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      Impact of Seasonal and Pandemic Influenza on Emergency Department Visits, 2003–2010, Ontario, Canada

      research-article
      , MSc, , MD, CCFP(EM), FCFP
      , MD, MPH
      Academic Emergency Medicine
      Blackwell Publishing Ltd

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          Abstract

          Objectives

          Weekly influenza-like illness (ILI) consultation rates are an integral part of influenza surveillance. However, in most health care settings, only a small proportion of true influenza cases are clinically diagnosed as influenza or ILI. The primary objective of this study was to estimate the number and rate of visits to the emergency department (ED) that are attributable to seasonal and pandemic influenza and to describe the effect of influenza on the ED by age, diagnostic categories, and visit disposition. A secondary objective was to assess the weekly “real-time” time series of ILI ED visits as an indicator of the full burden due to influenza.

          Methods

          The authors performed an ecologic analysis of ED records extracted from the National Ambulatory Care Reporting System (NARCS) database for the province of Ontario, Canada, from September 2003 to March 2010 and stratified by diagnostic characteristics (International Classification of Diseases, 10th Revision [ICD-10]), age, and visit disposition. A regression model was used to estimate the seasonal baseline. The weekly number of influenza-attributable ED visits was calculated as the difference between the weekly number of visits predicted by the statistical model and the estimated baseline.

          Results

          The estimated rate of ED visits attributable to influenza was elevated during the H1N1/2009 pandemic period at 1,000 per 100,000 (95% confidence interval [CI] = 920 to 1,100) population compared to an average annual rate of 500 per 100,000 (95% CI = 450 to 550) for seasonal influenza. ILI or influenza was clinically diagnosed in one of 2.6 (38%) and one of 14 (7%) of these visits, respectively. While the ILI or clinical influenza diagnosis was the diagnosis most specific to influenza, only 87% and 58% of the clinically diagnosed ILI or influenza visits for pandemic and seasonal influenza, respectively, were likely directly due to an influenza infection. Rates for ILI ED visits were highest for younger age groups, while the likelihood of admission to hospital was highest in older persons. During periods of seasonal influenza activity, there was a significant increase in the number of persons who registered with nonrespiratory complaints, but left without being seen. This effect was more pronounced during the 2009 pandemic. The ratio of influenza-attributed respiratory visits to influenza-attributed ILI visits varied from 2.4:1 for the fall H1N1/2009 wave to 9:1 for the 2003/04 influenza A(H3N2) season and 28:1 for the 2007/08 H1N1 season.

          Conclusions

          Influenza appears to have had a much larger effect on ED visits than was captured by clinical diagnoses of influenza or ILI. Throughout the study period, ILI ED visits were strongly associated with excess respiratory complaints. However, the relationship between ILI ED visits and the estimated effect of influenza on ED visits was not consistent enough from year to year to predict the effect of influenza on the ED or downstream in-hospital resource requirements.

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          Most cited references36

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          Clinical signs and symptoms predicting influenza infection.

          New antiviral drugs are available for the treatment of influenza type A and type B infections. In clinical practice, antiviral use has rarely been guided by antecedent laboratory diagnosis. Defined clinical predictors of an influenza infection can help guide timely therapy and avoid unnecessary antibiotic use. To examine which clinical signs and symptoms are most predictive of influenza infection in patients with influenza-like illness using a large data set derived from clinical trials of zanamivir. This analysis is a retrospective, pooled analysis of baseline signs and symptoms from phase 2 and 3 clinical trial participants. It was conducted in mainly unvaccinated (mean age, 35 years) adults and adolescents who had influenza-like illness, defined as having fever or feverishness plus at least 2 of the following influenza-like symptoms: headache, myalgia, cough, or sore throat who underwent laboratory testing for influenza. Clinical signs and symptoms were evaluated in statistical models to identify those best predicting laboratory confirmation of influenza. Of 3744 subjects enrolled with baseline influenza-like symptoms, and included in this analysis, 2470 (66%) were confirmed to have influenza. Individuals with influenza were more likely to have cough (93% vs 80%), fever (68% vs 40%), cough and fever together (64% vs 33%), and/or nasal congestion (91% vs 81%) than those without influenza. The best multivariate predictors of influenza infections were cough and fever with a positive predictive value of 79% (P<. 001). The positive predictive value rose with the increase in the temperature at the time of recruitment. When influenza is circulating within the community, patients with an influenza-like illness who have both cough and fever within 48 hours of symptom onset are likely to have influenza and the administration of influenza antiviral therapy may be appropriate to consider. Arch Intern Med. 2000;160:3243-3247.
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            Methods for current statistical analysis of excess pneumonia-influenza deaths.

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              Estimates of US influenza‐associated deaths made using four different methods

              Abstract Background  A wide range of methods have been used for estimating influenza‐associated deaths in temperate countries. Direct comparisons of estimates produced by using different models with US mortality data have not been published. Objective  Compare estimates of US influenza‐associated deaths made by using four models and summarize strengths and weaknesses of each model. Methods  US mortality data from the 1972–1973 through 2002–2003 respiratory seasons and World Health Organization influenza surveillance data were used to estimate influenza‐associated respiratory and circulatory deaths. Four models were used: (i) rate‐difference (using peri‐season or summer‐season baselines), (ii) Serfling least squares cyclical regression, (iii) Serfling–Poisson regression, (iv) and autoregressive integrated moving average models. Results  Annual estimates of influenza‐associated deaths made using each model were similar and positively correlated, except for estimates from the summer‐season rate‐difference model, which were consistently higher. From the 1976/1977 through the 2002/2003 seasons the, the Poisson regression models estimated that an annual average of 25 470 [95% confidence interval (CI) 19 781–31 159] influenza‐associated respiratory and circulatory deaths [9·9 deaths per 100 000 (95% CI 7·9–11·9)], while peri‐season rate‐difference models using a 15% threshold estimated an annual average of 22 454 (95% CI 16 189–28 719) deaths [8·6 deaths per 100 000 (95% CI 6·4–10·9)]. Conclusions  Estimates of influenza‐associated mortality were of similar magnitude. Poisson regression models permit the estimation of deaths associated with influenza A and B, but require robust viral surveillance data. By contrast, simple peri‐season rate‐difference models may prove useful for estimating mortality in countries with sparse viral surveillance data or complex influenza seasonality.
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                Author and article information

                Contributors
                Role: Supervising Editor
                Journal
                Acad Emerg Med
                Acad Emerg Med
                acem
                Academic Emergency Medicine
                Blackwell Publishing Ltd
                1069-6563
                1553-2712
                April 2013
                16 April 2013
                : 20
                : 4
                : 388-397
                Affiliations
                Centre for Communicable Diseases and Infection Control, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada Ottawa, Ontario
                Public Health Ontario and the Department of Family and Community Medicine, University of Toronto Toronto, Ontario, Canada
                Author notes
                Address for correspondence and reprints: Dena Schanzer, e-mail: dena.schanzer@ 123456phac-aspc.gc.ca .

                The authors have no relevant financial information or potential conflicts of interest to disclose.

                Article
                10.1111/acem.12111
                3748786
                23701347
                746b9157-c3f3-4774-9376-bb2e89a4bc50
                © 2013 by the Society for Academic Emergency Medicine

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 16 July 2012
                : 30 September 2012
                : 06 October 2012
                Categories
                Original Research Contributions

                Emergency medicine & Trauma
                Emergency medicine & Trauma

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