Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

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.).

Prevalence and changing antimicrobial resistance profiles of Shigella spp. isolated from diarrheal patients in Kolkata during 2011-2019

BACKGROUND

The primary aim of this study was to investigate the occurrence, characteristics, and antimicrobial resistance patterns of various Shigella serogroups isolated from patients with acute diarrhea of the Infectious Diseases Hospital in Kolkata from 2011-2019.

PRINCIPAL FINDINGS

During the study period, Shigella isolates were tested for their serogroups, antibiotic resistance pattern and virulence gene profiles. A total of 5.8% of Shigella spp. were isolated, among which S. flexneri (76.1%) was the highest, followed by S. sonnei (18.7%), S. boydii (3.4%), and S. dysenteriae (1.8%). Antimicrobial resistance against nalidixic acid was higher in almost all the Shigella isolates, while the resistance to β-lactamases, fluoroquinolones, tetracycline, and chloramphenicol diverged. The occurrence of multidrug resistance was found to be linked with various genes encoding drug-resistance, multiple mutations in the topoisomerase genes, and mobile genetic elements. All the isolates were positive for the invasion plasmid antigen H gene (ipaH). Dendrogram analysis of the plasmid and pulsed-field electrophoresis (PFGE) profiles revealed 70-80% clonal similarity among each Shigella serotype.

CONCLUSION

This comprehensive long-term surveillance report highlights the clonal diversity of clinical Shigella strains circulating in Kolkata, India, and shows alarming resistance trends towards recommended antibiotics. The elucidation of this study's outcome is helpful not only in identifying emerging antimicrobial resistance patterns of Shigella spp. but also in developing treatment guidelines appropriate for this region.

Automated detection of hospital outbreaks of multi-drug resistant pathogens in one Italian region

BACKGROUND

Automated tools for antimicrobial resistance surveillance are critical for improving detection of drug-resistant organisms and informing prevention and control interventions. In this study, the WHONET-SaTScan software was used at a multi-hospital level in Tuscany, Italy to identify case clusters consistent with hospital outbreaks caused by drug-resistant pathogens.

METHODS

Antimicrobial resistance surveillance data from all Tuscany hospitals between January 2018 and December 2020 were analyzed using WHONET. The SaTScan package was used to detect case clusters applying a simulated prospective approach and the space-time permutation algorithm. Clusters were identified using resistance profiles and two distinct spatial variables: single medical services ("service") or groups of related services ("metaservice").

RESULTS

Data from eight bacterial pathogens were provided from 49 hospitals for 312,779 isolates from 158,809 patients. Single service-based analysis detected 693 hospital clusters, while metaservice-based analysis identified 635. There was no evidence for a difference between the two methods in terms of cluster length, cluster size, recurrence intervals, number of alerts, distribution across years or hospitals. Among clusters involving multiple services identified by both analyses, metaservice-detected clusters were usually larger and more statistically significant.

CONCLUSIONS

WHONET-SaTScan proved to be a valuable multi-facility cluster detection tool that can be implemented for real-time surveillance.

A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.

Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review

Background

Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action. Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.

Objective

The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.

Methods

We conducted a systematic search of the published literature across four databases (MEDLINE via OVID, Web of Science Core Collection, ProQuest Science, and Google Scholar) for peer-reviewed journal papers from January 1998 to October 2019 using a keyword search. Papers with the full text available were extracted for review, and exclusion criteria were applied to identify eligible papers.

Results

Of the 6649 retrieved papers, 23 remained, describing 15 web-based apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both. We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations. Evaluation studies (6 out of 15 apps) were mostly cross-sectional, with some evidence of reduction in time to notification of outbreak; however, studies lacked user-based needs assessments and evaluation of implementation.

Conclusions

Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

Staphylococcus aureus antimicrobial susceptibility trends and cluster detection in Vermont: 2012-2018

Objectives: This study presents demographic and temporal trends in the isolation of Staphylococcus aureus in Vermont clinical microbiology laboratories and explores the use of statistical algorithms and multi-resistance phenotypes to improve outbreak detection.Methods: Routine microbiology test results downloaded from Vermont clinical laboratory information systems were used to monitor S. aureus antimicrobial resistance trends. The integrated WHONET-SaTScan software used multi-resistance phenotypes to identify possible acute outbreaks with the space-time permutation model and slowly emerging geographic clusters using the spatial-only multinomial model.Results: Data were provided from seven hospital laboratories from 2012 to 2018 for 19,224 S. aureus isolates from 14,939 patients. Statistically significant differences (p ≤ 0.05) in methicillin-resistant S. aureus (MRSA) isolation were seen by age group, specimen type, and health-care setting. Among MRSA, multi-resistance profiles permitted the recognition and tracking of 6 common and 21 rare 'phenotypic clones.' We identified 43 acute MRSA clusters and 7 significant geographic clusters (p ≤ 0.05).Conclusions: There was significant heterogeneity in MRSA strains between facilities and the use of multi-resistance phenotypes facilitated the recognition of possible outbreaks. Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging resistance threats.

Surveillance of antimicrobial resistance and evolving microbial populations in Vermont: 2011-2018

OBJECTIVE

This study presents trends in organism isolation and antimicrobial resistance in routine microbiology test results from acute-care hospital microbiology laboratories in Vermont.

METHODS

Organism identifications and antimicrobial susceptibility test results were captured from acute-care hospital laboratories to monitor geographic and temporal trends in resistance and emerging microbial threats with the free WHONET software.

RESULTS

Data were provided from 12 acute care hospital laboratories from 2011 through 2018 for 318,833 isolates from 148,994 patients (70% female, 74% outpatient, and 63% urine). Significant differences (p < 0.05) in age, gender, and antimicrobial susceptibility results (e.g. Escherichia coli and levofloxacin) between outpatient and inpatient isolates were identified with temporal increases in certain species (e.g. Aerococcus urinae) and resistance (e.g. Streptococcus pneumoniae and erythromycin). The use of multi-resistance phenotypes demonstrated significant heterogeneity (p < 0.05) in MRSA strains between facilities, for example Staphylococcus aureus resistant to six priority antimicrobials were found in no critical access hospitals (fewer than 25 inpatient beds) but in all non-critical access hospitals.

CONCLUSIONS

Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging community and healthcare resistance threats at the local and state level.

Effects of laboratory capabilities on combating antimicrobial resistance, 2013-2016: A static model panel data analysis

OBJECTIVES

Antimicrobial resistance (AMR) has become a serious global public health problem. The World Health Organization (WHO) and European Union (EU) have taken actions to combat this issue, in which laboratory capability construction is a crucial part. This study aimed to explore the relationship between laboratory capabilities and antimicrobial resistance from a macro perspective.

METHODS

The study used annual national level penal data from the EU Laboratory Capability Monitoring System and Antimicrobial Resistance Surveillance Europe 2013-2016. A conventional static panel data analysis was constructed to establish the relationship between the antimicrobial resistance rates and laboratory capabilities.

RESULTS

Laboratory capability on antimicrobial drug resistance characterisation and monitoring (LC8) showed a positive effect on Escherichia coli (E. coli) combined resistance rate (Y5), E. coli resistant rate of aminoglycosides (Y4), and Klebsiella pneumoniae resistant rate of carbapenems (Y8) (OR=0.929, 0.957, and 0.861; P=0.035, 0.007, and 0.026, respectively). However, following the diagnostic testing guidelines (LC2) caused higher resistance rates of Klebsiella pneumoniae to fluoroquinolones (Y6), third-generation cephalosporins (Y7), and aminoglycosides (Y9) (OR=1.076, 1.093, and1.065; P=0.011, 0.032, and 0.002, respectively).

CONCLUSIONS

Antimicrobial drug resistance characterisation and monitoring by laboratories has contributed to minimising antimicrobial resistance, while the mechanism of laboratory capabilities to pose an ineffective or negative impact on AMR remains to be further studied.

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.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space–time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

Systematic review of electronic surveillance of infectious diseases with emphasis on antimicrobial resistance surveillance in resource-limited settings

BACKGROUND

Electronic surveillance of infectious diseases involves rapidly collecting, collating, and analyzing vast amounts of data from interrelated multiple databases. Although many developed countries have invested in electronic surveillance for infectious diseases, the system still presents a challenge for resource-limited health care settings.

METHODS

We conducted a systematic review by performing a comprehensive literature search on MEDLINE (January 2000-December 2015) to identify studies relevant to electronic surveillance of infectious diseases. Study characteristics and results were extracted and systematically reviewed by 3 infectious disease physicians.

RESULTS

A total of 110 studies were included. Most surveillance systems were developed and implemented in high-income countries; less than one-quarter were conducted in low-or middle-income countries. Information technologies can be used to facilitate the process of obtaining laboratory, clinical, and pharmacologic data for the surveillance of infectious diseases, including antimicrobial resistance (AMR) infections. These novel systems require greater resources; however, we found that using electronic surveillance systems could result in shorter times to detect targeted infectious diseases and improvement of data collection.

CONCLUSIONS

This study highlights a lack of resources in areas where an effective, rapid surveillance system is most needed. The availability of information technology for the electronic surveillance of infectious diseases, including AMR infections, will facilitate the prevention and containment of such emerging infectious diseases.

Use of WHONET-SaTScan system for simulated real-time detection of antimicrobial resistance clusters in a hospital in Italy, 2012 to 2014

Resistant pathogens infections cause in healthcare settings, higher patient mortality, longer hospitalisation times and higher costs for treatments. Strengthening and coordinating local, national and international surveillance systems is the cornerstone for the control of antimicrobial resistance (AMR). In this study, the WHONET-SaTScan software was applied in a hospital in Italy to identify potential outbreaks of AMR. Data from San Filippo Neri Hospital in Rome between 2012 and 2014 were extracted from the national surveillance system for antimicrobial resistance (AR-ISS) and analysed using the simulated prospective analysis for real-time cluster detection included in the WHONET-SaTScan software. Results were compared with the hospital infection prevention and control system. The WHONET-SaTScan identified 71 statistically significant clusters, some involving pathogens carrying multiple resistance phenotypes. Of these 71, three were also detected by the hospital system, while a further 15, detected by WHONET-SaTScan only, were considered of relevant importance and worth further investigation by the hospital infection control team. In this study, the WHONET-SaTScan system was applied for the first time to the surveillance of AMR in Italy as a tool to strengthen this surveillance to allow more timely intervention strategies both at local and national level, using data regularly collected by the Italian national surveillance system.

Online platform for applying space-time scan statistics for prospectively detecting emerging hot spots of dengue fever

Background

Cases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level.

Methods

A total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space–time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC’s surveillance statistics.

Results

Among the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map.

Conclusions

We designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan

BACKGROUND

While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time.

METHODS

Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time.

RESULTS

Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others.

CONCLUSION

Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting

OBJECTIVES

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013.

METHODS

Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs).

RESULTS

During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved.

CONCLUSIONS

Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks.

A cohort study to define the age-specific incidence and risk factors of Shigella diarrhoeal infections in Vietnamese children: a study protocol

BACKGROUND

Shigella spp. are one of the most common causes of paediatric dysentery globally, responsible for a substantial proportion of diarrhoeal disease morbidity and mortality, particularly in industrialising regions. Alarming levels of antimicrobial resistance are now reported in S. flexneri and S. sonnei, hampering treatment options. Little is known, however, about the burden of infection and disease due to Shigella spp. in the community.

METHODS/DESIGN

In order to estimate the incidence of this bacterial infection in the community in Ho Chi Minh City, Vietnam we have designed a longitudinal cohort to follow up approximately 700 children aged 12-60 months for two years with active and passive surveillance for diarrhoeal disease. Children will be seen at 6 month intervals for health checks where blood and stool samples will be collected. Families will also be contacted every two weeks for information on presence of diarrhoea in the child. Upon report of a diarrhoeal disease episode, study nurses will either travel to the family home to perform an evaluation or the family will attend a study hospital at a reduced cost, where a stool sample will also be collected. Case report forms collected at this time will detail information regarding disease history, risk factors and presence of disease in the household.Outcomes will include (i) age-specific incidence of Shigella spp. and other agents of diarrhoeal disease in the community, (ii) risk factors for identified aetiologies, (iii) rates of seroconversion to a host of gastrointestinal pathogens in the first few years of life. Further work regarding the longitudinal immune response to a variety of Shigella antigens, host genetics and candidate vaccine/diagnostic proteins will also be conducted.

DISCUSSION

This is the largest longitudinal cohort with active surveillance designed specifically to investigate Shigella infection and disease. The study is strengthened by the active surveillance component, which will likely capture a substantial proportion of episodes not normally identified through passive or hospital-based surveillance. It is hoped that information from this study will aid in the design and implementation of Shigella vaccine trials in the future.

Clinical microbiology informatics

The clinical microbiology laboratory has responsibilities ranging from characterizing the causative agent in a patient's infection to helping detect global disease outbreaks. All of these processes are increasingly becoming partnered more intimately with informatics. Effective application of informatics tools can increase the accuracy, timeliness, and completeness of microbiology testing while decreasing the laboratory workload, which can lead to optimized laboratory workflow and decreased costs. Informatics is poised to be increasingly relevant in clinical microbiology, with the advent of total laboratory automation, complex instrument interfaces, electronic health records, clinical decision support tools, and the clinical implementation of microbial genome sequencing. This review discusses the diverse informatics aspects that are relevant to the clinical microbiology laboratory, including the following: the microbiology laboratory information system, decision support tools, expert systems, instrument interfaces, total laboratory automation, telemicrobiology, automated image analysis, nucleic acid sequence databases, electronic reporting of infectious agents to public health agencies, and disease outbreak surveillance. The breadth and utility of informatics tools used in clinical microbiology have made them indispensable to contemporary clinical and laboratory practice. Continued advances in technology and development of these informatics tools will further improve patient and public health care in the future.

Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection

Background

Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers.

Methodology/Principal Findings

Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as “nuisance” biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms.

Conclusions

The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.