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Burkom Howard

Description

The mission of the ISDS TCC is to bridge the gap between the analytic needs of public health practitioners and the expertise of researchers from other fields for the enhancement of disease surveillance, including situational awareness of chronic as well as infectious threats and follow-up activities such as case linkage and contact tracing. Committee activities to achieve this mission are identifying practical use cases, refining technical specifications in open forums, obtaining benchmark datasets for controlled dissemination, validating candidate methods, and sharing method documentation. In its first 2 years, the TCC has worked on three use cases and assisted with development of data use agreements to permit posting of benchmark datasets, http://www.syndromic.org/ communities/technical-conventions. Recent polling of the Biosense User Group indicated widespread interest in developing additional use cases. The proposed panel is intended to focus on practical applications of common interest, refine the use case development and dissemination process, and foster global interest in this process.

Objective

The main objective is to broaden the collection of use cases developed by the ISDS Technical Conventions Committee (TCC) to enhance effective collaboration between public health practice and analyst researchers in various disciplines and institutions. Panellists will present and motivate use case concepts including requirements for practical solution methods. Component objectives are to refine the presented use cases and to stimulate formation of new ones at local, state, and national levels.

Submitted by teresa.hamby@d… on
Description

Biosurveillance Portal (BSP) is a web-based enterprise environment that is aimed to facilitate international collaboration, communication, and information-sharing in support of the detection, management, and mitigation of biological events in Korea. In Oct 2013, Republic of Korea (ROK) Ministry of National Defense has made the project agreement with United States (US) Department of Defense Joint Program Executive Office of Chemical and Biological Defense to develop Biosurveillance Portal which will provide tools and capabilities to facilitate timely identification and detection of biological events to minimize operational impacts on ROK-US Forces. As a part of this project, Armed Forces Medical Command (AFMC) undertook the initiative to develop the Military Active Realtime Syndromic Surveillance system.

Objective

This presentation aims to elaborate our experiences from initiating a syndromic surveillance system as a part of current biosurveillance developments in Korea. We developed Military Active Realtime Syndromic Surveillance (MARSS) system with data from all of 19 Korean military hospitals as a part of the US-ROK joint Biosurveillance Project.

Submitted by teresa.hamby@d… on
Description

Biosurveillance systems commonly depend on free-text chief complaints (CC)s for timely situational awareness. However, diagnosis codes may not be available soon enough and may have uncertain value because they are assigned for billing purposes rather than for population monitoring. Existing systems use syndrome categories to classify records based on these free-text fields. A syndromic cluster determination method (TOA) based on patient arrival times has been implemented in versions of ESSENCE and in NCDETECT [1]. While effective for finding case clusters whose CC terms are classifiable into syndromes, TOA implementations do not find clusters whose CC terms share only uncategorized terms. 

Objective

Explain and demonstrate the performance of a statistical method for detection of anomalous terms in pooled, contiguous blocks of freetext chief complaints from a health facility with emergent or urgent care capability.

Submitted by rmathes on
Description

Public health departments need enhanced surveillance tools for population monitoring, and external researchers have expertise and methods to provide these tools. However, collaboration with potential solution developers and students in academia, industry, and government has not been sufficiently close or well informed for rapid progress. Many peer-reviewed papers on biosurveillance methods have been published by researchers, but few methods have been adopted in systems used by health departments. In a 2013 BioSense User Group survey with responses from users in more than 40 U.S. states, access to improved analytic methods was a top priority. Among the tools most desired by respondents were the ESSENCE biosurveillance system with multiple analytic tools and statistical software packages such as SAS. Multiple obstacles have slowed the progress of practitioners and developers who seek the development and implementation of useful analytic tools. First, the epidemiological challenges and associated operational constraints are not sufficiently understood among academic developers. Many health departments do not have the resources to hire such developers beyond maintenance of information technology, and the health monitors are typically too busy to publish in peer-reviewed journals. Second, data cannot be shared because of privacy and proprietary limitations with varying local rules. Data-sharing has posed difficult administrative problems, both within and external to health departments, in the course of ISDS Technical Conventions committee efforts to promote interactions through use case problems. Third, aspects of situational awareness vary widely among health monitors at different jurisdictional levels, so analytical challenges and constraints vary widely among potential users. Practitioners have pointed out that “surveillance is local”, but local operational and data environments vary widely. A fourth main issue is cross-cultural: Understaffed health departments must respond to successive crises and often lack the time for requirements analysis and technical publication. Such client work situations complicate interaction with academic environments of semester schedules and limited grants and transient student support. This panel brings together academic statisticians who have had successful direct relationships with public health departments to discuss how they have dealt with these challenges.

Objective

The session will explore past collaborations between the scientist panelists and public health departments to highlight approaches that have and have not been effective and to recommend effective, sustainable relationship strategies for the mutual advancement of practical disease surveillance and relevant academic research.

Submitted by teresa.hamby@d… on
Description

NPDS is a near real-time surveillance system and national database operated by the American Association of Poison Control Centers. NPDS receives records of all calls made to the 55 regional US poison centers (PCs). The Centers for Disease Control and Prevention (CDC) use NPDS to 1) provide public health surveillance for chemical, radiological and biological exposures and illnesses, 2) identify early markers of chemical, radiological, and biological incidents, and 3) find potential cases and enhance situational awareness during a known incident. Anomalies are reviewed daily by a distributed team of PC medical and clinical toxicologists for potential incidents of public health significance (IPHS). Information on anomalies elevated to IPHS is promptly relayed to state epidemiologists or other designated officials for situational awareness and public health response.

Current NPDS surveillance algorithms utilize the Historical Limits Method, which identifies a data anomaly when call volumes exceed a statistical threshold derived from multiple years of historical data. Alternative analysis tools such as those employed by ESSENCE and other computerized data surveillance systems have been sought to enhance NPDS signal analysis capability. Technical improvements have been implemented in 2013 to expand NPDS surveillance capabilities but have not been thoroughly tested. Moreover, other data aberration detection algorithms, such as temporal scan statistics, have not yet been tested on real-time poison center data.

Objective

To compare the effectiveness of current surveillance algorithms used in the National Poison Data System (NPDS) to identify incidents of potential public health significance with 1) new algorithms using expanded NPDS surveillance capabilities and 2) methods beyond the NPDS’ generalized historical limits model.

Submitted by teresa.hamby@d… on
Description

The APCC hotline fields daily calls regarding potential animal intoxications from the US, its territories, and Canada. We explored the value of these data for identifying increased occurrences of intoxications related to livestock and poultry species, toxicant product categories, clinical syndromes, and illness severity. These data proved valuable for identifying risks of toxicant exposures by species, product category, and season. In addition to identifying intoxication risks to animal health, these data could be used to monitor for infectious outbreaks that may initially be confused for intoxications.

Objective

To describe the value of the American Society for the Prevention of Cruelty to Animals (ASPCA) Poison Control Center (APCC) livestock animal calls as a passive data stream for biosurveillance of number of calls, species affected, toxicant exposures, and clinical syndromes.

 

Submitted by Magou on
Description

The Risk Identification Unit (RIU) of the US Dept. of Agriculture’s Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. In an initiative to increase the monitored species, health issues, and data sources, CEAH epidemiologists are building a surveillance system based on weekly counts of laboratory test orders along with Colorado State Univ. laboratorians and statistical analysts from the Johns Hopkins Univ. Applied Physics Lab. Initial efforts used 12 years of equine test records from 3 state labs covering most Colorado horse testing. Trial syndrome groups were formed based on RIU experience and published articles. Data analysis, stakeholder input, and discovery of laboratory workflow details were needed to modify these groups and filter test records to eliminate alerting bias. Customized statistical monitoring methods were sought based on specialized lab information characteristics and on likely presentation and health significance of syndrome-associated diseases.

Submitted by teresa.hamby@d… on
Description

Syndromic surveillance systems have historically focused on aggregating data into syndromes for analysis and visualization. These syndromes provide users a way to quickly filter large amounts of data into a manageable number of streams to analyze. Additionally, ESSENCE users have the ability to build their own case definitions to look for records matching particular sets of criteria. Those user- defined queries can be stored and analyzed automatically, along with the pre-defined syndromes. Aside from these predefined and user- defined syndromic categories, ESSENCE did not previously provide alerts based on individual words in the chief complaint text that had not been specified a priori. Thus, an interesting cluster of records linked only by non-syndromic keywords would likely not be brought to a user’s attention. 

Objective

The objective of this presentation is to describe the new word alert capability in ESSENCE and how it has been used by the Florida Department of Health (FDOH). Specifically, this presentation will describe how the word alert feature works to find individual chief complaint terms that are occurring at an abnormal rate. It will then provide usage statistics and first-person accounts of how the alerts have impacted public health practice for the users. Finally, the presentation will offer future enhancement possibilities and a summary of the benefits and shortcomings of this new feature. 

Submitted by Magou on
Description

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States PCs. PCs provide triage and treatment advice for hazardous exposures through a free national hotline. Information on demographics, health effects, implicated substance(s), medical outcome of the patient, and other variables are collected.

CDC uses automated algorithms to identify anomalies in both pure call volume and specific clinical effect volume, and to identify calls reporting exposure to high priority agents. Pure and clinical effect volume anomalies are identified when an hourly call count exceeds a threshold based on historical data using HLM.1 Clinical toxicologists and epidemiologists at the American Association of Poison Control Centers and CDC apply standardized criteria to determine if the anomaly identifies a potential incident of public health significance (IPHS) and to notify the respective health departments and local PCs as needed. Discussions with NPDS users and analysis of IPHS showed that alerting based on pure call volume yielded excessive false positives. A study using a 5-year NPDS call dataset assessed the positive predictive value (PPV) of the call volume-based approach. This study showed that less than 4% of anomalies were IPHS.2 A low PPV can cause unnecessary waste of staff time and resources analyzing false positive anomalies.

As an alternative to pure call volume-based detection where all calls to each PC are aggregated for anomaly detection, we considered separating calls by toxicologically-relevant exposure categories for more targeted anomaly detection. We hypothesized that this stratified approach would reduce the number of false positives. 

Objective

Our objective was to compare the effectiveness of applying the historical limits method (HLM) to poison center (PC) call volumes with vs without stratifying by exposure type. 

Submitted by Magou on