A Retrospective Study to Understand the Risk Factors/Drivers of "Inappropriate" Antimicrobial Use and the Performance Evaluation of a Clinical Decision Support Tool That Facilitates Prediction of Outbreaks of Inappropriate Antibiotic Use
Conditions: Infectious Disease
Sponsor: Midwestern University

Cover page 
 
 
STU00205629: A Retrospective Study to Understand the Risk Factors/Drivers of "Inappropriate" 
Antimicrobial Use and the Performance Evaluation of a Clinical Decision Support Tool that 
Facilitates Prediction of Outbreaks of Inappropriate Antibiotic Use 
 
 
  
Approved 
Entered IRB: 
8/2/2017 2:39 PM 
Initial approval: 
10/26/2017 
Effective: 
2/7/2018 
Approval end: 
10/25/2018 
Last updated: 
2/7/2018 12:02 PM 
 

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 1 of 10
HRP-593 / v011017
PROTOCOL TITLE:  A Retrospective Study to Understand the Risk Factors/Drivers of "Inappropriate" 
Antimicrobial Use and the Performance Evaluation of a Clinical Decision Support Tool that Facilitates 
Prediction of Outbreaks of Inappropriate Antibiotic Use
PRINCIPAL INVESTIGATOR:
Marc H. Scheetz, PharmD, MSc, BCPS AQ-ID
Infectious Diseases Pharmacist
Northwestern Memorial Hospital
251 E. Huron St.
Feinberg Pavilion, LC 700
Chicago, IL 60611
312.926.2546 office
mscheetz@nm.org
Associate Professor of Pharmacy Practice
Midwestern University Chicago College of Pharmacy
555 31st Street 
Downers Grove, IL 60515
630.515.6116 office
mschee@midwestern.edu 
VERSION NUMBER: 2
VERSION DATE:  06/6/17
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 2 of 10
HRP-593 / v011017
OBJECTIVES:
Objective 1. The first objective of this study is to comprehensively classify and predict vancomycin 
never-events. 
Vancomycin is selected as a prototypical broad-spectrum antibiotic that is regularly overused 
and is known to cause downstream harms (e.g., acute kidney injury). We will classify the 
proportion of vancomycin use in which potential for harm outweighs the potential benefit, 
herein referred to as “never events”. We will categorize the rate of never events as a function of 
patient, provider, and disease/organism characteristics. 
Objective 2. We will develop decision rules and thresholds (e.g. decision support tools) to predict time 
periods with frequent vancomycin never events based on antimicrobial consumption trends. 
Objective 2a. Identify the relationship between clusters of high antibiotic use, herein referred to 
as “outbreaks,” and occurrence of never events. We will use our previously published strategies 
to identify antibiotic outbreaks and correlate to vancomycin never events. 
Objective 2b. Compare thresholds for prediction of never events based on Optimal Data Analysis 
(ODA) methodologies with exact statistics [3, 4] versus vancomycin never events.
BACKGROUND:
Appropriateness in antimicrobial prescribing has become a focal national and international issue.  It has 
been estimated that upwards of 50% of antibiotic use is inappropriate [6-9]. With this backdrop, a 
national strategic goal has been set by the United States White House to decrease inappropriate 
antibiotic use by 20% and 50%, respectively for inpatient and outpatient settings [10].  In order to 
decrease inappropriate use, drivers of incorrect use must be identified at each local setting.  The actual 
drivers of confirmed inappropriate use have been difficult to identify except when using time and 
resource intense chart reviews.  Even the largest contemporary antibiotic consumption studies have not 
assessed appropriateness as it was ‘outside of study scope’ [11-13].  Further, there is no consensus or 
agreement on what constitutes inappropriate use.  These apparent omissions underscore the difficulty 
and complexity in attributing appropriateness of use for antimicrobials.  Importantly, this study will 
focus on the MOST inappropriate use, which we are defining as ‘never events’.  Never events represent 
the most clearly inappropriate use.  By defining these never events unambiguously, electronic data 
capture strategies can quickly and accurately identify areas of antibiotic use concern.
Such novel methods are sorely needed to fill this gap and identify signals/signatures of inappropriate 
antimicrobial use so that strategies can be defined to improve use.  This study will use an electronic data 
capture strategy to identify vancomycin consumption outbreaks and vancomycin never events.  The 
latter will be confirmed by chart review to determine the correctness of the electronic strategy for 
defining never events.  Our study is innovative since methods will use rapid electronic data capture 
strategies that can be tailored and scaled to many US hospitals; thus, providing conduits to conducting 
appropriateness assessments.
Our methods are expected to improve the status quo.  To communicate effectively about antibiotic 
consumption and work towards improvement, there is a need for a benchmarking process that is 
validated, simple to perform, and uses common and persuasive language for reporting. Antimicrobial 
use can be measured in quality and/or quantity; however, the standard measures for benchmarking 
antimicrobial use are focused almost entirely on quantity.  As shown by our group, both the CDC’s 
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 3 of 10
HRP-593 / v011017
National Healthcare Safety Network Antibiotic Use (AU) and Resistance (R) module [1] [Scheetz, et al. 
ICHE, accepted article in-press] and the previous standard Days of Therapy (DOT) method perform well 
[14] for creating internal trend benchmarks.  One challenge then, is determining thresholds of 
antimicrobial use that should be considered abnormal and used to trigger further evaluation. This study 
will identify qualitative vancomycin never events based on quantitative thresholds.  Our group (including 
collaborators) is comprised of national leaders in antimicrobial stewardship (see PI and Co-I CVs) that 
are poised and eager to conduct these studies.
RATIONALE.  While antimicrobial consumption trends are part of routine tracking and reporting for 
antimicrobial stewardship programs, consumption metrics like days of therapy are not well understood 
outside of ASPs themselves. To move away from relying on quantitative factors like drug consumption 
and cost, there is a considerable need for measures that reflect patient safety and outcome, as these 
considerations are more likely to resonate with clinicians and health system leaders. Vancomycin is 
chosen as a prototypical antimicrobial that is widely and indiscriminately used and has been closely 
associated with a common adverse effect (i.e. nephrotoxicity).  As vancomycin is one of the most 
frequently prescribed antibiotics in US hospitals [13, 15], results of this research will be widely 
applicable immediately.  Further, the method can be transferred to other antibiotics, allowing individual 
hospitals to tailor antimicrobial stewardship efforts.  
SIGNIFICANCE. While antimicrobial use tracking and reporting is widely recommended, this data is 
inconsistently applied in real time to facilitate measurable improvements in antimicrobial use.  One 
considerable barrier to this is the lack of a consistent approach for identifying and reporting trends that 
represent inappropriate or unsafe antimicrobial use.  The proposed work will provide a framework to 
identify vancomycin never events using only vancomycin consumption data and ultimately provide 
pragmatic targets for antimicrobial stewardship intervention. After creating antibiotic use thresholds 
that predict antibiotic never events, the next logical step will be to implement these real-time threshold 
decision support tools in a prospective quality improvement study.
PRELIMINARY DATA. Our group has demonstrated that quantifying antibiotic consumption via the AUR 
methodology performs well compared to the old standard, DOT methodology [14].  That is, there is a 
high level of correlation between AUR and DOT estimates of antibiotic use within an individual hospital.  
Hence, either metric can be used for creating internal trend benchmarks at the hospital level.  We have 
also described methods for individual hospitals to track antibiotic use and predict anomalies of use.  
Normal use is comprised of the hospital’s usual consumption of an antibiotic for appropriate indications 
plus some small random amount of “inappropriate” use.  However, periods exist when use rises beyond 
normal variation (i.e. presumed inappropriate use), and we have defined these periods as antibiotic 
outbreaks [1].  The definition is similar to The World Health Organization definition of a disease 
outbreak: “the occurrence of cases of disease in excess of what would normally be expected in a defined 
community, geographical area or season” [16]. The proposed study will define the environment/triggers 
in which vancomycin never events occur and establish thresholds of vancomycin outbreaks that predict 
time periods with frequent ‘never events’.    
Our method for describing anomalies of antibiotic use (i.e. antibiotic outbreaks) follows [1] [Scheetz et 
al., ICHE, accepted in press].  In brief, linear regressive strategies can be automated based on user 
parameters.  DOTs can be trended according to time (in months) as the dependent variable (Figure 1). 
Typical linear regressive strategies can define a mean regression line and a confidence interval (most 
typically 95%) for the mean trend.  However, it is also possible to define prediction intervals.  Prediction 
intervals are highly useful as they determine the likelihood of the result at any given month, instead of 
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 4 of 10
HRP-593 / v011017
Figure 1.  Time trended antibiotic use.
Figure 2.  Standardized residuals, visualizing 
prediction intervals
the trend.  That is, a 75% prediction interval will 
define the likelihood that the number of DOTs in 
any single month (e.g. December 2016) will fall 
within the range.  Since stewardship programs are 
more interested in individual events (e.g. is the 
amount of vancomycin being used in December of 
2016 beyond expected?), the prediction interval 
has a natural interpretation and may be of great 
value to programs seeking to address antibiotic 
outbreaks. We have suggested that that the 
prediction interval has more meaning and 
translative capacity than the more commonly 
reported 95% confidence interval for the mean 
[1].  These concepts can be visually appreciated in 
Figure 1.  The confidence interval for the mean 
predicts where the mean prediction would fall 
with 95% confidence.  The purple dashed circles represent potential outbreaks and green double lined 
circles are potential under-utilizations.  Residuals (i.e. difference between observed and predicted) can 
be plotted and those that are outside of the 75% prediction interval of interest can be visually and 
mathematically identified (Figure 2).  It is important to note that the optimal prediction interval for 
identifying specific events (such as thresholds for antibiotic outbreaks that predict never events) has not 
yet been established.  Our proposed research will do this. 
Our strategy [1] has received much attention.  It has been presented 1) on a CDC / AU Option partner 
quarterly call, 2) at the American Society of Health System Pharmacists meeting with over 900 
presentation attendees, and 3) the abstract/manuscript has been downloaded over 5000 times since the 
electronic release in May 2016. 
However, identifying anomalies in 
antibiotic use is just the first step to 
predicting antibiotic inappropriateness 
and antibiotic never events.   The 
ultimate utility of these methods will 
be realized when strategies based only 
on antibiotic use are linked to the more 
concerning antibiotic never events.  
This is important since identifying the 
environment of overuse that gives rise 
to never events will facilitate root-
cause analyses at individual 
institutions.  These strategies will lay 
the groundwork for data-driven 
solutions to mitigate inappropriate 
antibiotic prescribing.
INCLUSION AND EXCLUSION CRITERIA:
The retrospective study population will be based on all inpatient intravenous vancomycin used during 
the proposed 36-month study period.  Vancomycin ordered for any other route of administration or for 
outpatient use will be excluded.  We will not collect variables that identify:  

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 5 of 10
HRP-593 / v011017
-
Adults unable to consent
-
Individuals who are not yet adults (infants, children, teenagers)
-
Pregnant women
-
Prisoners
Age Range: 
Adults 18 years of age or older and less than 90 years of age.
Indicate if this is a retrospective and/or prospective chart review
__X__   Retrospective Chart Review (Retrospective means the data is already in 
existence when the project is submitted to the IRB for initial review.
__NA___   Prospective Chart Review (Prospective means the data is not in existence 
when the project is submitted to the IRB for initial review)
STUDY-WIDE NUMBER OF PARTICIPANTS:
2000
STUDY-WIDE RECRUITMENT METHODS:
Subjects will not be recruited for this study as it is retrospective.
MULTI-SITE RESEARCH:
This study will be conducted at Northwestern Memorial Hospital, Chicago, IL and Midwestern University, 
Downers Grove, IL (PI: Marc Scheetz); Wayne State University and Henry Ford Hospital, Detroit, MI (Co-I: 
Susan Davis); University of Michigan, Ann Arbor, MI (Co-I:  Keith Kaye).  Study coordination will occur at 
Midwestern University under the direction of Dr. Scheetz.  IRB approval from all sites (coordinated by 
NU) is required. will be required individually from all sites.  MWU will serve as Lead site.  NU will be a 
performance site, where data will be collected.
All sites will have the most current version of the protocol.  IRB is required from all sites before data may 
be sent to Dr. Scheetz.  A waiver of consent and Waiver of HIPAA Authorization are required from each 
site.  Individual IRB approval will verify that:
­
All required approvals have been obtained at each site (including approval by the site’s IRB of 
record).
­
All engaged participating sites will safeguard data as required by local information security 
policies.
­
All local site investigators conduct the study appropriately.
­
All non-compliance with the study protocol or applicable requirements will be reported in 
accordance with local policy.
The study will be discussed on semi-annual conference calls and all problems will be reported to Dr. 
Scheetz via email (within 48 hours of actionable event).  
STUDY TIMELINES:
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 6 of 10
HRP-593 / v011017
This is a retrospective study that will analyze date from 1/1/14-10/1/17.  All events have already 
occurred.  It is estimated that the study will take approximately 18 months to complete (i.e. official 
protocol development, IRB coordination for all sites through a central IRB (i.e. MWU), data collection, 
data review/cleaning and data analysis). 
It is anticipated that it will take 3 months to draft and revise a manuscript, including incorporation of 
input from all study authors
STUDY ENDPOINTS:
1.
Descriptive level antimicrobial use (containing no PHI)
2.
Patient level demographics (intermediary, not study endpoint)
a.
Age (if between 18-89, >90 will be classified categorically only)
b.
Gender
c.
Infecting organism
d.
Infecting organism susceptibility
e.
Antibiotic indication
f.
Duration of therapy
g.
Duration of time between identification of pathogen/susceptibility and vancomycin 
discontinuation
h.
Hospital location
3.
Provider demographics
a.
Ordering provider classification (i.e. medical resident, PGY1, 2, 3; medical 
b.
fellow; attending physician; other (e.g. Nurse practitioner).
c.
Number of years since terminal clinical training.
d.
Specialty (e.g. critical care, infectious diseases, internal medicine).
e.
Average monthly patient census during prescription
4.
Patient level outcomes data including:
a.
Length of stay
b.
Duration of therapy
c.
Discharge disposition
d.
30-day readmission
STUDY METHODS
A retrospective, non-interventional study will be completed.
SOURCE (LOCATION) OF RECORDS TO BE REVIEWED:
See Section (MULTISITE RESEARCH)
DESCRIBE HOW THE CHARTS TO BE REVIEWED WILL BE IDENTIFIED:
Charts will be identified from electronic data sources (e.g. EDW, Powerchart, Theradoc) on the basis of 
having received vancomycin while inpatient at NMH
DESCRIBE WHO WILL IDENTIFY CHARTS TO BE REVIEWED:  

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 7 of 10
HRP-593 / v011017
Charts to be reviewed will be identified by the principal investigator, Dr. Marc Scheetz.
CONFIDENTIALITY OF DATA  
DESCRIBE HOW DATA (BOTH PAPER AND ELECTRONIC) WILL BE STORED TO SAFE-GUARD 
CONFIDENTIALITY (E.G. IN A LOCKED CABINET, PASSWORD PROTECTED COMPUTER):  
Redcap will be used for data confidentiality.  De-identification will be maintained through their site.  The 
Redcap data collection tool is being built based on the documents submitted to the IRB. Patient-specific 
data will be kept behind NM firewalls.
SPECIFY WHO WILL HAVE ACCESS TO HARVESTED PATIENT DATA:  
Patient name and MRN will be entered into RedCap.  The coded identifier will only ever be opened 
behind NM firewalls.  No patient identifying information will be co-located on research databases for 
analysis.
CLARIFY LONG HARVESTED PATIENT DATA WILL BE STORED AND HOW IT WILL BE DESTROYED WHEN 
NO LONGER NEEDED:  
Data will be stored in RedCap. It is the investigator’s responsibility to retain study essential documents 
for at least 3 years after the study is completed. After that, the records can be shipped to off-site 
storage or destroyed at the investigator’s discretion.
CONSENT:
A waiver of consent is sought.
RISKS AND BENEFITS: (MODIFY AS NEEDED)
RISKS: 
There is minimal, but measurable, risk of a confidentiality breach in chart review research
BENEFITS: 
The subject’s whose charts are reviewed are not likely to receive any benefit from the proposed 
research; however, society and investigators will benefit from the knowledge gained.  
STATISTICAL CONSIDERATIONS 
PROPOSED SAMPLE SIZE (NUMBER OF RECORDS TO BE REVIEWED): 
We expect to enroll about 2000 subjects between the 3 sites.  Locally, we expect approximately 1000 
subjects
PROPOSED TIME PERIOD TO BE EVALUATED: 
36 months (minimum over the requested time period).
SPECIFY HOW DATA WILL BE ANALYZED AND BY WHOM: 
The PI, Dr. Scheetz, will be responsible for the statistical analysis in Objective 1 and 2a. Objective 2b will 
be analyzed by Dr. Paul Yarnold.  Dr. Yarnold will receive a completely de-identified dataset.
APPENDICES: THE FOLLOWING APPENDICES MUST BE ATTACHED TO THE PROTOCOL
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 8 of 10
HRP-593 / v011017
APPENDIX A: DATA COLLECTION FORM (THIS FORM SHOULD LIST THE DATA ELEMENTS THAT WILL BE 
COLLECTED FROM THE MEDICAL RECORD.  IT SHOULD NOT CONTAIN ANY DIRECT OR INDIRECT 
IDENTIFIERS EXCEPT FOR A UNIQUE SUBJECT CODE.)
APPENDIX B: CODED IDENTIFIER LIST (THIS FORM SHOULD SERVE AS THE LINK BETWEEN THE UNIQUE 
SUBJECT CODE AND ANY IDENTIFIERS YOU WILL NEED TO CONDUCT THIS CHART REVIEW STUDY [E.G., 
NAME , MEDICAL RECORD NUMBER, DATE OF BIRTH, ADDRESS, TELEPHONE NUMBER, SOCIAL 
SECURITY NUMBER])
PROCEDURES INVOLVED:
NA
DATA AND SPECIMEN BANKING:
Data banking in RedCap as described above.
DATA AND SPECIMEN MANAGEMENT:
Data management in RedCap as described above.
PROVISIONS TO MONITOR THE DATA TO ENSURE THE SAFETY OF PARTICIPANTS:
NA
WITHDRAWAL OF PARTICIPANTS:
NA
RISKS TO PARTICIPANTS:
Risks to the subjects are loss of data confidentiality.  To minimize this risk, patient specific data will be 
kept secure in RedCap.  De-identified data will be utilized for analyses.
POTENTIAL BENEFITS TO PARTICIPANTS:
There is no direct benefit to study participants.
VULNERABLE POPULATIONS:
NA
COMMUNITY-BASED PARTICIPATORY RESEARCH:
NA
SHARING OF RESULTS WITH PARTICIPANTS:
No results will be shared with study participants.  Notifying them could increase the risk of 
confidentiality breach and there is no common medium to contact them as all events occurred in the 
past.
SETTING:
This study will be conducted at Northwestern Memorial Hospital, Chicago, IL and Midwestern University, 
Downers Grove, IL (PI: Marc Scheetz); Wayne State University and Henry Ford Hospital, Detroit, MI (Co-I: 
Susan Davis); University of Michigan, Ann Arbor, MI (Co-I:  Keith Kaye).  Study coordination will occur at 
Midwestern University under the direction of Dr. Scheetz.  IRB approval from all sites (coordinated by 
NU) is required. 
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 9 of 10
HRP-593 / v011017
RESOURCES AVAILABLE:
Dr. Scheetz is an internationally recognized researcher and holds an appointment at Northwestern 
Medicine.  Hi PI status has been approved (see attachment), and he has adequate time for study 
oversight.  This study is similar to those regularly conducted by Dr. Scheetz and his team at NMH.
https://www.ncbi.nlm.nih.gov/sites/myncbi/1Bw-
VFHOUgI53/bibliography/46519885/public/?sort=date&direction=descending 
https://scholar.google.com/citations?user=4-dax_DquKAC&hl=en
All study personnel will have access to the protocol.  It will be reviewed with Dr. Scheetz.  Dr. Scheetz is 
available 24/7 for questions related to the protocol (from any study personnel).  He can be called at 
312.545.7943.  
PRIOR APPROVALS:
NA
RECRUITMENT METHODS:
Subjects are not recruited.
NUMBER OF LOCAL PARTICIPANTS:
We expect to enroll ~2000 subjects between the 3 sites.  Locally, we expect ~1000.
CONFIDENTIALITY:
Redcap will be used for data confidentiality.  De-identification will be maintained at each local study site.   
MRNs and subject identifying numbers will be removed completely for analysis documents.  All dates 
will be scrambled in RedCap when downloaded for analysis to maintain confidentiality.
PROVISIONS TO PROTECT THE PRIVACY INTERESTS OF PARTICIPANTS:
PHI will be accessed for retrospective events.  Privacy will be protected as described under heading 
‘Confidentiality’.
COMPENSATION FOR RESEARCH-RELATED INJURY:
No compensation is being offered.
ECONOMIC BURDEN TO PARTICIPANTS:
NA
CONSENT PROCESS:
NA
Waiver or Alteration of Consent Process:
A waiver of consent is requested.
DRUGS OR DEVICES:
NA
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.

#STU00205629
Version #: 2
Version Date:  10/06/17
Page 10 of 10
HRP-593 / v011017
REFERENCES
[1] M.H. Scheetz, P.E. Crew, C. Miglis, E.M. Gilbert, S.H. Sutton, J.N. O'Donnell, M. Postelnick, T. 
Zembower, N.J. Rhodes, Investigating the Extremes of Antibiotic Use with an Epidemiologic Framework, 
Antimicrob Agents Chemother, 60 (2016) 3265-3269.
[2] https://psnet.ahrq.gov/primers/primer/3/never-events.  accessed 03/16/17, DOI.
[3] P.R. Yarnold, R.C. Soltysik, Refining two-group multivariable classification models using univariate 
optimal discriminant analysis., Decision Sciences, 22 (1991) 1158-1164.
[4] P.R. Yarnold, R.C. Soltysik, Maximizing Predictive Accuracy, ODA Books2016.
[5] N.J. Rhodes, J.N. O'Donnell, B.D. Lizza, M.M. McLaughlin, J.S. Esterly, M.H. Scheetz, Tree-Based 
Models for Predicting Mortality in Gram-Negative Bacteremia: Avoid Putting the CART before the Horse, 
Antimicrob Agents Chemother, 60 (2016) 838-844.
[6] A. Cusini, S.K. Rampini, V. Bansal, B. Ledergerber, S.P. Kuster, C. Ruef, R. Weber, Different patterns of 
inappropriate antimicrobial use in surgical and medical units at a tertiary care hospital in Switzerland: a 
prevalence survey, PLoS One, 5 (2010) e14011.
[7] R.C. Glowacki, D.N. Schwartz, G.S. Itokazu, M.F. Wisniewski, P. Kieszkowski, R.A. Weinstein, Antibiotic 
combinations with redundant antimicrobial spectra: clinical epidemiology and pilot intervention of 
computer-assisted surveillance, Clin Infect Dis, 37 (2003) 59-64.
[8] M.T. Hecker, D.C. Aron, N.P. Patel, M.K. Lehmann, C.J. Donskey, Unnecessary use of antimicrobials in 
hospitalized patients: current patterns of misuse with an emphasis on the antianaerobic spectrum of 
activity, Arch Intern Med, 163 (2003) 972-978.
[9] K.E. Fleming-Dutra, A.L. Hersh, D.J. Shapiro, M. Bartoces, E.A. Enns, T.M. File, Jr., J.A. Finkelstein, J.S. 
Gerber, D.Y. Hyun, J.A. Linder, R. Lynfield, D.J. Margolis, L.S. May, D. Merenstein, J.P. Metlay, J.G. 
Newland, J.F. Piccirillo, R.M. Roberts, G.V. Sanchez, K.J. Suda, A. Thomas, T.M. Woo, R.M. Zetts, L.A. 
Hicks, Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-
2011, JAMA, 315 (2016) 1864-1873.
[10] National Strategy for Combatting Antibiotic Resistant Bacteria.  
https://www.cdc.gov/drugresistance/pdf/carb_national_strategy.pdf.  Official White House 
communication.  2014, DOI.
[11] T. Kelesidis, N. Braykov, D.Z. Uslan, D.J. Morgan, S. Gandra, B. Johannsson, M.L. Schweizer, S.A. 
Weisenberg, H. Young, J. Cantey, E. Perencevich, E. Septimus, A. Srinivasan, R. Laxminarayan, Indications 
and Types of Antibiotic Agents Used in 6 Acute Care Hospitals, 2009-2010: A Pragmatic Retrospective 
Observational Study, Infect Control Hosp Epidemiol, 37 (2016) 70-79.
[12] S.S. Magill, J.R. Edwards, Z.G. Beldavs, G. Dumyati, S.J. Janelle, M.A. Kainer, R. Lynfield, J. Nadle, 
M.M. Neuhauser, S.M. Ray, K. Richards, R. Rodriguez, D.L. Thompson, S.K. Fridkin, I. Emerging Infections 
Program Healthcare-Associated, T. Antimicrobial Use Prevalence Survey, Prevalence of antimicrobial use 
in US acute care hospitals, May-September 2011, JAMA, 312 (2014) 1438-1446.
[13] J. Baggs, S.K. Fridkin, L.A. Pollack, A. Srinivasan, J.A. Jernigan, Estimating National Trends in Inpatient 
Antibiotic Use Among US Hospitals From 2006 to 2012, JAMA Intern Med, 176 (2016) 1639-1648.
[14] N.J. Rhodes, J.L. Wagner, E.M. Gilbert, P.E. Crew, S.L. Davis, M.H. Scheetz, Days of Therapy and 
Antimicrobial Days: Similarities and Differences Between Consumption Metrics, Infect Control Hosp 
Epidemiol, DOI 10.1017/ice.2016.109(2016) 1-3.
[15] T. Kelesidis, N. Braykov, D.Z. Uslan, D.J. Morgan, S. Gandra, B. Johannsson, M.L. Schweizer, S.A. 
Weisenberg, H. Young, J. Cantey, E. Perencevich, E. Septimus, A. Srinivasan, R. Laxminarayan, Indications 
and Types of Antibiotic Agents Used in 6 Acute Care Hospitals, 2009-2010: A Pragmatic Retrospective 
Observational Study, DOI.
[16] The World Health Organization.  Health Topics: Disease Outbreaks, 2015.
IRB #: STU00205629 Approved by NU IRB for use on or after 10/26/2017 through 10/25/2018.