Frequency estimates from prescription drug datasets (revision of #04-11-066A)

Authors

  • Swu-Jane Lin PhD,

    Corresponding author
    1. Department of Pharmacy Administration, University of Illinois at Chicago, Chicago, IL, USA
    • 833 S. Wood Street, Rm. 241 (M/C 871), Chicago, IL 60612-7231, USA.
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  • Bruce Lambert PhD,

    1. Department of Pharmacy Administration, University of Illinois at Chicago, Chicago, IL, USA
    2. Department of Pharmacy Practice, University of Illinois at Chicago, Chicago, IL, USA
    3. Department of Communication, University of Illinois at Chicago, Chicago, IL, USA
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  • Hiangkiat Tan BPharm, MS,

    1. Department of Pharmacy Administration, University of Illinois at Chicago, Chicago, IL, USA
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  • Sengwee Toh BPharm

    1. Department of Pharmacy Administration, University of Illinois at Chicago, Chicago, IL, USA
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  • No conflict of interest was declared.

Abstract

Purpose

Accurate information about the number of times a drug is prescribed or dispensed annually is important to marketers, pharmacoepidemiologists, and patient safety researchers. Yet there is no standard reference for prescribing frequency data. The multiple sources that do exist vary in their sampling methods, target populations, nomenclature, and methods of tallying individual medications prescribed or dispensed. These differences are likely to create ambiguity and contradictions in the scientific literature, but they are not well understood.

Methods

We conducted a descriptive study to examine the similarities and differences between five well-known sources of prescribing frequency data: the National Ambulatory Medical Care Survey (NAMCS), the National Hospital Ambulatory Medical Care Survey (NHAMCS, emergency department and outpatient department), the IMS National Prescription Drug Audit, the Solucient outpatient dataset, and the Solucient inpatient dataset. We compared survey methods, costs, overall frequencies, number of unique names in each database, correlations between frequency estimates from different databases, the extent of overlap in the databases, and nomenclature differences between and within datasets.

Results

All the correlations between frequency estimates derived from different datasets were significant, but the frequency estimates differed considerably. The lowest correlation (0.17) was found between the IMS and emergency department of the NHAMCS, and the highest correlation (0.93) was between IMS and Solucient outpatient data.

Conclusions

Although there were significant correlations between frequency estimates for comparable datasets, sampling methods and nomenclature choices resulted in important differences both for individual drug products and for overall frequency statistics. Researchers need to be aware of the differences when deriving drug frequency with these datasets. Copyright © 2005 John Wiley & Sons, Ltd.

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