Research
Infection Control & Surveillance in Hospitals
John R. Cange
Principal Investigator
Copyright Midwest Informatics, LLC, 2014
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Abstract
This paper summarizes the work being done to drive the identification,
prevention, and prediction of healthcare associated infections (HAI), also known
as nosocomial infections in hospital settings. A summary of recent successes and
failures with reducing the major types of HAI interventions, and as well as a
synopsis of recent HAI prevention efforts is also included. A description of the
government and industry organizations that oversee infection surveillance and
reporting provide historical and healthcare industry context.
Keywords: healthcare associated infections, nosocomial infections
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Infection Control & Surveillance in Hospitals
One of the primary goals of the Centers for Disease Control and
Prevention (CDC) and the Centers for Medicare and Medicaid Services (CMS) is
to reduce the number of HAIs in the U.S. CMS’ goals for measurement and
reporting HAI for fiscal year 2013 focused on 3 sources of HAI:

central line-associated blood stream infections

catheter-associated urinary tract infections, and

surgical site infections.
CMS, the CDC, and the World Health Organization (WHO) all share the goal of
preventing infectious outbreaks.
Literature Review
In their study of real-time surveillance and decision support, Young and
Stevenson (2008) estimated that 10% of American hospital patients (about 2
million per year) will acquire a clinically significant HAI. According to the
mortality rates cited, these infections could result in 100,000 deaths per year.
According to a study by Roberts et al.(2010), the cost of these infections total
$4.5B per year.
HAIs are one of the leading causes of death in the United States. Prior efforts
to reduce the number of HAIs have tried to break the chain of transmission by
removing the opportunistic bacteria or virus from the environment and by
isolating the infected patients. Research into improving control and prevention of
infections has been a continuing effort. According to Black (1996), nosocomial
infections are difficult to remove from hospitals due to the:
INFECTION CONTROL & SURVEILLANCE
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high prevalence of pathogens

high prevalence of compromised hosts

efficient mechanisms of transmission from patient to patient.
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According to the Health.gov website (2014), the US Department of Health
and Human Services (HHS) launched an Action Plan to Prevent Healthcareassociated Infections in 2009. The Action Plan set national targets for reducing
specific infections and though HHS is accountable for change across the
healthcare system, federal agencies actually have limited control. The Action Plan
also established the model by which different government agencies would
coordinate efforts at the federal level. The Plan recommends changes that address
transparency, financial incentives, support of state and regional HAI prevention
efforts, changes in safety culture, and mechanisms to engage stakeholders. As part
of the Plan, the essential infrastructure required to stimulate change throughout
the healthcare system was also implemented.
According to the CMS.gov website (2014), CMS seeks to reduce the extra
hospital days and treatment costs associated with these types of infections, as
described in the Hospital Inpatient Prospective Payment System and Long-Term
Care Hospital Prospective Payment System. HAI measure data are collected by
the CDC via tools managed by the National Healthcare Safety Network (NHSN).
Hospitals must enroll and complete NHSN training to comply with CMS Hospital
Inpatient Quality Reporting (IQR) Program HAI requirements. The CDC requires
that multiple users complete the required NHSN training, gain access to the
NHSN tool for reporting, and submit data on a monthly basis. Hospitals must
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adjust their guidelines and utilize CDC-provided report templates used and follow
reporting protocols, as required. Individual hospitals establish or adjust their
routines and practices with prevention in mind as the best way to control
nosocomial infections.
According to Black (1996), the most common nosocomial infection
pathways are by direct person-to-person transmission between an infected patient,
staff member, or visitor and non-infected patients; indirect transmission through
equipment, supplies, and hospital procedures; or transmission through air.
Nosocomial infections are generally caused by opportunistic infection types,
particularly by:

Enterococcus spp., including vancomycin-resistant enterococci (VRE)

Escherichia coli

Pseudomonas spp.

Staphylococcus aureus, including methicillin-resistant strains (MRSA)

Clostridium difficile
In their paper on developing an automated early warning system, Mellmann et
al. (2006), the common method used by hospitals to determine if an outbreak has
occurred is to apply a few simple rules, such as the occurrence of 3 new cases of a
pathogen within 2 weeks on the same ward. Hospitals generally focus, however,
on a few pathogens and don’t often look at clusters of infections that span
multiple wards or those associated with specialty services. Ideally, outbreak
detection programs should use a wide array of data sources and consider multiple
pathogens to be comprehensive and effective.
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Young and Stevenson (2008) studied the use of claims information from
administrative databases to help identify HAI outbreaks and noted a significant
discrepancy between hospital and HMO-based systems in identifying patients
who underwent central venous catheter insertion. Other studies have demonstrated
that claims data alone are poor predictors of true infections. A well-designed
automated surveillance system could easily integrate data from multiple sources
to detect HAIs in all health care settings, including the outpatient arena.
Sources of HAI
Recent publications and papers describe some effective prevention and
intervention efforts, as well as those that include ineffective or inconsistent
results. The following section summarizes recent case studies for each of the 5
main sources or types of HAI. This information provides insight into the
effectiveness and uncertainties of recent HAI surveillance and prevention
programs.
Central Line-Associated Bloodstream Infections. Mendel et al. (2014)
reported in their study of central line-associated bloodstream infections (CLABSI)
in a pediatric hematology/oncology population, the overall CLABSI rate fell
from 9 infections per 1000 line days to zero at the conclusion of the study. At the
study team's institution, an initiative that standardized blood culturing techniques,
lab draw times, line care techniques, and provided physician and nurse education
was able to eliminate CLABSI among pediatric hematology/oncology patients.
Catheter-Acquired Urinary Tract Infections. Parry, Grant, and Sestovic
(2013) studied how one hospital reduced catheter use by 50% and experienced a
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70% reduction in catheter-acquired urinary tract infections (CAUTI) over a 36month period. Results varied by unit with improvements of as little as 4%
(maternity) versus 74% (telemetry). Overall, urinary catheter use and CAUTI
rates were reduced by the use of evidence-based interventions, such as the nursedirected catheter removal protocol, which was shown to lower catheter use rates
and reduce infection rates.
Surgical Site Infections. Lübbeke et al. (2013) showed that using a
surgical safety checklist can result in significant reduction in postoperative
surgical site infections (SSI), morbidity, and mortality. The hospital implemented
a pre-surgery checklist to ensure proper safeguards were in place before the
surgery began. Comparing pre-implementation to post-implementation periods:
checklist use during 77 interventions prevented 1 reoperation for SSI, which was
a 50% improvement. The number of unplanned returns to the operating room was
reduced by 19%, though the unplanned returns to the ICU made only small
improvements. The number of in-hospital deaths actually increased by 17%,
though use of the checklist is not considered a causal agent.
Ventilator-Associated Pneumonia. In a study of how guidelines can
help prevent ventilator-associated pneumonia (VAP), Flodgren et al. (2013)
noted the low quality of the evidence, which raises uncertainty about which
interventions are most effective in changing professional behavior and in what
contexts. However, educational interventions that are repeatedly administered
over time and interventions employing specialized personnel have shown some
positive results.
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Clostridium difficile infection. In a study by Novak-Weekley et al.
(2010), it was then noted that the incidence of Clostridium difficile Infection
(CDI) has risen almost 300% in the United States in the last 10 years,
emphasizing the need for better interventions, including more rapid and accurate
tests for CDI. In a computer simulation developed by Rubin et al. (2013) using
existing clinical data, six interventions were introduced - both alone and
"bundled" together: aggressive C. difficile testing; isolation and treatment of
symptomatic patients; improved adherence to hand hygiene and contact
precautions; improved use of soap and water for hand hygiene; improved
environmental cleaning. The "bundled" intervention involved better hand hygiene,
better isolation of infected patients, and better treatment of suspected cases and
was found to be more likely to reduce CDI than any of the other interventions
alone. As described by Alcala´ et al. (2012) in their paper on the under-diagnosis
of CDI in Spain, the Spanish Clostridium difficile Study Group performed C.
difficile cultures on all stool specimens sent to a series of laboratories in Spain on
a single day. The specimens were cultured and characterized at a central reference
laboratory. Results: a total of 807 specimens from 118 laboratories were
collected, covering 75.4% of the Spanish population. The estimated rate of
hospital-acquired CDI was .24% during this time. However, 2 of every 3 episodes
went undiagnosed or were misdiagnosed, owing to non-sensitive diagnostic tests
(19.0%) or lack of recognition (47.6%; mostly young people or non-hospitalized
patients). It was found, however, that all CDI strains were fully susceptible to
metronidazole and vancomycin. Consequently, the Spanish electronic medical
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records systems were programmed to recommend these medications when the
reference lab provided positive CDI results.
Discussion
Several mathematical/statistical models and methods have been used to
identify, prevent, and predict HAI. A brief review of the different methods
demonstrates that no one method or model has been shown to be effective and
effectiveness varied by the type of infection/outbreak and whether the goal was to
identify and prevent the spread of the infection or to predict it based on
environmental factors.
Outbreak Identification/Prevention. In a retrospective cohort study by
Huang et al. (2009) at Brigham Women’s Hospital (BWH), the organization used
the World Health Organization’s (WHO) WHONET-SaTSan laboratory
information software to identify clusters of infections using a space-time
permutation scan statistic. This method defines the space or location in which the
pathogen was identified, e.g. within 5 miles. The method then describes a cylinder
that will result when the space is multiplied by the time, from 1 to 7 days. The
resulting cylinder contains all of the identified cases that have occurred within
that space and during the 1 to 7 day bounds. The number of cylinders will vary
and cylinders may overlap as they are centered on patient medical information
from over-the-counter drug sales at pharmacies, nurse hotline calls, regular
physician visits, ER department visits, ambulance dispatches, or lab test results.
The WHO application considered the likelihood of clusters occurring by chance
as being less than once per year. For MRSA and VRE clusters, the WHONET-
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generated clusters were compared to those identified by BWH’s Infection Control
program’s existing rule-based criterion of 3 occurrences in 2 weeks within a
single ward. Current detection methods involve case counting and subjective
judgments on whether a cluster is occurring in a ward. Generally, these rule-based
clusters trigger precautionary measures across the hospital ward, including alerts
sent to nursing and physician leadership, weekly screening and culture collection
from all ward patients, use of gloves for all patient contact, and the potential need
to disinfect rooms, surfaces, and devices. These interventions often continue until
no new cases are identified over a 4 week period or until all cases are discharged
from the ward. Due to the resource-intensive nature of these measures, the costs
of false-positives are very high.
The
WHONET-SaTScan
outperformed
traditional
infection
control
surveillance in 3 ways. This approach has the potential to be more comprehensive
than infection control resources; reduce the number of false-positives identified
by the current, rules-based infection control system; and it identifies events that
are statistically unusual and may represent an opportunity for intervention.
Statistical methods like those utilized in the WHONET-SaTSan application can be
run every day to identify occurrences of pathogens as quickly as possible. The
statistical and probabilistic nature of the space-time scan adjusts for “normal”
(random) appearance of such pathogens in a hospital. The number of false
positives is almost 0 in a space-time permutation scan, which indicates that any
new, non-random clusters deserve attention.
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From an epidemiological standpoint, the WHONET tool could be expanded to
include any number of hospitals and medical groups to take a broader view of the
overlapping cylinders. This approach may also improve the ability to better
identify the location of the first appearance of a nosocomial infection before it can
become an outbreak.
Outbreak Prediction. Though the WHONET statistical method may prove to
be better at identifying infection clusters and potential outbreaks, Buczak,
Koshute, Babin, Feighner, and Lewis (2012) described a rules-based model they
used to detect outbreaks of dengue fever which experienced a 68% success rate as
much as 4 weeks in advance. This data-driven model looks at multiple data
sources, such as telephone calls to physicians, physician visits, ER department
visits, lab test requests, lab test results, and drug prescriptions. The model then
constructs an association tree that is trained to build rules and classifiers. The
‘best’ classifier is chosen by using validation data and looking at the model’s
predictive power. They extracted relationships between clinical, meteorological,
climatic, and socio-political data from Peru. The relationships are in the form of
rules and the best set of rules is automatically chosen and forms a classifier. That
classifier is then used to predict future dengue incidence as either HIGH
(outbreak) or LOW (no outbreak). Results: The automated method built three
different fuzzy association rule models. The first two weekly models predicted
dengue incidence three and four weeks in advance, respectively. The third
prediction encompassed a four-week period, used previously unused test data for
the period 4-7 weeks from time of prediction and yielded a positive predictive
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value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a
specificity of 0.982.
Chang et al. (2011) developed a scoring system to predict HAI, derived
from Logistic Regression (LR) and simultaneously validated by Artificial Neural
Networks (ANN). This study looked at 16 variables extracted from their EHR and
fed into ANN and LR models. This method demonstrated ~97% accuracy in
identifying internal infections and 87% success with external infections.
Conclusions and Future Study
The ability to identify and prevent nosocomial infections is critical, but the
best methods for doing so for a discrete HAI type are sometimes inconsistent and
ineffective. In a recent study by Kahn and Battles (2014), researchers indicate
good progress and identify lessons learned. By taking a comprehensive approach
to surveillance, identifying outbreaks is greatly improved. Use of statistical,
artificial neural networks and rules-based systems have all had recent successes.
Advances have been made in preventing several of the core types of nosocomial
infections, though research and new interventions need to attempted and studied
on a continuing basis. Advances in statistical analysis tools, like the WHONETSaTSan application, provides a way to identify nosocomial infections more
accurately and to potentially identify the sources of infections more quickly.
Further study into the best-practice methods and models for identifying, treating,
preventing, and predicting HAI outbreaks is needed.
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