ICD9-CM Claims Data are Insufficient for Influenza Surveillance





Influenza, biosurveillance, public health


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.

Author Biography

Peter L. Elkin, University at Buffalo

Dr. Elkin serves as Professor and Chair of the UB Department of Biomedical Informatics.  He is also the Vice Chairman for Quality and Patient Safety and Professor of Internal Medicine at the University at Buffalo.  Dr. Peter L. Elkin has served as a tenured Professor of Medicine at the Mount Sinai School of Medicine.  In this capacity he was the Center Director of Biomedical Informatics, Vice-Chairman of the Department of Internal Medicine and the Vice-President of Mount Sinai hospital for Biomedical and Translational Informatics.  Prior to Mount Sinai Dr. Elkin was a Professor at the Mayo Clinic in Rochester, MN.  Dr. Elkin has published over 170 peer reviewed publications.  He received his Bachelors of Science from Union College and his M.D. from New York Medical College.  He did his Internal Medicine residency at the Lahey Clinic and his NIH/NLM sponsored fellowship in Medical Informatics at Harvard Medical School and the Massachusetts General Hospital. Dr. Elkin has been working in Biomedical Informatics since 1981 and has been actively researching health big data science since 1987.  He is the primary author of the American National Standards Institute’s (ANSI) national standard on Quality Indicators for Controlled Health Vocabularies ASTM E2087, which has also been approved by ISO TC 215 as a Technical Specification (TS17117).  He has chaired Health and Human Service’s HITSP Technical Committee on Population Health.  Dr. Elkin served as the co-chair of the AHIC Transition Planning Group.  Dr. Elkin is a Master of the American College of Physicians and a Fellow of the American College of Medical Informatics and a Fellow of the New York Academy of Medicine.  Dr. Elkin was the founding chair of the International Medical Informatics Associations Working Group on Human Factors Engineering for Health Informatics.  Dr. Elkin is the Editor of the Springer Informatics Textbook, Terminology and Terminological Systems.  He was awarded the Mayo Department of Medicine’s Laureate Award for 2005.  Dr. Elkin is the index recipient of the Homer R. Warner award for outstanding contribution to the field of Medical Informatics.  Dr. Elkin is the Informatics lead on the University at Buffalo CTSA award. 


US Centers for Disease Control and Prevention. CDC Seasonal Influenza (Flu) - Flu Activity and Surveillance. US Centers for Disease Control; 2011.

Call SA, Vollenweider MA, Hornung CA, Simel DL, McKinney WP. Does this patient have influenza? JAMA. 2005;293(8):987-97.

Teodoro D, Pasche E, Gobeill J, Emonet S, Ruch P, Lovis C. Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation. J Med Internet Res. 2012 May 29;14(3):e73.

Elkin PL, Froehling D, Wahner-Roedler D, Brown SH, Bailey K. “Comparison of NLP Biosurveillance Methods for Identifying Influenza from Encounter Notesâ€; Ann Intern Med. 2012 Jan 3;156(1 Pt 1):11-8.

Elkin PL, Brown SH, Balas A, Temesgen Z, Wahner-Roedler DL, Froehling D, Liebow M, Trusko B, Rosenbloom ST, Poland G. Biosurveillance evaluation of SNOMED CT’s terminology (BEST Trial): coverage of chief complaints. Stud Health Technol Inform. 2008;136:797-802.

Adisasmito W, Chan PK, Lee N, Oner AF, Gasimov V, Aghayev F Zaman M, Bamgboye E, Dogan N, Coker R, Starzyk K, Dreyer NA, Toovey S. Effectiveness of antiviral treatment in human influenza A(H5N1) infections: analysis of a Global Patient Registry. J Infect Dis. 2010;202(8):1154-60.


Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011 Aug 24;306(8):848-55.

Brown SH, Elkin PL, Fielstein E, Speroff T. eQuality for all – extending automated quality measurement from free text. AMIA Annu Symp Proc. 2008 Nov 6:71-5.

Brown SH, Speroff T, Fielstein EM, Bauer BA, Wahner-Roedler DL, Greevy R, Elkin PL. eQuality: Automatic assessment from narrative clinical reports. Mayo Clin Proc 2006 81(11):1472-1481






Medical Education