Search

Contexte : La surveillance sentinelle du syndrome d’allure grippale est une composante essentielle d’un programme exhaustif de surveillance de la grippe. Les systèmes de surveillance communautaires du syndrome d’allure grippale qui comptent uniquement sur des pratiques de soins de santé sentinelles omettent des segments importants de la population, notamment ceux qui ne consultent pas un médecin. La surveillance participative, qui compte sur la participation de la communauté à la surveillance, peut combler certaines lacunes des systèmes traditionnels de surveillance du syndrome d’allure grippale. Objectif : Nous avions pour objectif d’évaluer le programme ActionGrippe, une application de surveillance du syndrome d’allure grippale à externalisation ouverte développée pour compléter et apporter un complément à la surveillance du syndrome d’allure grippale au Canada. Méthodes : En utilisant les cadres établis pour les évaluations des données de surveillance, nous avons évalué l’acceptabilité, la fiabilité, l’exactitude et l’utilité du système ActionGrippe de 2015–2016 à 2018–2019. Les indicateurs d’évaluation ont été comparés aux indicateurs de surveillance nationale du syndrome d’allure grippale et des infections virales respiratoires confirmées en laboratoire. Résultats : L’acceptabilité du programme ActionGrippe a été démontrée par une croissance de 50 % à 100 % de la participation d’une saison à l’autre, et un taux de maintien constant de 80 % d’une saison à l’autre. La fiabilité était plus grande pour le programme ActionGrippe que pour notre système traditionnel de surveillance du syndrome d’allure grippale, bien que les deux systèmes aient enregistré des fluctuations d’une semaine à l’autre en ce qui concerne le nombre de participants ayant répondu. Les taux de syndrome d’allure grippale du programme ActionGrippe présentaient une corrélation modérée avec les taux de détection hebdomadaires de la grippe en laboratoire et d’autres détections de virus respiratoires saisonniers hivernaux, notamment le virus respiratoire syncytial et les coronavirus saisonniers. Enfin, le programme ActionGrippe a démontré son utilité en tant que source d’informations de base pour la surveillance du programme ÉpiGrippe et qu’il peut combler les lacunes de données dans les programmes actuels de surveillance et de contrôle de la grippe. Conclusion : Le programme ActionGrippe est un exemple de programme de surveillance participative numérique novateur qui a été créé pour remédier aux limites de la surveillance traditionnelle du syndrome d’allure grippale au Canada. Il répond aux critères d’évaluation du système de surveillance, qui sont l’acceptabilité, la fiabilité, l’exactitude et l’utilité.

Contexte : Le programme ÉpiGrippe est le système de surveillance national du Canada chargé de suivre la propagation de la grippe. Son volet de surveillance syndromique surveille la propagation du syndrome d’allure grippale en temps quasi réel pour détecter les signaux d’une activité inhabituelle ou accrue. Les données de surveillance syndromique sont recueillies à partir de deux sources principales : le Programme de surveillance des praticiens sentinelles et le programme ActionGrippe. Nous avons évalué la représentativité de la population participante la plus récente afin de comprendre les variations du taux de représentation depuis 2015, de cerner les écarts démographiques et géographiques ainsi que les corrélats ou déterminants de la participation pour caractériser un participant type. Méthodes : Dans cette étude transversale en série, les caractéristiques des participants au cours de quatre saisons grippales consécutives (2015–2016, 2016–2017, 2017–2018 et 2018–2019) ont été comparées à celles du Recensement canadien de 2016 et des enquêtes nationales sur la couverture vaccinale contre la grippe saisonnière de 2015–2016, de 2016–2017, de 2017–2018 et de 2018–2019. Les associations entre les facteurs démographiques et le niveau de participation des utilisateurs ont également été analysées parmi la population participant au programme ActionGrippe en 2018–2019. Résultats : Les enfants en bas âge (0 à 4 ans) et les personnes âgées (65 ans et plus) étaient sous-représentés dans le programme ActionGrippe au cours des quatre saisons grippales. Les femmes et les participants urbains étaient largement surreprésentés. La couverture vaccinale est restée considérablement plus élevée parmi les populations participant au programme ActionGrippe au cours des quatre dernières saisons grippales dans tous les groupes d’âge. Le niveau de participation au programme ActionGrippe était associé à l’âge et au statut d’immunisation, mais pas au sexe ni à la géographie. Au cours des quatre années de mise en œuvre, la population participant au programme ActionGrippe est devenue plus représentative de la population canadienne en ce qui concerne l’âge et la géographie (urbaine/rurale et provinciale/territoriale). Conclusion : Les participants au programme ActionGrippe sous-représentent les enfants en bas âge et les personnes âgées du Canada et surreprésentent ceux et celles qui adoptent des comportements favorables à la santé, comme l’indique la couverture vaccinale élevée contre la grippe, ce qui correspond aux biais de réponse typiques des enquêtes volontaires. La représentativité serait probablement améliorée par un recrutement ciblé des groupes sous-représentés, tels que les hommes, les personnes âgées et les Canadiens vivant dans les régions rurales.

Background: FluWatch is Canada’s national surveillance system that monitors the spread of influenza. Its syndromic surveillance component monitors the spread of influenza-like illness (ILI) in near-real time for signals of unusual or increased activity. Syndromic surveillance data are collected from two main sources: the Sentinel Practitioner ILI Reporting System and FluWatchers. We evaluated the representativeness of the most recent participant population to understand changes in representativeness since 2015, to identify demographic and geographic gaps and correlates/determinants of participation to characterize a typical participant. Methods: In this serial cross-sectional study, characteristics of participants during four consecutive influenza seasons (2015–2016, 2016–2017, 2017–2018 and 2018–2019) were compared with the 2016 Canadian Census and the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 National Seasonal Influenza Vaccination Coverage Surveys. Associations between demographic factors and the level of user participation were also analyzed among the 2018–2019 FluWatchers population. Results: Infants (0–4 years) and older adults (65 years and older) were under-represented in FluWatchers across all four influenza seasons. Female and urban participants were significantly over-represented. Vaccination coverage remained significantly higher among the FluWatchers populations from the past four influenza seasons across all age groups. Level of participation among FluWatchers was associated with age and vaccination status, but not with sex or geography. Over its four years of implementation, the FluWatchers participant population became more representative of the Canadian population with respect to age and geography (urban/rural and provincial/territorial). Conclusion: FluWatchers participants under-represent the tails of Canada’s age distribution and over-represent those who engage in health promoting behaviours as indicated by high influenza vaccine coverage, consistent with typical volunteer-based survey response biases. Representativeness would likely improve with targeted recruitment of under-represented groups, such as males, older adults and Canadians living in rural areas.

Background: Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. Objective: We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. Methods: Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015–2016, through 2018–2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. Results: The acceptability of FluWatchers was demonstrated by growth of 50%–100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers’ ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. Conclusion: FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.

Background Public health surveillance is essential to inform programs that aim to eradicate child maltreatment (CM) and to provide services to children and families. However, collection of CM data imposes a burden on child welfare workers (CWWs). This study assesses the feasibility of hiring coders to abstract the required information from administrative records and case narratives. Methods Based on a convenience sample of child welfare data from Manitoba, Canada, two coders abstracted information on 181 alleged CM cases. The coders completed a short web-based questionnaire for each case to identify which of five types of CM had been investigated, level of substantiation for each type, and risk of future CM. The CWWs responsible for each case completed the same questionnaire. Percentages of the occurrence of CM by the three sources were compared. The validity of the coders’ classifications was assessed by calculating sensitivity, specificity, and positive and negative predictive values, against the CWWs’ classifications as the “gold standard.” Cohen’s kappa was also calculated. Results The coders’ classifications of physical abuse, sexual abuse and neglect generally matched those of CWWs; for exposure to intimate partner violence, agreement was weak for one coder. Coding of emotional maltreatment and risk investigations could not be evaluated. Conclusion Results were promising. Abstraction was not time-consuming. Differences between coders and CWWs can be largely explained by the administrative data system, child welfare practice, and legislation. Further investigation is required to determine if additional training could improve coders’ classifications of CM.

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.

Objectives: To review user signal rating activity within the Canadian Network for Public Health Intelligence’s (CNPHI’s) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating?Methods: KIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff’s alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating.Results: The 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal’s 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once.Discussion: A diverse community of raters participated in rating the signals. Krippendorff’s Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals.Conclusions: Diversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal’s 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.

Objectives: The Canadian Network for Public Health Intelligence (CNPHI) is a secure, web-based scientific informatics and biosurveillance platform that leverages disparate public health information resources and expertise for the direct benefit of local, regional and national decision makers. CNPHI fosters ‎collaboration and consultation through innovation in disease surveillance, intelligence exchange, research and response to protect, promote and support public health. The objective of this article is to present the CNPHI ‘on the go’ mobile application, and to discuss preliminary evaluation of the technology. The mobile application is intended to enable rapid mobile data collection using both online and offline modes supporting various stages of surveillance and response through the extension of data collection and analysis to the mobile environment. Methods: Two needs assessment meetings were held with stakeholders representing individuals from government, academia and research institutions, to inform the development of the CNPHI “on the go” mobile application. An initial version of the mobile technology (an “app”) was developed and piloted by end-users with expertise in the field. Two focused pilots were conducted to test the technology: Pilot 1: 17-7-2017 to 21-11-2017 (6 participants); Pilot 2: 25-7-2017 to 15-9-2017 (2 participants). An initial consultation was held with the project leads to identify data elements for mobile data collection. A custom data collection form was designed using CNPHI’s Web Data technology for each pilot, which was then made available through the mobile app. The technology was assessed using feedback received during each pilot as well as through a survey that was conducted at the conclusion of pilots. Results: Pilot participants reported that the mobile technology allowed seamless data collection, data management and rapid information sharing. Participants also reported that the entire process was seamless, simple, efficient, and that fewer steps were required for data collection and management. Further, significant efficiencies were gained by directly entering information using the mobile app without having to transfer handwritten information into an electronic database. An overall positive experience was reported by participants from both pilots. Discussion: Literature suggests that traditional methods of surveillance and data collection using a paper based methodology pose many challenges such as data loss and duplication, difficulty in managing the database, and lack of timely access to the data. Accurate and rapid access is critical for public health professionals in order to effectively make decisions and respond to public health emergencies. Results show that the CNPHI “on the go” app is well poised to address some of the suggested challenges. A limitation of this study was that sample size for pilot participation was small for capturing overall feedback on the readiness of the technology for integration into regular surveillance activities and response procedures. Conclusions: CNPHI “on the go” is a customizable technology developed within an already thriving collaborative CNPHI platform used by public health professionals, and performs well as a tool for rapid data collection and secure information sharing.

Influenza A virus in swine is of significant importance to human and veterinary public health. Environmental sampling techniques that prove practical would enhance surveillance for influenza viruses in swine. The primary objective of this study was to demonstrate the feasibility of bioaerosol and surface sampling for the detection of influenza virus in swine barns with a secondary objective of piloting a mobile application for data collection. Sampling was conducted at a large swine operation between July 2016 and August 2017. Swine oral fluids and surface swabs were collected from multiple rooms. Room-level air samples were collected using four bioaerosol samplers: a low volume polytetrafluoroethylene (PTFE) filter sampler, the National Institute for Occupational Safety and Health's low volume cyclone sampler, a 2-stage Andersen impactor and/or one high volume cyclonic sampler. Samples were analysed using quantitative RT-PCR. Data and results were reported using a mobile data application. Eighty-nine composite oral fluid samples, 70 surface swabs and 122 bioaerosol samples were analysed. Detection rates for influenza virus RNA in swine barn samples were 71.1% for oral fluids, 70.8% for surface swabs and 71.1% for the PTFE sampler. Analysis revealed a statistically significant relationship between the results of the PTFE sampler and the surface swabs with oral fluid results (p < 0.001 and p < 0.01 respectively). In addition, both the PTFE sampler (p < 0.01) and surface swabs (p = 0.03) significantly correlated with, and predicted oral fluid results. Bioaerosol sampling using PTFE samplers is an effective hands-off approach for detecting influenza virus activity among swine. Further study is required for the implementation of this approach for surveillance and risk assessment of circulating influenza viruses of swine origin. In addition, mobile data collection stands to be an invaluable tool in the field by allowing secure, real-time reporting of sample collection and results.

BACKGROUND: Enterovirus D68 (EV-D68) has been detected infrequently and has not been associated with severe disease in Canada. In the early fall of 2014, following an unusual case increase in the United States, clusters of EV-D68 among children and some adults manifesting severe symptoms were reported in Canada. OBJECTIVE: To provide an initial epidemiological summary of pediatric cases hospitalized with EV-D68 in Canada. METHODS: A time-limited surveillance pilot was conducted collecting information on pediatric cases (less than 18 years of age) hospitalized with EV-D68 between September 1 and 30, 2014. RESULTS: In total, 268 cases were reported from Ontario (n=210), Alberta (n=45), and British Columbia (n=13). Of the 268 reported cases, 64.9% (n=174) were male; the sex difference was statistically significant (p

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.

Objectives: To introduce the Canadian Network for Public Health Intelligence’s new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to perform 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.

Purpose: To validate the utility and effectiveness of a standardized tool for prioritization of information sources for early detection of diseases. Methods: The tool was developed with input from diverse public health experts garnered through survey. Ten raters used the tool to evaluate ten information sources and reliability among raters was computed. The Proc mixed procedure with random effect statement and SAS Macros were used to compute multiple raters’ Fleiss Kappa agreement and Kendall's Coefficient of Concordance. Results: Ten disparate information sources evaluated obtained the following composite scores: ProMed 91%; WAHID 90%; Eurosurv 87%; MediSys 85%; SciDaily 84%; EurekAl 83%; CSHB 78%; GermTrax 75%; Google 74%; and CBC 70%. A Fleiss Kappa agreement of 50.7% was obtained for ten information sources and 72.5% for a sub-set of five sources rated, which is substantial agreement validating the utility and effectiveness of the tool. Conclusion: This study validated the utility and effectiveness of a standardized criteria tool developed to prioritize information sources. The new tool was used to identify five information sources suited for use by the KIWI system in the CEZD-IIR project to improve surveillance of infectious diseases. The tool can be generalized to situations when prioritization of numerous information sources is necessary.

We investigated the proportions of mono vs. mixed infections for human metapneumovirus (hMPV) as compared to adenovirus (ADV), four types of coronavirus (CRV), parainfluenza virus (PIV), RSV, and enterovirus/rhinovirus (ERV) in Alberta, Canada. Using the Data Integration for Alberta Laboratories (DIAL) platform, 26,226 respiratory specimens at ProvLab between 1 July 2009 and 30 June 2012 were selected and included in the study. Using the Respiratory Virus Panel these specimens tested positive for one or more respiratory virus and negative for influenza A and B. From our subset hMPV was the fourth most common virus (n=2,561) with 373 (15%) identified as mixed infection using DIAL. Mixed infection with hMPV was most commonly found in infants less than 6 months old and ERV was most commonly found in mixed infection with hMPV (230/373, 56%) across all age groups. The proportion of mixed-infection vs. mono-infection was highest for ADV (46%), followed by CRV 229E (32%), CRV HKU1 (31%), CRV NL63 (28%), CRV OC43 (23%), PIV (20%), RSV (17%), hMPV (15%) and ERV (13%). hMPV was significantly more likely to be identified in mono infection as compared with ADV, CRV, PIV, and RSV with the exception of ERV [p < 0.05].

This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as 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 and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.

There are many benefits that derive from real-time knowledge of the health status of the national livestock population. Effective animal disease surveillance is a requirement for countries that trade in live animals and their products in order to comply with the World Organization for Animal Health (OIE) guidelines. Rapid identification of introduced and emerging disease allows rapid response and mitigation of the economic consequences. Connections between animal and human disease caused by a common pathogen can be recognized and control measures implemented, thereby protecting public health and maintaining public confidence in the food supply. Production-limiting diseases can be monitored, and control programmes be evaluated with benefits accruing from decreased economic losses associated with disease as well as reducing the welfare concerns associated with diseased animals. Establishing a surveillance programme across a wide area with diverse ecosystems and political administrations as Canada is a complex challenge. When funding became available from a government programme to enable early detection of a bio-terrorist attack on livestock, the Canadian Animal Health Surveillance Network (CAHSN) became officially established. An existing web-based information platform that supports intelligence exchange, surveillance and response for public health issues in Canada was adapted to link the network animal health laboratories. A minimum data set was developed that facilitated sharing of results between participating laboratories and jurisdictions as the first step in creating the capacity for national disease trend analysis. In each of the network laboratories, similar quality assurance and bio-containment systems have been funded and supported, and diagnostic staff have been trained and certified on a suite of diagnostic tests for foreign animal diseases. This ensures that national standards are maintained throughout all of the diagnostic laboratories. This paper describes the genesis of CAHSN, its current capability and governance, and potential for future development.

In the spring of 2003, the Public Health Agency of Canada (then, Health Canada) partnered with several provincial/territorial and regional public health stakeholders to improve pan-Canadian public health surveillance, communications and response through the application of new technologies. This resulted in the creation of the Canadian Network for Public Health Intelligence (CNPHI), a comprehensive framework of applications and resources designed to fill critical gaps in Canada's national public health infostructure. Over the past four years, the CNPHI has evolved into Canada's only pan-Canadian public health information management system. With over 2000 registered users, the current CNPHI environment consists of more than 30 integrated applications and systems that can be loosely categorized into four functional groups: data exchange; data analysis and integration; communication, collaboration and coordination; and knowledge management. Despite poor data repositories, legacy information management systems, and the lack of standards and agreements, the CNPHI has demonstrated that much can be accomplished in these areas. Over the next decade, significant barriers impeding additional advances will be bridged through the implementation of the Electronic Health Record, and through ongoing efforts to address gaps in standards, and data- and information-sharing agreements. Together with new technologies coming on-line, opportunities to further enhance public health surveillance and response will be limited only by one's imagination.

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.

Environmental Health Surveillance is a critical function of public health laboratories, where monitoring water quality trends helps create information to be used for health promotion, disease mitigation, resource planning, risk analyses and other decision making.

This perspective paper presents a simple serious game on a mobile platform (Smartphone game). The game has the integrated capability to track a person’s play by storing player metadata on start time, end time, and moves within the game. These data can be analyzed to infer cognitive processes of strategy learning, retention, and recall over a brief period of time for potential future applications in pre-symptomatic assessment of mild cognitive impairment (MCI). Through machine learning (ML), the data are demonstrated to be of utility in providing a “cognitive fingerprint” of play. The ML methods used to classify play use synthetic data generated by robots (bots), ranging from bots playing perfectly to bots playing with various degrees of impairment. The findings include guidance on the volume of data required, as well as the features deemed effective for ML classification of various degrees of bot impairment. The work illustrates several significant considerations when applying ML to simple serious games and the data they can generate.

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 HI was associated with infection in older adults. Therefore, H3 was more commonly found than HI in outbreaks in LTCFs.

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.

This paper briefly outlines the combination of an Agent Based Modeling (ABM) framework for modeling the flow of traffic in an urban center, using a 3D gaming platform and incorporating real world data extracted from cell phone trajectories to guide agent movements. Results are compared against two other sets of real world data. The model validation shows considerable promise both for the simulation itself and the use of cellular location data to infer traffic patterns.

Laboratories play a critical role in facilitating timely recognition of and response to public health threats. However, capabilities and capacities vary widely among laboratories around the world. The scientific community recognizes that: 1) no single laboratory or network can effectively cover all health hazard threats and 2) connecting laboratories through networks enables scientific communities to harness and contribute their expertise in response to public health threats, while adding value and enhancing opportunities to enrich their own work. However, a consolidated and accessible inventory of laboratories that would enable this to happen does not exist. Public health laboratories serve the essential function of identifying etiologic agents of disease in an accurate and timely manner. However, the practicality and potential of these laboratories in the detection, monitoring, and reporting of threats over a wider geographic range is limited by unclear case definitions, inadequate laboratory capacity, and often, limited political will of local authorities to comply with International Health Regulations (IHR) 2005 (Baker and Fidler, 2006) to be prepared to respond to public health emergencies of international concern (PHEIC). Furthermore, some countries are not member states of World Health Organization (WHO) and therefore have no obligation to comply with IHR. Global, regional, and national laboratory networks serve to alleviate these issues by streamlining the detection, monitoring, and reporting procedures for communicable diseases in order to effectively and significantly reduce the global or regional burden of disease. Laboratory networks are useful in establishing and maintaining standards such as molecular disease confirmation by providing member laboratories with standardized testing and reporting procedures, reagents, equipment, training, reference materials, quality control indicators, and technical support. Such collaboration between and among laboratories facilitated by networks allows for rapid and accurate provision of information regarding the magnitude of disease and the strains that are circulating in particular regions, leading to faster response and more effective control of the threat. Some example networks include the Global Polio Network (Hull et al, 1997) and the Global Measles and Rubella Network (Featherstone et al, 2003).

BACKGROUND: Emergency Department (ED) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been studied. In theory, optimal ED patient flow may be assisted via balancing patient loads between EDs (in essence spreading patients more evenly throughout this system). This investigation utilizes simulation to explore “Crowdinforming” as a basis for a process control strategy aimed to balance patient loads between six Emergency Departments within a mid-sized Canadian city. METHODS: Anonymous patient visit data comprising 120,000 ED patient visits over six months to six ED facilities were obtained from the region’s Emergency Department Information System (EDIS) to (1) determine trends in ED visits and interactions between parameters; (2) to develop a process control strategy integrating crowdinforming; and, (3) apply and evaluate the model in a simulated environment to explore the potential impact on patient self-redirection and load balancing between EDs. RESULTS: As in reality, the data available and subsequent model demonstrated that there are many factors that impact ED patient flow. Initial results suggest that for this particular data set used, ED arrival rates were the most useful metric for ED ‘busyness’ in a process control strategy, and that Emergency Department performance may benefit from load balancing efforts. CONCLUSIONS: The simulation supports the use of crowdinforming as a potential tool when used in a process control strategy to balance the patient loads between emergency departments. The work also revealed that the value of several parameters intuitively expected to be meaningful metrics of ED ‘busyness’ was not evident, highlighting the importance of finding parameters meaningful within one’s particular data set. The information provided in the crowdinforming model is already available in a local context at some Emergency Department sites. The extension to a wider dissemination of information via an Internet web service accessible by smart phones is readily achievable. Similarly, the system could be extended to help direct patients by including future estimates or predictions in the crowdinformed data. The contribution of the simulation is to allow for effective policy evaluation to better inform the public of ED ‘busyness’ as part of their decision making process in attending an emergency department. In effect, this is a means of providing additional decision support insights garnered from a simulation, prior to a real world implementation.

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.

This paper presents a novel means of collecting and analyzing data related to personal social contact networks. The work developed a custom application for Smart phones that support Bluetooth connectivity, as representative of the ensemble of many consumer electronic products and can be used to infer users' proximity, contact duration, and GPS-based information. In many cases this is augmented by device meta identity. The 3G application, data storage and retrieval is discussed in detail. Preliminary data were collected (device-device proximity, duration, and location) gathered in pilot testing on the Blackberry Storm and HTC Hero (Android). Data are presented as distributions and visualization tools for evolving contact graphs, including Pareto distributions and power law exponents generated representative of face to face contacts. Finally, data are then demonstrated to be useful in estimating the potential of infection spread (e.g. respiratory illness), where a key transmission vector is person-person contact. A variant of the standard SIR agent based model is developed, with agent contacts guided by contact distributions extracted from the data.

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.

Emergency Department overcrowding is an important healthcare issue garnering significant public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been considered and implemented to various degrees. In theory, a more optimal emergency department patient flow may be assisted via balancing patient loads between emergency departments within the region - in essence spreading patients more evenly throughout this system. This investigation uses available data for one regional health authority, and utilizes simulation to explore a process control strategy built on ‘crowdinforming’, aimed to balance patient loads between six Emergency Departments within a mid-sized Canadian city. The preliminary model uses patient arrival rates as an indicator of ‘busyness’ and as the basis for crowdinforming. Intuitively expected metrics such as the number of patients waiting, the average length of stay, and time waiting to be seen were not found to be meaningful metrics of emergency department ‘busyness’ with this particular data set for the purpose of a process control strategy.

This paper presents a novel means of collecting and analyzing personal social network data, using 3G Smartphone that support Bluetooth connectivity. The application discovers and logs other close-proximity Bluetooth-discoverable devices, and this can be used to infer individuals' proximity, contact duration, as well as geographical location information. The contact data are presented in terms of distributions and visualization tools for evolving contact graphs. Finally, contact data are then demonstrated to be of utility in estimating the potential of the spread of a contact-based infectious disease, where a vector of transmission is proximity to infected agents.

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.

This paper presents three novel means of collecting and analyzing data to generate contact graphs of personal social networks. The means of collection include a web based service, an RFID system, and a network application running on a wireless sensor network. Data generated from networks of personal contact are demonstrated to be of utility in estimating the potential of the spread of a infection such as would be associated with the recent outbreak of H1N1 influenza.

Health surveillance can be viewed as an ongoing systematic collection, analysis, and interpretation of data for use in planning, implementation, and evaluation of a given health system, in potentially multiple spheres (ex: animal, human, environment). As we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. The main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. Identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. Proposed here is a novel framework that would enable a user and/or a system to interpret the anomaly detection results generated via multiple aberration detection algorithms with some indication of confidence. A framework that takes into account the relationships between algorithms and produces an unbiased confidence measure for identification of start of an outbreak.