BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

The prevalence of cubital tunnel syndrome: A cross-sectional study in a U.S. metropolitan cohort

BACKGROUND: Although cubital tunnel syndrome is the second most common peripheral mononeuropathy (after carpal tunnel syndrome) encountered in clinical practice, its prevalence in the population is unknown. The objective of this study was to evaluate the prevalence of cubital tunnel syndrome in the general population. METHODS: We surveyed a cohort of adult residents of the St. Louis metropolitan area to assess for the severity and localization of hand symptoms using the Boston Carpal Tunnel Questionnaire Symptom Severity Scale (BCTQ-SSS) and the Katz hand diagram. We identified subjects who met our case definitions for cubital tunnel syndrome and carpal tunnel syndrome: self-reported hand symptoms associated with a BCTQ-SSS score of >2 and localization of symptoms to the ulnar nerve or median nerve distributions. RESULTS: Of 1,001 individuals who participated in the cross-sectional survey, 75% were women and 79% of the cohort was white; the mean age (and standard deviation) was 46 ± 15.7 years. Using a more sensitive case definition (lax criteria), we identified 59 subjects (5.9%) with cubital tunnel syndrome and 68 subjects (6.8%) with carpal tunnel syndrome. Using a more specific case definition (strict criteria), we identified 18 subjects (1.8%) with cubital tunnel syndrome and 27 subjects (2.7%) with carpal tunnel syndrome. CONCLUSIONS: The prevalence of cubital tunnel syndrome in the general population may be higher than that reported previously. When compared with previous estimates of disease burden, the active surveillance technique used in this study may account for the higher reported prevalence. This finding suggests that a proportion of symptomatic subjects may not self-identify and may not seek medical treatment. CLINICAL RELEVANCE: This baseline estimate of prevalence for cubital tunnel syndrome provides a valuable reference for future diagnostic and prognostic study research and for the development of clinical practice guidelines

Approaches to canine health surveillance

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance

Viral Gastroenteritis Outbreaks in Europe, 1995–2000

To gain understanding of surveillance and epidemiology of viral gastroenteritis outbreaks in Europe, we compiled data from 10 surveillance systems in the Foodborne Viruses in Europe network. Established surveillance systems found Norovirus to be responsible for >85% (N=3,714) of all nonbacterial outbreaks of gastroenteritis reported from 1995 to 2000. However, the absolute number and population-based rates of viral gastroenteritis outbreaks differed markedly among European surveillance systems. A wide range of estimates of the importance of foodborne transmission were also found. We review these differences within the context of the sources of outbreak surveillance information, clinical definitions, and structures of the outbreak surveillance systems

Annual Report to the Secretary, Department of Health and Human Services, 2012

The Food Safety Modernization Act (FSMA) authorized CDC to create a diverse Working Group of experts and stakeholders to provide routine and ongoing guidance to improve foodborne illness surveillance systems in the United States. This report summarizes the Working Group\ue2\u20ac\u2122s activities during its first year, FY 2012.The Working Group met twice for two days each at CDC, providing guidance on selection criteria for the FSMA-mandated Integrated Food Safety Centers of Excellence, reviewing and providing feedback on the Interagency Food Safety Analytics Collaboration strategic plan and on several CDC FSMA-related initiatives to enhance foodborne disease surveillance, and identifying priority areas to focus on in the coming years. The Working Group recognized that since the passage of FSMA, considerable progress has been made in collaboration around foodborne illness surveillance, but felt further improvement in surveillance is needed and could be made in a number of areas including\ue2\u20ac\ua2 Interagency linkages and coordination at local, state, federal, and tribal levels;\ue2\u20ac\ua2 Development and use of meaningful foodborne illness surveillance performance measures;\ue2\u20ac\ua2 More complete collection, standardization, and analysis of information on factors contributing to foodborne illness;\ue2\u20ac\ua2 Response to the potential loss of the ability to track and link organisms to detect outbreaks and suspect foods due to increased use of culture-independent diagnostic tests;\ue2\u20ac\ua2 Building of state and local surveillance capacity on which national surveillance is based; and\ue2\u20ac\ua2 Communication with partners and external stakeholders, especially when investigating and responding to widespread outbreaks affecting many states.In the course of its work, the Working Group repeatedly noted the importance of national and state/local surveillance for foodborne illness and that the data gathered are critical to detecting outbreaks and new food vehicles causing illness; to monitoring the safety of the food supply; and to directing risk-based food safety efforts by CDC, FDA, and USDA. Further, the Working Group noted the recent loss of capacity at state and local levels due to the recession and that additional resources will be needed to build on existing surveillance systems, better integrate them, and fill existing and emerging data gaps.Publication date from document properties.Summary -- Introduction -- Working Group Activities \ue2\u20ac\u201c FY 2012 -- Selection Criteria for Integrated Food Safety Centers of Excellence -- Figure 1: Desired CoE capabilities based on the commentary of the BSC FSMA-SWG -- Interagency Food Safety Analytics Collaboration -- Improving Foodborne Illness Surveillance Systems: Focus Areas for Future Discussion -- Governmental Coordination and Integration of Foodborne Surveillance -- Evaluating and Improving Surveillance Systems -- Enhancing Communication and Collaboration among Partners and External Stakeholders -- Resources -- Next Steps -- Appendix 1: Surveillance Working Group (Members) -- Appendix 2: Centers of Excellence Duties and Capabilities -- Appendix 3: Selected CDC Accomplishments in Implementing FSMA Surveillance Requirements -- Appendix 4: 2011 Recalls -- Appendix 5: New Food Vehicles in the United States Identified as Risk Factors -- References

Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry

2012 Fall.Includes bibliographical references.Epidemiologic modeling is an increasingly common method of estimating the potential impact of outbreaks of highly contagious diseases, such as foot-and-mouth disease (FMD) and highly pathogenic avian influenza (HPAI), in populations of domesticated animals. Disease models are also used to inform policy decisions regarding disease control methods and outbreak response plans, to estimate the possible magnitude of an outbreak, and to estimate the resources needed for outbreak response. Although disease models are computationally sophisticated, the quality of the results of modeling studies depends on the quality and accuracy of the data on which they are based, and on the conceptual soundness and validity of the models themselves. For such models to be credibly applied, they should realistically represent the systems they are intended to reflect, should be based to as great an extent as possible on valid data, and should be subjected to careful and ongoing scrutiny. Two key steps in the evaluation of epidemiologic models are model verification and model validation. Verification is the demonstration that a computer-driven model is operating correctly, and conforms to its intended design. Validation refers to the process of determining how well a model corresponds to the system that it intended to represent. For a veterinary epidemiologic model, validation would address issues such as how well the model represents the dynamics of the disease in question in a population to which the model is applied, and how well the model represents the application of different measures for disease control. Among the steps that can be taken by epidemiologic modelers to facilitate the processes of model verification and validation are to clearly state the purpose, assumptions, and limitations of a model; to provide a detailed description of the conceptual model for use by everyone who might be tasked with evaluation of a model; document steps already taken to test the model; and thoroughly describe the data sources and the process used to produce model input parameters from data. The realistic representation of the dynamics of spread of disease within individual herds or flocks can have important implications for disease detection and surveillance, as well as for disease transmission between herds or flocks. We have developed a simulation model of within-unit (within-herd or within-flock) disease spread that operates at the level of the individual animal, and fully incorporates sources of individual-level variation such as variability in the durations of incubating and infectious periods, the stochastic nature of disease spread among individuals, and the effects of vaccination. We describe this stochastic model, along with the processes employed for verification and validation. The incorporation of this approach to modeling of within-unit disease dynamics into models of between-unit disease spread should improve the utility of these models for emergency preparedness and response planning by making it possible to assess the value of different approaches to disease detection and surveillance, in populations with or without some existing level of vaccine immunity. Models rely not only on realistic representations of the systems of interest, but also on valid and realistic information. For spatially explicit models of the spread and control of disease in populations of livestock and poultry, this means a heavy reliance upon valid spatial representations of the populations of interest, including such characteristics as the geographic locations of farms and their proximity to others in the population. In the United States, limited information regarding the locations of actual farm premises is available, and modeling work often makes use of artificially generated population datasets. In order to evaluate the accuracy and validity of the use of such artificially generated datasets, we compared the outcomes of mechanistic epidemiologic simulation models that were run using an empirical population dataset to those of models that made use of several different synthetic population datasets. Although we found generally good qualitative agreement among models run using various population datasets, the quantitative differences in model outcomes could be substantial. When quantitative outcomes from epidemiologic models are desired or required, care should be taken to adequately capture or describe the uncertainty in model-based outcomes due to the use of synthetic population datasets

Animal disease investigations : Comparison of methods for information collection and identification of attributes for information management systems

In an infectious animal disease outbreak, effective management of the event requires timely and accurate information collection, processing, storage and distribution. This thesis focuses on the tools to assist information collection and management. The first study describes the comparison of questionnaire methodology for the information collection in the initial epidemiologic investigation of a Canadian federally reportable disease. The second study defines attributes of an animal disease outbreak information management system (IMS). The studies were performed within a one-year period (July 2013-July 2014). The first study performed two comparisons to determine differences in the information quality (completeness and accuracy) between differing questionnaire methodology and modes of completion (hard copy and electronic). The study was conducted with 24 Canadian Food Inspection Agency (CFIA) inspectors and veterinarians using a fictitious Canadian reportable disease scenario. The first comparison used a hard copy of a Canadian Food Inspection Agency (CFIA) questionnaire designed to be applicable (or generic) for all highly infectious reportable disease investigations with a supplementary disease specific section compared to an electronic disease specific reportable disease questionnaire. There was no significant difference in the information quality (N = 22; P = 0.09). The mean difference in completeness and accuracy scores was 3.5% (95% CI -0.6, 7.6). The second comparison focused on the hard copy disease questionnaire and assessed differences in information quality between using only the generic sections of the questionnaire compared to the supplementation of a disease specific section. A difference in information quality was determined (N = 24; P < 0.0001). The mean completeness and accuracy score for the generic only sections was 50.2% (95% CI 43.6, 57.2) compared to 80.2% (95% CI 76.2, 84.5) with the inclusion of the disease-specific section. The greatest difference in information quality occurred in the tracing specific information categories (P < 0.0001) with a mean difference of completeness and accuracy scores of 67.7% (95% CI 52.0, 83.4) for the trace-in (exposure history) category and 38.3% (95% CI 28.3, 48.3) for the trace-out (potential spread of disease) category. The absence of disease-specific questions were determined to be the primary factor in the difference in information quality. The second study determined a comprehensive list of user-defined attributes of an animal disease outbreak IMS and further identified the most important (key) attributes. A list of 34 attributes and associated definitions were determined through a series of focus group sessions and two surveys of Canadian animal health stakeholders. The animal health stakeholders included federal and provincial governments, veterinary academia and animal production industry representatives. The key attributes of an animal disease outbreak IMS identified were: ‘user friendly’, ‘effectiveness’, ‘accessibility’, ‘data accuracy’, ‘reliability’ and ‘timeliness’. ‘User friendly’ received the highest frequency of ranking as the most important attribute, followed by ‘effectiveness’. Information management was identified as the main purpose of an animal disease outbreak IMS with a median rating of 10 (rating scale of 0-10 with 10 = strongly agree). The occurrence of a federally reportable disease or a large-scale animal disease outbreak can have a great impact on the animal agriculture sector, regulatory government agencies and the economy. Information collection and management are essential to assist with the epidemiologic investigation and disease control measures. The study provided a novel opportunity to study information management for an animal disease outbreak from a Canadian perspective. The knowledge obtained will add value to the future development of tools and systems designed for information collection and management involving an animal disease outbreak

Guide to global digital tools for COVID-19 response

Updated Oct. 23, 2020The guide compares the District Health Information Software (DHIS2), the Surveillance, Outbreak Response Management and Analysis System (SORMAS), Go.Data, Open Data Kit (ODK), Epi Info, CommCare, KoboToolbox, Excel, and paper. Each has been deployed in various countries for contact tracing, investigations, and/or, in the case of DHIS2 and SORMAS, national surveillance. Paper is also included because it continues to be used and there are a number of resources available online for the COVID-19 response.\u200b\u200bThis guide is not meant to be an all encompassing guide to all available tools or features. Rather is it focused on the primary tools that are being reported to CDC and the functions that are commonly asked about. It is meant to be a dynamic resource that will be updated as additional tools are reported from the field offices and as additional questions about the functional elements arise.District Health Information Software (DHIS2) -- Surveillance, Outbreak Response Management and Analysis System (SORMAS\uae) -- Go.Data -- Epi Info -- Open Data Kit (ODK) -- CommCare -- KoboToolbox -- Excel -- Paper.2020E:\cpapFiles\WebServer\COVIDglobal-covid-19-compare-digital-tools2020oct23.pdfhttps://www.cdc.gov/coronavirus/2019-ncov/global-covid-19/compare-digital-tools.html855

Guide to global digital tools for COVID-19 response

Updated Sept. 12, 2020The guide compares the District Health Information Software (DHIS2), the Surveillance, Outbreak Response Management and Analysis System (SORMAS), Go.Data, Open Data Kit (ODK), Epi Info, CommCare, KoboToolbox, Excel, and paper. Each has been deployed in various countries for contact tracing, investigations, and/or, in the case of DHIS2 and SORMAS, national surveillance. Paper is also included because it continues to be used and there are a number of resources available online for the COVID-19 response.\u200b\u200bThis guide is not meant to be an all encompassing guide to all available tools or features. Rather is it focused on the primary tools that are being reported to CDC and the functions that are commonly asked about. It is meant to be a dynamic resource that will be updated as additional tools are reported from the field offices and as additional questions about the functional elements arise.District Health Information Software (DHIS2) -- Surveillance, Outbreak Response Management and Analysis System (SORMAS\uae) -- Go.Data -- Epi Info -- Open Data Kit (ODK) -- CommCare -- KoboToolbox -- Excel -- Paper.2020E:\cpapFiles\WebServer\COVIDglobal-covid-19-compare-digital-tools2020sep12.pdfhttps://www.cdc.gov/coronavirus/2019-ncov/global-covid-19/compare-digital-tools.html847