The impact of alternative diagnoses on the utility of influenza-like illness case definition to detect the 2009 H1N1 pandemic
DOI:
https://doi.org/10.5055/ajdm.2012.0086Keywords:
H1N1, influenza-like illness, public health, syndromic surveillanceAbstract
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 (χ2 = 9.09, p < 0.001) and significant improvement in specificity (χ2 = 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.
References
US Department of Health and Human Services: H1N1 influenza: The year in review, 2011. Available at http://www.flu.gov/firstyear/index.html. Accessed January 20, 2011.
Chan M:World now at the start of 2009 influenza pandemic, 2011. Available at http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_phase6_20090611/en/index.html. Accessed January 20, 2011.
Centers for Disease Control and Prevention: CDC estimates of 2009 H1N1 influenza cases, hospitalizations and deaths in the united states, April 2009-March 13, 2010. Available at http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm#Table%20Cumulative. Accessed January 20, 2011.
Centers for Disease Control and Prevention: Overview of influenza surveillance in the United States, 2010. Available at http://www.cdc.gov/flu/weekly/overview.htm. Accessed January 20, 2011.
Bellazzini M, Minor K: ED syndromic surveillance for novel H1N1 spring 2009. Am J Emerg Med. 2011; 29(1): 70-74.
Fleischauer A, Silk B, Schumacher M, et al.: The validity of chief complaint and discharge diagnosis in emergency department-based syndromic surveillance. Acad Emerg Med. 2004; 11(12): 1262-1267.
Townes J, Kohn M, Southwick K, et al.: Investigation of an electronic emergency department information system as a data source for respiratory syndrome surveillance. J Publ Health Manag Pract. 2004; 10(4): 299-307.
Centers for Disease Control and Prevention: Morbidity and mortality weekly report—update: Influenza activity—United States, 2009–10 season, 2010. Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5929a2.htm?s_cid_mm5929a2_w. Accessed January 11, 2012.
Mandell L,Wunderink R, Anzueto A, et al.: Infectious diseases society of America/American thoracic society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis. 2007; 44 (Suppl 2): S27-S72.
Waddell MJ, Doseck SM, Silva JC, et al.:GUARDIAN: Geographic utilization of artificial intelligence in real-time for disease identification and alert notification. Adv Dis Surveillance. 2007; 4: 63.
Bodenreider O: The unified medical language system (UMLS): Integrating biomedical terminology. Nucleic Acids Res. 2004; 32 (Database issue): D267-D270.
Chapman WW, Bridewell W, Hanbury P, et al.: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001; 34(5): 301-310.
Silva JC, Rumoro DP, Bayram J, et al.: Comparison of multiple syndrome surveillance programs to detect influenza-like illness. Poster Presentation at American Medical Informatics Association Annual Conference (http://proceedings.amia.org/127j35/1),Washington, DC, November 2010.
Trajman A, Luiz RR: McNemar chi2 test revisited: Comparing sensitivity and specificity of diagnostic examinations. Scand J Clin Lab Invest. 2008; 68(1): 77-80.
National Institutes of Health: Rapid response was crucial to containing the 1918 flu pandemic: Historical analyses help plan for future pandemics. Published April 02, 2007. Available at http://www.nih.gov/news/pr/apr2007/niaid-02b.htm.Accessed March 15, 2012.
Wong WN, Sek ACH, Lau RFL, et al.: Accuracy of clinical diagnosis versus the World Health Organization case definition in the Amoy Garden SARS cohort. Can J Emerg Med. 2003; 5(6): 384-391.
Boivin G, Hardy I, Tellier G, et al.: Predicting influenza infections during epidemics with use of a clinical case definition. Clin Infect Dis. 2000; 31(5): 1166-1169.
Kasper M, Wierzba T, Sovann L, et al.: Evaluation of an influenza-like illness case definition in the diagnosis of influenza among patients with acute febrile illness in Cambodia. BMC Infect Dis. 2010; 10: 320-320.
Centers for Disease Control and Prevention: H1N1 flu: Interim recommendations for clinical use of influenza diagnostic tests during the 2009-10 influenza season. Published September 29, 2009. Available at http://www.cdc.gov/h1n1flu/guidance/diagnostic_tests.htm. Accessed January 11, 2012.
Bravata D, McDonald K, Smith W, et al.: Systematic review: Surveillance systems for early detection of bioterrorism-related diseases. Ann Intern Med. 2004; 140(11): 910-922.
Tsui F, Espino J, Dato V, et al.: Technical description of RODS: A real-time public health surveillance system. J Am Med Informatics Assoc. 2003; 10(5): 399-408.
RODS Laboratory: Real-time outbreak and disease surveillance at the department of biomedical informatics, 2011. Available at https://www.rods.pitt.edu. Accessed January 20, 2011.
Lombardo J, Burkom H, Pavlin J: ESSENCE II and the framework for evaluating syndromic surveillance systems. Morb Mortal Weekly Rep. 2004; 53 (Suppl): 159-165.
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