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

BMC Infectious Diseases

Marie-Hélène Metzger²  Véronique Potinet-Pagliaroli¹  Solweig Gerbier-Colomban² 

[1] Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Service des urgences, F-69317 Lyon, France;Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France

关键词: Population surveillance;    Disease outbreak;    Infection control;    Human;    Influenza;    Syndromic surveillance;    Hospital;    Emergency service;   

Others  :  1127406
DOI  :  10.1186/1471-2334-14-381

 received in 2013-11-26, accepted in 2014-06-30,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Gerbier-Colomban et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Siegel JD, Rhinehart E, Jackson M, Chiarello L: Healthcare Infection Control Practices Advisory Committee: 2007 Guideline for Isolation Precautions: Preventing Transmission of Infectious Agents in Healthcare Settings. Am J Infect Control 2007, 35(Supplement 2):S65-S164.
• [2]Andrieu AG, Paute J, Glomot L, Jarlier V, Belmin J: Nosocomial influenza outbreak in a geriatrics department: Effectiveness of preventive measures. Presse Med 2006, 35:1419-1426.
• [3]Weinstock DM, Eagan J, Malak SA, Rogers M, Wallace H, Kiehn TE, Sepkowitz KA: Control of influenza A on a bone marrow transplant unit. Infect Control Hosp Epidemiol 2000, 21:730-732.
• [4]Sartor C, Zandotti C, Romain F, Simon S, Atlan-Gepner C, Sambuc R, Vialettes B, Drancourt M: Disruption of services in an internal medicine unit due to a nosocomial influenza outbreak. Infect Control Hosp Epidemiol 2002, 23:615-619.
• [5]Gerbier S, Yarovaya O, Gicquel Q, Millet A-L, Smaldore V, Pagliaroli V, Darmoni S, Metzger M-H: Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance. BMC Med Inform Decis Mak 2011, 11:50. BioMed Central Full Text
• [6]Gerbier-Colomban S, Gicquel Q, Millet A-L, Riou C, Grando J, Darmoni S, Potinet-Pagliaroli V, Metzger M-H: Evaluation of syndromic algorithms for detecting patients with potentially transmissible infectious diseases based on computerised emergency-department data. BMC Med Inform Decis Mak 2013, 13:101. BioMed Central Full Text
• [7]Josseran L, Nicolau J, Caillere N, Astagneau P, Brucker G: Syndromic surveillance based on emergency department activity and crude mortality: two examples. Euro Surveill 2006, 11:225-229.
• [8]Josseran L, Caillère N, Goncalvès N, Ringard D, Leroy C, Fournet N, Fouillet A, Delmas MC, Ilef D: Syndromic surveillance in the context of the A (H1N1) 2009 pandemic: interest and limits. Bull épidémiol hebd 2010, 24:25-26. 274-7
• [9]Carrat F, Flahault A, Boussard E, Farran N, Dangoumau L, Valleron AJ: Surveillance of influenza-like illness in France. The example of the 1995/1996 epidemic. J Epidemiol Community Health 1998, 52(Suppl 1):32S-38S.
• [10]Flahault A, Boussard E, Vibert JF, Valleron AJ: Sentiweb remains efficient tool for nationwide surveillance of disease. BMJ 1997, 314:1418.
• [11]Lina B, Valette M, Foray S, Luciani J, Stagnara J, See DM, Aymard M: Surveillance of community-acquired viral infections due to respiratory viruses in Rhone-Alpes (France) during winter 1994 to 1995. J Clin Microbiol 1996, 34:3007-3011.
• [12]Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L: Detecting influenza epidemics using search engine query data. Nature 2009, 457:1012-1014.
• [13]Valdivia A, López-Alcalde J, Vicente M, Pichiule M, Ruiz M, Ordobas M: Monitoring influenza activity in Europe with Google Flu Trends: comparison with the findings of sentinel physician networks – results for 2009-10. Euro Surveill 2010., 15(29) Available online: http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=19621 webcite
• [14]Société Française de Médecine d’Urgence, Ministère de la Santé et des Solidarités, Institut de Veille Sanitaire: Résumé de Passage aux Urgences (RPU). [http://www.informatique.sfmu.org.support-urgences.fr/content/view/28/77/ webcite]
• [15]Hutwagner L, Browne T, Seeman GM, Fleischauer AT: Comparing aberration detection methods with simulated data. Emerg Infect Dis 2005, 11:314-316.
• [16]Boutou-Kempf O, Casamatta D, Gay C, Cire R-A: Bulletin de veille sanitaire Rhône-Alpes. Numéro spécial - Surveillance de la grippe en Rhône-Alpes. Hiver 2010–2011. 2010. [http://www.invs.sante.fr/fr/Publications-et-outils/Bulletin-de-veille-sanitaire/Tous-les-numeros/Rhone-Alpes/Bulletin-de-veille-sanitaire-Rhone-Alpes.-Numero-special-Surveillance-de-la-grippe-en-Rhone-Alpes.-Hiver-2010-2011 webcite]
• [17]Cowling BJ, Wong IOL, Ho L-M, Riley S, Leung GM: Methods for monitoring influenza surveillance data. Int J of Epidemiol 2006, 35:1314-1321.
• [18]Bults M, Beaujean DJ, de Zwart O, Kok G, van Empelen P, van Steenbergen JE, Richardus JH, Voeten HA: Perceived risk, anxiety, and behavioural responses of the general public during the early phase of the Influenza A (H1N1) pandemic in the Netherlands: results of three consecutive online surveys. BMC Public Health 2011, 11:2. BioMed Central Full Text
• [19]Goulia P, Mantas C, Dimitroula D, Mantis D, Hyphantis T: General hospital staff worries, perceived sufficiency of information and associated psychological distress during the A/H1N1 influenza pandemic. BMC Infect Dis 2010, 10:322. BioMed Central Full Text
• [20]Saadatian-Elahi M, Facy F, Del Signore C, Vanhems P: Perception of epidemic’s related anxiety in the General French Population: a cross-sectional study in the Rhône-Alpes region. BMC Public Health 2010, 10:191. BioMed Central Full Text
• [21]Moore K, Black J, Rowe S, Franklin L: Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic. BMC Public Health 2011, 11:338. BioMed Central Full Text
• [22]Gicquel Q, Kergoulay I, Gerbier-Colomban S, Chariout S, Bittar A, Segond F, Darmoni S, Metzger M-H: Annotation methods to develop and evaluate an expert system based on natural language processing in electronic medical record. In MIE. Istanbul, Turkey; 2014. European Medical Informatics Conference - MIE2014 http://www.mie2014.org webcite