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A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization

Published online by Cambridge University Press:  03 May 2017

Cristina Miglis ,
Nathaniel J. Rhodes ,
Sean N. Avedissian ,
Teresa R. Zembower ,
Michael Postelnick ,
Richard G. Wunderink ,
Sarah H. Sutton  and
Marc H. Scheetz
Cristina Miglis
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Nathaniel J. Rhodes
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Sean N. Avedissian
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Teresa R. Zembower
Affiliation:
Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois
Michael Postelnick
Affiliation:
Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Richard G. Wunderink
Affiliation:
Division of Pulmonary Critical Care, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Sarah H. Sutton
Affiliation:
Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois
Marc H. Scheetz*
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
*
Address correspondence to Marc H. Scheetz, PharmD, MSc, Associate Professor of Pharmacy Practice, Midwestern University Chicago College of Pharmacy; 555 31st St, Downers Grove, IL 60515 ([email protected]).
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Abstract

Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level.

Infect Control Hosp Epidemiol 2017;38:860–862

Type
Concise Communications
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 7 , July 2017 , pp. 860 - 862
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Llor, C, Bjerrum, L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Ther Adv Drug Saf 2014;5:229–241.CrossRefGoogle ScholarPubMed
2. Antibiotic resistance threats in the United States. Centers for Disease Control and Prevention website. http://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.pdf. Published 2013. Accessed September 1, 2016.Google Scholar
3. Spellberg, B, Gilbert, DN. The future of antibiotics and resistance: a tribute to a career of leadership by John Bartlett. Clin Infect Dis 2014;59(Suppl 2):S71–S75.CrossRefGoogle ScholarPubMed
4. Spellberg, B, Srinivasan, A, Chambers, HF. New societal approaches to empowering antibiotic stewardship. JAMA 2016;315:1229–1230.CrossRefGoogle ScholarPubMed
5. Scheetz, MH, Crew, PE, Miglis, C, et al. Investigating the extremes of antibiotic use with an epidemiologic framework. Antimicrob Agents Ch 2016;60:3265–3269.CrossRefGoogle ScholarPubMed
6. Antimicrobial use and resistance (AUR) module. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/PDFs/pscManual/11pscAURcurrent.pdf. Published 2016. Accessed August 29, 2016.Google Scholar
7. Confidence and prediction intervals for forcasted values. Real Statistics University Excel website. http://www.real-statistics.com/regression/confidence-and-prediction-intervals/. Published 2016. Accessed March 27, 2017.Google Scholar
8. Altman, DG, Bland, JM. Statistics notes—standard deviations and standard errors. Brit Med J 2005;331:903–903.CrossRefGoogle ScholarPubMed
9. Using Excel to compute a confidence interval. Graphpad software website. http://www.graphpad.com/support/faqid/1432/. Published 2016. Accessed March 27, 2017.Google Scholar
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