In search of the perfect comorbidity measure for use with administrative claims data: does it exist?

Administrative claims data are used frequently in health services research and policy to examine patient health status and the quality and cost of care. Comorbid illness has a significant impact on these and other outcomes, and several measures of comorbidity have been developed for use in studies based on administrative claims.1–8 Claims-based measures of comorbidity are of particular importance to cancer care researchers, who increasingly use population-based cancer registry data linked with administrative claims (eg, Surveillance, Epidemiology, and End Results [SEER]-Medicare files) to examine such issues as racial or geographic disparities in receipt of recommended treatments or the association between treatment and survival.9–12 Adjustment for comorbidity is essential in these observational studies because baseline differences in health status between groups (eg, racial and ethnic groups, those with and without treatment) may modulate differences found in study outcomes. Health services researchers have a choice of comorbidity measures and must decide how best to apply them to their work. The measures themselves have been developed and used with claims from different places of services (eg, inpatient, outpatient) and time frames in relationship to the disease diagnosis (eg, prediagnosis, during treatment or diagnosis hospitalization). Some measures were developed to predict cost and modify payment systems,13–15 others to predict mortality and control for preexisting health status.1–8 A few studies have compared the performance of different measures in predicting mortality, generally concluding that measures incorporating more conditions and data sources are better predictors of mortality. Although some investigators have found no advantage to disease-specific indices, several have suggested creating condition- and/or outcome-specific comorbidity indices. Creating study-specific measures may not be possible, however, and investigators often apply measures in a manner at variance from how they were developed.10,12,16–18 In this study, we identified 4 administrative claims-based measures of comorbidity that have been used frequently in the health services literature to adjust for baseline health status and compared their performance in predicting 2 outcomes of importance to cancer care researchers—receipt of recommended adjuvant chemotherapy and mortality—among stage III colon cancer patients. We chose to study stage III colon cancer patients for several reasons. First, colon cancer is generally a disease of the elderly, who are more likely to have comorbid illness. Second, a 1990 National Institutes of Health Consensus Panel on Colorectal Cancer recommended routine adjuvant chemotherapy for stage III colon cancer, yet the reported rate of adjuvant chemotherapy use among the elderly is only about 55%.19 Because comorbidity may contribute to this low treatment rate, identifying the optimal comorbidity measure is important for analyses examining receipt of recommended treatment. Third, colon cancer patients’ level of comorbid illness could influence survival both through the influence on receipt of recommended treatment and through the association with noncancer death. This work compares the performance of a wide variety of comorbidity measures in predicting treatment use and mortality, and examines the influence of using different data sources on measure performance. Because one of our measures was developed specifically for colorectal cancer patients, we were able to compare its performance to that of other measures derived from more generalized populations. We hypothesized that a measure that combines multiple data sources with specificity to colorectal cancer patients would be the best predictor of our study outcomes.