Integrated Multilevel Surveillance of the World's Infecting Microbes and Their Resistance to Antimicrobial Agents

Epidemiology and molecular characterization of macrolide-resistant Streptococcus pyogenes in Taiwan

Our multicenter nationwide surveillance data indicated that erythromycin (ERY) resistance among group A Streptococcus (GAS) isolates in Taiwan declined from 53.1% in 1998 and 2000 to 14.6% in 2002 and 2004 and 10.7% in 2006 to 2010 (P < 0.01). The present study aimed to assess the epidemiology of GAS in Taiwan and identify factors associated with ERY resistance. All 127 ERY-resistant (ERY(r)) isolates and 128 randomly selected ERY-susceptible (ERY(s)) isolates recovered from 1998 to 2010 were emm typed. ERY(r) isolates were also characterized by ERY resistance phenotype and mechanisms and pulsed-field gel electrophoresis (PFGE). Multilocus sequence typing was performed on selected ERY(r) isolates. The predominant emm types in ERY(r) isolates were emm22 (n = 33, 26.0%), emm12 (n = 24, 18.9%), emm4 (n = 21, 16.5%), and emm106 (n = 15, 11.8%). In ERY(s) isolates, emm12 (n = 27, 21.9%), emm1 (n = 18, 14.1%), emm106 (n = 16, 12.5%), and emm11 (n = 9, 7.1%) predominated. The most common ERY resistance phenotype was the M phenotype (resistant to macrolides) (70.9%), with all but one isolate carrying mef(A), followed by the constitutive macrolide-lincosamide-streptogramin B resistance (cMLSB) phenotype (26.8%), with isolates carrying erm(B) or erm(TR). ERY(r) isolates of the emm12-sequence type 36 (ST36) lineage with the cMLSB phenotype were mostly present before 2004, while those of the emm22-ST46 lineage with the M phenotype predominated in later years. Recovery from respiratory (throat swab) specimens was an independent factor associated with ERY resistance. emm1 and emm11 GAS isolates were significantly associated with ERY(s), while emm22 was detected only in ERY(r) GAS. In addition, emm106 isolates were prevalent among the abscess/pus isolates, whereas emm12 isolates were strongly associated with a respiratory (throat) origin. In addition to identifying factors associated with ERY resistance in GAS, our study provides helpful information on the changing GAS epidemiology in Taiwan.

Resistant gram-negative infections in the outpatient setting in Latin America

Latin America has a high rate of community-associated infections caused by multidrug-resistant Enterobacteriaceae relative to other world regions. A review of the literature over the last 10 years indicates that urinary tract infections (UTIs) by Escherichia coli, and intra-abdominal infections (IAIs) by E. coli and Klebsiella pneumoniae, were characterized by high rates of resistance to trimethoprim/sulfamethoxazole, quinolones, and second-generation cephalosporins, and by low levels of resistance to aminoglycosides, nitrofurantoin, and fosfomycin. In addition, preliminary data indicate an increase in IAIs by Enterobacteriaceae producing extended-spectrum β-lactamases, with reduced susceptibilities to third- and fourth-generation cephalosporins. Primary-care physicians in Latin America should recognize the public health threat associated with UTIs and IAIs by resistant Gram-negative bacteria. As the number of therapeutic options become limited, we recommend that antimicrobial prescribing be guided by infection severity, established patient risk factors for multidrug-resistant infections, acquaintance with local antimicrobial susceptibility data, and culture collection.

Finding evidence for local transmission of contagious disease in molecular epidemiological datasets

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence.

AmWeb: a novel interactive web tool for antimicrobial resistance surveillance, applicable to both community and hospital patients

BACKGROUND

Antimicrobial resistance (AMR) is recognized as one of the most significant threats to human health. Local and regional AMR surveillance enables the monitoring of temporal changes in susceptibility to antibiotics and can provide prescribing guidance to healthcare providers to improve patient management and help slow the spread of antibiotic resistance in the community. There is currently a paucity of routine community-level AMR surveillance information.

METHODS

The HPA in England sponsored the development of an AMR surveillance system (AmSurv) to collate local laboratory reports. In the West Midlands region of England, routine reporting of AMR data has been established via the AmSurv system from all diagnostic microbiology laboratories. The HPA Regional Epidemiology Unit developed a web-enabled database application (AmWeb) to provide microbiologists, pharmacists and other stakeholders with timely access to AMR data using user-configurable reporting tools.

RESULTS

AmWeb was launched in the West Midlands in January 2012 and is used by microbiologists and pharmacists to monitor resistance profiles, perform local benchmarking and compile data for infection control reports. AmWeb is now being rolled out to all English regions.

CONCLUSIONS

It is expected that AmWeb will become a valuable tool for monitoring the threat from newly emerging or currently circulating resistant organisms and helping antibiotic prescribers to select the best treatment options for their patients.

Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends

Background

Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available.

Objective

To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems.

Methods

We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels.

Results

The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends.

Conclusion

The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends.

High rates of multidrug resistance in Enterococcus faecalis and E. faecium isolated from inpatients and outpatients in Taiwan

Longitudinal national data on resistance in Enterococcus faecalis and E. faecium from different sources in Taiwan are rare. The present study analyzed data from the Taiwan Surveillance of Antimicrobial Resistance program to address this issue. Between 2002 and 2010, a total of 1696 E. faecalis and 452 E. faecium isolates were studied. Although these 2 species together comprised similar percentages of all enterococci in each study year (94.1-97.2%, P = 0.19), the proportion of E. faecium increased from 12.4% in 2002 to 27.3% in 2010 (P < 0.001). The most noteworthy change in susceptibilities of these 2 species was vancomycin resistance in E. faecium (VREfm), which increased from 0.3% in 2004 to 24.9% in 2010 (P < 0.001). VREfm prevalence differed significantly between geographic regions, patient age groups, and locations. Multidrug resistance was very common in both species even in isolates from outpatients (82.7% for E. faecalis and 98.1% for E. faecium), at rates similar to those from intensive care unit (ICU) and non-ICU patients (80.5-80.9% in E. faecalis and 97.2-98.6% in E. faecium). Nonsusceptibility to linezolid was <0.5% in both species. All tested isolates were susceptible to daptomycin. Continuous surveillance of VRE prevalence and survey of community reservoirs of multidrug-resistant enterococci are warranted.

Clinical breakpoint changes and their impact on surveillance of antimicrobial resistance in Escherichia coli causing bacteraemia

Dutch laboratories are currently changing their breakpoint criteria from mostly Clinical Laboratory and Standards Institute (CLSI) breakpoints to European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints. To evaluate the impact of these changes, we studied antimicrobial resistance trends of Escherichia coli in blood specimens from January 2008 to January 2012 using CLSI and EUCAST breakpoints and compared them with the antimicrobial susceptibility test (AST) interpretations reported by Dutch laboratories participating in the Infectious Disease Surveillance Information System for Antibiotic Resistance (ISIS-AR). ISIS-AR collects AST interpretations, including underlying minimal inhibitory concentrations (MICs) of routinely cultured bacterial species on a monthly basis from Dutch laboratories. MICs of Etests or automated systems were reinterpreted according to the CLSI 2009 and EUCAST 2010 guidelines. Trends in non-susceptibility (i.e. intermediate resistant and resistant) over time were analysed by the Cochran-Armitage test for trend. The effects of the change from CLSI to EUCAST breakpoints on non-susceptibility were small. There were no differences in non-susceptibility to amoxicillin, amoxicillin/clavulanic acid, cefuroxim, gentamicin and co-trimoxazol and only small differences (1-1.5%) for ciprofloxacin between AST interpretations by CLSI or EUCAST. However, for ceftazidime, and cefotaxime/ceftriaxone the proportion of non-susceptibility was substantially higher when EUCAST breakpoints were used (2-3%). The effects on time trends of the change in guidelines were limited, with only substantial differences for the oxymino-cephalosporins. Our study shows that the implementation of EUCAST breakpoints has a limited effect on the proportion of non-susceptible isolates and time trends in E. coli for most, but not all, antimicrobial agents.

Enteric protozoa in the developed world: a public health perspective

Several enteric protozoa cause severe morbidity and mortality in both humans and animals worldwide. In developed settings, enteric protozoa are often ignored as a cause of diarrheal illness due to better hygiene conditions, and as such, very little effort is used toward laboratory diagnosis. Although these protozoa contribute to the high burden of infectious diseases, estimates of their true prevalence are sometimes affected by the lack of sensitive diagnostic techniques to detect them in clinical and environmental specimens. Despite recent advances in the epidemiology, molecular biology, and treatment of protozoan illnesses, gaps in knowledge still exist, requiring further research. There is evidence that climate-related changes will contribute to their burden due to displacement of ecosystems and human and animal populations, increases in atmospheric temperature, flooding and other environmental conditions suitable for transmission, and the need for the reuse of alternative water sources to meet growing population needs. This review discusses the common enteric protozoa from a public health perspective, highlighting their epidemiology, modes of transmission, prevention, and control. It also discusses the potential impact of climate changes on their epidemiology and the issues surrounding waterborne transmission and suggests a multidisciplinary approach to their prevention and control.

Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation

Background

Antimicrobial resistance has reached globally alarming levels and is becoming a major public health threat. Lack of efficacious antimicrobial resistance surveillance systems was identified as one of the causes of increasing resistance, due to the lag time between new resistances and alerts to care providers. Several initiatives to track drug resistance evolution have been developed. However, no effective real-time and source-independent antimicrobial resistance monitoring system is available publicly.

Objective

To design and implement an architecture that can provide real-time and source-independent antimicrobial resistance monitoring to support transnational resistance surveillance. In particular, we investigated the use of a Semantic Web-based model to foster integration and interoperability of interinstitutional and cross-border microbiology laboratory databases.

Methods

Following the agile software development methodology, we derived the main requirements needed for effective antimicrobial resistance monitoring, from which we proposed a decentralized monitoring architecture based on the Semantic Web stack. The architecture uses an ontology-driven approach to promote the integration of a network of sentinel hospitals or laboratories. Local databases are wrapped into semantic data repositories that automatically expose local computing-formalized laboratory information in the Web. A central source mediator, based on local reasoning, coordinates the access to the semantic end points. On the user side, a user-friendly Web interface provides access and graphical visualization to the integrated views.

Results

We designed and implemented the online Antimicrobial Resistance Trend Monitoring System (ARTEMIS) in a pilot network of seven European health care institutions sharing 70+ million triples of information about drug resistance and consumption. Evaluation of the computing performance of the mediator demonstrated that, on average, query response time was a few seconds (mean 4.3, SD 0.1×10² seconds). Clinical pertinence assessment showed that resistance trends automatically calculated by ARTEMIS had a strong positive correlation with the European Antimicrobial Resistance Surveillance Network (EARS-Net) (ρ = .86, P < .001) and the Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH) (ρ = .84, P < .001) systems. Furthermore, mean resistance rates extracted by ARTEMIS were not significantly different from those of either EARS-Net (∆ = ±0.130; 95% confidence interval –0 to 0.030; P < .001) or SEARCH (∆ = ±0.042; 95% confidence interval –0.004 to 0.028; P = .004).

Conclusions

We introduce a distributed monitoring architecture that can be used to build transnational antimicrobial resistance surveillance networks. Results indicated that the Semantic Web-based approach provided an efficient and reliable solution for development of eHealth architectures that enable online antimicrobial resistance monitoring from heterogeneous data sources. In future, we expect that more health care institutions can join the ARTEMIS network so that it can provide a large European and wider biosurveillance network that can be used to detect emerging bacterial resistance in a multinational context and support public health actions.