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Comparing prescribing and dispensing databases to study antibiotic use: a validation study of the Electronic Medical Record Administrative data Linked Database (EMRALD)

Schwartz KL, Wilton AS, Langford BJ, Brown KA, Daneman N, Garber G, Johnstone J, Adomako K, Achonu C, Tu K. J Antimicrob Chemother. 2019; 74(7):2091-7. Epub 2019 Feb 25. DOI: 10.1093/jac/dkz033.


Background — Monitoring and studying community antibiotic use is a critical component in combating rising antimicrobial resistance.

Objectives — To validate an electronic medical record dataset containing antibiotic prescriptions and to quantify some important differences between prescribing and dispensing databases.

Methods — We evaluated antibiotics prescribed and dispensed to patients ≥65 years of age during 2011-15. We compared the EMRALD prescribing database with the validated Ontario Drug Benefit (ODB) dispensing database. Using ODB as the gold standard and limiting to EMRALD physicians, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) with 95% CIs. We also compared the relative change in antibiotic use prescribed by all physicians to this population over time between the databases using Poisson regression models.

Results — In this population, 74% of all antibiotics dispensed were from non-EMRALD physicians. Trends in use were discordant over time. When we limited ODB to EMRALD prescribers only to assess the validity of EMRALD data, we observed good sensitivity and excellent specificity for correctly identifying antibiotics at 85% (95% CI 84%-85%) and 98% (95% CI 98%-98%), respectively. The PPV was 78% (95% CI 78%-78%) and the NPV was 99% (95% CI 99%-99%). All performance measures were higher among the highest prescribing physicians.

Conclusions — We demonstrated EMRALD is well suited for studying antibiotic prescribing by EMRALD physicians. However, due to the frequency with which patients receive antibiotic prescriptions from their non-primary care physicians, we caution against the use of non-population-based prescribing databases to infer antibiotic use rates or trends over time.

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