Original article
How to measure comorbidity: a critical review of available methods

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Abstract

The object of this article was to systematically review available methods to measure comorbidity and to assess their validity and reliability. A search was made in Medline and Embase, with the keywords comorbidity and multi-morbidity, to identify articles in which a method to measure comorbidity was described. The references of these articles were also checked, and using a standardized checklist the relevant data were extracted from these articles. An assessment was made of the content, concurrent, predictive and construct validity, and the reliability. Thirteen different methods to measure comorbidity were identified: one disease count and 12 indexes. Data on content and predictive validity were available for all measures, while data on construct validity were available for nine methods, data on concurrent validity, and interrater reliability for eight methods, and data on intrarater reliability for three methods. The Charlson Index is the most extensively studied comorbidity index for predicting mortality. The Cumulative Illness Rating Scale (CIRS) addresses all relevant body systems without using specific diagnoses. The Index of Coexisting Disease (ICED) has a two-dimensional structure, measuring disease severity and disability, which can be useful when mortality and disability are the outcomes of interest. The Kaplan Index was specifically developed for use in diabetes research. The Charlson Index, the CIRS, the ICED and the Kaplan Index are valid and reliable methods to measure comorbidity that can be used in clinical research. For the other indexes, insufficient data on the clinimetric properties are available.

Introduction

As early as 1970, Alvan Feinstein noted that “the failure to classify and analyze comorbid diseases has led to many difficulties in medical statistics” [1], because comorbidity can affect the moment of detection, prognosis, therapy, and outcome. Comorbidity can play an important role in different types of research. In etiologic studies the relationship between comorbid conditions and an index disease can be investigated. Comorbidity can be the cause or the consequence of an index disease. It is also possible that the index disease and the comorbid conditions share the same risk factors. In diagnostic studies, comorbidity can obscure the relationship between the test under study and the index disease. In these fields of research it might be particularly useful to analyze every disease as a separate variable, to gain insight into the relationship between individual diseases and the index disease at issue. However, this method is not feasible in small studies, because of reduced efficiency of the analysis. Randomized controlled trials (RCTs) and prognostic studies can also be complicated by comorbidity. Comorbidity can either act as a confounder, threatening the internal validity, or as an effect modifier, threatening the internal and external validity of the study. For these purposes an efficient method is needed to measure comorbidity.

There are four important reasons for measuring comorbidity. The first reason is to be able to correct for confounding, and thus improve the internal validity of studies. The second reason is to be able to identify effect modification. A third reason is the desire to use comorbidity as a predictor of study outcome or natural history. Finally, a comprehensive comorbidity measure, including many cooccurring comorbid conditions in one valid variable, is needed for reasons of statistical efficiency.

Because an overview of available methods to measure comorbidity is still lacking, the following research question was formulated: Which methods are available for measuring comorbidity that can be used in RCTs and prognostic studies?

Section snippets

Methods

A search was made in the electronic databases of Medline (from January 1966 to September 2000) and Embase (from January 1988 to September 2000). The following keywords were used to identify potentially useful articles: comorbidity, multimorbidity, and coexisting disease. Articles in which the focus was on comorbidity assessment or comorbidity was an important (prognostic) variable were considered for inclusion. In the literature several terms are being used in comorbidity research. Also, there

Results

Thirteen different methods to assess comorbidity were identified and presented in alphabetical order in Table 1: one disease count and 12 indexes. Data on content and predictive validity were available for all measures, while data on construct validity were available for nine methods, data on concurrent validity for eight methods, data on interrater reproducibility for eight methods, and data on intrarater reliability for three methods.

The Burden of Disease (BOD) index [4] consists of 59

Discussion

Measuring comorbidity is an aspect of research that is receiving increasing attention in the literature. Several authors have discussed and compared the use of various selected methods to measure comorbidity 50, 51, 52, 53. This review describes methods that can be used to measure comorbidity in clinical research, without limiting the focus to certain index diseases or diagnostic groups. Thirteen different methods were identified. Six indexes used a carefully developed list of clearly defined

References (63)

  • M Liu et al.

    Comorbidity measures for stroke outcome researcha preliminary study

    Arch Phys Med Rehabil

    (1997)
  • S.E Gabriel et al.

    A comparison of two comorbidity instruments in arthritis

    J Clin Epidemiol

    (1999)
  • S.M Kieszak et al.

    A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data

    J Clin Epidemiol

    (1999)
  • D.W West et al.

    Comorbidity and breast cancer survivala comparison between black and white women

    Ann Epidemiol

    (1996)
  • D.J Skiest et al.

    The importance of comorbidity in HIV-infected patients over 55a retrospective case–control study

    Am J Med

    (1996)
  • R.M Poses et al.

    Prediction of survival of critically ill patients by admission comorbidity

    J Clin Epidemiol

    (1996)
  • S Beddhu et al.

    A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients

    Am J Med

    (2000)
  • M.D Miller et al.

    Rating chronic medical illness burden in geropsychiatric practice and researchapplication of the Cumulative Illness Rating Scale

    Psychiatry Res

    (1992)
  • K Page-Shafer et al.

    Comorbidity and survival in HIV-infected men in the San Francisco Men's Health Survey

    Ann Epidemiol

    (1996)
  • M.G Stineman et al.

    Diagnostic coding and medical rehabilitation length of staytheir relationship

    Arch Phys Med Rehabil

    (1998)
  • G.R Parkerson et al.

    The Duke Severity of Illness Checklist (DUSOI) for measurement of severity and comorbidity

    J Clin Epidemiol

    (1993)
  • K Imamura et al.

    Reliability of a comorbidity measurethe Index of Co-Existent Disease (ICED)

    J Clin Epidemiol

    (1997)
  • M.H Kaplan et al.

    The importance of classifying initial co-morbidity in evaluating the outcome of diabetes mellitus

    J Chronic Dis

    (1974)
  • R Gijsen et al.

    Causes and consequences of comorbiditya review

    J Clin Epidemiol

    (2001)
  • J.M Guralnik

    Assessing the impact of comorbidity in the older population

    Ann Epidemiol

    (1996)
  • M Extermann

    Measurement and impact of comorbidity in older cancer patients

    Crit Rev Oncol Hematol

    (2000)
  • M Extermann

    Measuring comorbidity in older cancer patients

    Eur J Cancer

    (2000)
  • M van den Akker et al.

    Multimorbidity in general practiceprevalence, incidence, and determinants of co-occurring chronic and recurrent diseases

    J Clin Epidemiol

    (1998)
  • L.P Fried et al.

    Association of comorbidity with disability in older womenthe Women's Health and Aging Study

    J Clin Epidemiol

    (1999)
  • D.J Malenka et al.

    Using administrative data to describe casemixa comparison with the medical record

    J Clin Epidemiol

    (1994)
  • P.S Wang et al.

    Strategies for improving comorbidity measures based on Medicare and Medicaid claims data

    J Clin Epidemiol

    (2000)
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