Learning Objectives:

Class members will be familiarized with the general field of Biosurveillance, to include knowledge of the definition of the term, and the historical origins of the terminology.

Class members will be familiar with a brief description of Biosurveillance and how Biosurveillance relates to the field of health informatics.

Classmates will understand the background of Biosurveillance and practical tips for how megatrends of Biosurveillance will continue.

BIOSURVEILLANCE

Biosurveillance is defined as a systematic process that monitors the environment for bacteria, viruses, and other biological agents that cause disease; detects disease in people, plants, or animals caused by those agents; and detects and characterizes outbreaks of such disease.

The term biosurveillance is a combination of the terms disease surveillance public health surveillance, both focused on the methods for data collection and analysis for disease detection. Biosurveillance is a continuous process, which proceeds from continuous data collection, aggregating for analysis, conducting analysis, confirmation of cases or outbreaks, decisions, and responses, with a feedback loop back to the aggregation process.

The fundamental properties of the biosurveillance process are: multidisciplinary, multi-organizational, time critical, probabilistic, decision oriented, data intensive, dependency on information technology, knowledge intensive and complexity. Primary attention of the field of biosurveillance focuses on outbreaks and investigations. Case detection, outbreak detection, and characterization of outbreaks may be performed by clinicians, laboratories, sentinel systems, targeted monitoring, screening, and investigation. Outbreak characterization subsequent to investigation uses general analytic techniques, such as spatial distribution, temporal distribution, incidence, mortality, or attack rates, and cohort or case-control studies.

Diseases may be characterized by incubation and infectious periods, source, and transmission route. Environmental investigations include food chains and vectors. The number of people ill and the number of persons at risk are critical to any investigation.

Organizations which conduct biosurveillance are primarily governmental, to include state and local health departments. At the federal level the Center for Disease Control and Prevention, part of the Department of Health and Human Services, is responsible for collecting, analyzing, and disseminating national disease occurrence and mortality date to state and local health departments, as well as the general public. Other organizations which conduct Biosurveillance include the Department of Defense, the Department of Homeland Security, the World Health Organization, and private sector corporations.

Surveillance data include vital statistics, such as death and birth certificates, notifiable disease reports, sentinel surveillance data, and data collected during investigations. The healthcare system collects data to include demographics, history of current illness, physical examination, laboratory results, radiology results, travel and exposure history, vaccinations, personal history, past medical history, allergies, medications and diagnosis. The systems used to collect this data include myriad registration systems, billing systems, laboratory information systems, radiology systems, pathology systems,. Pharmacy systems, order-entry systems, point-of-care systems, patient care data warehouses, patient web portals and call centers—all of which are data sources for Biosurveillance systems. The increasing adoption of electronic medical records supports the objectives required for Biosurveillance.

Additionally animal health, agriculture, wildlife, and other food sources are monitored for the purpose of Biosurveillance. Laboratories providing Biosurveillance data include environmental, clinical and public health laboratories at state and local levels. Water supplies are monitored for dozens of biological agents with a sophisticated testing infrastructure. Foodborne illnesses are reported and tracked back to point of origin.

Sensors are used to monitor the health status of people and animals through remote physiological monitoring and space-based sensors. Remote monitoring of individuals was developed as a part of the space program, and monitoring of the vital functions has become quite advanced. Image analysis from space based systems may be used for a variety or purposes, to include crop, animal, and remote population health. Likewise weather and environmental monitoring may be integrated with Biosurveillance systems and geographic information systems are often used for that integration process.

Pre-2000 Biosurveillance information systems include the National Electronic Telecommunications System for Surveillance, Public Health Laboratory Information Systems and PulseNet. Post-2000 information systems include BioSense, with a variety of other early warning surveillance systems, the Electronic Laboratory Reporting, the Laboratory Response Network, and other academic systems and initiatives, such as Real-time Outbreak and Disease Surveillance System (RODS). Given the extraordinary number of organizations and systems which possess Biosurveillance data, there are an exponential number of possible means to collect and analyze Biosurveillance data.

Decision-making in the field of biosurveillance uses decision theory and decision analysis tools, principles such as the Principle of Maximum Expected Utility, probability theory, and models using computational decision analysis. Probabilistic interpretation of surveillance data integrates detection systems with conditional probabilities in applied decision scenarios.

The importance of the decision process to provide advisories or alerts to the public are similar to the decision whether to alert the public about a hurricane or other weather event. The cost and benefits of false alerts must be balanced with the goal of protection of the population at risk during the decision process. For example, the cost to a food industry for an alert of contaminated cattle, crops, or process foods may be very high regardless of the probability of risk cited within the communication process. Identifying the choices, uncertainties, costs and benefits are critical components of biosurveillance decision support.

The future of biosurveillance will respond to the megatrends of population growth, population density, globalization of travel and trade, bioterrorism, and the capabilities provided by future information technologies. Likewise, biosurveillance research will evolve, along with the education, organization, and leadership of biosurveillance infrastructure.

References

DeFraites, Robert F., Chambers, William C. (2007). Gaining Experience with Military Medical Situational Awareness and Geographic Information Systems in a Simulated Influenza Epidemic. Military Medicine, Vol. 172:10, 1071-1076.

Lazarus, R., Kleinman, K.P., Dashevsky, I., DeMaria, A., and Platt, R. (2001). Using Automated Medical Records for Rapid Identification of Illness Syndromes (Syndromic Surveillance): The Example of Lower Respiratory Infection. BMC Public Health, Vol. 1:9, Retrieved November 30, 2007, from http://www.biomedcentral.com/1471-2458/1/9

Lober W.B., Trigg L., Karras B. (2004). Information System Architectures for Syndromic Surveillance. Morbidity and Mortality Weekly Report, Vol. 53(Suppl):203-208, Retrieved November 30, 2007, from http://www.cdc.gov/mmwr/PDF/wk/mm53SU01.pdf

Olson K.L., Bonetti M., Pagano M., Mandl K.D. (2005). Real time spatial cluster detection using interpoint distances among precise patient location BMC Medical Informatics and Decision Making Vol. 5:19, Retrieved November 30, 2007, from http://www.biomedcentral.com/1472-6947/5/19

Wagner, M., Moore, A., Aryel, R. (eds.) (2006). Handbook of Biosurveillance, New York: Elsevier

External links
http://www.cdc.gov/epo/dphsi/syndromic/informatics_2.htm
http://www.cdc.gov/biosense/
https://www.rods.pitt.edu/site/
http://www.hhs.gov/healthit/ahic/biosurveillance/