This thesis is an investigation of various sources of animal health data used in disease surveillance for the swine industry in Ontario, Canada. Health data derived from veterinary diagnostic laboratory submission rates, veterinary diagnostic test results, an abattoir, and swine sentinel herds are examined. Based on data from 1998 to 2009, the rate of submissions to a veterinary diagnostic laboratory are dependent on economic variables associated with the swine industry and include: the price paid to swine producers for pigs, the cost of feed (corn), the United States/Canadian dollar exchange rate, and lean-hog futures. An outbreak of porcine circovirus-associated disease (PCVAD) that occurred in the Ontario swine industry from 2004-2006 increased the rate of diagnostic submissions despite the poor economic state of the swine industry at the time. The positivity of porcine reproductive and respiratory syndrome virus (PRRSV) polymerase chain reaction (PCR) tests ordered by veterinary practitioners decreased during the PCVAD outbreak. There was a strong seasonal and yearly affect in the model influencing the positivity of PRRSV PCR tests. However, there was no association between the positivity of PRRSV enzyme-linked immunosorbent assays (ELISA) and the PCVAD outbreak. These findings suggest that the results of tests ordered by veterinarians at diagnostic laboratories have the potential to be used as a form of syndromic surveillance. Swine tonsils (n=395) were collected from a federally-inspected abattoir to determine the prevalence of porcine pathogens, and the most predominant bacterial pathogens isolated were Streptococcus suis (53.7%), Arcanobacterium pyogenes (29.9%), Pasteurella multocida (27.3%), and Streptococcus porcinus (19.5%). Tonsils collected from the held-rail were more likely to be positive for, Staphylococcus hyicus, Streptococcus porcinus, and Streptococcus suis. PRRSV and porcine circovirus-2 were detected in 22.0% and 11.9% of the samples, respectively. Stored serum samples (n=500) from 50 Ontario swine sentinel herds and samples from 2 case herds with clinical disease suggestive of pestivirus infection were tested for Bovine Viral Diarrhea Virus (BVDV) to determine the prevalence of BVDV on Ontario swine farms. The prevalence of BVDV on Ontario swine farms was negligible and the presence of cattle on the same farm was not an identified risk factor.

This thesis examines the potential interface that exists between health information, specifically electronic health record (EHR) systems, and notifiable disease surveillance in Canada. It aims to highlight the benefits and barriers experienced by the current national notifiable disease surveillance strategy, as well as to highlight the successes and roadblocks to the successful implementation and adoption of EHR technologies in Canada. Qualitative methodologies, which include the 16 semi-structured interviews conducted with four key stakeholder groups, including public health experts, physicians, health administrators and academics that are concerned with EHR adoption and public health were used to obtain data. Data from interviews was analysed using grounded theory methodology and then verified using member checking and other data validation methods. Emergent themes from obtained data indicated that there is a large potential for the improvement of the current notifiable disease through the use of EHR technologies: however, the barriers currently faced by both the notifiable disease surveillance system and the state of implementation and adoption of EHR technologies prevent this from occurring. These barriers include political, financial, human, security/privacy, and technology barriers. Differences between stakeholder groups were explored, and potential solutions and insights into existing barriers were provided. The information gained from this study provides insight into the efficiency of the current infectious disease surveillance system and the progress of and need for the implementation of EHRs nationwide. In addition, the results of this study provide stakeholders with a deeper understanding of the barriers facing the use of EHR technologies for infectious disease surveillance and provide a starting place to address these issues. The results of this study can help to inform policy regarding public health surveillance and EHR implementation and adoption.

Traditional disease surveillance systems are instrumental in guiding policy-makers' decisions and understanding disease dynamics. The first study in this dissertation looks at sentinel surveillance network design. We consider three location-allocation models: two based on the maximal coverage model (MCM) and one based on the K-median model. The MCM selects sites that maximize the total number of people within a specified distance to the site. The K-median model minimizes the sum of the distances from each individual to the individual's nearest site. Using a ground truth dataset consisting of two million de-identified Medicaid billing records representing eight complete influenza seasons and an evaluation function based on the Huff spatial interaction model, we empirically compare networks against the existing volunteer-based Iowa Department of Public Health influenza-like illness network by simulating the spread of influenza across the state of Iowa. We compare networks on two metrics: outbreak intensity (i.e., disease burden) and outbreak timing (i.e., the start, peak, and end of the epidemic). We show that it is possible to design a network that achieves outbreak intensity performance identical to the status quo network using two fewer sites. We also show that if outbreak timing detection is of primary interest, it is actually possible to create a network that matches the existing network's performance using 42% fewer sites. Finally, in an effort to demonstrate the generic usefulness of these location-allocation models, we examine primary stroke center selection. We describe the ineffectiveness of the current self-initiated approach and argue for a more organized primary stroke center system. While these traditional disease surveillance systems are important, they have several downsides. First, due to a complex reporting hierarchy, there is generally a reporting lag; for example, most diseases in the United States experience a reporting lag of approximately 1-2 weeks. Second, many regions of the world lack trustworthy or reliable data. As a result, there has been a surge of research looking at using publicly available data on the internet for disease surveillance purposes. The second and third studies in this dissertation analyze Wikipedia's viability in this sphere. The first of these two studies looks at Wikipedia access logs. Hourly access logs dating back to December 2007 are available for anyone to download completely free of charge. These logs contain, among other things, the total number of accesses for every article in Wikipedia. Using a linear model and a simple article selection procedure, we show that it is possible to nowcast and, in some cases, forecast up to the 28 days tested in 8 of the 14 disease-location contexts considered. We also demonstrate that it may be possible in some cases to train a model in one context and use the same model to nowcast or forecast in another context with poor surveillance data. The second of the Wikipedia studies looked at disease-relevant data found in the article content. A number of disease outbreaks are meticulously tracked on Wikipedia. Case counts, death counts, and hospitalization counts are often provided in the article narrative. Using a dataset created from 14 Wikipedia articles, we trained a named-entity recognizer (NER) to recognize and tag these phrases. The NER achieved an F1 score of 0.753. In addition to these counts in the narrative, we tested the accuracy of tabular data using the 2014 West African Ebola virus disease epidemic. This article, like a number of other disease articles on Wikipedia, contains granular case counts and deaths counts per country affected by the disease. By computing the root-mean-square error between the Wikipedia time series and a ground truth time series, we show that the Wikipedia time series are both timely and accurate.

This document describes an evaluation of acute (meningitis)-encephalitis syndrome (AES/AMES) surveillance established in India, Bangladesh and China. The key objectives of the project included 1) building on existing networks for syndromic surveillance and laboratory confirmation, 2) establishing laboratory-based surveillance for vaccine-preventable causes of encephalitis and meningitis, 3) enhancing capacity to use data to guide disease control and prevention programs, and 4) improving capacity to recognize new or emerging diseases. The syndromes encompass several diseases, including Japanese encephalitis (JE), pneumococcal meningitis, Haemophilus influenzae type b (Hib), and meningococcal meningitis. The purpose of the evaluation is to assess the extent to which the key objectives were met in the three project countries, compare and contrast the experiences among the countries, document the strengths and weaknesses, and make recommendations. The indicators used in the evaluation include feasibility of integration, availability of country protocols, appropriate training, data quality, sensitivity, specificity, positive predictive value, negative predictive value, representativeness, timeliness, integration with AFP surveillance, simplicity and efficiency, acceptability, usefulness, flexibility, stability, and sustainability. The criteria and standards are based on WHO recommendations. Data sources include AES/AMES epidemiologic and laboratory data sets, trip reports, country reports, field observations, and published bulletins. All countries made substantial progress in a relatively short period of time using the infrastructure and technical tools of existing surveillance and laboratory networks for acute flaccid paralysis. After one year, India and Bangladesh collects and maintains high quality epidemiologic data, exceeds targets for timeliness of reporting, and has quality-assured capacity for laboratory confirmation of Japanese encephalitis (JE) virus infection. India now has regional laboratory capacity for reference testing on virology and bacteriology. After two years of operations, China has population-based surveillance data for JE that meets targets for timeliness. Several levels have well-established capacity for laboratory confirmation of JE virus infection. The national level has the technical ability to provide proficiency testing for virology and to provide reference testing for bacteriology. In all countries, challenges in building capacity for basic bacteriology, quality control and quality assurance for all laboratory testing, and management of laboratory data.

Contexte La maladie de Lyme (ML), une zoonose transmise par une tique, est en émergence au Canada. Elle est à déclaration obligatoire au Québec depuis novembre 2003. Récemment, des cas ont été diagnostiqués chez des personnes n'ayant pas voyagé à l'extérieur de la province. La description complète des cas déclarés n'a pas été faite. L'objectif de cette étude est de décrire l'incidence, les manifestations cliniques et les facteurs de risque de la ML au Québec chez les cas signalés du 1er janvier 2004 au 31 décembre 2010. Méthode Une copie des dossiers de tous les cas signalés a été obtenue des DSP. Une grille standardisée a été utilisée pour la collecte des données. Les dossiers ont été classés selon la définition nosologique de 2010 (cas confirmé ou probable) à laquelle on a ajouté la catégorie "cas possible". Résultats Parmi les 108 cas signalés, 88 ont été retenus. Lors du reclassement, 40 dossiers ont changé de catégorie dont 23 cas considérés à l'origine comme non retenus. Parmi les 23 cas acquis au Québec, il y 3 cas confirmés, 11 probables et 9 possibles. Le nombre annuel de cas augmente progressivement, l'incidence passant de 0,01 à 0,28/100 000 entre 2004 et 2008. Les cas ont entre 2 et 86 ans (médiane de 43); 8 % ont moins de 10 ans et 58 % sont de sexe masculin. Un érythème migrant (EM), une arthrite ou une paralysie faciale ont été retrouvés chez 78 %, 20 % et 13 % des cas respectivement. L'EM est associé au sexe féminin (89 % vs 69 %, p=0,03). Les cas avec une paralysie faciale sont plus souvent hospitalisés (45 % vs 13 %, p<0,01). Seulement 34 % des cas ont été déclarés par un médecin, même si 98 % d'entre eux ont passé une sérologie pour la ML. Une activité en plein air, un séjour dans une zone endémique ou une piqûre de tique ont été rapportés par 87 %, 76 % et 31 % des cas respectivement. Discussion Le nombre de cas de ML augmente lentement au Québec. Le sud-ouest du Québec est à risque dû à la proximité géographique des zones endémiques. Les médecins n'ont déclaré qu'un tiers des cas et il y a une divergence entre la classification des DSP et celle de l'étude. Les caractéristiques cliniques des cas québécois sont similaires à celles observées aux États-Unis et en Allemagne, sauf pour la proportion de cas chez les enfants qui est plus faible qu'ailleurs.

abstract: This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum. / Dissertation/Thesis / Ph.D. Biomedical Informatics 2012

No description available.

De nombreux documents concernant l'émergence, la propagation ou le suivi de maladies humaines et animales sont quotidiennement publiés sur le Web. Afin de prévenir l'expansion des maladies, les épidémiologistes doivent constamment rechercher ces documents et les étudier afin de détecter les foyers de propagation le plus tôt possible. Dans cette thèse, nous nous intéressons aux deux activités liées à ce travail de veille afin de proposer des outils visuels permettant de faciliter/accélérer l'accès aux informations pertinentes. Nous nous focalisons sur les maladies animales, qui ont été moins étudiées et qui pourtant peuvent avoir de lourdes conséquences sur les activités humaines (maladies transmises d'animaux à humains, épidémies dans les élevages, ...).La première activité du veilleur consiste à collecter les documents issus du Web. Pour cela, nous proposons EpidVis, un outil visuel permettant aux épidémiologistes de regrouper et structurer les mots-clés nécessaires à leurs recherches, construire visuellement des requêtes complexes, les lancer sur différents moteurs de recherche et visualiser les résultats retournés. La seconde activité du veilleur consiste à explorer un grand nombre de documents concernant les maladies. Ces documents contiennent non seulement des informations telles que les noms des maladies, les symptômes associés, les espèces infectées, mais aussi des informations de type spatio-temporelles. Nous proposons EpidNews, un outil de visualisation analytique permettant d'explorer ces données en vue d'en extraire des informations. Les deux outils ont été réalisés dans le cadre d'une étroite collaboration avec des experts en épidémiologie. Ces derniers ont réalisé des études de cas pour montrer que les fonctionnalités des propositions étaient complètement adaptées et permettaient de pouvoir facilement extraire de la connaissance. / Many documents concerning emergence, spread or follow-up of human and animal diseases are published daily on the Web. In order to prevent the spread of disease, epidemiologists must frequently search for these documents and analyze them to detect outbreaks as early as possible. In this thesis, we are interested in the two activities related to this monitoring work in order to produce visual tools facilitating the access to relevant information. We focus on animal diseases, which have been less studied but can have serious consequences for human activities (diseases transmitted from animals to humans, epidemics in livestock ...).The first activity is to collect documents from the Web. For this, we propose EpidVis, a visual tool that allows epidemiologists to group and organize the keywords used for their research, visually build complex queries, launch them on different search engines and view the results returned. The second activity is to explore a large number of documents concerning diseases. These documents contain not only information such as disease names, associated symptoms, infected species, but also spatio-temporal information. We propose EpidNews, a visual analytics tool to explore this data for information extraction. Both tools were developed in close collaboration with experts in epidemiology. The latter carried out case studies to show that the functionalities of the proposals were completely adapted and made it possible to easily extract knowledge.

The world in which we live has changed rapidly over the last few decades. Threats of bioterrorism, influenza pandemics, and emerging infectious diseases coupled with unprecedented population mobility led to the development of public health surveillance systems. These systems are useful in detecting and responding to infectious disease outbreaks but often operate with a considerable delay and fail to provide the necessary lead time for optimal public health response. In contrast, syndromic surveillance systems rely on clinical features (e.g., activities prompted by the onset of symptoms) that are discernible prior to diagnosis to warn of changes in disease activity. Although less precise, these systems can offer considerable lead time. Patient information may be acquired from multiple existing sources established for other purposes, including, for example, emergency department primary complaints, ambulance dispatch data, and over-the-counter medication sales. Unfortunately, these data are often expensive, sometimes difficult to obtain and almost always hard to integrate. Fortunately, the proliferation of online social networks makes much more information about our daily habits and lifestyles freely available and easily accessible on the web. Twitter, Facebook and FourSquare are only a few examples of the many websites where people voluntarily post updates on their daily behaviors, health status, and physical location. In this thesis we develop and apply methods to collect, filter and analyze the content of social media postings in order to make predictions. As a proof of concept we used Twitter data to predict public opinion in the form of the outcome of a popular television show. We then used the same methods to monitor and track public perception of influenza during the H1N1 epidemic, and even to predict disease burden in real time, which is a measurable advance over current public health practice. Finally, we used location specific social media data to model human travels and show how this data can improve our prediction of disease burden.

Excess inpatient length of stay (LOS) varies between hospitals and is burdensome to patients and the overall healthcare system. Variation in LOS has often been associated with hospital-level factors, such as hospital efficiency and quality. Clostridium difficile infection (CDI) is an increasingly common hospital-acquired (HA) infection. This thesis explores the connection between hospital incidence of CDI and excess LOS in patients without a CDI. It is hypothesized that HA-CDI incidence may act as a "proxy variable" to capture unobserved hospital characteristics, such as hospital quality or efficiency, associated with prolonged LOS. In addition, hospitals with longer LOS may tend to observe more HA-CDI cases prior to discharge. This thesis analyzes the ability of CDI incidence to capture excess LOS variation across hospitals, while controlling for CDI cases that occur after discharge. We use data on hospital inpatient visits, spanning the years 2005-2011, from three data sources distributed by the Healthcare Cost and Utilization Project: the Nationwide Inpatient Sample (NIS), and the State Inpatient Databases (SID) for California and New York. The NIS provides discharge records from a nationwide sampling of hospitals in a given year. The SIDs are longitudinal populations of inpatient records in each state, and patient records can be linked across stays. We compute a variety of different measures of hospital CDI incidence and identify HA-CDI cases that occur after a patient is discharged. Various multivariable regression models are analyzed to predict LOS at an individual patient level. A generalized linear modeling approach is used, and different distributions and link functions are compared using the Akaike information criterion. A multilevel modeling approach is also used to estimate the amount of between-hospital variation in LOS that can be explained by HA-CDI incidence. We find CDI incidence to be a strong predictive factor for explaining a patient's LOS and is one of the strongest predictive variables we identified. Moreover, CDI incidence appears to primarily capture between-hospital variation in excess LOS. Although we find evidence that present-on-admission indicators may underreport cases of HA CDI, our findings suggest the connection between CDI incidence and excess LOS is driven primarily by CDI cases that are HA. In addition, when we account for HA-CDI cases that occur post-discharge, the relationship between CDI incidence and LOS appears even stronger. Our results suggest that CDI incidence may be a powerful tool for making comparisons of excess LOS across hospitals.