Pharmaceutically-based severity stratification of an asthmatic population

Algorithms designed to precisely identify disease severity for a given pati ent within a managed care population are helpful in organizing targeted int erventions. These algorithms are also attracting considerable attention wit hin the medical research community. Several health risk screening instrumen ts have been developed; however, these involve survey methodologies and hav e several shortcomings. We present a valid and efficient method for predict ing healthcare resource utilization among asthmatics in an Health Maintenan ce Organization (HMO) population. First, various diagnosis, procedure and p harmacy billing codes were used to identify the asthmatics within the datab ase. The screening algorithm awards points each time one of these codes is identified for an HMO member. By varying the number of points necessary to consider a patient asthmatic, the sensitivity, specificity, positive and ne gative predictive values of the algorithm can be adjusted. Once identified as asthmatic, subjects were then stratified into severity levels based on p harmacy data. Severity stratification was validated directly by measuring a sthma-related bed days utilized during the 12 months following the date of stratification. Our identification algorithm estimated an asthma prevalence of 3.84% within the studied population, with age-specific prevalence estim ates that closely mirrored previously published survey data. There was a mo notonic relationship between pharmacy severity levels and inpatient resourc e utilization. For example, asthmatics in severity level 1 used only 92 hos pital days per 1000 asthmatics in the year following characterization, whil e those in levels 2-5 used 133, 156, 277 and 1168 hospital days (P<0.001), respectively. Results from this model can be used as adjusters in other pre dictive models or stand alone to represent a patient's severity of illness. (C) 1999 HARCOURT PUBLISHERS LTD.