Dissertations / Theses on the topic 'Syndromic surveillance system'

Thesis (M.S. in Applied Science (Operations Research))--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Ronald D. Fricker. "June 2007." Description based on title screen as viewed on August 14, 2007. Includes bibliographical references (p. 45-46). Also available in print.

After the terrorist attacks of September 11, 2001, questions developed over how quickly the country could respond if a bioterrorism attack was to occur. "Syndromic surveillance" systems are a relatively new concept that is being implemented and used by public health practitioners to attempt to detect a bioterrorism attack earlier than would be possible using conventional biosurveillance methods. The idea behind using syndromic surveillance is to detect a bioterrorist attack by monitoring potential leading indicators of an outbreak such as absenteeism from work or school, over-the-counter drug sales, or emergency room counts. The Center for Disease Control and Prevention's Early Aberration Reporting System (EARS) is one syndromic surveillance system that is currently in operation around the United States. This thesis compares the performance of three syndromic surveillance detection algorithms, entitled C1, C2, and C3, that are implemented in EARS, versus the CUSUM applied to model-based prediction errors. The CUSUM performed significantly better than the EARS' methods across all of the scenarios evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various levels of random daily variation. This results in the recommendation to replace the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.

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

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data.
QC 20110520

Animal disease surveillance has been integral in improving early warnings and predicting disease emergence and spread in humans as well as in animal hosts. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels, based on the distribution of clinical signs that occur in sick individuals. A number of disease syndromes in livestock may be associated with increased likelihood of endemic, emerging, re-emerging and zoonotic diseases. The overall aim of the current study was to implement a passive animal disease surveillance and response system with a capability of detecting emerging zoonotic diseases in domestic and peridomestic animals, in a trial site where active human disease surveillance is ongoing. The project was intended as a demonstration of the system on a short-term basis, with the intention of conducting an evaluation of its performance later. The specific objective was to evaluate and investigate four selected disease syndromes (abortions/stillbirths, sudden death, neurologic signs, red urine) affecting cattle, sheep and goats using a mobile phone-based rapid short message system (SMS) reporting system within the study site, to allow animal owners to report cases of animal disease. SMS reports were logged in a central database and generated a response from a veterinary team to investigate cases. The study was conducted in Asembo division of Rarieda Sub-county of Siaya County, Nyanza Province in western Kenya, from July 2011 through June 2012. A total of 185 events were reported during the study period. Of these, 46 (24.9%) were considered invalid on investigation. The 139 valid events comprised 75 cases of abortions, 43 cases of sudden death, 12 cases of red urine, and 9 cases of nervous signs. Goats were the most frequently affected species at 47% (n=64), cattle at 34% (n=47) and sheep at 19% (n=26). The incidence rate of events by species (per 1,000 population per year) was 7.3 in sheep, 6.5 in goats and 4.1 in cattle. The incidence rate of abortions by species per 1,000 population per year was 5.1 in sheep, 4.0 in goats and 1.5 in cattle. The incidence rate of sudden deaths by species per 1,000 population per year was 2.1 in goats, 2.0 in sheep and 1.3 in cattle. The incidence rate of neurological signs by species per 1,000 population per year was 0.6 in cattle, 0.3 in sheep and 0.2 in goats. The incidence rate of red urine by species per 1,000 population per year was 1.1 in cattle, 0.1 in goats and 0 in sheep. Presumptive diagnoses implicated haemoparasites infections; including theileriosis, babesiosis, anaplasmosis and heartwater and bacterial infections (septicaemia, clostridial infections and anthrax). This project demonstrated that passive animal surveillance using mobile technology is feasible in a resource-constrained setting, although it is likely that cases were underreported.
Dissertation (MSc)--University of Pretoria, 2014.
tm2015
Veterinary Tropical Diseases
MSc
Unrestricted

O acesso universal aos serviços de saúde e terapia antirretroviral foram importantes no aumento da sobrevida de pessoas vivendo com HIV/AIDS (PVHA). A incidência de comorbidades crônicas, porém, aumentou proporcionalmente. Considerando a epidemia de AIDS no RS, especialmente em Porto Alegre, é preciso investigar a sobrevida de PVHA no contexto local, especialmente entre coinfectados HIV/Tuberculose. Dados foram obtidos pelo relacionamento dos bancos SIM e SINAN 2007-2012. Foi utilizado o modelo de regressão de COX para obter o hazard ratio, o método de Kaplan-Meier para estimativas de sobrevida O banco compreendeu 1800 casos notificados em 2007. Correspondem a 60% homens, brancos em sua maioria (67%), com mediana de idade igual a 37 anos (AIQ=14), a maioria dos casos possui baixa escolaridade. Cerca de 70% adquiriu a doença via transmissão sexual. Comparados aos casos notificados por critério óbito, os óbitos totais tem maior proporção de indivíduos de raça/cor preta (46,1% e 32,5%, respectivamente) e maior freqüência de indivíduos do sexo masculino (40,0% e 27,8%, respectivamente). A proporção de dados incompletos também é maior para a escolaridade (72,1% e 68,7%, respect.), contagem de CD4+ (76% e 68%, respect.). Na análise bivariada a transmissão sanguínea, raça/cor negra e baixa escolaridade foram fatores associados a maior risco de óbito. Sexo feminino e idade acima de 30 anos foram fatores associados a menor risco de óbito por AIDS Raça/cor negra, transmissão do HIV pro via sanguínea, presença de infecção por TB e baixa escolaridade são fatores associados à mortalidade aumentada no modelo multivariado. A sobrevida média foi de 1.495 dias (IC 95% 1449 – 1550). O percentual de dados incompletos sugerem que o relacionamento dimensiona a subnotificação mas não qualifica o processo de vigilância. O Critério Óbito representa a perda de oportunidades de intervenção em eventos preveníveis e pode servir como indicador negativo da vigilância.
Universal Access to health services and antiretroviral therapy were crucial in improving the survival or people living with HIV/AIDS (PLHA). The incidence of cronic comorbidities, however, have proportionally rised. Considering the AIDS epidemics in the state of Rio Grande do Sul, specifically in the city of Porto Alegre, it is important to determine the survival of PLHA in a local context, specially between individuals coinfected with TB. Data were obtained by the linkage of SIM and SINAN databases from 2007-2012. A COX regression model was used for the hazard ratios, and the Kaplan-Meier method in order to determine survival estimates. The resulting database had 1800 cases notified in 2007. Sixty percent were male, mostly of white race (67%), median age of 37 years old (IQR=14), mosto f them had low schooling. Around 70% acquired the vírus through sexual transmission. Compared to the total of cases, those notified by the death criteria have proportionally more individuals of the black race (46,1% and 32,5%, respectively) and more males (40% and 27,8%, respectively). The proportion of incomplete data is also higher for scholarity (72,1% and 68,7% respectively) and CD4+ T lymphocite cell count (76% e 68%, respectively). Blood transmission, black race and low scholarity were associated with a higher risk of death in the bivariate analysis. Female gender and age over 30 years were factors associated with a low risk of AIDS-related death. In the multivariate model black race, blood transmission of the vírus e low scholarity were factors associated with higher mortality. Mean survival was of 1495 days (95% CI 1449 – 1550). The proportion of incomplete data suggests that the linkage scales the underreporting but doesn’t qualify the surveillance process. Death criterion represents missed opportunities of interventions in preventable events, and might serve as negative indicator of the surveillance.

FundaÃÃo de Amparo à Pesquisa do Estado do CearÃ
CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
A subnotificaÃÃo de casos de Aids representa um dos principais problemas enfrentados pela vigilÃncia epidemiolÃgica da Aids. Dessa forma este trabalho teve como objetivo, conhecer a subnotificaÃÃo dos casos de Aids em adultos no municÃpio de Fortaleza, nos anos de 2002 e 2003, aplicando o mÃtodo de captura e recaptura. Estimou-se ainda, a subnotificaÃÃo em dois hospitais de referÃncia para Aids, Hospital SÃo Josà â HSJ e Hospital Geral de Fortaleza â HGF. Nesse estudo foram utilizados dados secundÃrios do Sistema de InformaÃÃo de Agravo de NotificaÃÃo â Sinan, Sistema de Controle de Exames Laboratoriais - Siscel e Sistema de InformaÃÃes sobre Mortalidade - SIM, comparando os trÃs sistemas e considerando como notificados os casos confirmados no Sinan. Os casos foram emparelhados no Programa RecLink II. ApÃs o emparelhamento dos casos foram selecionados os elegÃveis para aplicaÃÃo do mÃtodo de captura e recaptura, utilizando os estimadores de Lincoln-Petersen e o de Chapman. A subnotificaÃÃo estimada para Fortaleza foi de 33,1%, e 14,1%, tendo como referÃncia o Sinan e comparando com as fontes Siscel e SIM, respectivamente. Para os hospitais S. Josà e HGF a subnotificaÃÃo foi de 5,4% e 90,5%, na mesma ordem, comparando o Sinan com o Siscel. Este trabalho demonstrou uma elevada subnotificaÃÃo de casos de Aids no municÃpio de Fortaleza nos anos de 2002 e 2003 e que a subnotificaÃÃo quando avaliada por fonte, comparando o Siscel com o Sinan, ela à mais de duas vezes superior à estimada quando comparada à das fontes SIM/Sinan, demonstrando ser o Siscel uma importante fonte de notificaÃÃo de casos de Aids. Quanto à subnotificaÃÃo nas duas unidades de SaÃde, observa-se um percentual bem mais elevado para o HGF. O Programa RecLink II mostrou ser uma opÃÃo simples para o reconhecimento dos casos nÃo notificados no Sinan e em relaÃÃo a tÃcnica de captura e recaptura, pode ser utilizada de forma simples e rÃpida e com baixo custo, comparando os sistemas dois a dois em pesquisas pontuais. Diante dos achados nesse estudo, recomenda-se que o Sinan-Aids seja comparado em periodicidade mensal, com todas as fontes de informaÃÃes disponÃveis que possam contribuir para reduÃÃo da subnotificaÃÃo de Aids
Underreporting is one of the major problems challenging epidemiologic AIDS surveillance. The objective of this study was to estimate the level of underreporting of AIDS in adults in Fortaleza in the period 2002-2003 using the capture-recapture method. In addition, the level of underreporting at two hospitals for AIDS referral (Hospital SÃo Josà â HSJ, and Hospital Geral de Fortaleza â HGF was estimated. The study relied on three secondary databases: SINAN (national disease surveillance), SISCEL (laboratory test control) and SIM (mortality information). The systems were compared and cases confirmed by SINAN were considered as reported. Cases from the two databases were paired using the software RecLink II. Subsequently cases eligible for the capture-recapture method were selected using the Lincoln-Petersen and Chapmam estimators. The levels of underreporting were estimated at 33.1% and 14.1% for SISCEL and SIM, respectively. Underreporting for SISCEL was 5.4% at HSJ and 90.5% at HGF. The study shows a considerable level of underreporting of AIDS cases in Fortaleza for the period 2002-2003 and suggests that SISCEL is an important source of AIDS reporting considering that it allowed to detect levels of underreporting more than twice the estimates derived from the sources SIM and SINAN. The level of underreporting was considerably higher at HF than at HSJ. The software RecLink II was shown to be a practical tool for identifying cases not reported to SINAN. The capture-recapture method is a simple, time-saving and inexpensive way to compare two systems when necessary. Considering the findings of the present study, SINAN-AIDS should be compared monthly with all relevant information systems in order to reduce levels of AIDS underreporting

INTRODUÇÃO: Em 1999 e 2000, a ocorrência de surtos de rubéola, com maior acometimento entre adultos jovens, refletiu no aumento da síndrome da rubéola congênita. Como estratégia de controle da doença, foram realizadas campanhas de vacinação contra a rubéola em mulheres em idade fértil em vários Estados do País. Em razão das controvérsias existentes na literatura geradas quanto ao emprego da vacina de vírus vivos atenuados em gestantes, não se recomendou sua utilização durante a gravidez e até um mês após a aplicação da vacina. No entanto, 6.473 mulheres foram inadvertidamente vacinadas no Estado de São Paulo, sendo encaminhadas a serviços de referência para acompanhamento dessas gestações, dentre eles, o HCFMUSP. OBJETIVO: Este estudo buscou descrever as características clínicas e epidemiológicas das gestantes atendidas no HCFMUSP e obter os resultados dessas gestações. MÉTODO: Foi realizado um estudo epidemiológico descritivo, utilizando-se como fonte de dados as notificações das gestantes inadvertidamente vacinadas contra a rubéola e atendidas no HCFMUSP entre novembro de 2001 a dezembro de 2002. Para obter o desfecho das gestações, utilizou-se a base de dados dos nascidos vivos do Município de São Paulo (SINASC). RESULTADOS: No HCMFUSP, foram atendidas e notificadas 409 gestantes. Destas, 49,1% foram vacinadas no primeiro mês de gravidez e 26,2% engravidaram até um mês após a vacinação. Em relação a condição sorológica durante o pré-natal, 16,9% das gestantes apresentaram sorologia reagente para rubéola. Do relacionamento com a base de dados do SINASC, foram localizados os dados do parto de 63,3% das gestantes, sendo detectadas duas malformações congênitas no SINASC e um abortamento, porém, não se pode atribuir estes resultados à vacina, pois, as sorologias das mães não permitem determinar se estas mulheres eram realmente suscetíveis. CONSIDERAÇÕES FINAIS: O estudo apresentou o fluxo de informação estabelecido frente a um evento inusitado. Além disso, o uso de bases de dados secundárias contribuiu para o aprimoramento dos dados coletados, resultando na melhora da qualidade das informações. Os Núcleos de Epidemiologia Hospitalar são fundamentais na articulação entre a equipe assistencial e o Sistema de Vigilância e colaboram para discussão na padronização de Sistemas de Informação para permitir melhor integração entre as informações geradas pelos Serviços de Saúde.
INTRODUCTION: In the years 1999 and 2000, rubella outbreaks reaching mostly young adults resulted in an increased number of cases of Congenital Rubella Syndrome in Brazil. State Vaccination Campaigns aiming at women at childbearing age were promoted around the country to control the disease, recommending that vaccination of pregnant women should be avoided and pregnancy should be postponed for at least a month after vaccination. Despite the recommendations, 6.473 pregnant women were accidentally vaccinated in the State of São Paulo and therefore sent to reference obstetrical services for prenatal care. A study was conducted to describe the cases assisted at the University of São Paulo, School of Medicine, General Hospital and notified to Public Health and also to obtain information on the pregnancy outcomes. METHODS: This descriptive epidemiological study used notification by the Hospital Epidemiology Service as source of information on pregnant women accidentally vaccinated against rubella that received care from November 2001 to December 2002 at the School of Medicine, General Hospital. The City of São Paulo Newborn Database was searched for pregnancy outcomes. RESULTS: Among the 409 notified cases, 49,1% were women accidentally vaccinated during fist trimester of pregnancy and 26,2% women that became pregnant within less than a month after vaccination. Positive serological tests were found in 16,9% of women during prenatal care. Newborn data base search yielded pregnancy outcome for 63,3%. The findings of 2 cases of Congenital Rubella Syndrome and 1miscarriage cannot be surely attributed to vaccination because immediate previous immunization status was unknown. CONCLUSIONS: The study described the information flow established for an unexpected adverse event and the use of secondary data to improve quality of information. Hospital Epidemiology Services have a fundamental role in connecting health assisting professionals to Public Surveillance Systems and in setting standards for information generated by Health Assistance.

This thesis is a summary of the work completed as part of the Masters of Applied Epidemiology (MAE) program at the Australian National University. The thesis is made up of 5 chapters, and 4 appendices that together describe all the work required to complete the MAE program. My field placement was at Healthdirect Australia, which is a Council of Australian Governments (COAG) organisation that delivers a range of telephone and digital health and the last chapter details the measles investigation. 1.0.1 Chapter 2: Surveillance This chapter discusses a syndromic surveillance system for influenza-like illness created using the healthdirect helpline and the healthdirect online symptom checker data. I discuss the data sources used for the surveillance system, describe the epidemiology of influenza-like illness contacts, then evaluate the proposed system using the US Centres for Disease Control and Prevention Guidelines. I conclude that the proposed surveillance system has excellent representativeness and timeliness, and submit a dashboard that could be used to publish the system. However, weaknesses of the system are flexibility and sensitivity, which are due to the limited details collected on each call or visit. 1.0.2 Chapter 3: Data Analysis The data analysis chapter describes three projects undertaken to complement the creation of the surveillance system discussed in Chapter 2. The aims of these three projects were: to identify unusual activity in the data; to determine the correlation between Healthdirect Australia data sets with seven other data sets; and to establish a case definition that maximises correlation with other data sets. I find that the healthdirect helpline data correlates very well with other data sources, particularly with emergency department surveillance. I was also able to identify unusual behaviour during the 2009 pandemic year, and to identify the start and finish of an influenza season. I conclude that, due to the high degree of correlation between the Healthdirect Australia data and other data sources, this system could contribute to influenza surveillance in Australia by improving knowledge about the community level of disease. 1.0.3 Chapter 4: Epidemiology Project In this chapter, I further explore the work of the previous 2 chapters by expanding to 6 further syndromes. Two of these syndromes - adverse reaction following immunisation and gastroenteritis - demonstrated high utility if they were to be added to the proposed influenza-like illness surveillance system. The adverse reaction following immunisation syndrome data shows the ability both to detect a spike in activity, and also to mirror the epidemiological characteristics known about the event. The healthdirect helpline data for gastroenteritis demonstrates similar behaviour to gastroenteritis visits to the emergency department. On the other hand, an investigation into geographical variations in gastroenteritis did not identify smaller regional outbreaks, nor did it show the geographical spread of illness. I conclude however that surveillance on both gastroenteritis and adverse reaction following immunisation could be useful to public health departments. 1.0.4 Chapter 5: Outbreak Investigation The final chapter discusses work conducted while on secondment at the Western Sydney public health department. My work here consisted of assisting in investigating a measles outbreak, which affected 17 people from March to April on 2017. In the chapter, I discuss the epidemiology of the outbreak and the public health resources committed to containing the outbreak, particularly through contact tracing. I conclude that responding to measles outbreaks pose a large burden on public health departments, particularly through extra staff hours. However, literature has shown that this effort does result in a significant reduction in secondary measles cases.

碩士
國立陽明大學
衛生資訊與決策研究所
93
Study Purpose: More and more evidences have shown already, the information technology to the modern public health will be more important. Using information science and technology to help public health personnel to oppose the emerging infectious diseases is a major subject nowadays. This study utilized the administrative data in the past three years to set up an automatic syndromic surveillance system and verified the system utilizing the first half year of 2003. Materials and Methods: This study utilized the administrative data of Taipei city collected from 2000 to 2002 and classified the patient's ICD-9-CM codes into fever, respiratory, and gastrointestinal syndromes established by the U.S.A.'s CDC and ESSENCE. We used linear regression with autoregressive error (abbreviated as Autoreg) and ARIMA statistic model to establish our prediction models for each syndrome. This research used eight kinds of statistical models to predict the counts of each three syndromes of the first half year of 2003. Influenza-like illness and diarrhea data provided by sentinel physicians and "SARS-related events" defined by the magazine of Taiwan Public Health Association were used as the gold standard for evaluation the performance of the system. Results: This study established a web-based automatic syndromic surveillance system which could utilize the Internet to upload the data files, implement statistical analysis regularly, insert new data into database automatically , offer various types of statistical graphs and GIS (geographical information system) maps, and present the latest epidemic situation materials on the webpage. The evaluation of the system is as follows: First, in the whole city prediction from January 1, 2003 to June 30, 2003, the sensitivity was 0.69 on average, all of the positive predictive values for four models of using ARIMA were 1, and the timeliness of the system was delayed two days on average. Second, in the assessment of the "fever syndrome" in SARS epidemic period in 12 districts from Mar 10, 2003 to Jun 30, 2003, all of the sensitivity rates of the four models using ARIMA were 1, the timeliness of the system was 5 days earlier on average. Conclusions and Suggestions: The whole city data may be useful for monitoring the naturally occurring outbreaks (e.g., influenza-like illness); the specific district data with fever syndrome may be useful for detecting SARS-related events. Because of the differences in demographics and medical resources in the individual district, how to choose an adequate statistical model and a significant-level value for the individual district is a great challenge of this kind of research.

碩士
國立臺灣大學
流行病學研究所
94
According to the challenges of emerging infectious diseases (EID) and bioterrorist attack in the 21st Century, the traditional infectious disease surveillance systems have lacked active surveillance and failed to detect the 2001 anthrax attacks and the 2003 cross-country spread of severe acute respiratory syndrome (SARS) early. In order to establish scientific evidence-based active infectious disease surveillance system, we initiated a hospital emergency department (ED)-based timely and automatic syndromic surveillance system (ED-SSS) in Taiwan for the basis of further epidemiological investigation. Through the introduction of the real-time outbreak and disease reporting system (RODS) from the University of Pittsburg to Taiwan, we established both a nation-wide ED-SSS involving 189 hospitals and set up more integrative procedures of local ED-SSS for daily operation, signal investigation, checking data quality and specimens taken at one regional hospital with 5 branches in metropolitan Taipei. In this study, we built up the daily operation procedures to perform further implementation and signal investigation and conducted epidemiological syndrome data analysis, using two data set of ICD-9 codes of ED visits: (1) 2,692,325 visits of nation-wide ED-SSS in Taiwan, April 1, 2004 - March 31, 2005. (2) 130,313 visits in one regional Taipei hospital, October 1, 2005- April 30, 2006. Daily patterns and trends of nation-wide ED-SSS ICD-9 codes involving 11 syndrome groups were analyzed, according to the international research experiences and recommendations of infectious disease experts in Taiwan. We found increasing trends in gastro-intestinal (GI) syndromes during winter season, and appearing peaks of respiratory or influenza-like illness (ILI) in summer periods, plus two waves of enterovirus (EV)-like infections under 6 years of age. In addition, striking increase of ED-visits were shown in weekends (1.3-fold p<0.001), and holidays, especially 2.0-fold higher during Chinese Lunar New Year (p<0.001). Most ED visits were children aged <10 y/o (18.6%) and 20-39 year-old young adults (17.4%). Male vs. female ED visits were 1.12:1. Starting from July of 2004, case numbers of fever, respiratory and ILI syndromes gradually increase, indicating possible presence of respiratory infections in Taiwan that had been seldom reported and worthwhile future investigating. Therefore, we examined the sensitivity, specificity and positive-predictive values of different ICD-9 code syndrome groupings of ILI cases from the 1,281 cases through medical charts review at C-hospital in Taipei, June 24, 2004 – July 8, 2004. The modified version of ILI syndrome that deleted asthma, fever and dizziness had 75.5% sensitivity and 84.4% specificity. We then applied it to detect ILI cases in Taipei ED-SSS. Increased numbers and earlier detected EVI syndrome cases of nation-wide ED-SSS were found in central Taiwan, but there was no significant difference in patterns by different age and gender groups. Compared with sentinel physicians’ hand-foot-mouth disease surveillance, the ED-SSS was 2 weeks earlier in detecting the increased EVI syndrome cases in Taiwan. Additionally, the Taipei X-Hospital ED-SSS also showed similar pattern. Among the patterns of other syndrome groups, GI syndrome cases in both nation-wide and Taipei ED-SSS increased from Nov. of 2004. On the other hand, Taipei X-Hospital ED-SSS with different branches showed regional differences. Moreover, 30-39 year-old male adults showed one week earlier in the increasing of ILI syndrome cases implying possible important transmission role in this age group in a metropolitan city. With the limit historical ED-SSS data, both historical control limits (HCL) using past four-week background data and cumulative sum (CUSUM) process control methods were used for our aberration detection and their required conditions in ED-SSS. The weekday and weekend stratified HCL method showed 67% sensitivity to detect the Taiwan-CDC identified outbreaks and the modified CUSUM method was too sensitive to demonstrate signals on weekends and holidays and needed to be improved due to its higher sensitivity for the changes of continuous baseline. Undoubtedly, both methods are applicable to public health use for immediate prevention and control of infectious diseases but they need further researches to reduce false signals. Evaluation of surveillance systems found that ED-SSS, providing more information for further epidemiological investigation, had the best timeliness and better flexibility to integrate with other database for multi-disciplinary research than other infectious disease surveillance systems existed. It can serve two-way frequent communications between public health agencies and medical departments. With the large baseline data of community health, it can also apply to research on other domains in public health. Future efforts are needed for continuous investment of medical informatics, to recruit more high quality infection control public health personnel, and to improve different statistical prediction models for maximizing effectiveness for early EID detection.

碩士
臺北醫學大學
醫學資訊研究所
96
Syndromic surveillance is the utillization of data that large amount of cases with prodromal phase symptoms and performs spatial-temporal clustering analysis through information technology for rapidly detecting disease outbreaks with visualization and aberration detection theory so that a further epidemiology investigation and disease control procedure could be taken in its very early time.In recent years, many countries have adopted syndromic surveillance systems as the frontline defense against emerging infectious diseases or bioterrorism attacks. Although Taiwan’s Centers for Disease Control, R.O.C.(Taiwan CDC) has established the system for several years, the disease control staff and investigators in local health agencies and hospitals are lack of experience in using syndromic surveillance systems for prevention of infectious disease outbreaks. Therefore, this study focuses on: (1) the establishment of an integrated syndromic surveillance system that can automatically collect data from hospital emergency departments (ED) in Taipei City in a timely and flexible fashion, (2) development of effective algorithms for early detection of disease outbreaks using data of routinely collected chief complaints or ICD-9 CM codes, and (3) provision of friendly interfaces for the presentation of surveillance data to provide information for healthcare workers and decision-makers at different levels of position in hospitals in order to enhance the capability in the detection and prevention of disease outbreaks. The study selected five hospitals located in different geographical areas of Taipei City. The system has established and collected data of patient visiting ED (totally about 500,000 visits) during the period of January 1, 2005 and June 30, 2008. There are 8 syndrome groups for routine surveillance. In addition, the system can dynamically define new groups based on the trends of disease occurrence. Until now 5 dynamically defined syndrome groups have been performed in this system as an extra targeted surveillance. The historical limit method with the short-term and long-term baseline data is used for analysis in aberration detection, and the alerts are delivered over to persons concerned via email once it is detected. Meanwhile, the analyzed surveillance data, which is accessible through pages on website, can be used to compare the parallel trends of disease occurrence in different years. Furthermore, they can also be linked to a geographic information system to view the dynamic changes in temporal and spatial patterns of disease occurrence, the occurrence scale of clustering cases, and the development of trends for any interested syndromes. Since our system is mainly used for surveillance of locally important disease and covered only Taipei area in geographic, in the future, we will make our effort to set our disease defense strategies by grading them into different stages in the processes of preparation and response, so that it can work as a whole with those conducted by the central government, to get better system performance.

Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID, PCI and FOS had significantly lower forecast error. Forecasts of the weekly aggregate number of visits showed little evidence of ability to accurately flag corresponding actual increases. However, when visits were aggregated over a four day period, increases could be flagged more accurately than chance in six of the 36 health units accounting for approximately half of the Ontario population. Conclusions: This thesis suggests that Telehealth Ontario data collected by a real-time syndromic surveillance system could play a role in forecasting health services demand for respiratory illness.
Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2009-08-11 16:20:44.553