Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance.
OBJECTIVE
Markers 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.


METHODS
From 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).


RESULTS
Predictive 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.


CONCLUSIONS
Age 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.


CONCLUSIONS
Incorporation of age and severity measures available in ED records can improve ILI surveillance algorithms.
Department of Biostatistics, Epidemiology and Occupational Health, McGill University, Montréal, Québec, Canada noemie.savard@mail.mcgill.ca.
Noémie
N
Savard
Direction de santé publique de l'Agence de la santé et des services sociaux de Montréal, Montréal, Québec, Canada Department of Social and Preventive Medicine, University of Montréal, Montréal, Québec, Canada.
Lucie
L
Bédard
Department of Biostatistics, Epidemiology and Occupational Health, McGill University, Montréal, Québec, Canada Direction de santé publique de l'Agence de la santé et des services sociaux de Montréal, Montréal, Québec, Canada.
Robert
R
Allard
Department of Biostatistics, Epidemiology and Occupational Health, McGill University, Montréal, Québec, Canada Direction de santé publique de l'Agence de la santé et des services sociaux de Montréal, Montréal, Québec, Canada.
David L
DL
Buckeridge
D000367:Age Factors; D057286:Electronic Health Records; D004636:Emergency Service, Hospital; D006760:Hospitalization; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology*; D058873:Pandemics; D011014:Pneumonia / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D011792:Quebec / Q000453:epidemiology; D012720:Severity of Illness Index; D014218:Triage
Age; Disposition; Influenza; Influenza-like illness – ILI; Syndromic surveillance; Triage score