Purpose: To develop a complete and consistent prescription drug monitoring program (PDMP) data set for use by drug safety researchers in evaluating patterns of high-risk use and potential abuse of scheduled drugs.
Methods: Using publically available data references from the US Food and Drug Administration and the Centers for Disease Control and Prevention, we developed a strategic methodology to assign drug categories based on pharmaceutical class for the majority of prescriptions in the PDMP data set. We augmented data elements required to calculate morphine milligram equivalents and assigned duration of action (short-acting or long acting) properties for a majority of opioids in the data set.
Results: About 10% of prescriptions in the PDMP data set did not have a vendor-assigned drug category, and 20% of opioid prescriptions were missing data needed to calculate risk metrics. Using inclusive methods, 19 133 167 (>99.9%) of prescriptions in the PDMP data set were assigned a drug category. For the opioid category, augmenting data elements resulted in 10 760 669 (99.8%) having required values to calculate morphine milligram equivalents and evaluate duration of action properties.
Conclusions: Drug safety researchers who require a complete and consistent PDMP data set can use the methods described here to ensure that prescriptions of interest are assigned consistent drug categories and complete opioid risk variable values. Copyright © 2016 John Wiley & Sons, Ltd.
Keywords: data sources; diversion measurement; metrics; pharmacoepidemiology; prescription drug abuse; prescription drug monitoring program; prescription opioid.
Copyright © 2016 John Wiley & Sons, Ltd.