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

Claims-based measures of comorbid illness severity have generally relied on the diagnoses listed for a single hospitalization. Unfortunately, such diagnostic information is often limited because patients have not been hospitalized during periods of interest, because of incomplete coding of diagnoses on claims forms, or because listed diagnoses represent complications of the hospitalization rather than preexisting comorbid conditions. To address these limitations, we developed and tested four comorbidity index scores for patients with breast cancer, each based on different sources of health services claims from Medicare and Medicaid: hospitalization for breast cancer surgery; outpatient care prior to the hospitalization; other inpatient care prior to the hospitalization; and all sources combined. Varying the number and type of sources of diagnostic information yielded only very small improvements in the prediction of mortality at 1 and 3 years. Surprisingly, even simpler measures of comorbidity (crude number of diagnoses) and of prior health care utilization (total days spent in the hospital) performed at least as well in predicting mortality as did the more complex index scores which assigned points and weights for specific conditions. The greatest improvement in explanatory power was observed when another source of clinical information (cancer stage derived from a population-based cancer registry) was used to supplement claims information. Expanding the source of claims diagnoses and focusing on time periods prior to an index hospitalization are insufficient for substantially improving the explanatory power of claims-based comorbidity indices. Other improvements suggested by our results should include: increasing the completeness and accuracy of claims diagnoses; supplementing diagnoses with health care utilization information in claims data; and supplementing claims data with other sources of clinical information. (C) 2000 Elsevier Science Inc. All rights reserved.