Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Australian Health Review Australian Health Review Society
Journal of the Australian Healthcare & Hospitals Association
RESEARCH ARTICLE

Comorbidity indexes from administrative datasets: what is measured?

Shyamala G. Nadathur
+ Author Affiliations
- Author Affiliations

Monash University, Clayton, VIC 3168, Australia. Email: shyamala.nadathur@med.monash.edu.au

Australian Health Review 35(4) 507-511 https://doi.org/10.1071/AH10933
Submitted: 6 June 2010  Accepted: 13 January 2011   Published: 30 September 2011

Abstract

It is important to factor-in the characteristics of patients that may affect treatment, outcome and resource when making clinical and administrative decisions, plans or policies. For some two and half decades there have been efforts to construct and refine instruments that endeavour to capture the concept of comorbidity. This paper focuses on such comorbidity measures that are derived from diagnoses information recorded in administrative datasets. The pros and cons of the popular weighted Charlson and Charlson-based indexes are discussed. Means to improve the comorbidity indexes are considered including the very concept and definition of comorbidity.

Additional keywords: complexity, hospital, international disease classification.


References

[1]  Atkins BZ, Shah AS, Hutcheson KA, Mangum JH, Pappas TN, Harpole DH, et al Reducing hospital morbidity and mortality following esophagectomy. Ann Thorac Surg 2004; 78 1170–6.
Reducing hospital morbidity and mortality following esophagectomy.Crossref | GoogleScholarGoogle Scholar |

[2]  Rabeneck L, Feinstein AR, Horwitz RI, Wells CK. A new clinical prognostic staging system for acute pancreatitis. Am J Med 1993; 95 61–70.
A new clinical prognostic staging system for acute pancreatitis.Crossref | GoogleScholarGoogle Scholar |

[3]  Miguel A, Garcia-Ramon R, Perez-Contreras J, Gomez-Roldan C, Alvarino J, Escobedo J, et al Comorbidity and mortality in peritoneal dialysis: a comparative study of type 1 and 2 diabetes versus non-diabetic patients. Peritoneal dialysis and diabetes (Multicenter group). Nephron 2002; 90 290–6.
Comorbidity and mortality in peritoneal dialysis: a comparative study of type 1 and 2 diabetes versus non-diabetic patients. Peritoneal dialysis and diabetes (Multicenter group).Crossref | GoogleScholarGoogle Scholar |

[4]  Wang MY, Green BA, Shah S, Vanni S, Levi AD. Complications associated with lumbar stenosis surgery in patients older than 75 years of age. Neurosurg Focus 2003; 14 e7
Complications associated with lumbar stenosis surgery in patients older than 75 years of age.Crossref | GoogleScholarGoogle Scholar |

[5]  Shah AN, Vail TP, Taylor D, Pietrobon R. Comorbid illness affects hospital costs related to hip arthroplasty: quantification of health status and implications for fair reimbursement and surgeon comparisons. J Arthroplasty 2004; 19 700–5.
Comorbid illness affects hospital costs related to hip arthroplasty: quantification of health status and implications for fair reimbursement and surgeon comparisons.Crossref | GoogleScholarGoogle Scholar |

[6]  SooHoo NF, Lieberman JR, Ko CY, Zingmond DS. Factors predicting complication rates following total knee replacement. J Bone Joint Surg Am 2006; 88 480–5.
Factors predicting complication rates following total knee replacement.Crossref | GoogleScholarGoogle Scholar |

[7]  Shwartz M, Iezzoni LI, Moskowitz MA, Ash AS, Sawitz E. The importance of comorbidities in explaining differences in patient costs. Med Care 1996; 34 767–82.
The importance of comorbidities in explaining differences in patient costs.Crossref | GoogleScholarGoogle Scholar |

[8]  Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA 1992; 267 2197–203.
Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?Crossref | GoogleScholarGoogle Scholar |

[9]  Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000; 53 1258–67.
Development of a comorbidity index using physician claims data.Crossref | GoogleScholarGoogle Scholar |

[10]  Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002; 55 573–87.
Measuring potentially avoidable hospital readmissions.Crossref | GoogleScholarGoogle Scholar |

[11]  Piccirillo JF. Importance of comorbidity in head and neck cancer. Laryngoscope 2000; 110 593–602.
Importance of comorbidity in head and neck cancer.Crossref | GoogleScholarGoogle Scholar |

[12]  Extermann M. Measuring comorbidity in older cancer patients. Eur J Cancer 2000; 36 453–71.
Measuring comorbidity in older cancer patients.Crossref | GoogleScholarGoogle Scholar |

[13]  Caughey GE, Vitry AI, Gilbert AL, Roughead EE. Prevalence of comorbidity of chronic diseases in Australia. BMC Public Health 2008; 8 221 [Review]
Prevalence of comorbidity of chronic diseases in Australia.Crossref | GoogleScholarGoogle Scholar |

[14]  McGregor JC, Kim PW, Perencevich EN, Bradham DD, Furuno JP, Kaye KS, et al Utility of the Chronic Disease Score and Charlson Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms. Am J Epidemiol 2005; 161 483–93.
Utility of the Chronic Disease Score and Charlson Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms.Crossref | GoogleScholarGoogle Scholar |

[15]  Cornoni-Huntley JC, Foley DJ, Guralnik JM. Co-morbidity analysis: a strategy for understanding mortality, disability and use of health care facilities of older people. Int J Epidemiol 1991; 20 S8–17.

[16]  Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol 2005; 58 595–602. [Review]
The development of a comorbidity index with physical function as the outcome.Crossref | GoogleScholarGoogle Scholar |

[17]  Reid BC, Alberg AJ, Klassen AC, Samet JM, Rozier RG, Garcia I, et al Comorbidity and survival of elderly head and neck carcinoma patients. Cancer 2001; 92 2109–16.
Comorbidity and survival of elderly head and neck carcinoma patients.Crossref | GoogleScholarGoogle Scholar |

[18]  Paleri V, Wight RG. A cross-comparison of retrospective notes extraction and combined notes extraction and patient interview in the completion of a comorbidity index (ACE-27) in a cohort of United Kingdom patients with head and neck cancer. J Laryngol Otol 2002; 116 937–41.
A cross-comparison of retrospective notes extraction and combined notes extraction and patient interview in the completion of a comorbidity index (ACE-27) in a cohort of United Kingdom patients with head and neck cancer.Crossref | GoogleScholarGoogle Scholar |

[19]  Nadathur SG. Maximising the value of hospital administrative datasets. Aust Health Rev 2010; 34 216–23.
Maximising the value of hospital administrative datasets.Crossref | GoogleScholarGoogle Scholar |

[20]  Needham DM, Scales DC, Laupacis A, Pronovost PJ. A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research. J Crit Care 2005; 20 12–9.
A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research.Crossref | GoogleScholarGoogle Scholar |

[21]  Riley GF. Administrative and claims records as sources of health care cost data. Med Care 2009; 47 S51–5. [Review]
Administrative and claims records as sources of health care cost data.Crossref | GoogleScholarGoogle Scholar |

[22]  Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived from ICD-9-CCM administrative data. Med Care 2002; 40 675–85.
Validity of information on comorbidity derived from ICD-9-CCM administrative data.Crossref | GoogleScholarGoogle Scholar |

[23]  Luthi JC, Troillet N, Eisenring MC, Sax H, Burnand B, Quan H, et al Administrative data outperformed single-day chart review for comorbidity measure. Int J Qual Health Care 2007; 19 225–31.
Administrative data outperformed single-day chart review for comorbidity measure.Crossref | GoogleScholarGoogle Scholar |

[24]  de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol 2003; 56 221–9.
How to measure comorbidity: a critical review of available methods.Crossref | GoogleScholarGoogle Scholar |

[25]  Kaplan MH, Feinstein AR. The importance of classifying initial comorbidity in evaluating the outcome of diabetes mellitus. J Chronic Dis 1974; 27 387–404.
The importance of classifying initial comorbidity in evaluating the outcome of diabetes mellitus.Crossref | GoogleScholarGoogle Scholar |

[26]  Hall SF. A user’s guide to selecting a comorbidity index for clinical research. J Clin Epidemiol 2006; 59 849–55.
A user’s guide to selecting a comorbidity index for clinical research.Crossref | GoogleScholarGoogle Scholar |

[27]  Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 373–83.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.Crossref | GoogleScholarGoogle Scholar |

[28]  Ghali WA, Hall RE, Rosen AK, Ash AS, Moskowitz MA. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data. J Clin Epidemiol 1996; 49 273–8.
Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data.Crossref | GoogleScholarGoogle Scholar |

[29]  Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992; 45 613–9.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.Crossref | GoogleScholarGoogle Scholar |

[30]  D’Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med 1993; 32 382–7.

[31]  Romano PS, Roos LL, Jollis JG. Variance and dissent presentation: adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 1993; 46 1075–9.
Variance and dissent presentation: adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.Crossref | GoogleScholarGoogle Scholar |

[32]  Romano PS, Roos LL, Jollis JG, Romano PS, Roos LL, Jollis JG. Further evidence concerning the use of a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 1993; 46 1085–90. [Response]
Further evidence concerning the use of a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.Crossref | GoogleScholarGoogle Scholar |

[33]  Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 8–27.
Comorbidity measures for use with administrative data.Crossref | GoogleScholarGoogle Scholar |

[34]  Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care 2004; 42 355–60.
Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data.Crossref | GoogleScholarGoogle Scholar |

[35]  Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43 1130–9.
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.Crossref | GoogleScholarGoogle Scholar |

[36]  Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004; 57 1288–94.
New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality.Crossref | GoogleScholarGoogle Scholar |

[37]  Zhu H, Hill MD. Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality. Neurology 2008; 71 283–7. [Comparative Study]
Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality.Crossref | GoogleScholarGoogle Scholar |

[38]  Schneeweiss S, Maclure M, Schneeweiss S, Maclure M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 2000; 29 891–8. [Review]
Use of comorbidity scores for control of confounding in studies using administrative databases.Crossref | GoogleScholarGoogle Scholar |

[39]  Glance LG, Dick AW, Osler TM, Mukamel DB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data? Health Serv Res 2006; 41 231–51.
Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?Crossref | GoogleScholarGoogle Scholar |

[40]  Monami M, Lambertucci L, Lamanna C, Lotti E, Marsili A, Masotti G, et al. Are comorbidity indices useful in predicting all-cause mortality in Type 2 diabetic patients? Comparison between Charlson index and disease count. Aging Clin Exp Res 2007; 19 492–6. [Comparative Study]

[41]  Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. J Clin Epidemiol 1999; 52 137–42.
A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data.Crossref | GoogleScholarGoogle Scholar |

[42]  D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson Comorbidity Index with administrative data bases. J Clin Epidemiol 1996; 49 1429–33.
Practical considerations on the use of the Charlson Comorbidity Index with administrative data bases.Crossref | GoogleScholarGoogle Scholar |

[43]  Macario A, Vitez TS, Dunn B, McDonald T, Brown B. Hospital costs and severity of illness in three types of elective surgery. Anesthesiology 1997; 86 92–100.
Hospital costs and severity of illness in three types of elective surgery.Crossref | GoogleScholarGoogle Scholar |

[44]  Lee WC, Arcona S, Thomas SK, Wang Q, Hoffmann MS, Pashos CL. Effect of comorbidities on medical care use and cost among refractory patients with partial seizure disorder. Epilepsy Behav 2005; 7 123–6.
Effect of comorbidities on medical care use and cost among refractory patients with partial seizure disorder.Crossref | GoogleScholarGoogle Scholar |

[45]  Iezzoni LI. Severity of illness measures: comments and caveats. Med Care 1990; 28 757–61. [Editorial]
Severity of illness measures: comments and caveats.Crossref | GoogleScholarGoogle Scholar |

[46]  Rubin HR, Wu AW. The risk of adjustment. Med Care 1992; 30 973–5. [Editorial]
The risk of adjustment.Crossref | GoogleScholarGoogle Scholar |

[47]  Froehner M, Koch R, Litz R, Oehlschlaeger S, Wirth MP. Which conditions contributing to the Charlson score predict survival after radical prostatectomy? J Urol 2004; 171 697–9.
Which conditions contributing to the Charlson score predict survival after radical prostatectomy?Crossref | GoogleScholarGoogle Scholar |

[48]  Liu M, Domen K, Chino N. Comorbidity measures for stroke outcome research: a preliminary study. Arch Phys Med Rehabil 1997; 78 166–72.
Comorbidity measures for stroke outcome research: a preliminary study.Crossref | GoogleScholarGoogle Scholar |

[49]  Goldstein LB, Samsa GP, Matchar DB, Horner RD. Charlson Index comorbidity adjustment for ischemic stroke outcome studies. Stroke 2004; 35 1941–5.
Charlson Index comorbidity adjustment for ischemic stroke outcome studies.Crossref | GoogleScholarGoogle Scholar |

[50]  Hemmelgarn BR, Manns BJ, Quan H, Ghali WA. Adapting the Charlson Comorbidity Index for use in patients with ESRD. Am J Kidney Dis 2003; 42 125–32.
Adapting the Charlson Comorbidity Index for use in patients with ESRD.Crossref | GoogleScholarGoogle Scholar |

[51]  Tang J, Wan JY, Bailey JE, Tang J, Wan JY, Bailey JE. Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive Medicaid population. Stroke 2008; 39 1938–44.
Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive Medicaid population.Crossref | GoogleScholarGoogle Scholar |

[52]  Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000; 53 1258–67.
Development of a comorbidity index using physician claims data.Crossref | GoogleScholarGoogle Scholar |

[53]  Feinstein AR. Clinical judgment. Baltimore, MD: Williams & Wilkins; 1967.

[54]  Feinstein AR. “Clinical Judgment” revisited: the distraction of quantitative models. Ann Intern Med 1994; 120 799–805.

[55]  Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009; 7 357–63. [Review]
Defining comorbidity: implications for understanding health and health services.Crossref | GoogleScholarGoogle Scholar |

[56]  Bonavita V, De Simone R. Towards a definition of comorbidity in the light of clinical complexity. Neurol Sci 2008; 29 99–102.
Towards a definition of comorbidity in the light of clinical complexity.Crossref | GoogleScholarGoogle Scholar |