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1.  SAGES Update: Electronic Disease Surveillance in Resource-Limited Settings 
Objective
The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, open-source software tools for electronic disease surveillance in resource-limited settings. This demonstration will illustrate several new innovations and update attendees on new users in Africa and Asia.
Introduction
The new 2005 International Health Regulations (IHR), a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance with particular emphasis on resource-limited settings. More than a decade ago, in collaboration with the US Department of Defense (DoD), the Johns Hopkins University Applied Physics Laboratory (JHU/APL) developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE collects, processes, and analyzes non-traditional data sources (i.e. chief complaints from hospital emergency departments, school absentee data, poison control center calls, over-the-counter pharmaceutical sales, etc.) to identify anomalous disease activity in a community. The data can be queried, analyzed, and visualized both temporally and spatially by the end user. The current SAGES initiative leverages the experience gained in the development of ESSENCE, and the analysis and visualization components of SAGES are built with the same features in mind.
Methods
SAGES tools are organized into four categories: 1) data collection, 2) analysis & visualization, 3) communications, and 4) modeling/simulation/evaluation. Within each category, SAGES offers a variety of tools compatible with surveillance needs and different types or levels of information technology infrastructure. SAGES tools are built in a modular nature, which allows for the user to select one or more tools to enhance an existing surveillance system or use the tools en masse for an end-to-end electronic disease surveillance capability. Thus, each locality can select tools from SAGES based upon their needs, capabilities, and existing systems to create a customized electronic disease surveillance system. New OpenESSENCE developments include improved data query ability, improved mapping functionality, and enhanced training materials. New cellular phone developments include the ability to concatenate single SMS messages sent by simple or Smart Android cell phones. This ‘multiple-SMS’ message ability allows use of SMS technology to send and receive health information exceeding normal SMS message length in a manner transparent to the users.
Conclusions
The SAGES project is intended to enhance electronic disease surveillance capacity in resource-limited settings around the world. We have combined electronic disease surveillance tools developed at JHU/APL with other freely-available, interoperable software tools to create SAGES. We believe this suite of tools will facilitate local and regional electronic disease surveillance, regional public health collaborations, and international disease reporting. SAGES development, funded by the US Armed Forces Health Surveillance Center, continues as we add new international collaborators. SAGES tools are currently deployed in locations in Africa, Asia and South America, and are offered to other interested countries around the world.
PMCID: PMC3692858
software; surveillance; electronic; open-source
2.  SAGES: A Suite of Freely-Available Software Tools for Electronic Disease Surveillance in Resource-Limited Settings 
PLoS ONE  2011;6(5):e19750.
Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.
doi:10.1371/journal.pone.0019750
PMCID: PMC3091876  PMID: 21572957
3.  Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance 
PLoS ONE  2014;9(1):e83730.
In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.
doi:10.1371/journal.pone.0083730
PMCID: PMC3879288  PMID: 24392093
4.  Landscape of international event-based biosurveillance 
Event-based biosurveillance is a scientific discipline in which diverse sources of data, many of which are available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events and situational awareness. The Global Health Security Action Group of the Global Health Security Initiative is developing a biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these biosurveillance systems and identifies needed future studies.
doi:10.3134/ehtj.10.003
PMCID: PMC3167659  PMID: 22460393
5.  Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System 
Objective
To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).
Introduction
Telephone triage is a relatively new data source available to biosurveillance systems.1–2 Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, over-the-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.
Methods
Veterans phoning VA’s TT system, and those admitted or seen at a VA facility with influenza or influenza-like-illness (ILI) diagnosis were included in this analysis. Influenza-specific ICD-9-CM coded emergency department (ED) and urgent care (UC) visits, hospitalizations, TT calls, and ILI outpatient visits were analyzed covering 2010–2011 and 2011–2012 influenza seasons (July 11, 2010–April 14, 2012). Data came from 80 VA Medical Centers and over 500 outpatient clinics with complete reporting data for the time period of interest. We calculated Spearman rank-order coefficients, 95% confidence intervals and p-values using Fisher’s z transformation to describe correlation between TT data and other influenza healthcare measures. For comparison of time trends, we plotted data for hospitalizations, ED/UC visits and outpatient ILI syndrome visits against TT encounters. We applied ESSENCE detection algorithms to identify high-level alerts for influenza activity. ESSENCE aberration detection was restricted to the 2011–2012 season because limited historical TT and outpatient data from 2009–2010 was available to accurately predict aberrancy in the 2010–2011 season. We then calculated the peak measure of healthcare utilization during both influenza seasons (2010–2011 and 2011–2012) for each data source and compared timing of peaks and alerts between TT and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance.
Results
There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ED/UC visits (0.85); and ILI-outpatient visits (0.88), respectively (P< 0.0001 for all correlations). Peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. For the 2011–2012 season, TT alerted with increased influenza activity before all other settings.
Conclusions
Data from VA telephone care correlates well with other VA data sources for influenza activity. TT may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. As a national health care system with a large patient population, VA could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with TT data. Additional analyses are needed to understand and correlate TT with healthcare utilization and severity of illness.
PMCID: PMC3692747
Surveillance; Influenza; Telephone triage; Veterans
6.  National Collaborative for Bio-Preparedness 
Objective
Demonstrate the functionality of the National Collaborative for Bio-Preparedness system.
Introduction
The National Collaborative for Bio-Preparedness (NCB-Prepared) was established in 2010 to create a biosurveillance resource to enhance situational awareness and emergency preparedness. This joint-institutional effort has drawn on expertise from the University of North Carolina- Chapel Hill, North Carolina State University, and SAS Institute, leveraging North Carolina’s role as a leader in syndromic surveillance, technology development and health data standards. As an unprecedented public/private alliance, they bring the flexibility of the private sector to support the public sector. The project has developed a functioning prototype system for multiple states that will be scaled and made more robust for national adoption.
Methods
NCB-Prepared recognizes that the capability of any biosurveillance system is a function of the data is analyzes. NCB-Prepared is designed to provide information services that analyze and integrate national data across a variety of domains, such as human clinical, veterinary and physical data. In addition to this one-health approach to surveillance, a primary objective of NCB-Prepared is to gather data that is closer in time to the event of interest. NCB-Prepared has validated the usefulness of North Carolina emergency medical services data for the purposes of biosurveillance of both acute outbreaks and seasonal epidemics (1).
A unique model of user-driven valuing of data-providing value back to the provider in their terms-motivates collaboration from potential data providers, along with timely and complete data. NCB-Prepared approaches potential data providers, partners and users with the proposition that enhanced data quality and analysis is valuable to them individually and that an integrated information system can be valuable to all. With the onboarding of new data sources, NCB-Prepared implements a formal process of data discovery and integration. The goal of this process is three-fold: 1) to develop recommendations to enhance data quality going forward, 2) to integrate information across data sources, and 3) to develop novel analytic techniques for detecting health threats. NCB-Prepared is committed to both utilizing standard methods for event detection and to developing innovative analytics for biosurveillance such as the Text Analytics and Proportional charts method (TAP). The sophisticated analytic functionality of the system, including improved time to detection, query reporting, alert detection, forecasting and predictive modeling, can be attributed to collaboration between analysts from private industry, academia and public health.
NCB-Prepared followed the formal software development process known as agile development to create the user interface of the system. This method is based on iterative cycles wherein requirements evolve from regular sessions between user groups and developers. The result of agile development and collaborative relationships is a system which visualizes signals and diverse data sources across time and place while providing information services across all levels of users and stakeholders.
Conclusions
Lessons Learned: Understand the functionality of new biosurveillance system, NCB-PreparedIdentify the benefit of creating collaborative relationships with data providers and usersAppreciate the value of a public/private partnership for biosurveillance and bio-preparedness
PMCID: PMC3692851
Biosurveillance; Analytics; Preparedness; Emergency
7.  Coverage and Timeliness of Combined Military and Veteran Surveillance Systems 
Objective
We determined the utility and effective methodology for combining patient record information from the Departments of Veterans Affairs (VA) and Defense (DoD) health surveillance systems.
Introduction
An objective of the Joint VA/DoD BioSurveillance System for Emerging Biological Threats project is to improve situational awareness of the health of combined VA and DoD populations. DoD and VA both use versions of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). With a retrospective outpatient data collection available, we analyzed relative coverage and timeliness of the two systems to understand potential benefits of a joint system.
Methods
We used the US Office of Management and Budget’s core-based statistical area (CBSA) to group data from the respective systems by megapolitan (>1 million), metropolitan (50,000-1 million) and micropolitan (10,000–50,000) areas. We performed frequency analyses and mapped coverage of the VA and DoD medical systems in these CBSAs. To determine comparability, we compared International Classification of Diseases, 9th Revision (ICD-9) code usage from 2007–2010 by age group in the respective systems and then formulated a working definition of influenza-like illness (ILI). We then compared CBSA-level temporal detection timeliness in the two systems for the H3N2 epidemic of 2007–9 and the H1N1 pandemic in 2009.
Results
We identified a total of 939 CBSAs, with generally diffuse geographic coverage by VA facilities and higher concentration in larger metro and mega areas for DoD facilities. Of the 51 mega CBSAs, all have at least one VA facility and 63% have a DoD facility. Coverage is sparser for the metro CBSAs and lighter still for the micro CBSAs (Table 1). Although the VA coverage is greater, in many CBSAs with dual coverage, the DoD visit volume is comparable or greater. Patient age distribution differs sharply, with >85% of the VA patients over 45 years of age compared to 22% of DoD patients. For all CBSAs, the overall VA/DoD visit ratio is 1.92, but the ratios for 0–17 years is 0.004, 18–44 years 0.33, 45–64 years 5.20 and >65 years 11.63.
Based on an analysis of ICD-9 codes used in the two systems, the DoD uses symptom-based ILI codes far more frequently than the VA, especially codes for diseases often seen in children (e.g., otitis media). Analysis of ILI-related codes assigned in both systems led to a common code set for comparative analysis. From applying alerting algorithms to visit counts based on this code set, detection was better in DoD data for 57% and 77% of CBSAs for seasonal and pandemic influenza, respectively, and better in VA data for 37% and 14% of CBSAs (Table 2). The VA system performed better during the typical H3N2 seasonal flu compared to the H1N1 outbreak. The DoD system performed better during the H1N1 pandemic, although outperformed the VA for both.
Conclusions
The coverage analysis demonstrates two complementary surveillance systems with evident benefits to a fused national health picture. The VA system patient volume roughly doubles the DoD system, and provides better geographic coverage in smaller CBSAs; however, the DoD includes younger populations, better coverage in strategic metro areas, and more pre-diagnostic ILI coding. From analysis of both outbreaks, relative timeliness could be improved in 92% of CBSAs with access to both systems, with more information provided in CBSAs where only one type of facility exists.
PMCID: PMC3692883
Syndromic surveillance; Merged systems; Government
8.  Evaluation of ESSENCE in the Cloud Using Meaningful Use Syndromic Surveillance Data 
Objective
This project represents collaboration among CDC’s BioSense Program, Tarrant County Public Health and the ESSENCE Team at the Johns Hopkins University APL. For over six months the Tarrant County Public Health Department has been sending data through the BioSense 2.0 application to a pilot version of ESSENCE on the Amazon GovCloud. This project has demonstrated the ability for local hospitals to send meaningful use syndromic surveillance data to the Internet cloud and provide public health officials tools to analyze the data both using BioSense 2.0 and ESSENCE. The presentation will describe the tools and techniques used to accomplish this, an evaluation of how the system has performed, and lessons learned for future health departments attempting similar projects.
Introduction
In November of 2011 BioSense 2.0 went live to provide tools for public health departments to process, store, and analyze meaningful use syndromic surveillance data. In February of 2012 ESSENCE was adapted to support meaningful use syndromic surveillance data and was installed on the Amazon GovCloud. Tarrant County Public Health Department agreed to pilot the ESSENCE system and evaluate its performance compared to a local version ESSENCE they currently used. The project determined the technical feasibility of utilizing the Internet cloud to perform detailed public health analysis, necessary changes needed to support meaningful use syndromic surveillance data, and any public health benefits that could be gained from the technology or data.
Methods
This project investigated database and visualization changes necessary to support meaningful use syndromic surveillance data in ESSENCE. It evaluated the Internet cloud environment and determined the benefits and disadvantages to using this technology as a platform for ESSENCE. This included scalability, performance, and cost analysis of the Internet cloud platform. After using the system for a period of time, the Tarrant County users evaluated the Internet cloud version of the system.
Results
Many technical adaptations to the ESSENCE system were made to support the new meaningful use syndromic surveillance elements. Several optimizations, including a new database schema and cube table structures, were developed to improve performance of ESSENCE in the Internet cloud and incorporating the meaningful use requirements. The Internet cloud platform offered many levels of performance that could alter the ESSENCE user experience. Smaller configurations allowed for 100 concurrent users to experience 16 second response times, whereas larger configurations supported experiences of 2 second response times.
Conclusions
Public health departments are dealing with new meaningful use syndromic surveillance data elements and the cost of maintaining local systems. This collaborative team have researched and evaluated tools, technologies, and solutions that can be used throughout the country.
PMCID: PMC3692900
Electronic Medical Records for Public Health; Interoperability; Meaningful Use; Syndromic Surveillance; Internet Cloud
9.  Event-based internet biosurveillance: relation to epidemiological observation 
Background
The World Health Organization (WHO) collects and publishes surveillance data and statistics for select diseases, but traditional methods of gathering such data are time and labor intensive. Event-based biosurveillance, which utilizes a variety of Internet sources, complements traditional surveillance. In this study we assess the reliability of Internet biosurveillance and evaluate disease-specific alert criteria against epidemiological data.
Methods
We reviewed and compared WHO epidemiological data and Argus biosurveillance system data for pandemic (H1N1) 2009 (April 2009 – January 2010) from 8 regions and 122 countries to: identify reliable alert criteria among 15 Argus-defined categories; determine the degree of data correlation for disease progression; and assess timeliness of Internet information.
Results
Argus generated a total of 1,580 unique alerts; 5 alert categories generated statistically significant (p < 0.05) correlations with WHO case count data; the sum of these 5 categories was highly correlated with WHO case data (r = 0.81, p < 0.0001), with expected differences observed among the 8 regions. Argus reported first confirmed cases on the same day as WHO for 21 of the first 64 countries reporting cases, and 1 to 16 days (average 1.5 days) ahead of WHO for 42 of those countries.
Conclusion
Confirmed pandemic (H1N1) 2009 cases collected by Argus and WHO methods returned consistent results and confirmed the reliability and timeliness of Internet information. Disease-specific alert criteria provide situational awareness and may serve as proxy indicators to event progression and escalation in lieu of traditional surveillance data; alerts may identify early-warning indicators to another pandemic, preparing the public health community for disease events.
doi:10.1186/1742-7622-9-4
PMCID: PMC3493297  PMID: 22709988
Biosurveillance; Infectious disease; Epidemiology; Disease-specific alerts; Internet media; Early warning; Pandemic (H1N1) 2009; Situational awareness; Outbreak detection
10.  Does Antimicrobial Prescription Data Improve Influenza Surveillance in VA? 
Introduction
Antimicrobial prescriptions are a new data source available to the Veterans Health Administration (VHA) biosurveillance program. Little is known about whether antiviral or antibacterial prescription data correlates with influenza ICD-9-CM coded encounters. We therefore evaluated the utility and timeliness of antiviral and antibacterial utilization for influenza surveillance.
Methods
Antiviral (oseltamivir, zanamivir) and antibacterial (azithromycin) outpatient (OP) prescriptions and OP ESSENCE coded respiratory syndrome, influenza-like-illness (ILI) or influenza-specific ICD-9-CM coded visits were analyzed covering the 2010–2011 and 2011–2012 influenza seasons (July 1, 2010–July 31, 2012) for 152 VA medical centers and 971 outpatient clinics using VA Corporate Data Warehouse and ESSENCE biosurveillance tool. Correlation analysis and peak comparisons were performed.
Results
For this time period, there were 2,880,415 respiratory, 1,578,421 ILI, and 5,158 influenza-specific coded visits. For both influenza seasons, respiratory and ILI visits peaked at weeks 1–2 whereas influenza-specific visits had two peaks between weeks 37–40 and weeks 6–11 (See Figure 1 and 2). The total number of prescriptions was 631,272 azithromycin; 8,362 oseltamivir; and 88 zanamivir (See Figure 2). Spearman rank correlation coefficients for daily antiviral prescriptions and influenza-coded visits were (0.70); ILI visits (0.64), and respiratory illness visits (0.62), respectively; and for azithromycin prescriptions 0.77, 0.98, and 0.97 respectively. Oseltamivir and zanamivir prescriptions only increased in 2010–2011 starting with week 51 and peaking week 6 and in 2011–2012 starting with week 8 and peaking week 14. However, azithromycin prescriptions tracked better across the entire influenza season (peaking at weeks 1–2 for both influenza seasons).
Conclusions
VA outpatient prescription data indicated that significantly more ILI and respiratory syndrome visits occurred compared to antiviral prescriptions dispensed with marginal temporal correlation between visits and antiviral prescriptions. Reasons for this finding require further investigation. Although we did not chart review the visit code and antimicrobial prescription in individual records, possible factors may be related to later presentation of cases, perceived lack of efficacy of antivirals, or insufficient coding of influenza. Thus, antiviral prescription data provided minimal additional information for influenza trend monitoring in VA although may still be useful a marker of more severe illness. Interestingly, azithromycin use tracked better with the onset and peaks of the influenza season. Further investigation is also needed to determine whether patients with influenza-specific coded encounters were also prescribed azithromycin and why relatively few encounters were coded with an influenza-specific code.
PMCID: PMC3692879
ESSENCE; Surveillance; Influenza; Veterans; Antimicrobials
11.  Factors Influencing Performance of Internet-Based Biosurveillance Systems Used in Epidemic Intelligence for Early Detection of Infectious Diseases Outbreaks 
PLoS ONE  2014;9(3):e90536.
Background
Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.
Method and Findings
Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables.
Conclusion
Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.
doi:10.1371/journal.pone.0090536
PMCID: PMC3944226  PMID: 24599062
12.  Localized Cluster Detection Applied to Joint and Separate Military and Veteran Subpopulations 
Objective
We examined the utility of combining surveillance data from the Departments of Defense (DoD) and Veterans Affairs (VA) for spatial cluster detection.
Introduction
The Joint VA/DoD BioSurveillance System for Emerging Biological Threats project seeks to improve situational awareness of the health of VA/DoD populations by combining their respective data. Each system uses a version of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE); a combined version is being tested.
The current effort investigated combining the datasets for disease cluster detection. We compared results of retrospective cluster detection studies using both separate and joined data. — Does combining datasets worsen the rate of background cluster determination?
— Does combining mask clusters detected on the separate datasets?
— Does combining find clusters that the separate datasets alone would miss?
Methods
Cluster determination runs were done with a spatial scan statistics implementation previously verified [1] by comparison with SaTScan software [2] using DoD data from the Biosense system.
Input data files were extracted from a repository of outpatient records from both DoD and VA facilities covering 4 years beginning Jan. 1, 2007. This repository includes over 37 million DoD records and over 86 million VA records. Input files were matrices of daily Influenza-like-Illness (ILI) or gastrointestinal (GI) visit counts. Matrix rows were consecutive days, columns were patient residence zip codes, and entry (i, j) was the number of visits on day i from with zip code j. These files were made for DoD data, VA data, and combined data.
For assessing the alerting burden from combining datasets, three sets of runs were executed using data from three regions, Baltimore/Washington D.C. (dominated by DoD data), Los Angeles (mainly VA data), and Tampa (representation of both). For each region, sets of 1672 daily runs were executed for ILI and GI syndrome data. Lastly, focused runs were done to investigate known outbreaks in New York (GI, Jan–Mar 2010), San Diego (ILI, Dec 2007–Apr 2008 and Fall 2009), and New Jersey (GI, Jan–Mar 2010).
Results
Combining the data sources increased the rate of significant cluster alerting by a manageable 1–10% across run sets. Some clusters found only when the data were combined persisted over several days and may have indicated small events not reported in either system; however, we were unable to validate minor events that may have occurred in past years.
Retrospective looks at known outbreaks were successful in that clustering evidence found in separate DoD and VA runs persisted when data sets were combined. For the New York run, a West Point outbreak was seen in repeated clusters of combined data, beginning days before the event report. However, clustering did not consistently produce alerts before outbreak report dates. In the New Jersey DoD runs, repeated clusters indicated a 10-week GI outbreak at Fort Dix; adding VA data that dominated the record counts gave the same clusters with no added cases, so the DoD event was probably self-contained. The San Diego runs were aimed at detecting unusually severe influenza epidemics in February 2008 and in the fall of 2009, and numerous clusters were found but did not enhance regional disease tracking.
Conclusions
From the analysis, combining DoD and VA data enhances cluster detection capability without loss of sensitivity to events isolated in either population and with manageable effect on the customary alert rate. For cluster detection, there may be many geographic regions where a health monitor in one of the systems would benefit from combined data. More detailed outbreak information is needed to quantify the timeliness/sensitivity advantages of combining datasets. In events examined, clustering itself yielded an occasional but not consistent timeliness advantage.
PMCID: PMC3692743
ESSENCE; Department of Defense; scan statistics; cluster detection; Veterans Administration
13.  Incorporation of School Absenteeism Data into the Maryland Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) 
Objective
The state of Maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. This session will discuss the process, challenges, and best practices for expanding the ESSENCE system to include school absenteeism data as part of disease surveillance. It will also discuss the plans that Maryland has for using this new data source, as well as the potential for further expansion.
Introduction
Syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. The Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) conducts syndromic surveillance using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Since its inception, ESSENCE has been a vital tool for DHMH, providing continuous situational awareness for public health policy decision makers. It has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. Schools have the potential to play a major role in the spread of disease during an epidemic. Therefore, having school absenteeism data in ESSENCE would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease.
Methods
DHMH partnered with the Maryland State Department of Education (MSDE), local health departments, and local school systems to incorporate school absenteeism data into the syndromic surveillance program. There are 24 local public school systems and 24 local health departments in the state of Maryland. OP&R contacted each local school superintendent and each local health officer to arrange a joint meeting to discuss the expansion of the ESSENCE program to include school absenteeism data. Once the meetings were arranged, OP&R epidemiologists traveled to each local jurisdiction and presented their plan for the ESSENCE expansion. At each meeting were representatives from the local health department, as well as school health, school attendance, and school IT staff. This allowed all questions and concerns to be addressed from both sides. In addition to the targeted meetings and presentations, the Secretary of Health issued an executive order which required all local school systems to sign a memorandum of understanding (MOU) with DHMH. This MOU detailed the data elements to be shared with the ESSENCE program and the process by which this would be shared. While this order made data contribution mandatory, the site visits by the OP&R staff created a working relationship and partnership with the local jurisdictions. Data was collected from all public schools in the state including elementary, middle, and high schools.
Results
As of June 30, 2012, Maryland became the first state in the United States to incorporate 100% of its public school systems (1,424 schools) into ESSENCE. Each school system reports absenteeism data daily via an automated secure FTP (sFTP) transfer to DHMH. Due to its unique properties, Johns Hopkins Applied Physics Laboratory (JHUAPL) designed a new detection algorithm in ESSENCE specifically for this data source. OP&R epidemiologist review and analyze this data for disease surveillance purposes in conjunction with other data sources in ESSENCE (emergency department chief complaints, poison control center data, thermometer sales data, and over-the-counter medication sales data). Integrating school absenteeism data will provide a more complete analysis of potential public health threats. The process by which Maryland incorporated their public school systems’ data could potentially be used as a best practice for other jurisdictions. Not only was DHMH able to obtain data from all public schools in the state, but the process also enhanced collaboration between local health departments and public school systems.
PMCID: PMC3692827
ESSENCE; Surveillance; Absenteeism
14.  Optimizing A Syndromic Surveillance Text Classifier for Influenza-like Illness: Does Document Source Matter? 
Syndromic surveillance systems that incorporate electronic free-text data have primarily focused on extracting concepts of interest from chief complaint text, emergency department visit notes, and nurse triage notes. Due to availability and access, there has been limited work in the area of surveilling the full text of all electronic note documents compared with more specific document sources. This study provides an evaluation of the performance of a text classifier for detection of influenza-like illness (ILI) by document sources that are commonly used for biosurveillance by comparing them to routine visit notes, and a full electronic note corpus approach. Evaluating the performance of an automated text classifier for syndromic surveillance by source document will inform decisions regarding electronic textual data sources for potential use by automated biosurveillance systems. Even when a full electronic medical record is available, commonly available surveillance source documents provide acceptable statistical performance for automated ILI surveillance.
PMCID: PMC2655960  PMID: 18999051
15.  New Strategy and Innovative Projects at the National Biosurveillance Integration Center 
Objective
Enhance knowledge of the vision, mission, strategic goals, and objectives of the National Biosurveillance Integration Center (NBIC). Learn about innovative biosurveillance projects ongoing in NBIC.
Introduction
For a number of years, the federal government has provided biosurveillance in various domains within different departments and agencies. Congress recognized the need for a means of integrating these separate information sources into a more useable resource by chartering NBIC within the Department of Homeland Security.
Methods
NBIC engaged the biosurveillance community within and beyond the federal government through a series of extensive discussions, workshops, and symposia to define a strategy for future development of integrated biosurveillance activities grounded in legislative and presidential direction. The NBIC Strategic Plan was extensively reviewed by the twelve federal Departments that comprise the National Biosurveillance Integration System (NBIS) as well as the White House Office of Management and Budget. The NBIC Strategic Plan is currently being revised for release of a public version. The NBIC also engaged partners in the development of projects designed to develop and test new approaches to biosurveillance.
Results
The NBIC Strategic Plan was delivered to Congress in August, 2012. The plan explains the Center’s approach, why it is needed, and how it seeks to execute the mission of integrating national biosurveillance information to provide relevant and timely information that effectively supports decision making. Projects are underway involving text analyses of emergency medical system data, changes to poison control center data collection and analysis, and the application of machine learning to social media analyses. A sub-working group of the NBIS has been established to guide selection of future pilot project areas to address prioritized requirements for integrated biosurveillance.
Conclusions
NBIC has increased flexibility in its commitment to collaboration and coordination, engaged in bold new approaches, and is defining requirements that will encourage buy-in and support of the users across the levels of government and the private sector. With success in its mission, NBIC will support its partners’ missions and provide relevant and timely information that effectively supports decision making.
PMCID: PMC3692927
NBIC; NBIS; strategy
16.  A Review of Evaluations of Electronic Event-based Biosurveillance Systems 
Objective
To assess evaluations of electronic event-based biosurveillance systems (EEBS’s) and define priorities for EEBS evaluations.
Introduction
EEBS’s that use near real-time information from the Internet are an increasingly important source of intelligence for public health organizations (1, 2). However, there has not been a systematic assessment of EEBS evaluations, which could identify uncertainties about current systems and guide EEBS development to effectively exploit digital information for surveillance.
Methods
We searched PubMed and consulted EEBS experts to identify EEBS’s that met the following criteria: uses publicly-available Internet info sources, includes events that impact humans, and has global scope. We constructed a list of 17 key evaluation variables using guidelines for evaluating health surveillance systems, and identified the key variables included in evaluations per EEBS, as well as the number of EEBS’s evaluated for each key variable (3,4).
Results
We identified 10 EEBS’s and 17 evaluations (Table 1). The number of evaluations per EEBS ranged from 1 (Gen-Db, GODsN) to 7 (GPHIN, HealthMap). The median number of variables assessed per EEBS was 6 (range, 3–12), with 5 (25%) evaluations assessing 7+ variables. Nine (53%) published evaluations contained quantitative assessments of at least 1 variable. The least-frequently studied variable was cost. No papers examined usefulness as specific public health decisions or outcomes resulting from early event detection, though 8 evaluations assessed usefulness by citing instances where the EEBS detected an outbreak earlier, or by eliciting user feedback.
Conclusions
While EEBS’s have demonstrated their usefulness and accuracy for early outbreak detection, no evaluations have cited specific examples of public health decisions or outcomes resulting from the EEBS. Future evaluations should discuss these critical indicators of public health utility. They also should assess the novel aspects of EEBS and include variables such as policy readiness, system redundancy, input/output geography (5); and test the effects of combining EEBS’s into a “super system”.
PMCID: PMC3692778
evaluation; biosurveillance; event-based surveillance
17.  SMART Platforms: Building the App Store for Biosurveillance 
Objective
To enable public health departments to develop “apps” to run on electronic health records (EHRs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. We describe a novel health information technology platform with substitutable apps constructed around core services enabling EHRs to function as iPhone-like platforms.
Introduction
Health care information is a fundamental source of data for biosurveillance, yet configuring EHRs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations.
Despite a $48B investment in HIT, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. An alternative approach is to reimagine EHRs as iPhone-like platforms supporting substitutable apps-based functionality. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality.
Methods
Substitutability requires that the purchaser of an app can replace one application with another without being technically expert, without requiring re-engineering other applications that they are using, and without having to consult or require assistance of any of the vendors of previously installed or currently installed applications. Apps necessarily compete with each other promoting progress and adaptability.
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project is funded by a $15M grant from Office of the National Coordinator of Health Information Technology’s Strategic Health IT Advanced Research Projects (SHARP) Program. All SMART standards are open and the core software is open source.
The SMART project promotes substitutability through an application programming interface (API) that can be adopted as part of a “container” built around by a wide variety of HIT, providing readonly access to the underlying data model and a software development toolkit to readily create apps. SMART containers are HIT systems, that have implemented the SMART API or a portion of it. Containers marshal data sources and present them consistently across the SMART API. SMART applications consume the API and are substitutable.
Results
SMART provides a common platform supporting an “app store for biosurveillance” as an approach to enabling one stop shopping for public health departments—to create an app once, and distribute it everywhere.
Further, such apps can be readily updated or created—for example, in the case of an emerging infection, an app may be designed to collect additional data at emergency department triage. Or a public health department may widely distribute an app, interoperable with any SMART-enabled EMR, that delivers contextualized alerts when patient electronic records are opened, or through background processes.
SMART has sparked an ecosystem of apps developers and attracted existing health information technology platforms to adopt the SMART API—including, traditional, open source, and next generation EHRs, patient-facing platforms and health information exchanges. SMART-enabled platforms to date include the Cerner EMR, the WorldVista EHR, the OpenMRS EHR, the i2b2 analytic platform, and the Indivo X personal health record. The SMART team is working with the Mirth Corporation, to SMART-enable the HealthBridge and Redwood MedNet Health Information Exchanges. We have demonstrated that a single SMART app can run, unmodified, in all of these environments, as long as the underlying platform collects the required data types. Major EHR vendors are currently adapting the SMART API for their products.
Conclusions
The SMART system enables nimble customization of any electronic health record system to create either a reporting function (outgoing communication) or an alerting function (incoming communication) establishing a technology for a robust linkage between public health and clinical environments.
PMCID: PMC3692876
Electronic health records; Biosurveillance; Informatics; Application Programming Interfaces
18.  Sharing Public Health Information with Non-Public Health Partners 
Objective
The objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health ESSENCE users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. This capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. These interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. This project provides tools that can enhance emergency management situational awareness of public health events. It also allows external partners a mechanism for providing feedback to support public health investigations.
Introduction
Automated Electronic Disease Surveillance has become a common tool for most public health practitioners. Users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. The insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other non-public health officials. Disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. This abstract describes the development of tools necessary to support information sharing between public health and partner organizations.
Methods
The project initially brought together public health and emergency management officials to determine current gaps in technology and policy that prevent sharing of information on a consistent basis. Officials from across the National Capital Region (NCR) in Maryland, Virginia, and the District of Columbia determined that a web portal in which public health information could be securely posted on and captured by non-public health users (humans and computer systems) would be best. The development team then found open source tools, such as the Pebble blogging system, that would allow information to be posted, tagged, and searched in an easily navigable site. The system also provided RSS feeds both on the site as whole and specific tags to support notification. The team made modifications to the system to incorporate spring security features to allow the site to be securely hosted requiring usernames and passwords for access. Once the Pebble system was completed and deployed, the NCR’s aggregated ESSENCE system was adapted to allow users to submit daily reports and post time series images to the new site. An additional feature was created to post visualizations every evening to the site summarizing the day’s reports.
Results
The system has been in testing since March of 2012 and users of the system have provided valuable feedback. Based on the success of the tests, public health users in the NCR have begun working on the policy component of the project to determine when and how it should be used. Modifications to the system since deployment have included a single sign on capability for ESSENCE users and the desire to allow other features of ESSENCE to be posted beyond time series graphs, such as GIS maps and statistical reports.
Conclusions
Having tools that can promote exchange of information between public health and non-public health partners such as emergency management, law enforcement, and first responders can greatly enhance the situational awareness and impact overall preparedness and response. By having tools embedded in ESSENCE, users are able to integrate the information sharing aspects into their daily routines with a small amount of effort. With the use of open source tools, the same type of capability can be easily replicated in other jurisdictions. This presentation will describe the lessons learned and potential improvements the project will incorporate in the future.
PMCID: PMC3692892
Open Source; Emergency Management; Information Sharing
19.  Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic Winter Games. 
The key to minimizing the effects of an intentionally caused disease outbreak is early detection of the attack and rapid identification of the affected individuals. The Bush administration's leadership in advocating for biosurveillance systems capable of monitoring for bioterrorism attacks suggests that we should move quickly to establish a nationwide early warning biosurveillance system as a defense against this threat. The spirit of collaboration and unity inspired by the events of 9-11 and the 2002 Olympic Winter Games in Salt Lake City provided the opportunity to demonstrate how a prototypic biosurveillance system could be rapidly deployed. In seven weeks we were able to implement an automated, real-time disease outbreak detection system in the State of Utah and monitored 80,684 acute care visits occurring during a 28-day period spanning the Olympics. No trends of immediate public health concern were identified.
PMCID: PMC2244330  PMID: 12463832
20.  An ISDS-Based Initiative for Conventions for Biosurveillance Data Analysis Methods 
Objective
The panel will present the problem of standardizing analytic methods for public health disease surveillance, enumerate goals and constraints of various stakeholders, and present a straw-man framework for a conventions group.
Introduction
Twelve years into the 21st century, after publication of hundreds of articles and establishment of numerous biosurveillance systems worldwide, there is no agreement among the disease surveillance community on most effective technical methods for public health data monitoring. Potential utility of such methods includes timely anomaly detection, threat corroboration and characterization, follow-up analysis such as case linkage and contact tracing, and alternative uses such as providing supplementary information to clinicians and policy makers.
Several factors have impeded establishment of analytical conventions. As immediate owners of the surveillance problem, public health practitioners are overwhelmed and understaffed. Goals and resources differ widely among monitoring institutions, and they do not speak with a single voice. Limited funding opportunities have not been sufficient for cross-disciplinary collaboration driven by these practitioners. Most academics with the expertise and luxury of method development cannot access surveillance data. Lack of data access is a formidable obstacle to developers and has caused talented statisticians, data miners, and other analysts to abandon the field. The result is that older research is neglected and repeated, literature is flooded with papers of varying utility, and the decision-maker seeking realistic solutions without detailed technical knowledge faces a difficult task.
Regarding conventions, the disease surveillance community can learn from older, more established disciplines, but it also poses some unique challenges. The general problem is that disease surveillance lies on the fringe of disparate fields (biostatistics, statistical process control, data mining, and others), and poses problems that do not adequately fit conventional approaches in these disciplines.
In its eighth year, the International Society of Disease Surveillance is well positioned to address the standardization problem because its membership represents the involved stakeholders including progressive programs worldwide as well as resource-limited settings, and also because best practices in disease surveillance is fundamental to its mission. The proposed panel is intended to discuss how an effective, sustainable technical conventions group might be maintained and how it could support stakeholder institutions.
Methods
Members of a Technical Conventions Group would have experience and dedication to advancing the science of disease surveillance. Primary functions would include: Specify and communicate technical problems facing professionals involved in routine monitoring of population health. Alternative use applications would also be considered, such as the use of epidemiological findings to inform clinical diagnoses.Independently evaluate the utility of proposed analytical solutions to well-defined problems in public health surveillance and confer approval or certification, perhaps on several levels, such as whether results can be replicated with shareable data. Approved solutions might be restricted to commonly available software such as the R language or Microsoft EXCEL.Facilitate sharing of tools and methodologies to evaluate methods and to visualize their results
The framework to be discussed in the proposed panel would be a means of keeping open lines of collaboration and idea-sharing. Overcoming obstacles toward this goal is worthy of a conference panel discussion whether or not it concludes that a conventions group is a viable approach.
Results
Three 15-minute panelist talks are proposed: Background: in-depth description of the dimensions of the problem aboveConstraints facing public health practitioners and requirements for practical analytic toolsStrawman conventions group: role, logistics, inclusiveness, methods of communicating with stakeholders and related organizations and producing/disseminating output.
For the 45 minutes of discussion, the first 15–20 will invite reactions to the first two talks to sharpen the scope of the effort. The remainder of the session will cover the advisability, feasibility, and logistics of an ISDS-based conventions group, and modify the straw-man group concept.
PMCID: PMC3692949
Standards; Data Analysis; Statistical Algorithms; Certification
21.  A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II) 
The Electronic Surveillance System for the Early Notification of Community-Based Epidemics, or ESSENCE II, uses syndromic and nontraditional health information to provide very early warning of abnormal health conditions in the National Capital Area (NCA). ESSENCE II is being developed for the Department of Defense Global Emerging Infections System and is the only known system to combine both military and civilian health care information for daily outbreak surveillance. The National Capital Area has a complicated, multijurisdictional structure that makes data sharing and integrated regional surveillance challenging. However, the strong military presence in all jurisdictions facilitates the collection of health care information across the region. ESSENCE II integrates clinical and nonclinical human behavior indicators as a means of identifying the abnormality as close to the time of onset of symptoms as possible. Clinical data sets include emergency room syndromes, private practice billing codes grouped into syndromes, and veterinary syndromes. Nonclinical data include absenteeism, nurse hotline calls, prescription medications, and over-the-counter self-medications. Correctly using information marked by varying degrees of uncertainty is one of the more challenging as pects of this program. The data (without personal identifiers) are captured in an electronic format, encrypted, archived, and processed at a secure facility. Aggregated information is then provided to users on secure Web sites. When completed, the system will provide automated capture, archiving, processing, and notification of abnormalities to epidemiologists and analysts. Outbreak detection methods currently include temporal and spatial variations of odds ratios, autoregressive modeling, cumulative summation, matched filter, and scan statistics. Integration of nonuniform data is needed to increase sensitivity and thus enable the earliest notification possible. The performance of various detection techniques was compared using results obtained from the ESSENCE II system.
doi:10.1007/PL00022313
PMCID: PMC3456555  PMID: 12791777
Evaluation; Nontraditional; Surveillance; Syndromes; Test bed
22.  Effective Detection of the 2009 H1N1 Influenza Pandemic in U.S. Veterans Affairs Medical Centers Using a National Electronic Biosurveillance System 
PLoS ONE  2010;5(3):e9533.
Background
The 2008–09 influenza season was the time in which the Department of Veterans Affairs (VA) utilized an electronic biosurveillance system for tracking and monitoring of influenza trends. The system, known as ESSENCE or Electronic Surveillance System for the Early Notification of Community-based Epidemics, was monitored for the influenza season as well as for a rise in influenza cases at the start of the H1N1 2009 influenza pandemic. We also describe trends noted in influenza-like illness (ILI) outpatient encounter data in VA medical centers during the 2008–09 influenza season, before and after the recognition of pandemic H1N1 2009 influenza virus.
Methodology/Principal Findings
We determined prevalence of ILI coded visits using VA's ESSENCE for 2008–09 seasonal influenza (Sept. 28, 2008–April 25, 2009 corresponding to CDC 2008–2009 flu season weeks 40–16) and the early period of pandemic H1N1 2009 (April 26, 2009–July 31, 2009 corresponding to CDC 2008–2009 flu season weeks 17–30). Differences in diagnostic ICD-9-CM code frequencies were analyzed using Chi-square and odds ratios. There were 649,574 ILI encounters captured representing 633,893 patients. The prevalence of VA ILI visits mirrored the CDC's Outpatient ILI Surveillance Network (ILINet) data with peaks in late December, early February, and late April/early May, mirroring the ILINet data; however, the peaks seen in the VA were smaller. Of 31 ILI codes, 6 decreased and 11 increased significantly during the early period of pandemic H1N1 2009. The ILI codes that significantly increased were more likely to be symptom codes. Although influenza with respiratory manifestation (487.1) was the most common code used among 150 confirmed pandemic H1N1 2009 cases, overall it significantly decreased since the start of the pandemic.
Conclusions/Significance
VA ESSENCE effectively detected and tracked changing ILI trends during pandemic H1N1 2009 and represents an important temporal alerting system for monitoring health events in VA facilities.
doi:10.1371/journal.pone.0009533
PMCID: PMC2832014  PMID: 20209055
23.  Utility of the ESSENCE Surveillance System in Monitoring the H1N1 Outbreak 
Online Journal of Public Health Informatics  2010;2(3):ojphi.v2i3.3028.
The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) enables health care practitioners to detect and monitor health indicators of public health importance. ESSENCE is used by public health departments in the National Capital Region (NCR); a cross-jurisdictional data sharing agreement has allowed cooperative health information sharing in the region since 2004. Emergency department visits for influenza-like illness (ILI) in the NCR from 2008 are compared to those of 2009. Important differences in the rates, timing, and demographic composition of ILI visits were found. By monitoring a regional surveillance system, public health practitioners had an increased ability to understand the magnitude and character of different ILI outbreaks. This increased ability provided crucial community-level information on which to base response and control measures for the novel 2009 H1N1 influenza outbreak. This report underscores the utility of automated surveillance systems in monitoring community-based outbreaks. There are several limitations in this study that are inherent with syndrome-based surveillance, including utilizing chief complaints versus confirmed laboratory data, discerning real disease versus those healthcare-seeking behaviors driven by panic, and reliance on visit counts versus visit rates.
doi:10.5210/ojphi.v2i3.3028
PMCID: PMC3615770  PMID: 23569593
H1N1; swine flu; surveillance
24.  Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance application 
Background
The establishment of robust biosurveillance capabilities is an important component of the U.S. strategy for identifying disease outbreaks, environmental exposures and bioterrorism events. Currently, U.S. Departments of Defense (DoD) and Veterans Affairs (VA) perform biosurveillance independently. This article describes a joint VA/DoD biosurveillance project at North Chicago-VA Medical Center (NC-VAMC). The Naval Health Clinics-Great Lakes facility physically merged with NC-VAMC beginning in 2006 with the full merger completed in October 2010 at which time all DoD care and medical personnel had relocated to the expanded and remodeled NC-VAMC campus and the combined facility was renamed the Lovell Federal Health Care Center (FHCC). The goal of this study was to evaluate disease surveillance using a biosurveillance application which combined data from both populations.
Methods
A retrospective analysis of NC-VAMC/Lovell FHCC and other Chicago-area VAMC data was performed using the ESSENCE biosurveillance system, including one infectious disease outbreak (Salmonella/Taste of Chicago-July 2007) and one weather event (Heat Wave-July 2006). Influenza-like-illness (ILI) data from these same facilities was compared with CDC/Illinois Sentinel Provider and Cook County ESSENCE data for 2007-2008.
Results
Following consolidation of VA and DoD facilities in North Chicago, median number of visits more than doubled, median patient age dropped and proportion of females rose significantly in comparison with the pre-merger NC-VAMC facility. A high-level gastrointestinal alert was detected in July 2007, but only low-level alerts at other Chicago-area VAMCs. Heat-injury alerts were triggered for the merged facility in June 2006, but not at the other facilities. There was also limited evidence in these events that surveillance of the combined population provided utility above and beyond the VA-only and DoD-only components. Recorded ILI activity for NC-VAMC/Lovell FHCC was more pronounced in the DoD component, likely due to pediatric data in this population. NC-VAMC/Lovell FHCC had two weeks of ILI activity exceeding both the Illinois State and East North Central Regional baselines, whereas Hines VAMC had one and Jesse Brown VAMC had zero.
Conclusions
Biosurveillance in a joint VA/DoD facility showed potential utility as a tool to improve surveillance and situational awareness in an area with Veteran, active duty and beneficiary populations. Based in part on the results of this pilot demonstration, both agencies have agreed to support the creation of a combined VA/DoD ESSENCE biosurveillance system which is now under development.
doi:10.1186/1472-6947-11-56
PMCID: PMC3188469  PMID: 21929813
25.  NLP-based Identification of Pneumonia Cases from Free-Text Radiological Reports 
Radiological reports are a rich source of clinical data which can be mined to assist with biosurveillance of emerging infectious diseases. In addition to biosurveillance, radiological reports are an important source of clinical data for health service research. Pneumonias and other radiological findings on chest xray or chest computed tomography (CT) are one type of relevant finding to both biosurveillance and health services research. In this study we examined the ability of a Natural Language Processing system to accurately identify pneumonias and other lesions from within free-text radiological reports. The system encoded the reports in the SNOMED CT Ontology and then a set of SNOMED CT based rules were created in our Health Archetype Language aimed at the identification of these radiological findings and diagnoses. The encoded rule was executed against the SNOMED CT encodings of the radiological reports. The accuracy of the reports was compared with a Clinician review of the Radiological Reports. The accuracy of the system in the identification of pneumonias was high with a Sensitivity (recall) of 100%, a specificity of 98%, and a positive predictive value (precision) of 97%. We conclude that SNOMED CT based computable rules are accurate enough for the automated biosurveillance of pneumonias from radiological reports.
PMCID: PMC2656026  PMID: 18998791

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