Investigating the Extremes of Antibiotic Use with an Epidemiologic Framework

Abstract

Benchmarks for judicious use of antimicrobials are needed. Metrics such as defined daily doses (DDDs) and days of therapy (DOTs) quantify antimicrobial consumption. However, benchmarking with these metrics is complicated by interhospital variability. Thus, it is important for each hospital to monitor its own temporal consumption trends. Time series analyses allow trends to be detected; however, many of these methods are complex. We present simple regressive methods and caveats in using them to define potential antibiotic over- and underutilizations.

Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Figures

FIG 1

Box-Jenkins autoregressive integrated moving average (ARIMA) model: vancomycin consumption from January 2012 to June 2015, with predictions through December 2015.

FIG 2

Linear regression of vancomycin consumption from January 2012 to June 2015. Dashed purple circles represent potential overuse. Double-lined green circles represent potential underuse.

FIG 3

Residual diagnostic plots. (Left) residuals; (right) standardized residuals. Predictions are color coded as follows: within 80%, blue; within 90%, orange; within 95%, red.