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Biosurveillance Systems

Biosurveillance systems commonly use emergency department (ED) patient chief complaint data (CC) for surveillance of influenza-like illness (ILI). Daily volumes are tracked using a computerized patient CC classifier for fever (CC Fever) to identify febrile patients. Limitations in this method have led to efforts to identify other sources of ED data. At many EDs the triage nurse measures the patient’s temperature on arrival and records it in the electronic medical record. This makes it possible to directly identify patients who meet the CDC temperature criteria for ILI: temperature greater than 100 degrees F (T>100F).

Objective

To evaluate whether a classifier based on temperature >100F would perform similarly to CC Fever and might identify additional patients.

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Objective

The National Biosurveillance Integration System (NBIS) is a consortium of federal agencies, whose joint objective is to enhance the identification, location, characterization, and tracking of biological events potentially impacting homeland security. Together, the consortium members benefit from a joint awareness of potentially significant biological events that are unfolding or imminent, based on information shared among the group. This presentation describes the framework, activities and benefits for NBIS participants, and invites participation by other agencies.

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The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) obtains electronic data from 153 Veterans Affairs (VA) Medical Centers plus outpatient clinics in all 50 states, American Samoa, Guam, Philippines, Puerto Rico, and U.S. Virgin Islands. Currently, there is no centralized VA reporting requirement for nationally notifiable infectious conditions detected in VA facilities. Surveillance and reporting of cases to local public health authorities are performed manually by VA Infection Preventionists and other clinicians. In this analysis, we examined positive predictive value of ICD-9-CM diagnosis codes in VA ESSENCE to determine the utility of this system in electronic detection of reportable conditions in VA.

 

Objective

To determine the utility of ICD-9-CM diagnosis codes in the VA ESSENCE for detection and public health surveillance of nationally notifiable infectious conditions in veteran patients.

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On 20 April 2010, an explosion on an offshore drilling rig in the Gulf of Mexico led to a prolonged uncontrolled release of crude oil. Both clean-up workers and coastal residents were potentially at high risk for respiratory and other acute health effects from exposure to crude oil and its derivatives, yet there was no surveillance system available to monitor these health effects. The Department of Veterans Affairs (VA) conducts routine surveillance for biological threats using the Electronic Surveillance System for Early Notification of Community Based Epidemics (ESSENCE). ESSENCE captures specific patient care visit ICD-nine codes belonging to selected conditions that could represent a biological threat. VA operates 153 medical centers and over 1000 free standing patient care facilities across the United States. We describe the adaptation of ESSENCE to allow surveillance of health conditions potentially related to the oil spill.

 

Objective

To describe a surveillance system created to identify acute health issues potentially associated with the Deepwater Horizon oil spill among Veterans in the Gulf of Mexico coastal region.

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Presented January 31, 2018

 

David Swenson presented the following slides during the 2018 ISDS Annual Conference in Orlando, Florida. This presentation provides a use case for developing and implementing surveillance prodocols to conduct public health monitoring, analyze data collected, and engage partners/leadership in follow-up procedures.

 

Presenter: David Swenson, AHEDD Project Manager, Infectious Disease Surveillance Section DPHS, DHHS, New Hampshire

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On 27 April 2005, a simulated bioterrorist event—the aerosolized release of Francisella tularensis in the men’s room of luxury box seats at a sports stadium—was used to exercise the disease surveillance capability of the National Capital Region (NCR). The objective of this exercise was to permit all of the health departments in the NCR to exercise inter-jurisdictional epidemiological investigations using an advanced disease surveillance system. Actual system data could not be used for the exercise as it both is proprietary and contains protected, though de-identified, health information about real people; nor is there much historical data describing how such an outbreak would manifest itself in normal syndromic data. Thus, it was essential to develop methods to generate virtual health care records that met specific requirements and represented both ‘normal’ endemic visits (the background) as well as outbreak-specific records (the injects).

 

Objective

This paper describes a flexible modeling and simulation process that can create realistic, virtual syndromic data for exercising electronic biosurveillance systems.

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The eleven syndrome classifications for clinical data records monitored by BioSense include rare events such as death or lymphadenitis and also common occurrences such as respiratory infections. BioSense currently uses two statistical methods for prediction and alerting with respect to the eleven syndromes. These are a modified CUSUM; and small area regression and testing (SMART), described by Ken Kleinman. At the inception of BioSense, these prediction methods were implemented as one-model-fits-all, and they remain largely unmodified. An evaluation of the predictive value of these methods is required. The SMART method, as used in BioSense, uses long-term data. As covariate predictors, day-of-week, a holiday indicator, day after holiday, and sine/cosine seasonality variables are used. Lengthy, stable historical data is not always available in BioSense data sources, and this obstacle is expected to grow as data sources are added. We wish to test regression methods of surveillance that use shorter time periods, and different sets of predictors.

 

Objective

This paper compares the prediction accuracy of regression models with different covariates and baseline periods, using a subset of data from CDC’s BioSense initiative. Accurate predictions are needed to achieve sensitivity at practical false alarm rates in anomaly detection for biosurveillance.

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To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata analyzed, small numbers for some strata, and wide variances in daily counts, the statistical algorithms will generate flags too often. Experience has shown that these flags usually have little or no public health significance. As a result, syndromic surveillance systems suffer from the ‘boy who cried wolf’ syndrome. It is clear that the analyst’s ability to use professional judgment to sift through multitudes of flags is very important to the success of the system, which suggests that statistics alone cannot identify issues of public health importance from ED data.

Objective

This study's aim was to refine an automated biosurveillance system in order to better suit the daily monitoring capabilities and resources of a health department.

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“The ultimate measure of whether a surveillance system has achieved the optimal balance of attributes lies in its usefulness.” No one is better qualified to comment on usefulness than the users. As system developers, we are well advised to consider the opinions of users when building, evaluating, and considering revisions to surveillance systems. 

Health Monitoring Systems, Inc. is a for-profit company that provides biosurveillance capabilities to public health agencies and hospitals using a software-as-a-service model.

 

Objective

This paper describes results from a survey of public health department users of biosurveillance. The survey solicited input regarding sophistication of analytic methods, perceived value of potential data sources, and utilization resulting in timelier public health interventions.

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