Cluster Linkage Using Statistics to Trigger and Evaluate Response (CLUSTER) Trial for Outbreak Detection and Response
Phase: NA
Conditions: Cluster Detection
Sponsor: Harvard Pilgrim Health Care

 
A Trial of Automated Outbreak Detection to Reduce Hospital 
Pathogen Spread 
NCT#: 04053075 
6/5/2019 

  
 
CLUSTER (Cluster Linkage Using Statistics to Trigger and Evaluate Response) 
The CLUSTER Trial Protocol  
 
 
1.0   Background and Goals   
1.1 Outbreaks and Patient Safety in Hospitals 
Healthcare-associated infections (HAIs) are a leading cause of preventable morbidity and mortality.1 
Some of these infections occur as part of outbreaks resulting from transmission of microorganisms from 
patient-to-patient via healthcare personnel, contaminated medical equipment, or supplies.2 For patient safety, 
The Joint Commission requires hospital infection prevention programs to include outbreak detection and 
response protocols.3 Nevertheless, there are no standardized, validated outbreak detection and response 
strategies.4  
1.2 
The Problem of Detecting Hospital-Associated Outbreaks 
 
Despite the critical importance of identifying hospital-associated outbreaks as early as possible in order 
to limit their spread,5-7 there are currently no standardized approaches to outbreak detection.4 Ideally, outbreak 
detection methods not only detect HAIs, but also hospital acquisition of colonizing pathogens even before 
infection occurs. For this reason, clinical microbiology laboratory results are most commonly used for outbreak 
detection, since these can reflect both colonizing and infecting pathogens regardless of patient symptoms.  
 
In this proposal, we refer to the co-occurrence of similar organisms as “clusters”; some of these 
represent true outbreaks. Many infection prevention programs rely on infection preventionists noticing unusual 
clustering of organisms on manual review of microbiology results. Other programs employ a fixed rule 
method,8,9 commonly using ≥3 isolates of a hospital-associated organism within 2 weeks detected on the same 
unit to indicate a cluster. Although a wide variety of pathogens can spread in hospitals,10-12 resource limitations 
typically limit outbreak detection to a small set of multidrug-resistant organisms (MDROs) such as methicillin-
resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococcus (VRE), and resistant gram-
negative bacteria such as extended-spectrum beta-lactamase producers (ESBLs). Another major limitation of 
fixed rules is their inability to account for the background prevalence of specific organisms, leading to both 
over- and under-identification of true outbreaks.   
 
The lack of national investments in this important problem is reflected by the paucity of validated 
outbreak detection tools and high quality studies to assess existing tools or rules. Ideally, an outbreak 
detection tool would provide timely alerts, high sensitivity and specificity, ease of operating in real time, and 
cluster containment.4,8,13,14 To address these needs, our research group has developed and refined a tool to 
provide standardized, automated, statistically-based and reliable outbreak detection. 
1.3 
WHONET-SaTScan as a Solution for Detecting and Tracking Hospital-Associated Clusters 
 
The use of space-time and higher dimensional scan statistics is a promising method to improve 
hospital-associated cluster detection.15-17 Our team has developed a statistically-based cluster detection tool by 
integrating two publicly available software programs created by members of our investigative team and prior 
faculty collaborators for use in public health epidemiology and clinical microbiology analytics.8,18-22 WHONET is 
an analytics software program for management and descriptive analysis of microbiology data, including 
complex bacterial antibiotic resistance patterns.23 It has been used for over 25 years in 90+ countries for 
developing antibiograms and other reports for clinical microbiology laboratories. SaTScan is a disease 
surveillance software containing various spatial and space-time scan statistics which has been widely used to 
detect and evaluate geographical disease clusters .15,16,24-27 The integrated tool (WHONET-SaTScan) has been 
successfully used to detect clusters  across South America and other large regions.8,18,28,29 It not only detects 
clusters when pre-specified parameters are reached, but also determines when an cluster has resolved based 
upon cessation of statistically relevant signals. 
We retrospectively applied WHONET-SaTScan to five years of daily microbiology laboratory data from 
Brigham and Women’s Hospital (BWH), a 793-bed academic medical center.8 WHONET-SaTScan identified 
an average of 12 clusters annually. All were deemed to be of clinical interest by hospital epidemiologist 
reviewers and one-third would have warranted investigation or active intervention had the alerts occurred in 
real time. Half of the clusters involved gram-negative bacteria that were not routinely tracked by the infection 
control program. WHONET-SaTScan identified all previously known clusters that had undergone confirmation 
by genetic typing methods. In each of those clusters, WHONET-SaTScan identified the cluster several days 
prior to routine surveillance methods.  

  
Furthermore, when comparing clusters of MRSA determined by routine hospital infection prevention 
surveillance (using the 3-in-2-week fixed rule) versus WHONET-SaTScan, we found that many more MRSA 
clusters were triggered by the fixed rule criteria (n=73) than by the WHONET-SaTScan statistical thresholds (n 
= 7) over a five-year period.8 Similar findings were noted for other pathogens such as VRE. This suggests that 
many fixed rule alerts may represent random variation rather than true clusters when the baseline prevalence 
of these pathogens is considered. This has implications for hospital infection prevention resources since 
programs using non-statistically-based, fixed rule triggers may be allocating limited resources to investigation 
and containment of false clusters while the majority of statistically unusual events are missed. 
We recently expanded this single center retrospective study to 43 geographically diverse hospitals, 
including both academic medical centers and community hospitals.30 The cluster detection tool detected an 
average of 1 cluster/100 beds/year. Based on survey responses, infection preventionists would have wanted to 
be notified about 81% of the clusters. Half of the clusters increased in size after detection, which would have 
allowed cluster response interventions to potentially curtail the clusters. All infection preventionists indicated 
that an automated tool would improve cluster detection at their hospital. 
1.4 The Need for a Trial of Cluster Detection Strategies 
 
Until now, there has been no robust alternative to fixed rule cluster detection methods despite the many 
limitations described above. Current vendor software programs either do not offer cluster detection, fail to 
separate out community-associated events from healthcare-associated events, or use undisclosed proprietary 
software. In addition, hospitals do not have standard guidance for how to respond to clusters.4 
 
We aim to validate a publicly available system that has already shown promise in over 40 hospitals and 
to develop a standardized protocol for cluster response.8,30 In this proposal, we will first conduct a prospective 
pilot to develop and test the standardized cluster response protocol. Then, we will conduct a cluster 
randomized trial to compare the impact of the statistical cluster detection tool to the impact of routine cluster 
detection methods when using the standardized response protocol. 
1.5 Defining Valid Cluster Detection 
  
The confirmation of a cluster is usually based upon both epidemiologic investigation that finds a 
common source and genomic testing that confirms transmission of a clonal strain. Unfortunately, formal 
investigation and genetic confirmation is uncommonly pursued by infection prevention programs due to time 
and resource constraints and insufficient training to appropriately conduct either evaluation. For these reasons, 
standard operating procedure usually dictates responses based solely on total numbers of apparently related 
isolates.8 For this proposal, we will use a pragmatic outcomes-based approach to define the validity of our 
cluster detection tool, namely that responding to the alerts significantly reduces cluster size and duration.  
1.6   Evaluate a Statistically-based Cluster Detection Tool to Identify Clusters 
This study will be a novel validation of a statistically-based cluster detection tool. Although the tool has 
been assessed in retrospective evaluations, this will be the first prospective, cluster-randomized trial to assess 
impact and validity. The WHONET-SaTScan Cluster Detection Tool merges two Harvard-developed, publicly 
available software products which have been re-designed to assess hospital clusters. WHONET,23 originally 
developed for management and descriptive analysis of microbiology data, includes a data conversion utility, 
BacLink, which standardizes and imports data from nearly all microbiology systems into the WHONET format. 
SaTScan software contains spatial and space-time scan statistics and is widely used to detect and evaluate 
geographical disease clusters.15,16,24-27 The tool uses space-time permutation and Poisson software with a 
novel function that accommodates multiple additional dimensions, including antibiograms, units, services, and 
sets of units or services that share personnel. Thus another innovation is the ability to pre-specify groups of 
services or units that are part of the a priori search for clusters (e.g. cardiac surgery and cardiology).  
1.7   Development of a Standardized Cluster Response Protocol  
The approaches that hospital infection prevention programs use to respond to possible outbreaks vary 
widely.4 Responses include, but are not limited to, watchful waiting, alerting unit personnel, increasing cleaning 
frequency, increased attention to hand hygiene compliance, implementation of unit-wide contact precautions, 
and screening of asymptomatic patients and/or health care workers to identify carriers. Decisions about when 
to intervene and which approaches to take vary widely between hospitals and even within hospitals over time. 
We have assembled a working group of healthcare epidemiology subject matter experts from geographically 
diverse academic and community hospitals to develop a standardized response protocol to cluster alerts.  
 
2.0  Trial Study Design & Population  
 
The CLUSTER trial will be the first randomized clinical trial to assess the impact of a hospital-based 
cluster detection system compared to routine cluster detection approaches when combined with a 

  
standardized response protocol. If successful, this trial could provide the first evidence-based protocol for 
efficient cluster detection and response in U.S. hospitals. 
2.1 Hypothesis 
Using the refined cluster response protocol and implementation techniques from the pilot study in Aim 
1, we hypothesize that the use of WHONET-SaTScan for cluster detection coupled with a robust response 
protocol will enable rapid containment of hospital clusters as measured by a reduction in cluster size and 
duration compared to routine infection prevention cluster detection methods combined with the same robust 
response protocol.  
2.2 Health System Partner and Decision Support Software Systems 
Hospital Corporation of America (HCA Healthcare) is the largest private inpatient healthcare 
organization in the U.S. with over 160 primarily community-based hospitals in 20 states. HCA provides 5% of 
all U.S. inpatient care. HCA’s national scope and centralized infrastructure make it an ideal partner for 
healthcare-associated infection research. Its largely community-based hospitals are more representative of 
U.S. hospitals than academic medical centers and its centralized electronic data warehouse enables collection 
of standardized data from all hospitals throughout its health system.  
HCA maintains a centralized corporate data warehouse including hospital and unit census data, patient 
demographic data, nursing documentation, admission and discharge information, diagnosis and procedure 
codes, and laboratory and microbiology data derived from the clinical information system (MEDITECH). HCA 
uses TheraDoc (Premier Inc.), clinical decision support software for infection surveillance, tracking, and 
reporting.  
2.3 Study Design and Population  
The CLUSTER Trial (Cluster Linkage Using Statistics to Trigger and Evaluate Response) is an 82-
hospital cluster-randomized trial to evaluate the impact of the WHONET-SaTScan cluster detection system and 
response protocol on reducing the size and duration of signaled clusters. The study population will include all 
patients hospitalized at 82 HCA-affiliated hospitals between February 2017 and January 2022 (Table 1).  
 
  Table 1. CLUSTER Trial - Design and Outcomes 
Study Design 
Cluster-randomized clinical trial 
Unit of Randomization 
Hospitals* 
Study Population 
All patients in 82 participating HCA hospitals  
Exclusions 
HCA hospitals not in U.S. 
Group Assignments 
     Arm 1 (N=41) 
     Arm 2 (N=41) 
Routine cluster detection and response protocol for infection prevention programs 
WHONET-SaTScan cluster alerts, routine cluster detection and response protocol for infection 
prevention programs 
Study Period 
24-Month Full Baseline Period (Retrospective Data): February 2017 - January 2019 (Note: we 
will utilize a 24-month lookback period preceding baseline period for the WHONET-SaTScan 
analysis) 
30-Month Intervention Period: July 2019-January 2022 
Primary Outcome 
Size of cluster (number of cases added to a cluster after the first signal) 
Secondary Outcome 
Duration of cluster (number of days from the first signal to the last isolate collection date in a 
cluster) 
* patient-level data will be analyzed by hospital 
2.4 Installation of WHONET-SaTScan 
The HCA corporate-wide use of the TheraDoc infection prevention surveillance and decision support 
system will enhance the implementation of WHONET-SaTScan alerts. Dr. Stelling will work with Premier Inc. to 
facilitate data feeds from TheraDoc that will integrate into WHONET-SaTScan. Output will flow back to a 
TheraDoc tracker board. Since TheraDoc requires standardized laboratory feeds, we do not anticipate any 
unresolved microbiology terminology. We will work with local infection preventionists to map units that share in 
patient care in order to evaluate clusters that occur across related units. Clusters will be presented to infection 
preventionists within the daily work flow of TheraDoc, including cluster details, line lists, and option to annotate. 
Infection preventionists will follow the progression of the cluster using the TheraDoc “tracker board”.  
2.5 Recruitment 
HCA will use its leadership and communications systems to inform hospital decision makers (chief  
executive officers, chief medical officers, chief nursing officers, infection prevention directors) about the trial 
and to advocate for participation. Solicitation for hospital participation will be extended to all 165 HCA 
hospitals. We will also provide webinars through HCA’s webhosting system to introduce the trial and the 
process for participation. Participation will be confirmed by an electronic eligibility survey and a signed letter of 
participation from each hospital’s senior leadership.  

  
2.6 Randomization 
Randomization will be performed at the hospital level in order to detect clusters at the hospital and unit 
levels.63 Randomizing entire hospitals will ensure uniform cluster detection and response practices within each 
hospital. During randomization, we will evaluate the need to account for hospital case mix using general 
hospital data (e.g., average daily census, median length of stay, average elixhauser score) and baseline 
outcome measures (e.g., number of preventable isolates or preventable days), among others, to improve 
balance across the arms. We will use a novel approach we developed for a prior cluster randomized trial.64 In 
this approach, we will calculate the weighted Mahalanobis distance between pairs of facilities across all key 
variables for weights representing importance of achieving balance and choose the pairings with the minimum 
average within-pair distance. We will then assign one member of each pair to each arm.  
2.7 Implementation and Compliance 
WHONET-SaTScan will run daily in the intervention arm. Alerts will display in TheraDoc with weekday 
monitoring by infection preventionists. Alerts will also be accessible to investigators and research coordinators 
who will follow up with compliance assessments. In the control arm, WHONET-SaTScan will be run daily in the 
background, on all microbiology and census data in the baseline and intervention period to identify clusters that 
would have been detected had WHONET-SaTScan been running in real time.  The output will be saved to a 
secure folder to which study staff will have no access. On a monthly basis, a de-identified version of this output 
will be uploaded to the HCA Research Server for a quality assurance check, to confirm that the program is 
running appropriately.  
 
2.8 Data Collection and Outcomes 
Clusters will be identified based upon WHONET-SaTScan analyses occurring in real time, in both 
intervention hospitals and in routine surveillance hospitals. The primary outcome is cluster size: the number of 
cluster cases identified after the initial cluster signal from the WHONET-SaTScan software. The secondary 
outcome is the cluster duration: the number of days from the initial cluster signal through the last cluster case. 
Additional data will include the number of clusters, cluster type (unit or meta-unit with antibiotic profile when 
available), pathogen type (gram-positive, gram-negative, fungal) and pathogen species.  
 
 
2.9 Standardized Cluster Response Protocol for Real Time Implementation  
 
We will leverage the infrastructure of infection prevention programs at participating hospitals to 
implement the response protocol when a cluster alert is triggered by WHONET-SaTScan in intervention 
hospitals or when a cluster is detected by routine methods in both control and intervention hospitals. Both 
intervention and control hospitals will use the same response protocol and receive the same support from 
study investigators and HCA corporate leaders. The protocol will be predicated on alerting unit personnel to the 
cluster, assessing and improving compliance with hand hygiene and appropriate precautions, and enhanced 
environmental cleaning until the cluster ceases (Table 2).   
 
 
Table 2. Elements of the Standardized Cluster Response Protocol 
Elements 
Description 
Alert unit personnel 
Standardized email and call to unit nursing and/or medical directors 
Assess compliance with prevention practices 
Audits 
Hand hygiene  
Appropriately performed  
Contact Precautions 
Check appropriate use, donning and doffing 
Adequacy of environmental cleaning   
Mark high touch room surfaces and verify next day removal 
Interventions  
Based on deficiencies noted during audits (see Figure 1) 
 
Cluster alerts will trigger an activation checklist for designated responders and rapidly assess 
adherence to basic infection prevention practices to identify performance gaps. The on-site responders 
(infection preventionists or unit director) will perform audits of the rooms of patients belonging to the cluster to 
assess compliance with hand hygiene and contact precautions, and assess the thoroughness of environmental 
cleaning by applying an ultraviolet (UV) or ATP bioluminescence marker to high touch room surfaces and 
assessing the adequacy of removal after daily cleaning.31-35 Deficiencies noted during these initial 
assessments will trigger selection and implementation of pre-defined interventions (Tier 1), including improved 
hand hygiene, improved or enhanced contact precautions (if appropriate), and improved or enhanced 
environmental cleaning (see Figure 1). Project coordinators at HPHCI will support the on-site responders by 
encouraging use of the cluster response protocol guidance and forms, and tracking assessment results and 
interventions. If the cluster progresses, audits will be reinstituted for all patients in the cluster and the response 

  
will be intensified to consider additional Tier 2 through 5 responses, such as pre-emptive precautions, isolation 
of patients involved in the cluster, screening of asymptomatic patients and healthcare workers, and/or closure 
of the unit for deep cleaning (Figure 1).  
 
 
Figure 1. CLUSTER Pilot Study Cluster Response Protocol 
 
 
3.0 Statistical Analysis 
3.1 Background 
The CLUSTER (Cluster Linkage Using Statistics to Trigger and Evaluate Response) Trial is a cluster-
randomized trial in 82 HCA Healthcare (HCA) affiliated hospitals designed to assess a statistically‐based 
automated method for detecting clusters of hospital acquired infections, compared to routine assessment. The 
CLUSTER Trial will assess the impact of a space-time statistical software tool, WHONET-SaTScan, on guiding 
response and containment of hospital-based clusters.  
 

  
Hospitals have been randomized to: 
• 
Arm 1–Routine cluster detection coupled with an evidence‐based cluster response protocol 
• 
Arm 2–Enhanced cluster detection using an automated cluster detection tool and routine cluster 
detection, coupled with an evidence‐based cluster response protocol 
Trial Outcomes 
Clusters will be identified based upon WHONET-SaTScan analyses occurring in real time, in both intervention 
hospitals and in routine surveillance hospitals. The primary outcome is cluster size: the number of cluster 
cases identified after the initial cluster signal from the WHONET-SaTScan software. The secondary outcome is 
the cluster duration: the number of days from the initial cluster signal through the last cluster case. WHONET-
SaTScan identifies the beginning of a cluster using a p-value threshold, and we define the end of a cluster as 
the date of the last case meeting the p-value threshold, by the close of the 180 day window, or end of the trial, 
whichever date is earlier.   
 
Trial outcomes are found in the below table. 
 
Outcome 
Metric 
Primary Trial Outcome 
Cluster size 
Number of cases identified after the initial 
cluster signal through the last cluster case 
Secondary Trial Outcome 
Cluster duration 
Number of days from the initial cluster signal 
through the last cluster case 
 
3.2 Analysis 
Our primary analysis will be an unadjusted generalized linear mixed model that assumes a negative 
binomial distribution and accounts for groupings of clusters within each hospital. Model terms will include arm 
(intervention or routine surveillance group), trial period (baseline vs. intervention) and an interaction term 
between trial period and arm. The assessment of trial success in decreasing the size of clusters will be 
determined by the significance of the interaction term, which assesses whether the difference in cluster size 
between the baseline and intervention period differs significantly between the two arms. This model will be 
used for both the primary and the secondary outcome.   
We can write this model symbolically as:  
 
where  is a hospital,  is a signal within the hospital, 
 and 
 are indicator variables and are = 0 for 
signals in a hospital in the routine surveillance arm or baseline period and 1 if in the intervention arm or period. 
The 
 and 
 are random effects, and account for the clustering within a given hospital, equivalent to usual 
length or size of cluster at each hospital.  These may differ in the baseline and intervention periods. The 
ultimate effect of the intervention is assessed through 
: as parameterized, if it is negative and has p-value < 
.05 (or 95% CI excluding 0) then the intervention reduces the size (primary outcome) or duration (secondary 
outcome) of clusters.  
 
Subsequent analyses will include as-treated and adjusted models. As treated models in this setting 
correspond to coding hospitals according to whether they actually implemented the WHONET-SaTScan 
system. All analyses will be performed using current versions of SAS (9.3, as of writing, SAS Institute, Cary 
NC) and/or R (3.6.1, as of writing).36 These analyses will not be included in formal multiple comparisons 
adjustment because these are non-independent assessments related to the as-randomized unadjusted 
analyses. Results will be reported only as estimated effects and confidence limits, in keeping with current 
recommendations.37 Types of clusters based on pathogen type will be provided as descriptive tables without 
including statistical analysis.   
3.3 Power Assessment 
In many settings, an analytic approach to power is possible given that the assumptions of the model 
(e.g., logistic regression) are met and a relatively simple solution exists. However, generating the expected 
values to plug in may be difficult. In addition, some settings are complex enough that closed-form solutions 
may be difficult to generate. Many cluster-randomized designs fall into this class. In cluster-randomized 

  
studies, it is also difficult to obtain reliable estimates of the additional parameters that are required, most 
notably the between-cluster variance or, equivalently, the intra-class correlation coefficient.  
We assessed power using a custom-built simulation/resampling approach. This allowed us to 
incorporate detailed information from data collected from the hospitals in the study. Briefly, to calculate power 
by simulation, we 1) generated data under the alternative hypothesis-the data we anticipated seeing; 2) fit the 
model we planned to fit in the actual study; and 3) recorded whether we rejected the null hypothesis of no 
detectable effect in the simulated data. We repeated this process many times, and the proportion times we 
rejected the null hypothesis was used as an estimate of the power under the alternative hypothesis which we 
used to simulate the data.   
Our simulation is based on the actual arm assignments for the trial. For the simulated baseline period in 
each arm, we use the observed clusters, rather than simulating new clusters; we assume that all clusters are 
as observed, with a probability of additional cases or days in each arm.  In the simulated intervention period, 
the routine surveillance arm has the observed number of cases from the observed data, while the intervention 
arm has the effect described below. 
 We assumed that the intervention would have no effect in 20% of the intervention hospitals. Using the 
clusters observed in the baseline period within each hospital, we assumed that we prevent 30-50% of the 
cases after an initial signal during the intervention period in the intervention hospitals, with the actual reduction 
chosen by a uniform distribution over the described range on a per-outbreak basis. We also assumed that we 
would shorten the clusters by 50-70% of the observed length after an initial signal in the intervention period in 
the intervention hospitals.  In addition, the extra days and cases added probabilistically to each cluster in the 
baseline period are not added in the intervention period. Thus, the simulation assumes a secular effect of 
smaller and shorter clusters.  
We then fit the model described above. For our primary analysis, we achieved a power of 100% (95% 
CI 99.6%,100%) for the main (size) outcome and power of 79% (95% CI 77%, 82%) for the secondary duration 
outcome. Other assumptions reflected in this estimate include an alpha error level of 0.05.  

  
 
4.0 Bibliography and References Cited 
 
1. 
Klevens RM, Edwards JR, Richards CL, Jr., Horan TC, Gaynes RP, Pollock DA, Cardo DM. Estimating 
health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160-6. 
PubMed PMID: 17357358. PubMed Central PMCID: PMC1820440.  
2. 
Archibald LK, Jarvis WR. Health care-associated infection outbreak investigations by the Centers for 
Disease Control and Prevention, 1946-2005. American journal of epidemiology 2011;174:S47-64. 
3. 
The Joint Commission. National Patient Safety Goals.  PubMed PMID: 22135394.  
http://www.jointcommission.org/standards_information/npsgs.aspx Accessed 11 January, 2016. 
4. 
Baker MA, Huang SS, Letourneau AR, Kaganov RE, Peeples JR, Drees M, Platt R, Yokoe DS. Lack of 
comprehensive outbreak detection in hospitals is the norm. Infect Control Hosp Epidemiol. In press. 
5. 
Epstein L, Hunter JC, Arwady MA, Tsai V, Stein L, Gribogiannis M, Frias M, Guh AY, Laufer AS, Black 
S, Pacilli M, Moulton-Meissner H, Rasheed JK, Avillan JJ, Kitchel B, Limbago BM, MacCannell D, 
Lonsway D, Noble-Wang J, Conway J, Conover C, Vernon M, Kallen AJ. New Delhi metallo-beta-
lactamase-producing carbapenem-resistant Escherichia coli associated with exposure to 
duodenoscopes. JAMA 2014;312:1447-55. PubMed PMID: 25291580.  
6. 
Palmore TN, Henderson DK. Managing transmission of carbapenem-resistant enterobacteriaceae in 
healthcare settings: a view from the trenches. Clinical infectious diseases : an official publication of the 
Infectious Diseases Society of America 2013;57:1593-9. PubMed PMID: 23934166. PubMed Central 
PMCID: PMC3888298.  
7. 
Maragakis LL, Cosgrove SE, Song X, Kim D, Rosenbaum P, Ciesla N, Srinivasan A, Ross T, Carroll K, 
Perl TM. An outbreak of multidrug-resistant Acinetobacter baumannii associated with pulsatile lavage 
wound treatment. JAMA 2004;292:3006-11. PubMed PMID: 15613669.  
8. 
Huang SS, Yokoe DS, Stelling J, Placzek H, Kulldorff M, Kleinman K, O'Brien TF, Calderwood MS, 
Vostok J, Dunn J, Platt R. Automated detection of infectious disease outbreaks in hospitals: a 
retrospective cohort study. PLoS medicine 2010;7:e1000238. PubMed PMID: 20186274. PubMed 
Central PMCID: PMC2826381.  
9. 
Mellmann A, Friedrich AW, Rosenkotter N, Rothganger J, Karch H, Reintjes R, Harmsen D. Automated 
DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus 
aureus outbreaks. PLoS medicine 2006;3:e33. PubMed PMID: 16396609. PubMed Central PMCID: 
PMC1325475.  
10. 
Hansen S, Stamm-Balderjahn S, Zuschneid I, Behnke M, Ruden H, Vonberg RP, Gastmeier P. Closure 
of medical departments during nosocomial outbreaks: data from a systematic analysis of the literature. 
The Journal of hospital infection 2007;65:348-53. PubMed PMID: 17350731.  
11. 
Magill SS, Edwards JR, Bamberg W, Beldavs ZG, Dumyati G, Kainer MA, Lynfield R, Maloney M, 
McAllister-Hollod L, Nadle J, Ray SM, Thompson DL, Wilson LE, Fridkin SK. Multistate point-prevalence 
survey of health care-associated infections. The New England journal of medicine 2014;370:1198-208. 
PubMed PMID: 24670166.  PubMed Central PMCID: PMC4648343.  
12. 
Romano-Bertrand S, Filleron A, Mesnage R, Lotthe A, Didelot MN, Burgel L, Jumas Bilak E, Cambonie 
G, Parer S. Staphylococcus aureus in a neonatal care center: methicillin-susceptible strains should be a 
main concern. Antimicrobial resistance and infection control 2014;3:21. PubMed PMID: 25089196. 
PubMed Central PMCID: PMC4118615.  
13. 
Klompas M, Yokoe DS. Automated surveillance of health care-associated infections. Clinical infectious 
diseases : an official publication of the Infectious Diseases Society of America 2009;48:1268-75. 
PubMed PMID: 19335166.  
14. 
Curran ET, Benneyan JC, Hood J. Controlling methicillin-resistant Staphylococcus aureus: a feedback 
approach using annotated statistical process control charts. Infection control and hospital epidemiology 
2002;23:13-8. PubMed PMID: 11868886.  
15. 
Kulldorff M. Tests for spatial randomness adjusting for an inhomogeneity: A general framework. Journal 
of the American Statistical Association, 2006, 101:1289-1305. 
16. 
Kulldorff M, Heffernan R, Hartman J, Assuncao R, Mostashari F. A space-time permutation scan statistic 
for disease outbreak detection. PLoS medicine 2005;2:e59. PubMed PMID: 15719066.  
17. 
Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Statistics in medicine 
1995;14:799-810. PubMed PMID: 7644860.  

  
18. 
Stelling J, Yih WK, Galas M, Kulldorff M, Pichel M, Terragno R, Tuduri E, Espetxe S, Binsztein N, 
O'Brien TF, Platt R. Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. 
using antimicrobial resistance phenotypes. Epidemiology and infection 2010;138:873-83. PubMed 
PMID: 19796449.  PubMed Central PMCID: PMC4093803.  
19. 
Stelling JM, O'Brien TF. Surveillance of antimicrobial resistance: the WHONET program. Clinical 
infectious diseases : an official publication of the Infectious Diseases Society of America 1997;24 Suppl 
1:S157-68. PubMed PMID: 8994799.  
20. 
Kleinman KP, Abrams AM, Kulldorff M, Platt R. A model-adjusted space-time scan statistic with an 
application to syndromic surveillance. Epidemiology and infection 2005;133:409-19. PubMed PMID: 
15962547.  
21. 
Kleinman K, Abrams A, Katherine Yih W, Platt R, Kulldorff M. Evaluating spatial surveillance: detection 
of known outbreaks in real data. Statistics in medicine 2006;25:755-69. PubMed PMID: 16453375.  
22. 
Kulldorff M, Mostashari F, Duczmal L, Katherine Yih W, Kleinman K, Platt R. Multivariate scan statistics 
for disease surveillance. Statistics in medicine 2007;26:1824-33. PubMed PMID: 17216592.  
23. 
WHONET Software (2009) from the World Health Organization (WHO) Collaborating Centre for 
Surveillance of Antimicrobial Resistance. Available: 
http://www.who.int/drugresistance/whonetsoftware/en/ Accessed 11 January 2016.  
24. 
Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR. Evaluating cluster alarms: a space-time scan 
statistic and brain cancer in Los Alamos, New Mexico. American journal of public health 1998;88:1377-
80. PubMed PMID: 9736881.  PubMed Central PMCID: PMC1509064.  
25. 
Kulldorff M. A spatial scan statistic. Communications in Statistics: Theory and Methods, 1997;26:1481-
96. 
26. 
Kulldorff M. Prospective time-periodic geographical disease surveillance using a scan statistic. Journal 
of the Royal Statistical Society, 2001;A164:61-72. 
27. 
SaTScan User Guide v9.4.2, 2015, http://www.satscan.org/techdoc.html Accessed 11 January 2016. 
28. 
O'Brien TF, Stelling J. Integrated Multilevel Surveillance of the World's Infecting Microbes and Their 
Resistance to Antimicrobial Agents. Clinical microbiology reviews 2011;24:281-95. PubMed PMID: 
21482726.  PubMed Central PMCID: PMC3122493.  
29. 
Vinas MR, Tuduri E, Galar A, Yih K, Pichel M, Stelling J, Brengi SP, Della Gaspera A, van der Ploeg C, 
Bruno S, Roge A, Caffer MI, Kulldorff M, Galas M. Laboratory-based prospective surveillance for 
community outbreaks of Shigella spp. in Argentina. PLoS neglected tropical diseases 2013;7:e2521. 
PubMed PMID: 24349586. PubMed Central PMCID: PMC3861122.  
30. 
Baker MA, Yokoe DS, Stelling J, Kaganov RE, Letourneau AR, O’Brien T, Kulldorff M, Babalola D, 
Lishman H, Drees M, Platt R, Huang SS for the CDC Prevention Epicenters Program. Automated 
Outbreak Detection of Hospital-Associated Infections: Value to Infection Prevention Programs. IDWeek: 
A Joint Meeting of the Infectious Disease Society of America, Society for Healthcare Epidemiology of 
America, HIV Medicine Association, and the Pediatric Infectious Disease Society. San Diego, CA. 
October 7-11, 2015.  
31. 
Datta R, Platt R, Yokoe DS, Huang SS. Environmental Cleaning Intervention and Risk of Acquiring 
Multidrug-Resistant Organisms From Prior Room Occupants. Arch Intern Med. 2011;171(6):491-494. 
PubMed PMID: 21444840.   
32. 
Goodman ER, Platt R, Bass R, Onderdonk AB, Yokoe DS, Huang SS. Impact of an environmental 
cleaning intervention on the presence of methicillin-resistant Staphylococcus aureus and vancomycin-
resistant enterococci on surfaces in intensive care unit rooms. Infection control and hospital 
epidemiology 2008;29:593-9. PubMed PMID: 18624666. PubMed Central PMCID: PMC2670228.  
33. 
Murphy CR, Eells SJ, Quan V, Kim D, Peterson E, Miller LG, Huang SS. Methicillin-resistant 
Staphylococcus aureus burden in nursing homes associated with environmental contamination of 
common areas. Journal of the American Geriatrics Society 2012;60:1012-8. PubMed PMID: 22670708.  
PubMed Central PMCID: PMC3376013.  
34. 
Carling PC, Perkins J, Ferguson J, Thomasser A. Evaluating a new paradigm for comparing surface 
disinfection in clinical practice. Infection control and hospital epidemiology 2014;35:1349-55. PubMed 
PMID: 25333429.  
35. 
Carling PC, Parry MM, Rupp ME, Po JL, Dick B, Von Beheren S. Improving cleaning of the environment 
surrounding patients in 36 acute care hospitals. Infection control and hospital epidemiology 
2008;29:1035-41. PubMed PMID: 18851687.  

  
36. 
Platt R, Takvorian SU, Septimus E, Hickok J, Moody J, Perlin J, Jernigan JA, Kleinman K, Huang SS. 
Cluster randomized trials in comparative effectiveness research: randomizing hospitals to test methods 
for prevention of healthcare-associated infections. Medical care 2010;48:S52-7. PubMed PMID: 
20473200.  
37. 
Active BAThing to Eradicate Infection Trial (ABATE Infection Trial). PI: Huang. 
https://clinicaltrials.gov/ct2/show/NCT02063867. Last accessed January 6, 2016 
38. 
R Core Team (2018). R: A language and environment for statistical computing. RFoundation for 
Statistical Computing, Vienna, Austria. URL http://www.R‐project.org/. 
39. 
Harrington D, D’Agostino RB, Gatsonis C, et al. New Guidelines for Statistical Reporting in the Journal. 
NEJM. 2019;381:285‐6.