Original articles
Development of a comorbidity index using physician claims data

https://doi.org/10.1016/S0895-4356(00)00256-0Get rights and content

Abstract

Important comorbidities recorded on outpatient claims in administrative datasets may be missed in analyses when only inpatient care is considered. Using the comorbid conditions identified by Charlson and colleagues, we developed a comorbidity index that incorporates the diagnostic and procedure data contained in Medicare physician (Part B) claims. In the national cohorts of elderly prostate (n = 28,868) and breast cancer (n = 14,943) patients assessed in this study, less than 10% of patients had comorbid conditions identified when only Medicare hospital (Part A) claims were examined. By incorporating physician claims, the proportion of patients with comorbid conditions increased to 25%. The new physician claims comorbidity index significantly contributes to models of 2-year noncancer mortality and treatment received in both patient cohorts. We demonstrate the utility of a disease-specific index using an alternative method of construction employing study-specific weights. The physician claims index can be used in conjunction with a comorbidity index derived from inpatient hospital claims, or employed as a stand-alone measure.

Introduction

Comorbidities are coexisting medical conditions that are distinct from the principal diagnosis or the primary illness for which the patient seeks health care services 1, 2, 3. Comorbidities can be either chronic diseases or acute illnesses, and can increase a patient's total burden of illness 3, 4. Compared to patients who do not have these conditions, patients with comorbid illnesses may be at greater risk of complications or death, less able to tolerate particular medical procedures, and less responsive to therapy 1, 3. Furthermore, physicians may factor the presence of particular comorbidities into decisions concerning the most appropriate medical treatments for patients. For example, studies involving breast and prostate cancer patients have found that patients with more comorbidities receive less aggressive treatment for their tumors, even after controlling for patient age and cancer stage 5, 6. The reluctance to pursue a course of aggressive therapy, particularly surgery, in patients with a high burden of comorbid conditions likely stems from their substantially increased risk of complications and death [7].

The complexity of comorbidity data and its potential for creating unwieldy analyses has led to the development of summary comorbidity measures such as the Charlson index 8, 9. Based on medical record review, Charlson and colleagues developed a weighted index measure of comorbidity that was shown to predict 1-year all-cause mortality in a cohort of 559 hospitalized medical service patients and 10-year non-breast cancer mortality in cohort of 685 breast cancer patients. The index is comprised of 19 conditions, each of which is assigned a weight according to its potential for influencing mortality. Charlson and colleagues used as weights the adjusted hazard ratios (referred to in their article as “relative risks”) from a stepwise proportional hazards model, rounded to the nearest integer. The patient's comorbidity index, the sum of the weighted comorbidities, takes into account both the number and seriousness of the conditions. A higher score on the Charlson index indicates a greater burden of comorbid disease.

Deyo et al.[10] adapted the Charlson index for use with the ICD-9-CM diagnostic and procedure codes available in administrative datasets, and demonstrated the utility of this adapted measure in predicting risk of poor outcomes for patients following lumbar spine surgery. The Deyo adaptation involves searching a patient's hospital claims data for the presence of certain ICD-9-CM diagnosis and procedure codes corresponding to the Charlson comorbid conditions. An important limitation of the Deyo/Charlson comorbidity measure, however, is that it was developed and validated on the basis of inpatient hospital care. To date, studies that have used the measure have included only data from the inpatient setting 11, 12, 13. It is possible that important comorbidities recorded on outpatient claims are missed in analyses when only inpatient care is considered 11, 14, particularly because an estimated 80% of Medicare beneficiaries are not hospitalized in a given year [15]. Because of the ongoing shift toward delivery of health care services exclusively in outpatient settings, consideration of comorbidities recorded in outpatient claims is of particular importance when assessing treatment patterns in more recent years.

This article describes the development of a comorbidity measure using Charlson's conditions, the diagnostic and procedure data contained in Medicare physician (Part B) claims, and a new methodologic approach. Although other validated comorbidity measures such as the Kaplan-Feinstein index [16] and Index of Coexistent Disease [5] are available, we chose to build upon the Charlson index because of its wide use with claims data. The new measure is validated by assessing whether comorbidity information derived from physician claims significantly contributes to models of short-term noncancer mortality in national cohorts of elderly breast and prostate cancer patients. Prior studies 10, 17, 18, 19, 20, 21 also have demonstrated the utility of the Charlson comorbidity index, developed as a prospective means of evaluating risk of death, in predicting such nonmortality outcomes as complications, length of stay, and charges. In this study, we also evaluate whether inclusion of physician claims data significantly contributes to models of type of cancer treatment received in breast and prostate cancer patients. Finally, we propose a method of index construction that avoids arbitrary thresholds for condition exclusion and uses a scale more closely related to the model that produces the condition weights.

Section snippets

Data sources

Data for this study were derived from two sources: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program, and 2) Medicare claims.

Results

Table 1 displays condition prevalences, estimated coefficients, and hazard ratios for comorbidities appearing in inpatient hospital or only in physician claims for the prostate cancer cohort. To facilitate comparison to Charlson's original weights (see Table 1, Charlson et al. article), this table also includes the study-derived hazard ratios for these comorbid conditions rounded to the nearest integer, according to the method of Charlson et al.[9]. Diabetes, chronic pulmonary disease, and

Discussion

The Charlson comorbidity index, adapted for use with administrative datasets 10, 20, has been shown to predict a variety of patient outcomes, including mortality, postoperative complications, length of stay, and hospital charges 10, 17, 18, 19, 20, 21. The Charlson index, however, was developed and validated on the basis of inpatient hospital care [9]. We developed a new index measure of comorbidity derived from physician (Medicare Part B) claims data in two separate cohorts of elderly cancer

Acknowledgements

The authors are grateful to Nicki Schussler of Information Management Services, Inc., Silver Spring, MD, for expert assistance with dataset construction and to Rachel Ballard-Barbash, M.D., M.P.H., for careful review of and helpful comments on the manuscript.

References (42)

  • D.J Malenka et al.

    Using administrative data to describe casemixa comparison with the medical record

    J Clin Epidemiol

    (1994)
  • E.G Lowell-Smith

    Alternative forms of ambulatory careimplications for patients and physicians

    Soc Sci Med

    (1994)
  • A.R Finstein

    The pre-therapeutic classification of co-morbidity in chornic disease

    J Chron Dis

    (1970)
  • L.I Iezzoni et al.

    Comorbidities, complications, and coding biasdoes the number of diagnosis codes matter in predicting in-hospital mortality?

    JAMA

    (1992)
  • L.I Iezzoni

    Risk Adjustment for Measuring Health Care Outcomes

    (1994)
  • M Shwartz et al.

    The importance of comorbidities in explaining differences in patient costs

    Med Care

    (1996)
  • S Greenfield et al.

    Patterns of care related to age of breast cancer patients

    JAMA

    (1987)
  • C.L Bennett et al.

    Patterns of care related to age of men with prostate cancer

    Cancer

    (1991)
  • L.I Iezzoni et al.

    Chronic conditions and risk of in-hospital death

    Health Services Res

    (1994)
  • J.C Cornoni-Huntley et al.

    Co-morbidity analysisa strategy for understanding mortality, disability and use of health care facilities of older people

    Int J Epidemiol

    (1991)
  • R Ballard-Barbash et al.

    Factors associated with surgical and radiation therapy for early stage breast cancer in older women

    J Natl Cancer Inst

    (1996)
  • Cited by (0)

    View full text