Design of Algorithms for Rapid Identification of CDC Category A Bioterrorism Agents

Background:

Early recognition of a terrorist attack using biological weapons is difficult to predict, detect, or prevent. In an attack, rapid identification and containment of infected individuals would minimize disease spread and decrease the potentially catastrophic psychological, economic, and public health impact. Identification of these agents is generally not a part of nursing, medical or public health training. We describe the development of a case-based algorithmic approach to aid first responders in suspecting, detecting and managing disease caused by these agents.

Materials and Methods:

We conducted a literature search for CDC Category A bioterrorism agents and educational resources and interventions designed to facilitate their identification and treatment. We constructed case-based, algorithmic and pictorial-based modules for use in acute care settings as a print and Web-based resource.

Results:

We created multiple algorithms, based upon published data on clinical presentation, to be used by health professionals to rapidly identify individuals infected with CDC Category A Bioterrorism agents, which include: Anthrax, Botulism, Plague, Smallpox, Tularemia and the Viral Hemorrhagic Fevers. Accompanying the algorithms are pictorial-based guides with detailed, relevant information on clinical findings, natural history, diagnosis, isolation protocols, and treatment.

Conclusion:

Training in recognition, differentiation and management of bioterrorism agents is suboptimal in health care settings. As a result, delayed or missed diagnoses are possible. An algorithmic, case-based, accessible resource suitable for print or Web-based consultation is an important resource for first responders.