BioSense

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.

West Nile Virus (WNV) is a mosquito-borne virus that can cause meningitis and encephalitis. Since its discovery in New York City during an encephalitis outbreak in 1999, WNV has become endemic in North America. In the United States, 16,000 human WNV disease cases (including West Nile fever, meningitis, encephalitis, and unspecified clinical illness) and over 600 WNV-related deaths have been reported to the Centers for Disease Control from 46 states. Perennial WNV epidemics occur during summer months, peaking during late August. BioSense Early Event Detection and Situation Awareness System receives daily laboratory test order data feed in HL7 from Laboratory Corporation of America. In this study, test orders were studied for their correlation with WNV activity.

Objective

To determine the feasibility of using BioSense laboratory test order data for West Nile disease surveillance in the United States. 

BioSense is a Centers for Disease Control and Prevention (CDC) national near real-time public health surveillance system. CDC’s BioIntelligence Center (BIC) analysts monitor, analyze, and interpret BioSense data daily and provide support to BioSense users at state and local health departments and facilities sending data. The BioSense Application is continually being enhanced in concordance with public health and clinical partners. Ongoing dialogue between the BIC and these partners is required to gather user feedback, understand what would improve system utility, build collaborative relationships, and develop appropriate jurisdictionspecific communication protocols. In May 2006, BioSense hosted a face-to-face meeting in Atlanta with approximately 50 users to solicit recommendations for the program in general and the application. Also, every 1 to 2 months, a teleconference (“Real Time, Real Talk”) is held for all BioSense users. Because of confidentially issues, jurisdiction-specific data and issues can not be raised during such meetings, thus warranting the need for a forum in which such topics could be addressed.

Objective

To present lessons learned from the BioSense jurisdiction-specific webinars conducted in 2007.

In 2007, the CDC BioSense System received data from 450 non-federal hospitals. Hospitals provide data to Biosense based on their capability and willingness to supply electronic data. As of July 2008, Biosense is receiving data from 550 hospitals. The National Hospital Ambulatory Medical Care Survey (NHAMCS) is an annual national probability sample survey of hospitals that collects data on U.S. emergency department (ED) visits.

Objective

To assess the representativeness of BioSense ED data by comparing it with the NHAMCS results.

In October 2006, the Centers for Disease Control and Prevention funded four institutions, including Emory University, to conduct evaluations of the BioSense surveillance system. These evaluations include investigations of situations that represent actual or potential threats to public health in order to describe: 1) the pathways that health departments follow to assess and respond to such threats, 2) the role of various forms of surveillance, including BioSense and other syndromic surveillance systems, in enabling health departments to achieve critical milestones along these pathways, and 3) whether and how surveillance information informs healthcare practice during these events. We anticipate that these case studies will 1) identify approaches to improving BioSense and other syndromic surveillance systems, 2) describe the characteristics of events where syndromic surveillance is most apt to be useful, and 3) provide a baseline for assessing future impacts of advances in the development of BioSense and other forms of public health surveillance. This paper describes preliminary observations from initial case studies conducted by the Emory University team.

Objective

This paper describes preliminary observations from case study investigations of the uses of BioSense and other surveillance resources in public health practice.

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.

One of the standard approaches to public health surveillance for influenza is to monitor the percent of visits to about 2000 sentinel physicians for influenza-like illness (%ILI; fever plus cough or sore throat). The BioSense System currently receives (among other data) ICD-9 discharge diagnoses from Veteran’s Affairs (VA) and Department of Defense (DOD) outpatient clinics. A literature review found that, in addition to ICD-9 code 487 (the code specific for influenza), 29 other codes have been used previously to monitor influenza. We evaluated the utility of ICD-9 codes reported to BioSense for their utility in monitoring influenza.

Objective

To determine the utility of current CDC BioSense data sources in monitoring influenza activity at the national and state levels.

Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender.

BioSense is a CDC initiative to promote situational awareness through summarizing, analyzing, and presenting health related event information. Among the data sources collected and analyzed through the BioSense application are the Department of Defense and Department of Veterans Affairs ambulatory clinic care data. Clinical diagnoses and procedures are quantified, and analytic results are presented and categorized into 94 state and metropolitan areas.

Objective

Precise geographic location of health events is a challenging but critical component to determine the likely site of exposure for disease surveillance. This paper describes a method used by BioSense to develop and implement a reasonable set of rules in defining geographic locations of health events.

The  ability  to  accurately  predict  influenza  infection  by  symptoms  and  local  epidemiology  prior  to  lab  confirmation  warrants  further  study  and  is  particular  concern as the threat of pandemic flu heightens.  Antiviral drugs are effective when given within 48 hours of  symptom  onset,  but  this  usually  precludes  culture  confirmation. Further,  rapid  tests  can  be  clinically  helpful   but   lack   the   sensitivity   of   viral   culture. Hence,  ILI  symptoms  are  a  potentially  important  covariate  in  the  early  diagnosis  of  flu. However,  gaps  remain  in  several  areas  of  flu  symptom  research,  including  knowledge  of  potential  differences  between  symptoms  of  Influenza  A  and  of  Influenza  B  [1]. Therefore,  an  examination  of  symptoms  generally  associated  with  Influenza  infection  was  begun,  as  well  as  an  examination  of  symptoms  specifically  associated with Flu A and Flu B. An additional focus in  this  study  was  to  evaluate  the  performance  of  the  current  ILI  case  definition  used  by  the  DoD  flu  program.  This definition is useful to identify individuals who  are  likely  to  be  infected  with  influenza,  as  the  ability  to  capture  and  characterize  novel  strains  of  influenza is an important component to this program. Better yields of influenza mean less time and money spent processing negative specimens.

Objective

This study describes clinical symptoms reported in conjunction with influenza, non-influenza respiratory viruses, and negative viral cultures from the Department of Defense (DoD) Global Influenza Surveillance Program; influenza-like illness (ILI) case questionnaires were linked to corresponding laboratory specimen results for the 2005-06 influenza season for analysis.