peripheral vascular disorders | Peripheral artery disease, claudication | 2 |
| Hypertension (uncomplicated) | High blood pressure without end-organ damage | 0 |
| Hypertension (complicated) | High blood pressure with end-organ damage (e.g., renal failure) | -1 |
| Paralysis | Paraplegia, hemiplegia, quadriplegia | 7 |
| Other neurological disorders | Parkinson's disease, multiple sclerosis | 6 |
| Chronic pulmonary disease | COPD, asthma, pulmonary fibrosis | 3 |
| Diabetes (uncomplicated) | Diabetes without complications | 0 |
| Diabetes (complicated) | Diabetes with complications (e.g., retinopathy, neuropathy) | -3 |
| Hypothyroidism | Underactive thyroid | 0 |
| Renal failure | Chronic kidney disease, end-stage renal disease | 5 |
| Liver disease | Cirrhosis, hepatitis | 11 |
| Peptic ulcer disease | Gastric or duodenal ulcers | 0 |
| AIDS/HIV | Acquired immunodeficiency syndrome | 0 |
| Lymphoma | Hodgkin's or non-Hodgkin's lymphoma | 9 |
| Metastatic cancer | Cancer with metastasis | 12 |
| Solid tumor without metastasis | Localized cancer | 4 |
| Rheumatoid arthritis/collagen vascular diseases | Lupus, rheumatoid arthritis, scleroderma | 0 |
| Coagulopathy | Bleeding disorders, clotting disorders | 3 |
| Obesity | Body mass index (BMI) ≥ 30 | -4 |
| Weight loss | Malnutrition, cachexia | 9 |
| Fluid and electrolyte disorders | Hyponatremia, hyperkalemia | 5 |
| Blood loss anemia | Anemia due to acute blood loss | -2 |
| Deficiency anemia | Iron deficiency anemia, vitamin B12 deficiency | -2 |
| Alcohol abuse | Alcohol dependence or abuse | 0 |
| Drug abuse | Substance abuse | -7 |
| Psychoses | Schizophrenia, bipolar disorder | -5 |
| Depression | Major depressive disorder | -3 |
The Elixhauser Comorbidity Index (ECI) is a method for categorizing comorbidities in patients, based on diagnosis codes from the International Classification of Diseases (ICD) (16), and it is found in administrative data, like hospital abstracts data. ECI scoring ranges from −7 to +12 (19) (20). The score of >15 is used as a cut-off value (21) and it is with a 1-year mortality of 38% (22).
2.4 CDI
CDI is a critical metric used in epidemiology and public health research for assessing the prevalence and impact of chronic diseases in specific populations (27). This index has a role in understanding the burden of chronic diseases on healthcare systems individually. The CDI provides a comprehensive picture of the occurrence of chronic diseases like hypertension, diabetes, heart, cancer, stroke, and arthritis in a given population over an extended period (28).
In the research, various factors were calculated by researchers in the CDI, such as cases, disease duration, and overall population size (29). The aim was to make a standardized measure used to compare the prevalence of chronic diseases in different regions, timeframes, and demographics. CDI is a valuable tool for policymakers and healthcare professionals (30). It helps them to identify a high-burden area of chronic diseases, allocate resources effectively, and help target interventions to mitigate the impact of these conditions.
-
- AML
2.4 CDI
CDI is a critical metric used in epidemiology and public health research for assessing the prevalence and impact of chronic diseases in specific populations (27). This index has a role in understanding the burden of chronic diseases on healthcare systems individually. The CDI provides a comprehensive picture of the occurrence of chronic diseases like hypertension, diabetes, heart, cancer, stroke, and arthritis in a given population over an extended period (28).
In the research, various factors were calculated by researchers in the CDI, such as cases, disease duration, and overall population size (29). The aim was to make a standardized measure used to compare the prevalence of chronic diseases in different regions, timeframes, and demographics. CDI is a valuable tool for policymakers and healthcare professionals (30). It helps them to identify a high-burden area of chronic diseases, allocate resources effectively, and help target interventions to mitigate the impact of these conditions.
Table 3. The advantages and disadvantages of different comorbidity indices
| Comorbidity indices | Advantages | Disadvantages |
| CCI | Simple and widely validated for mortality prediction. Easy to apply in clinical and research settings. Focuses on a concise set of comorbidities. |
Limited to 17 conditions, potentially overlooking other significant comorbidities. Less comprehensive than newer indices. Does not include mental health conditions |
| ECI | Includes 30 comorbidities, offering a broader scope. Superior in predicting in-hospital mortality. Can be weighted (e.g., van Walraven score) for enhanced precision. |
More complex to implement due to the larger number of conditions Requires detailed administrative data May overcomplicate risk assessment in some settings |
| AML | Specific to hematologic malignancies, providing tailored risk assessment Useful for stratifying patients in oncology settings Incorporates genetic and molecular factors |
Limited to AML patients, not generalizable to other conditions. Requires specialized knowledge and data. Less applicable for non-cancer populations. |
| CDI | Focuses on chronic diseases, relevant for long-term health management Useful for population health studies Can be adapted to specific chronic conditions |
Less standardized compared to CCI or ECI May lack specificity for acute outcomes like mortality Limited validation in diverse clinical settings |
Conclusion
ECIand CCI are among the most widely used assessment tools globally. Among these, the ECI demonstrates superior accuracy in predicting in-hospital mortality. When a weighted scoring system is applied to evaluate patient outcomes, ECI score proves to be particularly precise. Its comprehensive inclusion of comorbidities enhances its predictive capability, making it a valuable tool for assessing healthcare outcomes and resource allocation.
Acknowledgments
We acknowledge all my teachers who made this study possible.
Conflicts of interest
All authors have declared no conflicts of interest.
Ethical considerations
Not applicable.
Code of ethics
Not applicable.
Funding
No external funding was received for this study.
Authors’ contributions
M. A. D. drafted the manuscript; A. A, M. T. S, and F. M, all helped with data analysis and critical appraisal of the manuscript. All co-authors
read and approved the final draft of the
manuscript.
Open access policy
JCHR does not charge readers and their institutions for access to its papers. Full text download of all new and archived papers is free of charge.
ECIand CCI are among the most widely used assessment tools globally. Among these, the ECI demonstrates superior accuracy in predicting in-hospital mortality. When a weighted scoring system is applied to evaluate patient outcomes, ECI score proves to be particularly precise. Its comprehensive inclusion of comorbidities enhances its predictive capability, making it a valuable tool for assessing healthcare outcomes and resource allocation.
Acknowledgments
We acknowledge all my teachers who made this study possible.
Conflicts of interest
All authors have declared no conflicts of interest.
Ethical considerations
Not applicable.
Code of ethics
Not applicable.
Funding
No external funding was received for this study.
Authors’ contributions
M. A. D. drafted the manuscript; A. A, M. T. S, and F. M, all helped with data analysis and critical appraisal of the manuscript. All co-authors
read and approved the final draft of the
manuscript.
Open access policy
JCHR does not charge readers and their institutions for access to its papers. Full text download of all new and archived papers is free of charge.
References
1. Trüeb RM, Trüeb RM. Patient expectation management. The Difficult Hair Loss Patient: Guide to Successful Management of Alopecia and Related Conditions. 2015: 31-48.
2. Svicher A. PAINMIG: Cohort Study for the Evaluation of Mental Pain in Migraine Patients; 2020.
3. Zulman DM, Asch SM, Martins SB, et al. Quality of care for patients with multiple chronic conditions: the role of comorbidity interrelatedness. Journal of general internal medicine. 2014; 29: 529-37.
4. Nanayakkara N, Curtis AJ, Heritier S, et al. Impact of age at type 2 diabetes mellitus diagnosis on mortality and vascular complications: systematic review and meta-analyses. Diabetologia. 2021; 64: 275-87.
5. Ssentongo P, Ssentongo AE, Heilbrunn ES, et al. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PloS one. 2020; 15(8): e0238215.
6. Austin SR, Wong Y-N, Uzzo RG, et al. Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work. Medical care. 2015; 53(9): e65-e72.
7. Sinvani L, Kuriakose R, Tariq S, et al. Using Charlson comorbidity index to predict short-term clinical outcomes in hospitalized older adults. The Journal for Healthcare Quality (JHQ). 2019; 41(3): 146-53.
8. Drosdowsky A, Gough K. The Charlson Comorbidity Index: problems with use in epidemiological research. Journal of clinical epidemiology. 2022; 148: 174-7.
9. Charlson ME, Carrozzino D, Guidi J, et al. Charlson comorbidity index: a critical review of clinimetric properties. Psychotherapy and psychosomatics. 2022; 91(1): 8-35.
10. Asai N, Ohashi W, Sakanashi D, et al. Combination of Sequential Organ Failure Assessment (SOFA) score and Charlson Comorbidity Index (CCI) could predict the severity and prognosis of candidemia more accurately than the Acute Physiology, Age, Chronic Health Evaluation II (APACHE II) score. BMC infectious diseases. 2021; 21: 1-11.
11. Grady PA, Gough LL. Self-management: a comprehensive approach to management of chronic conditions. American journal of public health. 2014; 104(8): e25-e31.
12. Fernando DT, Berecki-Gisolf J, Newstead S, et al. Effect of comorbidity on injury outcomes: a review of existing indices. Annals of epidemiology. 2019; 36: 5-14.
13. Zhang X-M, Wu X-J, Cao J, et al. Effect of the age-adjusted Charlson comorbidity index on all-cause mortality and readmission in older surgical patients: a national multicenter, prospective cohort study. Frontiers in Medicine. 2022; 9: 896451.
14. Whitney DG, Kamdar NS. Development of a new comorbidity index for adults with cerebral palsy and comparative assessment with common comorbidity indices. Developmental Medicine & Child Neurology. 2021; 63(3): 313-9.
15. Kirby JJ, Shaikh S, Bryant DP, et al. A simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019. Journal of Clinical Medicine Research. 2021; 13(4): 237.
16. Oterino-Moreira I, Lorenzo-Martínez S, López-Delgado Á, et al. Comparison of three comorbidity measures for predicting in-hospital death through a clinical administrative nacional database. International journal of environmental research and public health. 2022; 19(18): 11262.
17. Cai M, Liu E, Zhang R, et al. Comparing the performance of Charlson and Elixhauser comorbidity indices to predict in-hospital mortality among a Chinese population. Clinical Epidemiology. 2020: 307-16.
18. Epstein RH, Dexter F. Development and validation of a structured query language implementation of the Elixhauser comorbidity index. Journal of the American Medical Informatics Association. 2017; 24(4): 845-50.
19. Soroa G, Gorostiaga A, Aritzeta A, et al. A shortened Spanish version of the Emotional Creativity Inventory (the ECI-S). Creativity Research Journal. 2015; 27(2): 232-9.
20. Schneeweiss S, Seeger JD, Maclure M, et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. American journal of epidemiology. 2001; 154(9): 854-64.
21. Pot GK, Richards M, Prynne CJ, et al. Development of the Eating Choices Index (ECI): a four-item index to measure healthiness of diet. Public health nutrition. 2014; 17(12): 2660-6.
22. Huang Y-J, Chen J-S, Luo S-F, et al. Comparison of indexes to measure comorbidity burden and predict all-cause mortality in rheumatoid arthritis. Journal of Clinical Medicine. 2021; 10(22): 5460.
23. Döhner H, Estey EH, Amadori S, et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood, The Journal of the American Society of Hematology. 2010; 115(3): 453-74.
24. Roman E, Smith A, Appleton S, et al. Myeloid malignancies in the real-world: Occurrence, progression and survival in the UK’s population-based Haematological Malignancy Research Network 2004–15. Cancer epidemiology. 2016; 42: 186-98.
25. Delie A. Impact of new treatment guidelines pertaining to the indication for allogeneic stem cell transplantation in acute myeloid leukemia at the: Ghent University; 2018.
26. Porter CC, Druley TE, Erez A, et al. Recommendations for surveillance for children with leukemia-predisposing conditions. Clinical Cancer Research. 2017; 23(11): e14-e22.
27. Airlangga G. Comparative Analysis of Machine Learning Models for Chronic Disease Indicator Classification Using US Chronic Disease Indicators Dataset. MALCOM: Indonesian Journal of Machine Learning and Computer Science. 2024; 4(3): 1034-42.
28. Betancourt M, Roberts K, Bennett T-L, et al. Monitoring chronic diseases in Canada: the chronic disease indicator framework. Chronic Diseases & Injuries in Canada. 2014; 34.
29. Kuntz JL, Chrischilles EA, Pendergast JF, et al. Incidence of and risk factors for community-associated Clostridium difficile infection: a nested case-control study. BMC infectious diseases. 2011; 11: 1-7.
30. Scott RD, Slayton RB, Lessa FC, et al. Assessing the social cost and benefits of a national requirement establishing antibiotic stewardship programs to prevent Clostridioides difficile infection in US hospitals. Antimicrobial Resistance & Infection Control. 2019; 8: 1-17.
Review: Review |
Subject:
Epidemiology
Received: 2025/01/13 | Accepted: 2025/04/30 | Published: 2025/06/9
Received: 2025/01/13 | Accepted: 2025/04/30 | Published: 2025/06/9
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
