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

• Published in: BMC infectious diseases Vol. 14; no. 1; p. 381
• Main Authors: Gerbier-Colomban, Solweig, Potinet-Pagliaroli, Véronique, Metzger, Marie-Hélène
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.07.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Case studies; Comparative analysis; Complications and side effects; Computation and Language; Computer Science; Data processing; Diagnosis; Diagnosis-Related Groups - statistics & numerical data; Emergency medical services; Epidemics; Epidemiology; France - epidemiology; Health aspects; Hospitalization - statistics & numerical data; Hospitals; Humans; Infections; Infectious Diseases; Influenza; Influenza, Human - epidemiology; Information technology; Internal Medicine; Medical Microbiology; Medical personnel; Medical records; Medicine; Medicine & Public Health; Mortality; Outbreaks; Pandemics; Parasitology; Patient outcomes; Population Surveillance; Public health; Research Article; Risk factors; Sensitivity analysis; Studies; Technology application; Time series; Tropical Medicine; Viral diseases; France; Human; Emergency service; Influenza; Disease outbreak; Population surveillance; Hospital; Syndromic surveillance; Infection control
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/1471-2334-14-381

Background Early 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. Methods Time 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. Results The 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. Conclusions The 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.