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Abstract
Objectives. This study was performed to determine whether pulmonary function test results would appreciably alter asthma severity categorization determined by an algorithm using information readily available in administrative databases. Methods. Patients 6 to 64 years of age with asthma diagnosed from 1999–2005, who had at least one pulmonary function test, were identified from a claims database of a medical group practice located in central Massachusetts. Asthma severity for these patients was categorized using information available in an administrative database (claims-based algorithm) and by percent predicted forced expiratory volume in 1 second (FEV1) or peak expiratory flow (PEF) abstracted from medical charts (pulmonary function test method). Gamma rank correlation index was used to measure the association between the two severity categorization methods. Total and asthma-related healthcare costs for each severity category were compared between the two different approaches. Results. There was a significant ordinal association between severity categorization with the two classification approaches (p = 0.0002). The pulmonary function test method resulted in more frequent mild categorizations and less frequent moderate and severe categorizations than the claims-based algorithm. In only 10.9% of patients did the pulmonary function test method result in a more severe asthma category than the claims-based algorithm. Patients with more severe asthma, determined by both methods, had higher total and asthma-related health care costs. Total and asthma-related health care costs were similar for each asthma severity categorization for the two classification approaches, except for asthma-related costs in the moderate severity categories. Conclusion. The claims-based algorithm generally categorized patients as having more severe asthma than the approach using pulmonary function test results. Pulmonary function test results would have appreciably changed asthma severity categorization in only a small percent of patients. These findings add further support to the use of administrative database analyses for the evaluation of asthma care in large populations.