UCSF

Background

Pharmacy dispensing data are frequently used to identify prevalent medication use as a predictor or covariate in observational research studies. Although several methods have been proposed for using pharmacy dispensing data to identify prevalent medication use, little is known about their comparative performance.

Objectives

The authors sought to compare the performance of different methods for identifying prevalent outpatient medication use.

Research design

Outpatient pharmacy fill data were compared with medication reconciliation notes denoting prevalent outpatient medication use at the time of hospital admission for a random sample of 207 patients drawn from a national cohort of patients admitted to Veterans Affairs hospitals. Using reconciliation notes as the criterion standard, we determined the test characteristics of 12 pharmacy database algorithms for determining prevalent use of 11 classes of cardiovascular and diabetes medications.

Results

The best-performing algorithms included a 180-day fixed look-back period approach (sensitivity, 93%; specificity, 97%; and positive predictive value, 89%) and a medication-on-hand approach with a grace period of 60 days (sensitivity, 91%; specificity, 97%; and positive predictive value, 91%). Algorithms that have been commonly used in previous studies, such as defining prevalent medications to include any medications filled in the prior year or only medications filled in the prior 30 days, performed less well. Algorithm performance was less accurate among patients recently receiving hospital or nursing facility care.

Conclusion

Pharmacy database algorithms that balance recentness of medication fills with grace periods performed better than more simplistic approaches and should be considered for future studies which examine prevalent chronic medication use.