Spatio-temporal modeling of arthropod-borne zoonotic diseases: a proposed methodology to enhance macro-scale analyses

Zoonotic diseases are infectious diseases that can be transmitted from or through animals to humans, and arthropods often act as vectors for transmission. Emerging infectious diseases have been increasing both in prevalence and geographic range at alarming rates the last 30 years, and the majority of these diseases are zoonotic in nature. Many zoonotic diseases are considered notifiable by the Centers for Disease Control and Prevention (CDC). However, though state regulations or contractual obligations may require the reporting of certain diseases, significant underreporting is known to exist. Because of the rich volume of information captured in health insurance plan databases, administrative medical claims data could supplement the current reporting systems and allow for more comprehensive spatio-temporal analyses of zoonotic infections. The purpose of this dissertation is to introduce the use of electronic administrative medical claims data as a potential new source that could be leveraged in ecological field studies in the surveillance of arthropod-borne zoonotic diseases. If using medical claims data to study zoonoses is a viable approach, it could be used to improve both the temporal and spatial scale of study through the use of long-term longitudinal data covering large geographic expansions and more geographically refined ZIP code scales. Additionally, claims data could supplement the current reporting of notifiable diseases to the CDC. This effort may help bridge the disease incidence gap created by health care providers\u27 underreporting and thus allow for more effective tracking and monitoring of infectious zoonotic diseases across time and space. I specifically examined 5 tick-borne (Lyme disease [LD], babesiosis, ehrlichiosis, Rocky Mountain spotted fever [RMSF], and tularemia) and 2 mosquito-borne (West Nile virus, La Crosse viral encephalitis) diseases known to occur in the southeastern US. I first compared disease incidence rates from cases reported to the Tennessee Department of Health (TDH) state registry system with medically diagnosed cases captured in a southeastern managed care organization (MCO) claims data warehouse. I determined that LD and RMSF are significantly underreported in Tennessee. Three (3) cases of babesiosis were discovered in the claims data, a significant finding as this disease has never been reported in Tennessee. Next, I used a cluster scan statistic to statistically validate when (temporal) and where (spatial) these data sources differ. Findings highlight how the data sources do not overlap in their significant cluster results, supporting the need to integrate administrative and state registry data sources in order to provide a more comprehensive set of case information. Once the usefulness of administrative data was demonstrated, I focused on how these data could improve spatio-temporal macro-scale analyses by examining information at the ZIP code level as opposed to traditional county level assessments. I expanded on the current literature related to spatially explicit modeling by employing more advanced data mining modeling techniques. Four separate modeling techniques were compared (stepwise logistic regression, classification and regression tree, gradient boosted tree, and neural network) to describe the occurrence of tick-borne diseases as they relate to socio-demographic, geographic, and habitat characteristics. Covariates most useful in explaining LD and RMSF were similar and included co-occurrences of RMSF and LD, respectively, amount of forested and non-forested wetlands, pasture/grasslands, and urbanized/developed lands, population counts, and median income levels. Finally, I conclude with a ZIP code level spatio-temporal modeling exercise to determine areas and time periods in Tennessee where significant clusters of the studied diseases occurred. ZIP code level clusters were compared to the previously defined county-level clusters to discuss the importance of spatial scale. The findings suggest that focused disease/vector prevention efforts in non-endemic areas are warranted. Very little work exists using administrative claims data in the study of zoonotic diseases. This body of work thus adds to an area void of much knowledge. Administrative medical claims data are relatively easy to access given the appropriate permissions, have relatively no cost once access is granted, and provides the researcher with a volume rich dataset from which to study. This data source should be properly considered in the wildlife and biological sciences fields of research

A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis

Principles and practice of public health surveillance

Public health surveillance is the systematic, ongoing assessment of the health of a community including the timely collection, analysis, interpretation, dissemination and subsequent use of data. The book presents an organized approach to planning, developing, implementing, and evaluating public health surveillance systems. Chapters include: planning; data sources; system management and data quality control; analyzing surveillance data; special statistical issues; communication; evaluation; ethical issues; legal issues; use of computers; state and local issues; and surveillance in developing countries. The book is intended to serve as a desk reference for public health practitioners and as a text for students in public health.PB9 3-10 1129I: Introduction -- II: Planning a surveillance system -- III: Sources of routinely collected data for surveillance -- IV: Management of the surveillance system and quality control of data -- V: Analyzing and interpreting surveillance data -- VI: Special analytic issues -- VII: Communicating information for action -- VIII: Evaluating public health surveillance -- IX: Ethical issues -- X:Public health surveillance and the law -- XI: Computerizing public health surveillance systems -- XII: State and local issues in surveillance -- XIII: Important surveillance issues in developing countries -- Tables and figures.1992874

Évaluation de la validité des modèles de risque pour prédire l’incidence des gastroentérites d’origine hydrique au Québec

Les analyses de risque microbiologique, dont l'ÉQRM (évaluation quantitative du risque microbien) proposent de nouvelles techniques pour évaluer les conséquences sanitaires liées à la contamination microbiologique de l'eau potable. Ces modèles intègrent les données physico-chimiques et microbiologiques des usines de traitement d'eau pour quantifier un risque à la santé. Le projet visait à évaluer le lien entre le risque estimé selon un modèle ÉQRM et l’incidence de giardiase observée. Les banques de données des maladies à déclaration obligatoire et d’INFO-SANTÉ ont été utilisées pour comparer le résultat de l’analyse de risque à celui des analyses épidémiologiques. Les municipalités considérées les plus à risque par l'ÉQRM ont une incidence de gastroentérite et de parasitoses plus élevée. Cependant, l'ampleur du risque prédit ne correspond pas à celui observé. Il est souhaitable que les modèles d’ÉQRM incorporent des données populationnelles pour prédire avec une plus grande exactitude le risque épidémiologique

Disease surveillance systems

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

A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis

Syndromic Surveillance for Bioterrorism-related Inhalation Anthrax in an Emergency Department Population

Objective: To utilize clinical data from emergency department admissions and published clinical case reports from the 2001 bioterrorism-related inhalation anthrax (IA) outbreak to develop a detection algorithm for syndromic surveillance. Methods: A comprehensive review of case reports and medical charts was undertaken to identify clinical characteristics of IA. Eleven historical cases were compared to 160 patients meeting a syndromic case definition based on acute respiratory failure and the presence of mediastinal widening or lymphadenopathy on a chest radiograph. Results: The majority of syndromic group patients admitted were due to motor vehicle accident (52%), followed by fall (10%), or other causes (4%). Positive culture for a gram positive rod was the most predictive feature for anthrax cases. Among signs and symptoms, myalgias, fatigue, sweats, nausea, headache, cough, confusion, fever, and chest pain were found to best discriminate between IA and syndromic patients. When radiological findings were examined, consolidation and pleural effusions were both significantly higher among IA patients. A four step algorithm was devised based on combinations of the most accurate clinical features and the availability of data during the course of typical patient care. The sensitivity (91%) and specificity (96%) of the algorithm were found to be high. Conclusions: Surveillance based on late stage findings of IA can be used by clinicians to identify high risk patients in the Emergency Department using a simple decision tree. Implications for public health: Monitoring pre-diagnostic indicators of IA can provide enough credible evidence to initiate an epidemiological investigation leading to earlier outbreak detection and more effective public health response

MMWR. Morbidity and mortality weekly report

National Gay Men\u2019s HIV/AIDS Awareness Day--September 27, 2010 -- Prevalence and Awareness of HIV Infection Among Men Who Have Sex With Men--21 Cities, United States, 2008 -- Racial Differences by Gestational Age in Neonatal Deaths Attributable to Congenital Heart Defects--United States, 2003-2006 -- Update: Detection of a Verona Integron-Encoded Metallo-Beta-Lactamase in Klebsiella pneumoniae--United States, 2010 -- Announcements: World Heart Day--September 26, 2010 -- Announcements: Epi Info Training--December 2010 -- Announcements: Epidemiology in Action: Intermediate Analytic Methods Course -- Notifiable Diseases and Mortality Tables.2010626