Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France - Volume 21, Number 8—August 2015 - Emerging Infectious Disease journal - CDC

full-text ►

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France - Volume 21, Number 8—August 2015 - Emerging Infectious Disease journal - CDC

Volume 21, Number 8—August 2015

Synopsis

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France

On This Page

• Materials and Methods
• Results
• Discussion
• Suggested Citation

Figures

• Figure 1
• Figure 2
• Figure 3
• Figure 4

Tables

• Table 1
• Table 2
• Table 3
• Table 4

Downloads

• PDF[4.68 MB - 9 pgs]
• RIS[TXT - 2 KB]

Cédric Abat, Hervé Chaudet, Philippe Colson, Jean-Marc Rolain, and Didier Raoult 

Author affiliations: Aix-Marseille Université, Marseille, France (C. Abat, P. Colson, J.-M. Rolain, D. Raoult);Sciences Economiques et Sociales de la Santé et Traimtement de l’Information Médicale (SESSTIM), Marseille (H. Chaudet)

Suggested citation for this article

Abstract

Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data–based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved.

Although infectious diseases were declared under control and considered to be a past public health problem during the second half of the 20th century (1), these diseases, including those that are well-known, emerging, and reemerging, remain a major threat to humanity. Indeed, infectious pathogens possess an amazing common capacity to emerge and spread in unpredictable ways before they are detected by public health institutions (2). Infectious diseases have a substantial effect on both global human demographics (they are the second leading cause of death in humans worldwide, accounting for ≈15 million deaths) (3) and the economy (4), which has led the public health community to reconsider them as a real threat. This alarming observation has led public health authorities to try to improve infectious disease surveillance.

One of these strategies, known as traditional public health surveillance of infectious diseases, has been to use clinical case reports from sentinel laboratories or laboratory networks and direct reports of positive results from clinical laboratories to survey the presence of microbial agents known to be dangers to health in a precise population (5). Some examples of surveillance systems implemented by using this strategy are the National Tuberculosis Surveillance System in the United States (6), the surveillance system of the Netherlands Reference Laboratory for Bacterial Meningitis (7) and the European Gonococcal Antimicrobial Surveillance Programme (8).

Another strategy, known as syndromic surveillance, consists of developing real-time surveillance systems capable of detecting abnormal epidemiologic events, not on the basis of infectious disease diagnosis data, but rather on the basis of nonspecific health indicators, such as absenteeism, chief complaints, and prescription drug sales (5,9). Such surveillance systems can be implemented nationally, such as the Emergency Department Syndromic Surveillance System in England (10) or the National Retail Data Monitor in the United States (11), and regionally, such as the Emergency Department Syndromic Surveillance in Canada (12) or the European Antimicrobial Resistance Surveillance Network in Europe (13), or the systems can be administered by laboratories with large quantities of data and the financial and human resources to apply the information.

On the basis of our experience at the Assistance Publique–Hôpitaux de Marseille (AP-HM), we describe all the steps necessary for implementing a laboratory data–based syndromic surveillance system in a laboratory. Because of its simplicity, we believe that it can be rapidly applied and used as a first surveillance tool in well-established laboratories. We also show the advantages and limits of this surveillance system.

Mr. Abat is a PhD student at the Institut Hospitalo-Universitaire Méditerranée Infection, Aix-Marseille Université. His research interest is the implementation of computer tools for real-time epidemiologic surveillance of abnormal events based on clinical microbiology laboratory data.

Acknowledgments

We thank American Journal Experts for English corrections.

This work was partly funded by the Centre National de la Recherche Scientifique and the Institut Hospitalo–Universitaire Méditerranée Infection.

References

1. Raoult D. Les causes de l'émergence des agents infectieux. Responsabilité et Environnement. 2008;51:21–5.
2. Raoult D. Molecular, epidemiological, and clinical complexities of predicting patterns of infectious diseases. Front Microbiol. 2011;2:25. PubMed
3. Morens DM, Folkers GK, Fauci AS. The challenge of emerging and re-emerging infectious diseases. Nature. 2004;430:242–9 and. DOIPubMed
4. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Global trends in emerging infectious diseases. Nature. 2008;451:990–3.DOIPubMed
5. Zeng D, Chen H, Castillo-Chavez C, Thurmond M. Clinical laboratory data for biosurveillance. In: Zeng D., Chen H., Castillo-Chavez C., Lober W.B., Thurmond M., editors. Infectious disease informatics and biosurveillance. New York: Springer; 2011. p. 67–87.
6. Vinnard C, Winston CA, Wileyto EP, Macgregor RR, Bisson GP. Isoniazid resistance and death in patients with tuberculous meningitis: retrospective cohort study. BMJ. 2010;341:c4451. DOIPubMed
7. van Wessel K, Rodenburg GD, Veenhoven RH, Spanjaard L, van der Ende A, Sanders EA. Nontypeable Haemophilus influenzae invasive disease in The Netherlands: a retrospective surveillance study 2001–2008. Clin Infect Dis. 2011;53:e1–7 . DOIPubMed
8. Cole MJ, Unemo M, Hoffmann S, Chisholm SA, Ison CA, van de Laar MJ. The European gonococcal antimicrobial surveillance programme, 2009. Euro Surveill. 2011;16:19995 .PubMed
9. Sala Soler M, Fouillet A, Viso AC, Josseran L, Smith GE, Elliot AJ, Assessment of syndromic surveillance in Europe. Lancet. 2011;378:1833–4.DOIPubMed
10. Severi E, Heinsbroek E, Watson C, Catchpole M. Infectious disease surveillance for the London 2012 Olympic and Paralympic Games. Euro Surveill.2012;17:20232 .PubMed
11. Castillo-Salgado C. Trends and directions of global public health surveillance. Epidemiol Rev. 2010;32:93–109. DOIPubMed
12. van Dijk A, Aramini J, Edge G, Moore KM. Real-time surveillance for respiratory disease outbreaks, Ontario, Canada. Emerg Infect Dis.2009;15:799–801. DOIPubMed
13. Magiorakos AP, Suetens C, Monnet DL, Gagliotti C, Heuer OE. The rise of carbapenem resistance in Europe: just the tip of the iceberg? Antimicrob Resist Infect Control. 2013;2:6.
14. Colson P, Gouriet F, Badiaga S, Tamalet C, Stein A, Raoult D. Real-time laboratory surveillance of sexually-transmissible infections in Marseille University hospitals reveals rise of gonorrhoea, syphilis and human immunodeficiency virus seroconversions in 2012. Euro Surveill. 2013;18:4.PubMed
15. Parola P, Colson P, Dubourg G, Million M, Charrel R, Minodier P, Group A streptococcal infections during the seasonal influenza outbreak 2010/11 in South East England. Euro Surveill. 2011;16:19815 .PubMed
16. Kempf M, Rolain JM, Azza S, Diene S, Joly-Guillou ML, Dubourg G, Investigation of Acinetobacter baumannii resistance to carbapenems in Marseille hospitals, south of France: a transition from an epidemic to an endemic situation. APMIS. 2013;121:64–71. DOIPubMed
17. Seng P, Abat C, Rolain JM, Colson P, Lagier JC, Gouriet F, Identification of rare pathogenic bacteria in a clinical microbiology laboratory: impact of matrix-assisted laser desorption ionization–time of flight mass spectrometry. J Clin Microbiol. 2013;51:2182–94. DOIPubMed
18. Calain P. From the field side of the binoculars: a different view on global public health surveillance. Health Policy Plan. 2007;22:13–20.DOIPubMed
19. Lagier JC, Dubourg G, Cassir N, Fournier PE, Colson P, Richet H, Clostridium difficile 027 emerging outbreak in Marseille, France. Infect Control Hosp Epidemiol. 2013;34:1339–41. DOIPubMed
20. Kronenberg A, Hilty M, Endimiani A, Muhlemann K. Temporal trends of extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae isolates in in- and outpatients in Switzerland, 2004 to 2011. Euro Surveill. 2013;18:20284 .PubMed
21. Silva JC, Shah SC, Rumoro DP, Bayram JD, Hallock MM, Gibbs GS, Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports. Artif Intell Med. 2013;59:169–74. DOIPubMed
22. Chaudet H, Anceaux F, Beuscart MC, Pelayo S, Pellegrin L. Facteurs humains et ergonomie en informatique médicale. In: Venot A, Burgun A, Quantin C, editors. Informatique médicale, e-santé—fondements et applications. New York: Springer; 2013. p. 495–520.
23. Meyer E, Jonas D, Schwab F, Rueden H, Gastmeier P, Daschner FD. Design of a surveillance system of antibiotic use and bacterial resistance in German intensive care units (SARI). Infection. 2003;31:208–15 .PubMed
24. Meyer E, Schwab F, Schroeren-Boersch B, Gastmeier P. Dramatic increase of third-generation cephalosporin-resistant E. coli in German intensive care units: secular trends in antibiotic drug use and bacterial resistance, 2001 to 2008. Crit Care. 2010;14:R113. DOIPubMed
25. Das D, Weiss D, Mostashari F, Treadwell T, McQuiston J, Hutwagner L, Enhanced drop-in syndromic surveillance in New York City following September 11, 2001. J Urban Health. 2003;80(Suppl 1):i76–88 .PubMed
26. Enki DG, Noufaily A, Garthwaite PH, Andrews NJ, Charlett A, Lane C, Automated biosurveillance data from England and Wales, 1991–2011. Emerg Infect Dis. 2013;19:35–42. DOIPubMed

Figures

• Figure 1. Workflow of real-time surveillance systems used by Institut Hospitalo–Universitaire Méditérranée Infection, Assistance Publique-Hôpitaux de Marseille, Marseille, France.
• Figure 2. Screen shots from the Bacterial Real-Time Laboratory-based Surveillance System. A) List of the 652 bacterial species followed by the Bacterial Real-time Surveillance System and all of the contained information....
• Figure 3. Marseille Antibiotic Resistance Surveillance System (MARSS) interface for Escherichia coli. A) Screen shot showing list of most of the β-lactam antibiotic resistance profiles coded for E. coli in MARSS....
• Figure 4. Time chart of the confirmed and unconfirmed events identified by the Marseille Antibiotic Resistance Surveillance System (MARSS) and the Bacterial real-time Laboratory-based Surveillance System (BALYSES). A) List of all...

Tables

• Table 1. Validated alarms emitted by BALYSES and investigations from May 21, 2013 through June 4, 2014, Marseille, France
• Table 2. Summary of the normal phenotypes registered in MARSS
• Table 3. Summary of the alarm phenotypes defined in MARSS
• Table 4. Validated alarms emitted by MARSS and investigations from May 21, 2013, through June 4, 2014, Marseille, France

Suggested citation for this article: Abat C, Chaudet H, Colson P, Rolain JM, Raoult D. Real-time microbiology laboratory surveillance system to detect abnormal events and emerging infections, Marseille, France. Emerg Infect Dis. 2015 Aug [date cited]. http://dx.doi.org/10.3201/eid2108.141419

DOI: 10.3201/eid2108.141419