• Noémie Savard, Lucie Bédard, Robert Allard, and David L Buckeridge.
• Department of Biostatistics, Epidemiology and Occupational Health, McGill University, Montréal, Québec, Canada noemie.savard@mail.mcgill.ca.
• J Am Med Inform Assoc. 2015 May 1; 22 (3): 688-96.

ObjectiveMarkers of illness severity are increasingly captured in emergency department (ED) electronic systems, but their value for surveillance is not known. We assessed the value of age, triage score, and disposition data from ED electronic records for predicting influenza-related hospitalizations.Materials And MethodsFrom June 2006 to January 2011, weekly counts of pneumonia and influenza (P&I) hospitalizations from five Montreal hospitals were modeled using negative binomial regression. Over lead times of 0-5 weeks, we assessed the predictive ability of weekly counts of 1) total ED visits, 2) ED visits with influenza-like illness (ILI), and 3) ED visits with ILI stratified by age, triage score, or disposition. Models were adjusted for secular trends, seasonality, and autocorrelation. Model fit was assessed using Akaike information criterion, and predictive accuracy using the mean absolute scaled error (MASE).ResultsPredictive accuracy for P&I hospitalizations during non-pandemic years was improved when models included visits from patients ≥65 years old and visits resulting in admission/transfer/death (MASE of 0.64, 95% confidence interval (95% CI) 0.54-0.80) compared to overall ILI visits (0.89, 95% CI 0.69-1.10). During the H1N1 pandemic year, including visits from patients <18 years old, visits with high priority triage scores, or visits resulting in admission/transfer/death resulted in the best model fit.DiscussionAge and disposition data improved model fit and moderately reduced the prediction error for P&I hospitalizations; triage score improved model fit only during the pandemic year.ConclusionIncorporation of age and severity measures available in ED records can improve ILI surveillance algorithms.© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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