Joseph S. Lombardo

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.

Bayesian Information Fusion Networks for Biosurveillance Applications

This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.

Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event

OBJECTIVE We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm. METHODS Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event. RESULTS Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected. CONCLUSIONS Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly.

Bridging radiology and public health: the emerging field of radiologic public health informatics.

Radiology and public health have an emerging opportunity to collaborate, in which radiologys vast supply of imaging data can be integrated into public health information systems for epidemiologic assessments and responses to population health problems. Fueling the linkage of radiology and public health include (i) the transition from analog film to digital formats, enabling flexible use of radiologic data; (ii) radiologys role in imaging across nearly all medical and surgical subspecialties, which establishes a foundation for a consolidated and uniform database of images and reports for public health use; and (iii) the use of radiologic data to characterize disease patterns in a population occupying a geographic area at one time and to characterize disease progression over time via follow-up examinations. The backbone for this integration is through informatics projects such as Systematized Nomenclature of Medicine Clinical Terms and RadLex constructing terminology libraries and ontologies, as well as algorithms integrating data from the electronic health record and Digital Imaging and Communications in Medicine Structured Reporting. Radiologys role in public health is being tested in disease surveillance systems for outbreak detection and bioterrorism, such as the Electronic Surveillance System for the Early Notification of Community-based Epidemics. Challenges for radiologic public health informatics include refining the systems and user interfaces, adhering to privacy regulations, and strengthening collaborative relations among stakeholders, including radiologists and public health officials. Linking radiology with public health, radiologic public health informatics is a promising avenue through which radiology can contribute to public health decision making and health policy.

Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance application.

BackgroundThe 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.MethodsA 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.ResultsFollowing 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.ConclusionsBiosurveillance 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.

Mining electronic medical records for patient care patterns

A novel approach for generating full Electronic Medical Records of synthetic victims is described. Special emphasis is put on the data mining steps that build patient care models and perform clustering of this highly dimensional data set. A methodology for cluster validation is proposed. Results for a large data set with Staphylococcus aureus and Methicillin-Resistant Staphylococcus aureus infections are presented.

Construction and Validation of Synthetic Electronic Medical Records

There is a current and pressing need for a test bed of electronic medical records (EMRs) to insure consistent development, validation and verification of public health related algorithms that operate on EMRs. However, access to full EMRs is limited and not generally available to the academic algorithm developers who support the public health community. This paper describes a set of algorithms that produce synthetic EMRs using real EMRs as a model. The algorithms were used to generate a pilot set of over 3000 synthetic EMRs that are currently available on CDC’s Public Health grid. The properties of the synthetic EMRs were validated, both in the entire aggregate data set and for individual (synthetic) patients. We describe how the algorithms can be extended to produce records beyond the initial pilot data set.

Combining Surveillance Systems: Effective Merging of U.S. Veteran and Military Health Data

Background The U.S. Department of Veterans Affairs (VA) and Department of Defense (DoD) had more than 18 million healthcare beneficiaries in 2011. Both Departments conduct individual surveillance for disease events and health threats. Methods We performed joint and separate analyses of VA and DoD outpatient visit data from October 2006 through September 2010 to demonstrate geographic and demographic coverage, timeliness of influenza epidemic awareness, and impact on spatial cluster detection achieved from a joint VA and DoD biosurveillance platform. Results Although VA coverage is greater, DoD visit volume is comparable or greater. Detection of outbreaks was better in DoD data for 58% and 75% of geographic areas surveyed for seasonal and pandemic influenza, respectively, and better in VA data for 34% and 15%. The VA system tended to alert earlier with a typical H3N2 seasonal influenza affecting older patients, and the DoD performed better during the H1N1 pandemic which affected younger patients more than normal influenza seasons. Retrospective analysis of known outbreaks demonstrated clustering evidence found in separate DoD and VA runs, which persisted with combined data sets. Conclusion The analyses demonstrate two complementary surveillance systems with evident benefits for the national health picture. Relative timeliness of reporting could be improved in 92% of geographic areas with access to both systems, and more information provided in areas where only one type of facility exists. Combining DoD and VA data enhances geographic cluster detection capability without loss of sensitivity to events isolated in either population and has a manageable effect on customary alert rates.

An Informatics Solution for Informing Care Delivery of Immediate Public Health Risks to Their Patients

This paper describes a public health alerting approach that has the potential to improve patient care during a public health outbreak and reduce healthcare costs, streamline the process of public health alert management and dissemination, and heighten the crucial feedback loop between public health officials and clinicians. The approach ties public health alerts into the diagnostic process and allows clinicians to more easily determine when an observed medical condition may be related to a more widespread disease outbreak. A prototype Alert Knowledge Repository (AKR) service using this approach was demonstrated within the Health Information and Management Systems Society (HIMSS) and the Public Health Information Network (PHIN) interoperability showcases in April and September 2009, respectively.

The collaborative experience of creating the National Capital Region Disease Surveillance Network.

The Johns Hopkins University Applied Physics Laboratory (JHU/APL) implemented state and district surveillance nodes in a central aggregated node in the National Capital Region (NCR). Within this network, de-identified health information is integrated with other indicator data and is made available to local and state health departments for enhanced disease surveillance. Aggregated data made available to the central node enable public health practitioners to observe abnormal behavior of health indicators spanning jurisdictions and view geographical spread of outbreaks across regions.Forming a steering committee, the NCR Enhanced Surveillance Operating Group (ESOG), was key to overcoming several data-sharing issues. The committee was composed of epidemiologists and key public health practitioners from the 3 jurisdictions. The ESOG facilitated early system development and signing of the cross-jurisdictional data-sharing agreement. This agreement was the first of its kind at the time and provided the legal foundation for sharing aggregated health information across state/district boundaries for electronic disease surveillance.Electronic surveillance system for the early notification of community-based epidemics provides NCR users with a comprehensive regional view to ascertain the spread of disease, estimate resource needs, and implement control measures. This article aims to describe the creation of the NCR Disease Surveillance Network as an exceptional example of cooperation and potential that exists for regional surveillance activities.