Development and experience with an algorithm to evaluate suspected smallpox cases in the United States, 2002-2004.

Area of Science:

• Public Health
• Infectious Disease Epidemiology
• Clinical Diagnostics

Background:

• Smallpox, an eradicated disease, poses a bioterrorism threat.
• Limited recent experience with smallpox diagnosis in the US necessitates a structured approach.
• The potential resurgence of smallpox requires effective public health preparedness.

Purpose of the Study:

• To design and validate a clinical algorithm for classifying suspected smallpox cases.
• To guide diagnostic strategies and limit laboratory testing to high-risk individuals.
• To minimize false-positive results and optimize resource allocation for suspected smallpox.

Main Methods:

• Developed a clinical algorithm classifying suspected smallpox rashes into high, moderate, and low-risk categories based on classic smallpox features.
• Applied the algorithm to guide diagnostic decisions for suspected cases reported to the Centers for Disease Control and Prevention (CDC).
• Evaluated the algorithm's effectiveness in managing clinical and public health responses.

Main Results:

• From January 2002 to June 2004, 43 suspected smallpox consultations were received by the CDC.
• No patient evaluated was classified as high-risk for smallpox.
• Varicella (chickenpox) was diagnosed in 23 cases (53%); one patient was tested for variola virus.

Conclusions:

• The clinical algorithm effectively guided public health responses and diagnostic strategies for suspected smallpox cases.
• The algorithm proved useful in minimizing laboratory testing to high-risk individuals, reducing false positives.
• The CDC recommends the adoption of this risk-stratification algorithm for smallpox diagnosis in the US and globally.