Can epidemic detection systems at the hospital level complement regional surveillance networks: case study with the influenza epidemic?

Solweig Gerbier-Colomban,Véronique Potinet-Pagliaroli,Marie-Hélène Metzger

doi:10.1186/1471-2334-14-381

Abstract

BackgroundEarly knowledge of influenza outbreaks in the community allows local hospital healthcare workers to recognise the clinical signs of influenza in hospitalised patients and to apply effective precautions. The objective was to assess intra-hospital surveillance systems to detect earlier than regional surveillance systems influenza outbreaks in the community.MethodsTime series obtained from computerized medical data from patients who visited a French hospital emergency department (ED) between June 1st, 2007 and March 31st, 2011 for influenza, or were hospitalised for influenza or a respiratory syndrome after an ED visit, were compared to different regional series. Algorithms using CUSUM method were constructed to determine the epidemic detection threshold with the local data series. Sensitivity, specificity and mean timeliness were calculated to assess their performance to detect community outbreaks of influenza. A sensitivity analysis was conducted, excluding the year 2009, due to the particular epidemiological situation related to pandemic influenza this year.ResultsThe local series closely followed the seasonal trends reported by regional surveillance. The algorithms achieved a sensitivity of detection equal to 100% with series of patients hospitalised with respiratory syndrome (specificity ranging from 31.9 and 92.9% and mean timeliness from −58.3 to 20.3 days) and series of patients who consulted the ED for flu (specificity ranging from 84.3 to 93.2% and mean timeliness from −32.3 to 9.8 days). The algorithm with the best balance between specificity (87.7%) and mean timeliness (0.5 day) was obtained with series built by analysis of the ICD-10 codes assigned by physicians after ED consultation. Excluding the year 2009, the same series keeps the best performance with specificity equal to 95.7% and mean timeliness equal to −1.7 day.ConclusionsThe implementation of an automatic surveillance system to detect patients with influenza or respiratory syndrome from computerized ED records could allow outbreak alerts at the intra-hospital level before the publication of regional data and could accelerate the implementation of preventive transmission-based precautions in hospital settings.

Highlights

Knowledge of influenza outbreaks in the community allows local hospital healthcare workers to recognise the clinical signs of influenza in hospitalised patients and to apply effective precautions
The second objective of the system is to detect an increase in the number of patients who visit emergency department (ED) with potentially transmissible infectious diseases before other regional surveillance systems alert healthcare workers about outbreaks beginning in the community
The weekly number of ED visits for influenza in our hospital (ICD10-consultations) usually fluctuated between 0 and 6, even during outbreak periods, except during the global influenza A/ H1N1 pandemic when 59 visits were recorded during one week

Summary

Introduction

Knowledge of influenza outbreaks in the community allows local hospital healthcare workers to recognise the clinical signs of influenza in hospitalised patients and to apply effective precautions. Patients are classified by the surveillance system according to three syndromes: respiratory, cutaneous and gastrointestinal This would alert infection control practitioners and healthcare workers and help them to set up the appropriate transmission-based precautions as soon as the patients’ care begins in ED, without expecting the diagnosis confirmation. The second objective of the system is to detect an increase in the number of patients who visit ED with potentially transmissible infectious diseases before other regional surveillance systems alert healthcare workers about outbreaks beginning in the community. Such alert would help infection control practitioners to make as soon as possible healthcare workers sensitive to the epidemic risk

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