Overview of Canada's Antimicrobial Resistance Network (AMRNet): A data-driven One Health approach to antimicrobial resistance surveillance.

The Antimicrobial Resistance Network (AMRNet) is a laboratory-based antimicrobial resistance (AMR) surveillance system under development at the Public Health Agency of Canada's (PHAC's) National Microbiology Laboratory. The AMRNet surveillance system captures information on antimicrobial susceptibility testing from clinical and veterinary laboratories including both public and private facilities. In the future, the AMRNet system will also capture relevant data from existing PHAC surveillance systems for AMR including the Canadian Integrated Program for Antimicrobial Resistance Surveillance, the Canadian Nosocomial Infection Surveillance Program and the Enhanced Surveillance of Antimicrobial-Resistant Gonorrhea program, and contribute to the Canadian Antimicrobial Resistance Surveillance System. AMRNet's integrated "One Health" approach will allow health professionals and researchers to take a multi-dimensional perspective of AMR in both human and animal health in Canada and will make Canada a leader in AMR surveillance. AMRNet is a collaboration between PHAC, provincial and territorial public health organizations as well as clinical and veterinary laboratories across the country. As part of a phased rollout, AMRNet is now collecting human clinical data from three provinces, from both inpatients and outpatients. Ultimately, AMRNet aims to capture all antimicrobial susceptibility testing results from all bacterial and fungal pathogens across Canada. This article describes the AMRNet surveillance system, including program objectives, system structure and the data collected. The integration of human and animal data in AMRNet will inform One Health responses to AMR issues. The capacity to collect and to disseminate data to stakeholders in real time is a critical step to addressing emerging AMR issues in Canada.

Influenza-like illness-related emergency department visits: Christmas and New Year holiday peaks and relationships with laboratory-confirmed respiratory virus detections, Edmonton, Alberta, 2004-2014.

BACKGROUND: Emergency department (ED) visit volumes can be especially high during the Christmas-New Year holidays, a period occurring during the influenza season in Canada. METHODS: Using daily data, we examined the relationship between ED visits for the chief complaint "cough" (for Edmonton, Alberta residents) and laboratory detections for influenza A and respiratory syncytial virus (RSV) (for Edmonton and surrounding areas), lagged 0-5 days ahead, for non-pandemic years (2004-2008 and 2010-2014) using multivariable linear regression adjusting for temporal variables. We defined these cough-related visits as influenza-like illness (ILI)-related ED visits and, for 2004-2014, compared Christmas-New Year holiday (December 24-January 3) and non-holiday volumes during the influenza season (October-April). RESULTS: Adjusting for temporal variables, ILI-related ED visits were significantly associated with laboratory detections for influenza A and RSV. During non-pandemic years, the highest peak in ILI-related visit volumes always occurred during the holidays. The median number of holiday ILI-related visits/day (42.5) was almost twice the non-holiday median (24) and was even higher in 2012-2013 (80) and 2013-2014 (86). Holiday ILI-related ED visit volumes/100 000 population ranged from 56.0 (2010-2011) to 117.4 (2012-2013). In contrast, lower visit volumes occurred during the holidays of pandemic-affected years (2008-2010). CONCLUSIONS: During non-pandemic years, ILI-related ED visit volumes were associated with variations in detections for influenza A and RSV and always peaked during the Christmas-New Year holidays. This predictability should be used to prepare for, and possibly prevent, this increase in healthcare use; however, interventions beyond disease prevention strategies are likely needed.

KIWI: A technology for public health event monitoring and early warning signal detection.

OBJECTIVES: To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats. METHODS: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. RESULTS: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%). DISCUSSION: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities. CONCLUSIONS: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

An innovative web based system for reporting rare diseases in paediatrics.

BACKGROUND: Surveillance of rare diseases in children is an important aspect of public health. Rare diseases affect thousands of children worldwide. The Canadian Paediatric Surveillance Program (CPSP) has been in existence since 1996, and provides an innovative means to undertake paediatric surveillance and increase awareness of childhood disorders that are high in disability, morbidity, mortality, and economic costs to society, despite their low frequency. Traditionally, CPSP used manual paper-based reporting on a monthly basis, which although had an impressive response rate, it had inherent longer processing times and costs associated with it. OBJECTIVES: To provide an overview and evaluate an innovative web-based system that enables seamless reporting from participants across the country providing a quick, reliable and simple mechanism for the participants to submit data while yielding better data quality, timeliness and increased efficiencies. METHODS: In 2011, a proprietary electronic CPSP (eCPSP) system was developed to provide a simple, quick and reliable reporting environment for participants. It supports both the electronic and hardcopy reporting. The analysis presented in this paper was conducted based on usage data of this system. RESULTS: The response rates of the new eCPSP were found to be very favorable with adjusted rate of 80%, which equals the baseline. Approximately 50% of online participants report the first day they receive the notification e-mail. The response time was also reduced considerably. Furthermore, there has been significant reduction in data handling related activities (by almost 70%) from estimated 690 hours per year. Finally, the number of cases reported that do not fit the study case criteria has fallen, likely because participants can now immediately access the case definition and protocol via the online system. This has reduced both staff and investigator time for case processing. CONCLUSION: The eCPSP has modernized the CPSP program from paper-based reporting to efficient online technology while maintaining the core principles of the program. This simple and intuitive approach has proven to be an efficient approach cutting response times significantly while maintaining the desired response rates.

Bordetella pertussis in sporadic and outbreak settings in Alberta, Canada, July 2004-December 2012.

BACKGROUND: ProvLab Alberta provides all laboratory testing for Bordetella pertussis including sporadic cases and outbreak investigations through collaborations with provincial public health partners. We describe B. pertussis activity in Alberta from July 2004 to December 2012. METHODS: Laboratory testing for pertussis was analyzed using interpreted laboratory data that was generated by DIAL, a secure web-based platform. Duplicate specimens from the same individual ≤90 days were excluded to generate a case-based dataset. Immunization status of confirmed pertussis cases from the provincial immunization repository was reviewed. RESULTS: Overall, 7.1% of suspected pertussis cases tested positive with a higher positivity rate in outbreak as compared to sporadic setting. Annual variations in sporadic pertussis cases were observed across the province with higher positivity rates in 2005, 2008, 2009 and 2012. A significantly higher positivity rate was observed in a northern region of Alberta. While the positivity rate in sporadic setting was highest in adolescents aged 10 to <15 years old (14.8%), population-based disease burden was highest in young children <5 years old. Of the 81.6% (n = 1,348) pertussis cases with immunization records, 48.3% were up-to-date with immunization. The pertussis cases that were up-to-date with their immunization were older (median age 12.9 years) as compared to those with incomplete (median age 9.7 years) or no pertussis immunization (median age 3.8 years). CONCLUSIONS: Cyclic pattern of annual pertussis activity with geographic variation was observed in Alberta with no obvious case finding effect from outbreak investigations. The high positivity rates in adolescents suggested an underestimation of disease burden in this age group.

Swine influenza test results from animal health laboratories in Canada.

Due to its infrastructure and partnerships the Canadian Animal Health Surveillance Network was able to rapidly collect test results from 9 Canadian laboratories that were conducting primary testing for influenza on swine-origin samples, in response to the threat posed by the pandemic H1N1 influenza virus in 2009.

Résultats des tests de la grippe porcine provenant des laboratoires de santé animale au Canada. En raison de son infrastructure et de partenariats, le Réseau canadien de surveillance zoosanitaire a été capable de recueillir rapidement les résultats de tests de 9 laboratoires canadiens qui réalisaient des tests primaires pour la grippe sur des échantillons d'origine porcine, en réponse à la menace présentée par le virus de l'influenza H1N1 pandémique en 2009.(Traduit par Isabelle Vallières).

Impact of routine real-time PCR testing of imported malaria over 4 years of implementation in a clinical laboratory.

In clinical laboratories, diagnosis of imported malaria is commonly performed by microscopy. However, the volume of specimens is generally low and maintaining proficiency in reading blood smears, particularly at the species level, is challenging in this setting. To address this problem, the Provincial Laboratory for Public Health (ProvLab) in Alberta, Canada, implemented real-time PCR for routine confirmation of all smear-positive samples in the province. Here we report our experience over a 4-year period (2008 to 2012) with this new diagnostic algorithm. While detection of Plasmodium falciparum by microscopy alone was accurate, real-time PCR served as an important adjunct to microscopy for the identification of non-falciparum species. In 18% of cases, the result was reported as non-falciparum or the species could not be identified by microscopy alone, and in all cases, the species was resolved by real-time PCR. In another 4% of cases, the species was misidentified by microscopy. To enhance surveillance for malaria, we integrated our demographic, clinical, and laboratory data into a new system developed by the Canadian Network for Public Health Intelligence, called the Malaria System for Online Surveillance (SOS). Using this application, we characterized our patient populations and travel history to identify risk factors associated with malaria infection abroad.

A Web-Based System for Mapping Laboratory Networks: Analysis of GLaDMap Application.

Public health emergencies such as H1N1 and SARS pandemics have demonstrated and validated the necessity of a strong and cohesive laboratory response system that is able to respond to threats in an efficient and timely manner. Individual laboratories, through connection with other laboratories or networks, are able to enhance their capacity for preparedness and response to emergencies. Efficient networks often establish standards and maintain best practices within member laboratories. The Global Laboratory Directory Mapping tool (GLaDMap) supports the efforts of laboratory networks to improve their connectivity by providing a simple and efficient tool to profile laboratories by geographic location, function or expertise. The purpose of this paper is to evaluate the effectiveness of the GLaDMap search tool and the completeness of the descriptive content of networks and laboratories that are currently contained within the GLaDMap database. We determined the extent of information volunteered and how the system is being used. Although the system aims to attract an array of users from around the globe, our analysis reveals minimal participation and information sharing and that the low profile participation rate limits the tool's functionality. The Global Laboratory Directory platform has addressed barriers to participation by adding optional functionality such as restricted access to laboratory profiles to protect private information and by implementing additional functional applications complementary to GLaDMap.

Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study.

The objective of this paper was to develop an agent-based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent-based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C++ using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that became infected in one day within the simulation was the dependent variable. The results suggest that within the given instance context, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Determination of the relative economic impact of different molecular-based laboratory algorithms for respiratory viral pathogen detection, including Pandemic (H1N1), using a secure web based platform.

BACKGROUND: During period of crisis, laboratory planners may be faced with a need to make operational and clinical decisions in the face of limited information. To avoid this dilemma, our laboratory utilizes a secure web based platform, Data Integration for Alberta Laboratories (DIAL) to make near real-time decisions.This manuscript utilizes the data collected by DIAL as well as laboratory test cost modeling to identify the relative economic impact of four proposed scenarios of testing for Pandemic H1N1 (2009) and other respiratory viral pathogens. METHODS: Historical data was collected from the two waves of the pandemic using DIAL. Four proposed molecular testing scenarios were generated: A) Luminex respiratory virus panel (RVP) first with/without US centers for Disease Control Influenza A Matrix gene assay (CDC-M), B) CDC-M first with/without RVP, C) RVP only, and D) CDC-M only. Relative cost estimates of different testing algorithm were generated from a review of historical costs in the lab and were based on 2009 Canadian dollars. RESULTS: Scenarios A and B had similar costs when the rate of influenza A was low (< 10%) with higher relative cost in Scenario A with increasing incidence. Scenario A provided more information about mixed respiratory virus infection as compared with Scenario B. CONCLUSIONS: No one approach is applicable to all conditions. Testing costs will vary depending on the test volume, prevalence of influenza A strains, as well as other circulating viruses and a more costly algorithm involving a combination of different tests may be chosen to ensure that tests results are returned to the clinician in a quicker manner. Costing should not be the only consideration for determination of laboratory algorithms.

Innovative technology for web-based data management during an outbreak.

Lack of automated and integrated data collection and management, and poor linkage of clinical, epidemiological and laboratory data during an outbreak can inhibit effective and timely outbreak investigation and response. This paper describes an innovative web-based technology, referred to as Web Data, developed for the rapid set-up and provision of interactive and adaptive data management during outbreak situations. We also describe the benefits and limitations of the Web Data technology identified through a questionnaire that was developed to evaluate the use of Web Data implementation and application during the 2009 H1N1 pandemic by Winnipeg Regional Health Authority and Provincial Laboratory for Public Health of Alberta. Some of the main benefits include: improved and secure data access, increased efficiency and reduced error, enhanced electronic collection and transfer of data, rapid creation and modification of the database, conversion of specimen-level to case-level data, and user-defined data extraction and query capabilities. Areas requiring improvement include: better understanding of privacy policies, increased capability for data sharing and linkages between jurisdictions to alleviate data entry duplication.

Improving Agent Based Models and Validation through Data Fusion.

This work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. This work uses the integration of real data sources into an Agent Based Model (ABM) to simulate respiratory infection spread within a small municipality. Novelty is derived in that the data sources are not necessarily obvious within ABM infection spread models. The ABM is a spatial-temporal model inclusive of behavioral and interaction patterns between individual agents on a real topography. The agent behaviours (movements and interactions) are fed by census / demographic data, integrated with real data from a telecommunication service provider (cellular records) and person-person contact data obtained via a custom 3G Smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion that can be used by policy and decision makers. The data become real-world inputs into individual SIR disease spread models and variants, thereby building credible and non-intrusive models to qualitatively simulate and assess public health interventions at the population level.

Association between patient age and influenza A subtype during influenza outbreaks.

The distribution of influenza A subtypes was studied in specimens recovered from patients in long-term care facility (LTCF) outbreaks and in non-LTCF outbreaks in Alberta, Canada, for 3 years before the influenza pandemic of 2009. We found that H3 but not H1 was associated with infection in older adults. Therefore, H3 was more commonly found than H1 in outbreaks in LTCFs.

Agent based modeling of "crowdinforming" as a means of load balancing at emergency departments.

This work extends ongoing development of a framework for modeling the spread of contact-transmission infectious diseases. The framework is built upon Agent Based Modeling (ABM), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. The method presented here includes ABM modeling an outbreak of influenza-like illness (ILI) with concomitant surges at hospital emergency departments, and illustrates the preliminary modeling of 'crowdinforming' as an intervention. 'Crowdinforming', a component of 'crowdsourcing', is characterized as the dissemination of collected and processed information back to the 'crowd' via public access. The objective of the simulation is to allow for effective policy evaluation to better inform the public of expected wait times as part of their decision making process in attending an emergency department or clinic. In effect, this is a means of providing additional decision support garnered from a simulation, prior to real world implementation. The conjecture is that more optimal service delivery can be achieved under balanced patient loads, compared to situations where some emergency departments are overextended while others are underutilized. Load balancing optimization is a common notion in many operations, and the simulation illustrates that 'crowdinforming' is a potential tool when used as a process control parameter to balance the load at emergency departments as well as serving as an effective means to direct patients during an ILI outbreak with temporary clinics deployed. The information provided in the 'crowdinforming' model is readily available in a local context, although it requires thoughtful consideration in its interpretation. The extension to a wider dissemination of information via a web service is readily achievable and presents no technical obstacles, although political obstacles may be present. The 'crowdinforming' simulation is not limited to arrivals of patients at emergency departments due to ILI; it applies equally to any scenarios where patients arrive in any arrival pattern that may cause disparity in the waiting times at multiple facilities.

DIAL: A Platform for real-time Laboratory Surveillance.

Laboratory information systems fulfill many of the requirements for individual result management within a public health laboratory. However, access to the systems by data users, timely data extraction, integration, and data analysis are difficult tasks. These difficulties are further complicated by often having multiple laboratory results for specific analytes or related analytes per specimen tested as part of complex laboratory algorithms requiring specialized expertise for result interpretation. We describe DIAL, (Data Integration for Alberta Laboratories), a platform allowing laboratory data to be extracted, interpreted, collated and analyzed in near real-time using secure web based technology, which is adapted from CNPHI's Canadian Early Warning System (CEWS) technology. The development of DIAL represents a major technical advancement in the public health information management domain, building capacity for laboratory based surveillance.