Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

La France connaît un vieillissement de sa population sans précédent. La part des séniors s’accroît et notre société se doit de repenser son organisation pour tenir compte de ce changement et mieux connaître cette population.De nombreuses cohortes de personnes âgées existent déjà à travers le monde dont quatre en France et, bien que la part de cette population vivant dans des structures d’hébergement collectif (EHPAD, cliniques de soins de suite) augmente, la connaissance de ces seniors reste lacunaire.Aujourd’hui les groupes privés de maisons de retraite et d’établissements sanitaires comme Korian ou Orpéa s’équipent de grandes bases de données relationnelles permettant d’avoir de l’information en temps réel sur leurs patients/résidents. Depuis 2010 les dossiers de tous les résidents Korian sont dématérialisés et accessibles par requêtes. Ils comprennent à la fois des données médico-sociales structurées décrivant les résidents et leurs traitements et pathologies, mais aussi des données textuelles explicitant leur prise en charge au quotidien et saisies par le personnel soignant.Au fil du temps et alors que le dossier résident informatisé (DRI) avait surtout été conçu comme une application de gestion de base de données, il est apparu comme une nécessité d’exploiter cette mine d’informations et de construire un outil d’aide à la décision destiné à améliorer l’efficacité des soins. L’Institut du Bien Vieillir IBV devenu entretemps la Fondation Korian pour le Bien Vieillir a alors choisi, dans le cadre d’un partenariat Public/Privé de financer un travail de recherche destiné à mieux comprendre le potentiel informatif de ces données, d’évaluer leur fiabilité et leur capacité à apporter des réponses en santé publique. Ce travail de recherche et plus particulièrement cette thèse a alors été pensée en plusieurs étapes.- D’abord l’analyse de contenu du data warehouse DRI, l’objectif étant de construire une base de données recherche, avec un versant social et un autre de santé. Ce fut le sujet du premier article.- Ensuite, par extraction directe des informations socio-démographiques des résidents dès leur entrée, de leurs hospitalisations et décès puis, par un processus itératif d’extractions d’informations textuelles de la table des transmissions et l’utilisation de la méthode Delphi, nous avons généré vingt-quatre syndromes, ajouté les hospitalisations et les décès et construit une base de données syndromique, la Base du Bien Vieillir (BBV) . Ce système d’informations d’un nouveau type a permis la constitution d’une cohorte de santé publique à partir de la population des résidents de la BBV et l’organisation d’un suivi longitudinal syndromique de celle-ci. La BBV a également été évaluée scientifiquement dans un cadre de surveillance et de recherche en santé publique au travers d’une analyse de l’existant : contenu, périodicité, qualité des données. La cohorte construite a ainsi permis la constitution d’un outil de surveillance. Cet échantillon de population a été suivi en temps réel au moyen des fréquences quotidiennes d’apparitions des 26 syndromes des résidents. La méthodologie d’évaluation était celle des systèmes de surveillance sanitaire proposée par le CDC d’Atlanta et a été utilisée pour les syndromes grippaux et les gastro entérites aiguës. Ce fut l’objet du second article.- Enfin la construction d’un nouvel outil de santé publique : la distribution de chacun des syndromes dans le temps (dates de transmissions) et l’espace (les EHPAD de transmissions) a ouvert le champ de la recherche à de nouvelles méthodes d’exploration des données et permis d’étudier plusieurs problématiques liées à la personne âgée : chutes répétées, cancer, vaccinations et fin de vie. / French population is rapidly aging. Senior citizens ratio is increasing and our society needs to rethink its organization, taking into account this change, better knowing this fast growing population group.Even if numerous cohorts of elderly people already exist worldly with four in France and, even as they live in growing numbers in nursing homes and out-patient treatment clinics, knowledge of this population segment is still missing.Today several health and medico-social structures groups as Korian and Orpéa invest in big relational data bases enabling them to get real-time information about their patients/residents. Since 2010 all Korian residents’ files are dematerialized and accessible by requests. They contain at the same time, structured medico-social data describing the residents as well as their treatments and pathologies, but also free-textual data detailing their daily care by the medical staff.Through time and as the computerized resident file (DRI) was mainly conceived as a data base management application, it appeared essential to mine these data and build a decision-making tool intended to improve the care efficiency. The Ageing Well Institute becoming meanwhile the Korian Ageing Well Foundation chose then, working in a private/public partnership, to finance a research work intented to better understand these datas’ informative potential, to assess their reliability and response to public health threats. This research work and this thesis were then designed in several steps:- First, a content analysis of the data warehouse DRI, the objective being to build a research data base, with a social side and a health side. This was the first paper subject.- Then, by direct extraction of the residents’ socio-demographic information at nursing home (NH) entry, adding hospitalizations and deaths, and finally, by an iterative textual extraction process of the transmissions data and by using the Delphi method, we created twenty-four syndromes, added hospitalizations and deaths and built a syndromic data base, the Ageing Well data base. This information system of a new kind, allowed the constitution of a public health cohort for elderly people from the BBV residents’population and its syndromic longitudinal follow-up. The BBV was also scientifically assessed for surveillance and public health research through present situation analysis: content, periodicity and data quality. This cohort then gave us the opportunity to build a surveillance tool and follow the residents’ population in real-time by watching their 26 daily frequency syndromic distributions. The methodology for that assessment, Atlanta CDCs’ health surveillance systems method, was used for flu and acute gastro enteritis syndroms and was the second paper subject.- Finally, the building of a new public health tool: each syndrom’s distribution through time (transmissions dates) and space (transmissions NH ids) opened the research field to new data exploration methods. I used these to study different health problems afflicting senior citizens: frequent falls, cancer, vaccinations and the end of life.