Background: Influenza and Influenza like illness are representative of a class of epidemic infectious diseases that have important public health implications. Early detection via biosurveillance can speed lifesaving public heath responses. In the United States, biosurveillance is typically conducted using ICD9 coded visit diagnoses and uncoded chief complaint data.
Objective: To determine the accuracy of ICD9 diagnoses using laboratory confirmed cases as the gold standard.
Design: A six-year retrospective cohort study.
Setting: A tertiary referral center.
Patients: All 3,825 patients with an ICD9-CM diagnosis of Influenza and all 1455 patients with laboratory confirmed Influenza.
Results: Of the 3,828 patients assigned ICD9-CM visit codes indicating a diagnosis of Influenza, 2,825 were not confirmed by laboratory testing and 1,003 patients under went laboratory testing. Only 664 (66.2%) tested positive for Influenza. Of the 1,455 patients who tested positive for Influenza 45.6% were identified by ICD9-CM code.
Conclusion: ICD9-CM had a low 66.2% Positive Predictive Value (precision) for Influenza and a low 45.6% Sensitivity (recall) for Influenza in patients tested for Influenza. ICD9 coded visit diagnoses/claims data are insufficient alone to serve as the basis for Influenza Surveillance.
Primary funding source: CDC grants PH00022 and HK00014.
Keywords: Biosurveillance; Health Informatics; ICD9-CM; Influenza; Public Health.