Summary of data reported to CDC's national automated biosurveillance system, 2008

<p>Abstract</p> <p>Background</p> <p>BioSense is the US national automated biosurveillance system. Data regarding chief complaints and diagnoses are automatically pre-processed into 11 broader syndromes (e.g., respiratory) and 78 narrower sub-syndromes (e.g., asthma). The objectives of this report are to present the types of illness and injury that can be studied using these data and the frequency of visits for the syndromes and sub-syndromes in the various data types; this information will facilitate use of the system and comparison with other systems.</p> <p>Methods</p> <p>For each major data source, we summarized information on the facilities, timeliness, patient demographics, and rates of visits for each syndrome and sub-syndrome.</p> <p>Results</p> <p>In 2008, the primary data sources were the 333 US Department of Defense, 770 US Veterans Affairs, and 532 civilian hospital emergency department facilities. Median times from patient visit to record receipt at CDC were 2.2 days, 2.0 days, and 4 hours for these sources respectively. Among sub-syndromes, we summarize mean 2008 visit rates in 45 infectious disease categories, 11 injury categories, 7 chronic disease categories, and 15 other categories.</p> <p>Conclusions</p> <p>We present a systematic summary of data that is automatically available to public health departments for monitoring and responding to emergencies.</p

The cornerstone of public health practice : public health surveillance, 1961--2011

The roots of modern public health surveillance took hold in 17th century Europe (1), but the seed for CDC's role as America's national agency for collecting, analyzing, interpreting, and using data to protect the public's health was firmly planted only in 1961, when the Morbidity and Mortality Weekly Report (MMWR) was transferred to what was then the Communicable Disease Center (CDC; now the Centers for Disease Control and Prevention) (2). The advent of MMWR at CDC marked the beginning of CDC's responsibility for aggregating and publishing data weekly on nationally notifiable diseases and publishing the data annually in MMWR's Summary of Notifiable Diseases, United States.su6004a4.htm?s_cid=su6004a4_w2011974

Doctor of Philosophy

dissertationPublic health surveillance systems are crucial for the timely detection and response to public health threats. Since the terrorist attacks of September 11, 2001, and the release of anthrax in the following month, there has been a heightened interest in public health surveillance. The years immediately following these attacks were met with increased awareness and funding from the federal government which has significantly strengthened the United States surveillance capabilities; however, despite these improvements, there are substantial challenges faced by today's public health surveillance systems. Problems with the current surveillance systems include: a) lack of leveraging unstructured public health data for surveillance purposes; and b) lack of information integration and the ability to leverage resources, applications or other surveillance efforts due to systems being built on a centralized model. This research addresses these problems by focusing on the development and evaluation of new informatics methods to improve the public health surveillance. To address the problems above, we first identified a current public surveillance workflow which is affected by the problems described and has the opportunity for enhancement through current informatics techniques. The 122 Mortality Surveillance for Pneumonia and Influenza was chosen as the primary use case for this dissertation work. The second step involved demonstrating the feasibility of using unstructured public health data, in this case death certificates. For this we created and evaluated a pipeline iv composed of a detection rule and natural language processor, for the coding of death certificates and the identification of pneumonia and influenza cases. The second problem was addressed by presenting the rationale of creating a federated model by leveraging grid technology concepts and tools for the sharing and epidemiological analyses of public health data. As a case study of this approach, a secured virtual organization was created where users are able to access two grid data services, using death certificates from the Utah Department of Health, and two analytical grid services, MetaMap and R. A scientific workflow was created using the published services to replicate the mortality surveillance workflow. To validate these approaches, and provide proofs-of-concepts, a series of real-world scenarios were conducted

J Biomed Inform

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.1U38 HK000063-01/HK/PHITPO CDC HHS/United StatesP01 HK000086/HK/PHITPO CDC HHS/United StatesP01 HK000086/HK/PHITPO CDC HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesU38 HK000063/HK/PHITPO CDC HHS/United States2015-10-18T00:00:00Z23501015PMC460954

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way

Am J Public Health

Disaster epidemiology (i.e., applied epidemiology in disaster settings) presents a source of reliable and actionable information for decision-makers and stakeholders in the disaster management cycle. However, epidemiological methods have yet to be routinely integrated into disaster response and fully communicated to response leaders. We present a framework consisting of rapid needs assessments, health surveillance, tracking and registries, and epidemiological investigations, including risk factor and health outcome studies and evaluation of interventions, which can be practiced throughout the cycle. Applying each method can result in actionable information for planners and decision-makers responsible for preparedness, response, and recovery. Disaster epidemiology, once integrated into the disaster management cycle, can provide the evidence base to inform and enhance response capability within the public health infrastructure.CC999999/Intramural CDC HHS/United StatesU38 HM000414/HM/NCHM CDC HHS/United States5U38HM000414-05/HM/NCHM CDC HHS/United States2015-11-01T00:00:00Z25211748PMC4202981vault:330

National biosurveillance strategy for human health. V2.0

The United States faces many potential threats to human health, including natural disease outbreaks, environmental exposures, and acts of terrorism. In today\u2019s modern world of high- density population centers and global mass transit such threats and hazards can significantly impact human health. These potential threats speak to the need for an integrated all-hazards approach to health security by all sectors of society. At the same time, the broader and more sophisticated use of information technology, new information sources, and analytic techniques holds the potential to accelerate recognition of health threats and improve the accuracy of assessments. With greater availability and real-time delivery of health information it may be possible to maintain a comprehensive picture of the nation\u2019s health and detect aberrations in illness patterns faster and more accurately.These new opportunities and increasing threats demand a national vision for biosurveillance that builds on existing capabilities and relationships while investing in innovative and science-driven tools and methods. Effective biosurveillance embraces a complementary \u201csystem of systems\u201d that leverages the data collection and analyses performed at the local level while incorporating broader national perspectives.Biosurveillance in the context of human health is a new term for the science and practice of managing health-related data and information for early warning of threats and hazards, early detection of events, and rapid characterization of the event so that effective actions can be taken to mitigate adverse health effects. It represents a new health information paradigm that seeks to integrate and efficiently manage health-related data and information across a range of information systems toward timely and accurate population health situation awareness.The National Biosurveillance Strategy for Human Health (hereafter The Strategy) articulates a vision for enhanced biosurveillance and is intended to guide national interests to:\u2022 Implement a national enterprise of complementary biosurveillance systems that provides relevant, accurate and timely information for government, healthcare, business, and personal decision-making for planning and responding to population health emergencies;\u2022 Coordinate health-related information sharing, according to defined data sharing policies, both vertically and horizontally across all levels of government, jurisdictions, and health- related disciplines to improve the effectiveness of disease monitoring and response;\u2022 Prioritize improvements to existing biosurveillance efforts and infrastructures;\u2022 Ensure biosurveillance data is available for local use, where it is best understood and managed;\u2022 Develop new relationships while continuing to leverage and maintain existing ties between local public health professionals and data providers;\u2022 Engage a diverse consortium of governmental, non-governmental, academic, and business sector stakeholders in the complex and distributed national enterprise of biosurveillance for human health;\u2022 Optimize resources to support biosurveillance and broader public health needs; and\u2022 Address all hazards while assuring flexibility and specificity of biosurveillance methods as required to monitor and investigate cases at the local level.The Strategy provides the foundation for a long-term effort to improve a nationwide capability to manage health-related data and information. It is grounded in U.S. laws and Presidential Directives, including Homeland Security Presidential Directive-21 (HSPD-21), \u201cPublic Health and Medical Preparedness\u201d, which names biosurveillance as one of four critical priorities for improving public health preparedness. HSPD-21 also mandates the development of a nationwide integrated biosurveillance capability, as well as the establishment of a federal advisory committee. As a result, the National Biosurveillance Advisory Subcommittee (NBAS), which includes private sector representatives, and state and local government public health authorities, was established to help carry out these mandates. This subcommittee to the Advisory Committee to the Director (ACD), Centers for Disease Control and Prevention (CDC), Department of Health and Human Services (HHS), produced their first report which includes recommendations to strengthen the nation\u2019s biosurveillance capability and provides counsel to the federal government regarding a broad range of issues affecting a nationwide biosurveillance strategy for human health. That report has helped shape The Strategy document.C8308743-2National_Biosurveillance_Strategy_for_Human_Health_v_2.pdfAcknowledgements -- Introduction -- Biosurveillance in the 21st century -- Priority areas -- Electronic health information exchange -- Electronic laboratory information exchange -- Unstructured data -- Integrated biosurveillance information -- Global disease detection and collaboration -- Biosurveillance workforce of the future -- The way forward -- Appendix A: Lists of contributors -- Appendix B: Acronym glossary.2010736

Syndromic Surveillance using Poison Center Data: An Examination of Novel Approaches

Early detection of a new outbreak or new information about a public health issue could prevent morbidity and mortality and reduce healthcare expenditures for the economy. Syndromic surveillance is a subset of public health surveillance practice that uses pre-diagnostic data to monitor public health threats. The syndromic surveillance approach posits that patients first interface with the healthcare system in non-traditional ways (e.g., buying over-the-counter medications, calling healthcare hotlines) before seeking traditional healthcare avenues such as emergency rooms and outpatient clinics. Thus detection of public health issues may be more timely using syndromic surveillance data sources compared to diagnosis-based surveillance systems. One source of information not yet fully integrated in syndromic surveillance is calls to poison centers. United States poison centers offer free, confidential medical advice through a national help line to assist in poison exposures. Call data are transmitted and stored in an electronic database within minutes to the National Poison Data System (NPDS), which can be used for near-real-time surveillance for disease conditions or exposures. The studies presented in the dissertation explore new ways for poison center records to be used for early identification of public health threats and for evaluating policy and program impact by identifying changing trends in poison center records. The approach and findings from these three studies expand upon current knowledge of how poison center records can be used for syndromic surveillance and provide evidence that justifies expansion of poison center surveillance into avenues not yet explored by local, state, and federal public health