Lack of Comprehensive Outbreak Detection in Hospitals

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread

BACKGROUND

Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.

METHODS

We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.

RESULTS

Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).

CONCLUSIONS

Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

Epidemiology and genomics of a slow outbreak of methicillin-resistant Staphyloccus aureus (MRSA) in a neonatal intensive care unit: Successful chronic decolonization of MRSA-positive healthcare personnel

Objective:

To describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.

Design:

Prospective observational study.

Setting:

Neonatal intensive care unit (NICU).

Methods:

We conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.

Results:

A NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.

Conclusions:

In comparison to fast outbreaks, outbreaks that are “slow and sustained” may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.

Nosocomial outbreaks: A review of governmental reporting systems

OBJECTIVE

Identifying and describing components of existent governmental reporting systems of NO aiming at informing the design of the implementation of NO reporting systems in countries where they were not fully established.

DESIGN

A systematic search was carried out on PubMed, Embase, and the Latin American and Caribbean Health Sciences Literature database. We included studies published from January 2007 to June 2019 describing NO governmental reporting systems. Additionally, we included studies from the list of references in the identified papers, to gather more information about NO reporting systems. We also reviewed documents published in the governmental health department's Web sites, such as outbreak management guidelines and surveillance protocols, provided they were cited in the papers.

RESULTS

NO reporting systems were reported in France (Alsace Region), Germany, Norway, United Kingdom, United States (New York State; New York City), Australia (Victoria State), Sweden (Skane Region), Ireland, Scotland (Lothian Region), and Canada (Winnipeg; Ontario). These systems vary according to the type of targeted NO event, such as gastroenteritis, influenza-like illness, invasive group A streptococcal disease or all-health care-acquired infection NO. Germany, Norway, New York City, New York State, Ireland, Winnipeg, and Ontario have established a mandatory reporting for NO.

CONCLUSIONS

There is high variability among countries regarding governmental NO reporting systems. This may hinder opportune inter- and intracountries communication concerning NO of potential international public health relevance.

Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital

Background

Early detection of clusters of pathogens is crucial for infection prevention and control (IPC) in hospitals. Conventional manual cluster detection is usually restricted to certain areas of the hospital and multidrug resistant organisms. Automation can increase the comprehensiveness of cluster surveillance without depleting human resources. We aimed to describe the application of an automated cluster alert system (CLAR) in the routine IPC work in a hospital. Additionally, we aimed to provide information on the clusters detected and their properties.

Methods

CLAR was continuously utilized during the year 2019 at Charité university hospital. CLAR analyzed microbiological and patient-related data to calculate a pathogen-baseline for every ward. Daily, this baseline was compared to data of the previous 14 days. If the baseline was exceeded, a cluster alert was generated and sent to the IPC team. From July 2019 onwards, alerts were systematically categorized as relevant or non-relevant at the discretion of the IPC physician in charge.

Results

In one year, CLAR detected 1,714 clusters. The median number of isolates per cluster was two. The most common cluster pathogens were Enterococcus faecium (n = 326, 19 %), Escherichia coli (n = 274, 16 %) and Enterococcus faecalis (n = 250, 15 %). The majority of clusters (n = 1,360, 79 %) comprised of susceptible organisms. For 906 alerts relevance assessment was performed, with 317 (35 %) alerts being classified as relevant.

Conclusions

CLAR demonstrated the capability of detecting small clusters and clusters of susceptible organisms. Future improvements must aim to reduce the number of non-relevant alerts without impeding detection of relevant clusters. Digital solutions to IPC represent a considerable potential for improved patient care. Systems such as CLAR could be adapted to other hospitals and healthcare settings, and thereby serve as a means to fulfill these potentials.

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs

OBJECTIVE

To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.

DESIGN

Multicenter retrospective cohort study.

SETTING

The study included 43 hospitals using a common infection prevention surveillance system.

METHODS

A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.

RESULTS

We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.

CONCLUSIONS

Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals

Introduction

Outbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS.

Methods

Our AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013–2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system.

Results

During the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens’ overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen.

Conclusion

AODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments.

Statistical outbreak detection by joining medical records and pathogen similarity

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments.

Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan

BACKGROUND

Hospital outbreaks of antimicrobial-resistant (AMR) bacteria should be detected and controlled as early as possible.

AIM

To develop a framework for automatic detection of AMR outbreaks in hospitals.

METHODS

Japan Nosocomial Infections Surveillance (JANIS) is one of the largest national AMR surveillance systems in the world. For this study, all bacterial data in the JANIS database were extracted between 2011 and 2016. WHONET, a free software for the management of microbiology data, and SaTScan, a free cluster detection tool embedded in WHONET, were used to analyse 2015-2016 data of eligible hospitals. Manual evaluation and validation of 10 representative hospitals around Japan were then performed using 2011-2016 data.

FINDINGS

Data from 1031 hospitals were studied; mid-sized (200-499 beds) hospitals accounted for 60%, followed by large hospitals (≥500 beds; 24%) and small hospitals (<200 beds; 16%). More clusters were detected in large hospitals. Most of the clusters included five or fewer patients. From the in-depth analysis of 10 hospitals, ∼80% of the detected clusters were unrecognized by infection control staff because the bacterial species involved were not included in the priority pathogen list for routine surveillance. In two hospitals, clusters of more susceptible isolates were detected before outbreaks of more resistant pathogens.

CONCLUSION

WHONET-SaTScan can automatically detect clusters of epidemiologically related patients based on isolate resistance profiles beyond lists of high-priority AMR pathogens. If clusters of more susceptible isolates can be detected, it may allow early intervention in infection control practices before outbreaks of more resistant pathogens occur.

Implementation and evaluation of an automated surveillance system to detect hospital outbreak

BACKGROUND

The timely identification of a cluster is a critical requirement for infection prevention and control (IPC) departments because these events may represent transmission of pathogens within the health care setting. Given the issues with manual review of hospital infections, a surveillance system to detect clusters in health care settings must use automated data capture, validated statistical methods, and include all significant pathogens, antimicrobial susceptibility patterns, patient care locations, and health care teams.

METHODS

We describe the use of SaTScan statistical software to identify clusters, WHONET software to manage microbiology laboratory data, and electronic health record data to create a comprehensive outbreak detection system in our hospital. We also evaluated the system using the Centers for Disease Control and Prevention's guidelines.

RESULTS

During an 8-month surveillance time period, 168 clusters were detected, 45 of which met criteria for investigation, and 6 were considered transmission events. The system was felt to be flexible, timely, accepted by the department and hospital, useful, and sensitive, but it required significant resources and has a low positive predictive value.

CONCLUSIONS

WHONET-SaTScan is a useful addition to a robust IPC program. Although the resources required were significant, this prospective, real-time cluster detection surveillance system represents an improvement over historical methods. We detected several episodes of transmission which would have eluded us previously, and allowed us to focus infection prevention efforts and improve patient safety.