Objective: To investigate the impact of excluding cases with alternative diagnoses on the sensitivity and specificity of the Centers for Disease Control and Prevention's (CDC) influenza-like illness (ILI) case definition in detecting the 2009 H1N1 influenza, using Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification, a disease surveillance system.
Design: Retrospective cross-sectional study design.
Setting: Emergency department of an urban tertiary care academic medical center.
Patients: 1,233 ED cases, which were tested for respiratory viruses from September 5, 2009 to May 5, 2010.
Main outcome measure: The main outcome measures were positive predictive value, negative predictive value, sensitivity, specificity, and accuracy of the ILI case definition (both including and excluding alternative diagnoses) to detect H1N1.
Results: There was a significant decrease in sensitivity (chi2 = 9.09, p < 0.001) and significant improvement in specificity (chi2 = 179, p < 0.001), after excluding cases with alternative diagnoses.
Conclusion: When early detection of an influenza epidemic is of prime importance, pursuing alternative diagnoses as part of CDC's ILI case definition may not be warranted for public health reporting due to the significant decrease in sensitivity, in addition to the resources required for detecting these alternative diagnoses.