Outbreak investigation of Serratia marcescens neurosurgical site infections associated with a contaminated shaving razors.

BACKGROUND: Surgical site infection (SSI) is the most common healthcare-associated infection. We report an outbreak of neurosurgical site infections caused by Serratia marcescens after craniotomy in a tertiary care hospital. METHODS: Between August 6 and 21, 2018, five cases of early-onset SSI caused by S. marcescens after craniotomy were recorded in a 1786-bed tertiary care hospital. Cultures were collected from potential environmental sources and healthcare workers. Whole-genome sequencing (WGS) was used to investigate the genetic relationships among S. marcescens isolates. RESULTS: The outbreak involved five patients; S. marcescens was isolated from the cerebrospinal fluid, pus, tissue, and blood samples from these patients. S. marcescens was also isolated from shaving razors and brushes. All S. marcescens isolates from the infected patients and razors showed the same resistance patterns on antibiotic-susceptibility tests. WGS revealed close clustering among four of five isolates from the patients and among three of four isolates from the razors. No additional patient developed S. marcescens infection after we stopped using the razors for scalp shaving. CONCLUSIONS: We report an outbreak of neurosurgical site infections after craniotomy, which was associated with shaving razors contaminated by S. marcescens. Shaving scalps with razors should be avoided to prevent SSI.

When is an outbreak an outbreak? Using literature and discharge data to define Clostridioides difficile incidence changes referred to as outbreaks.

A clinically noticeable increase in Clostridioides difficile infections (CDI) in excess of expectations (i.e. outbreak) lacks a common definition. Understanding what magnitude of CDI incidence growth constitutes an outbreak from the hospital perspective is valuable for retrospective analyses. This study identified CDI incidence metrics reported as outbreaks in the literature and compared these with Healthcare Cost and Utilization Project (HCUP) discharge data. Publications reported a median 4.1x incidence change between outbreak periods and reference periods (N=26, minimum 2x incidence). However, this magnitude change in monthly incidence appeared once in HCUP data (20 hospitals, 2012-2014). Hence, the literature may present examples of extreme outbreaks.

Epizootics due to Yellow Fever Virus in São Paulo State, Brazil: viral dissemination to new areas (2016-2017).

Beginning in late 2016 Brazil faced the worst outbreak of Yellow Fever in recent decades, mainly located in southeastern rural regions of the country. In the present study we characterize the Yellow Fever Virus (YFV) associated with this outbreak in São Paulo State, Brazil. Blood or tissues collected from 430 dead monkeys and 1030 pools containing a total of 5,518 mosquitoes were tested for YFV by quantitative RT-PCR, immunohistochemistry (IHC) and indirect immunofluorescence. A total of 67 monkeys were YFV-positive and 3 pools yielded YFV following culture in a C6/36 cell line. Analysis of five nearly full length genomes of YFV from collected samples was consistent with evidence that the virus associated with the São Paulo outbreak originated in Minas Gerais. The phylogenetic analysis also showed that strains involved in the 2016-2017 outbreak in distinct Brazilian states (i.e., Minas Gerais, Rio de Janeiro, Espirito Santo) intermingled in maximum-likelihood and Bayesian trees. Conversely, the strains detected in São Paulo formed a monophyletic cluster, suggesting that they were local-adapted. The finding of YFV by RT-PCR in five Callithrix monkeys who were all YFV-negative by histopathology or immunohistochemistry suggests that this YFV lineage circulating in Sao Paulo is associated with different outcomes in Callithrix when compared to other monkeys.

A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies.

Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches.

Institutional gastroenteritis outbreaks and time to notify public health services.

AIM: We report a quantification and visualisation of the association between the time to notify public health service (PHS) and the duration and size of institutional gastroenteritis outbreaks, and explore the seasonality and trend of the outbreaks. METHOD: Descriptive analysis was performed on institutional gastroenteritis outbreak data from a North Island PHS (1 January 2009-31 December 2014). Time-series analysis was used to explore the seasonality and trend of outbreaks. Multivariate analyses were performed to quantify the association between the time to notify PHS and the duration and size of outbreaks. RESULTS: One hundred and seventy-five gastroenteritis outbreaks (from 58 facilities) were included in descriptive analyses. A significant increasing trend (p=0.01) without seasonal pattern was confirmed by time-series analysis. Shorter notification time was associated with shorter duration and smaller size of outbreaks, eg, duration of outbreaks when time to notify was ≥7 days, was 3.4 days (p=0.001, 95% CI=3.1-3.7) longer than baseline time to notify (0-1 day). CONCLUSION: Prompt notification to the PHS appears to be a factor associated with reduced outbreak duration and size.

Use of TaqMan Array Cards to Screen Outbreak Specimens for Causes of Febrile Illness in Tanzania.

We describe the deployment of a custom-designed molecular diagnostic TaqMan Array Card (TAC) to screen for 31 bacterial, protozoal, and viral etiologies in blood from outbreaks of acute febrile illness in Tanzania during 2015-2017. On outbreaks notified to the Tanzanian Ministry of Health, epidemiologists were dispatched and specimens were collected, transported to a central national laboratory, and tested by TAC within 2 days. This algorithm streamlined investigation, diagnosed a typhoid outbreak, and excluded dozens of other etiologies. This method is usable in-country and may be incorporated into algorithms for diagnosing outbreaks.

A Study on the Leptospirosis Outbreak Among US Marine Trainees in Okinawa, Japan.

Introduction: The US Marines operate the Jungle Warfare Training Center (JWTC) in Northern Okinawa, where leptospirosis is endemic. The JWTC endurance course involves prolonged fresh and stagnant water exposures in a simulated jungle warfare environment. Since a leptospirosis outbreak in 1987, JWTC has required prophylactic use of doxycycline at 200 mg weekly during exposure to the endurance course. This policy is based on a 1982 study in a similar environment in Panama. In August and September of 2014, an outbreak of leptospirosis occurred among 81 Marines training at JWTC. We analyzed data from the largest reported outbreak of leptospirosis among US military members. Materials and Methods: Two hundred and thirty nine US Marines who completed the endurance course were interviewed by trained personnel regarding their exposures and use of prophylactic medication utilizing a standardized questionnaire. All available personnel who went through the course during the outbreak period were interviewed regardless of whether or not they became ill. The Armed Forces Health Surveillance Branch's case definition was used to identify cases. Results: Eighty-one cases in 239 personnel were identified (attack rate = 33.9%). Exposures linked to being a case were swallowing water in the stagnant pond (attack rate ratio [ARR] = 2.3, 95% confidence interval [CI] = 1.4-3.7), cuts on the body (ARR = 1.5, 95% CI = 1.01-2.11), and insect bites (ARR = 2.0, 95% CI = 1.2-3.4). Exposures not linked to being a case were taking doxycycline before the exposure (ARR = 3.2, 95% CI = 0.5-22.2), taking doxycycline after the exposure (ARR = 0.9, 95% CI = 0.6-1.3), and swallowing water in the natural stream (ARR = 1.3, 95% CI = 0.9-1.9). Conclusions: Attack rate data indicate that whether or not personnel took pre- or post-exposure doxycycline made no difference statistically. Increased internal exposure (via swallowing water or through broken skin) increased risk of symptomatic disease. This new study combined with a growing body of literature should prompt researchers to re-examine recommendations for those with significant water exposure in areas with high leptospirosis levels.

A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics.

INTRODUCTION: In the era of genetic engineering of pathogens, distinguishing unnatural epidemics from natural ones is a challenge. Successful identification of unnatural infectious disease events can assist in rapid response, which relies on a sensitive risk assessment tool used for the early detection of deliberate attacks (i.e., bioterrorism). METHODS: A systematic review was conducted according to the outline of Preferred Reporting Items for Systematic Reviews. Published papers related to the detection of unnatural diseases were searched in MEDLINE (January 1927-April 2016), EMBASE (January 1937-March 2016), and Web of Science (January 1978-March 2016). Full texts were reviewed for the selection of studies on scoring systems specially designed to discern between unnatural and natural outbreaks. RESULTS: A total of 1,753 papers were reviewed, of which we identified the following five scoring systems specifically designed for detecting unnatural outbreaks: (1) the Grunow-Finke epidemiological assessment tool, (2) potential epidemiological clues to a deliberate epidemic, (3) bioterrorism risk assessment scoring, (4) and (5) two modified scoring systems based on (3). Various criteria ranging from the information on perpetrators, type of agents, spatial distribution, and intelligence of deliberate release were involved. Of these systems, the Grunow-Finke assessment tool remains the most widely used, but has low sensitivity for correctly identifying unnatural epidemics when tested against actual historical outbreaks. Others were applied into a few scenarios but provided different perspectives for bioterrorism detection and bio-preparedness. CONCLUSION: There are few risk assessment tools for differentiating unnatural from natural epidemics. These tools are increasingly necessary and valuable, but improved scoring systems with higher sensitivity, specificity, timeliness, and wider application to biological attacks must be developed.

Enhanced HIV-1 surveillance using molecular epidemiology to study and monitor HIV-1 outbreaks among intravenous drug users (IDUs) in Athens and Bucharest.

BACKGROUND: A significant increase in HIV-1 diagnoses was reported among Injecting Drug Users (IDUs) in the Athens (17-fold) and Bucharest (9-fold) metropolitan areas starting 2011. METHODS: Molecular analyses were conducted on HIV-1 sequences from IDUs comprising 51% and 20% of the diagnosed cases among IDUs during 2011-2013 for Greece and Romania, respectively. Phylodynamic analyses were performed using the newly developed birth-death serial skyline model which allows estimating of important epidemiological parameters, as implemented in BEAST programme. RESULTS: Most infections (>90%) occurred within four and three IDU local transmission networks in Athens and Bucharest, respectively. For all Romanian clusters, the viral strains originated from local circulating strains, whereas in Athens, the local strains seeded only two of the four sub-outbreaks. Birth-death skyline plots suggest a more explosive nature for sub-outbreaks in Bucharest than in Athens. In Athens, two sub-outbreaks had been controlled (Re<1.0) by 2013 and two appeared to be endemic (Re∼1). In Bucharest one outbreak continued to expand (Re>1.0) and two had been controlled (Re<1.0). The lead times were shorter for the outbreak in Athens than in Bucharest. CONCLUSIONS: Enhanced molecular surveillance proved useful to gain information about the origin, causal pathways, dispersal patterns and transmission dynamics of the outbreaks that can be useful in a public health setting.

Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting.

BACKGROUND: Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field. METHODS: 111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity. RESULTS: Formal source reports lagged behind informal source reports by a median of 1.26 days (p=0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting. CONCLUSION: The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.

Visualization of infectious disease outbreaks in routine practice.

Throughout the history of epidemiology, visualizations have been used as the interface between public-health professionals and epidemiological data. The aim of this study was to examine the impact of the level of abstraction when using visualizations on routine infectious disease control. We developed three interactive visualization prototypes at increasing levels of abstraction to communicate subsets of influenza outbreak surveillance information. The visualizations were assessed through workshops in an exploratory evaluation with infectious disease epidemiologists. The results show that despite the potential of processed, abstract, and information-dense representations, increased levels of abstraction decreased epidemiologists' understanding and confidence in visualizations. Highly abstract representations were deemed not applicable in routine practice without training. Infectious disease epidemiologists' work routines and decision-making need to be further studied in order to develop visualizations that meet both the quality requirements imposed by policy-makers and the contextual nature of work practice.

Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study.

BACKGROUND: Tuberculosis incidence in the UK has risen in the past decade. Disease control depends on epidemiological data, which can be difficult to obtain. Whole-genome sequencing can detect microevolution within Mycobacterium tuberculosis strains. We aimed to estimate the genetic diversity of related M tuberculosis strains in the UK Midlands and to investigate how this measurement might be used to investigate community outbreaks. METHODS: In a retrospective observational study, we used Illumina technology to sequence M tuberculosis genomes from an archive of frozen cultures. We characterised isolates into four groups: cross-sectional, longitudinal, household, and community. We measured pairwise nucleotide differences within hosts and between hosts in household outbreaks and estimated the rate of change in DNA sequences. We used the findings to interpret network diagrams constructed from 11 community clusters derived from mycobacterial interspersed repetitive-unit-variable-number tandem-repeat data. FINDINGS: We sequenced 390 separate isolates from 254 patients, including representatives from all five major lineages of M tuberculosis. The estimated rate of change in DNA sequences was 0.5 single nucleotide polymorphisms (SNPs) per genome per year (95% CI 0.3-0.7) in longitudinal isolates from 30 individuals and 25 families. Divergence is rarely higher than five SNPs in 3 years. 109 (96%) of 114 paired isolates from individuals and households differed by five or fewer SNPs. More than five SNPs separated isolates from none of 69 epidemiologically linked patients, two (15%) of 13 possibly linked patients, and 13 (17%) of 75 epidemiologically unlinked patients (three-way comparison exact p<0.0001). Genetic trees and clinical and epidemiological data suggest that super-spreaders were present in two community clusters. INTERPRETATION: Whole-genome sequencing can delineate outbreaks of tuberculosis and allows inference about direction of transmission between cases. The technique could identify super-spreaders and predict the existence of undiagnosed cases, potentially leading to early treatment of infectious patients and their contacts. FUNDING: Medical Research Council, Wellcome Trust, National Institute for Health Research, and the Health Protection Agency.

A Web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering.

BACKGROUND: The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. OBJECTIVES: To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. METHODS: Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system's performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. RESULTS: The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). CONCLUSION: This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.

Optimization of pulsed-field gel electrophoresis protocols for Salmonella Paratyphi A subtyping.

Salmonella enterica serovar Paratyphi A infection has caused public health problems in some countries in recent years. Pulsed-field gel electrophoresis (PFGE) has been used for the subtyping and epidemiological investigations of some serotypes of Salmonella, mainly in outbreaks caused by non-typhoidal Salmonella. In this study, different restriction endonucleases and electrophoresis parameters were compared for the PFGE subtyping by using Salmonella Paratyphi A strain panels. Two protocols for the enzymes SpeI and XbaI showed higher discriminatory power, which may facilitate epidemiological analysis for more accurate case definition, and clonality study of Salmonella Paratyphi A.

Economic high-throughput-identification of influenza A subtypes from clinical specimens with a DNA-oligonucleotide microarray in an outbreak situation.

Influenza A surface proteins H (haemagglutinin) and N (neuraminidase) occur in sixteen and nine distinct genotypes, respectively. The need for a timely production of vaccinations in case of pandemics or seasonal epidemics requires rapid typing methods for the determination of these alleles. The aim of the present study was to develop and improve a rapid and economic assay for determining H and N subtypes of influenza A from patient samples. The assay is based on the hybridisation of labelled amplicons from H and N reverse transcriptase-PCRs using consensus primer pairs to subtype-specific probes on microtiterstripe-mounted DNA-microarrays. An algorithm for semi-automatic data interpretation of raw data and assignment to H and N subtypes was proposed. Altogether, 191 samples were genotyped. This included 134 patient and 44 reference samples as well as controls. Under routine conditions sensitivity and specificity proved to be comparable to conventional nested or real-time PCRs. At least 130 out of 147 array-positive samples were unambiguously assignable. This included all sixteen variants of H as well as all nine variants of N. Furthermore, eighty-two samples from the 2009/2010 "novel H1N1/swine flu" (SF)-outbreak were correctly identified.

Phage types of Vibrio cholerae O1 and O139 in the past decade in India.

Cholera has been a prevalent disease worldwide since the early 19th century. Vibrio cholerae O1 and O139 are the two serogroups that have been mainly implicated in causing cholera. This study reports the results of biotyping, serotyping and phage typing of V. cholerae O1 and O139 (1998-2007) strains received from different parts of India for the identification of the trends in the occurrence and spread of cholera in the country. However, there has been a notable steep decline in the occurrence of V. cholerae O139 strains over the past few years resulting in no strain of V. cholerae O139 being received from any part of India in 2007 and 2008. Of the total strains received, 79.1% were serotyped as Ogawa and the remaining 20.9% were found to be Inaba, which indicates that Ogawa was the predominant serotype. Almost 100% typeability was observed with the new scheme of V. cholerae O1, with type 27 being the dominant phage type and V. cholerae O139 strains were clustered into the predominant phage type T-1. From the phage typing and serotyping results, it can be concluded that V. cholerae O1 (T-27) and O139 (T-1) strains circulate throughout the country at any given time.