Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-05T09:32:29.771Z Has data issue: false hasContentIssue false
  • English
  • Français

Local Hospital Perspective on a Nationwide Outbreak of Pseudomonas aeruginosa Infection in Norway

Published online by Cambridge University Press:  02 January 2015

Mette Walberg ,
Kathrine Frey Frøslie  and
Jo Røislien
Mette Walberg*
Affiliation:
Microbiology Section, Laboratory Centre, Asker and Baerum Hospital, Rud, Rikshospitalet Medical Centre, Norway Institute of Medical Microbiology, Rikshospitalet Medical Centre, Norway
Kathrine Frey Frøslie
Affiliation:
Biostatistics Group, Research Services Department, Rikshospitalet Medical Centre, Norway
Jo Røislien
Affiliation:
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway
*
Microbiology Section, Laboratory Centre, Asker and Basrum Hospital, 1309 Rud, Norway ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

To implement a system for monitoring of rare events based on statistical process control charts.

Design.

Statistical process control plotting by g chart of clinical microbiology laboratory data.

Setting.

Primary and secondary care Norwegian hospital with a 9-bed intensive care unit.

Results.

During the winter of 2001–2002 in Norway, there was a national monoclonal nosocomial outbreak of Pseudomonas aeruginosa infection mainly affecting patients in intensive care units. In the present work, we demonstrate how the use of SPC at one of the affected hospitals would have detected this outbreak several weeks before the alert from the Norwegian National Public Health Institute (NIPH). By plotting the monthly incidence rate of P. aeruginosa infection (with a c chart), we found that the hospital would have been alerted in February, by plotting the number of days between events (with a g chart), we found that the hospital would have detected a process already out of control in early January 2002. Not until 9 weeks later (ie, mid-March) did the NIPH declare the P. aeruginosa outbreak to be national, and a commercially produced mouth swab contaminated during the manufacturing process was found to be the source.

Conclusion.

The plotting of rare events, such as an outbreak of nosocomial infection, with a g chart may be used for early detection of a process out of control.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 29 , Issue 7 , July 2008 , pp. 635 - 641
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Carey, RG. How do you know that your care has improved? Eval Health Prof 2000;23:43–57.Google Scholar
2.Carey, RG. Improving Health Care with Control Charts. Milwaukee: ASQ Quality Press; 2002.Google Scholar
3.Benneyan, JC, Lloyd, RC, Plsek, PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 2003;12:458–464.Google Scholar
4.Humble, C. Caveats regarding the use of control charts. Infect Control Hosp Epidemiol 1998;19:865–868.Google Scholar
5.Nelson, EC, Splaine, ME, Batalden, PB, Plume, SK. Building measurement and data collection into medical practice. Ann Intern Med 1998;128:460–466.Google Scholar
6.Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology, part I: introduction and basic theory. Infect Control Hosp Epidemiol 1998;19:194–214.Google Scholar
7.Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology, part II: chart use, statistical properties, and research issues. Infect Control Hosp Epidemiol 1998;19:265–283.Google Scholar
8.Parachoor, SB, Rosov, E, Enderle, ID. Knowledge management system for benchmarking performance indicators using statistical process control (SPC) and virtual instrumentation (VI). Biomed Sci Instrum 2003;39:175–178.Google Scholar
9.Ross, TK. A statistical process control case study. Q Manage Health Care 2006;15:221–236.Google Scholar
10.Matthes, N, Ogunbo, S, Pennington, G, Wood, N, Hart, MK, Hart, R. Statistical process control for hospitals: methodology, user education, and challenges. Q Manage Health Care 2007;16:205–214.Google Scholar
11.Thor, J, Lundberg, J, Ask, J, et al. Application of statistical process control in health care improvement: systematic review. Qual Saf Health Care 2007;16:387–399.Google Scholar
12.Morton, AP, Whitby, M, McLaws et al. The application of statistical process control charts to the detection and monitoring of hospital-acquired infections. J Qual Clin Pract 2001;21:112–117.Google Scholar
13.Benneyan, JC. Caveats regarding the use of control charts. Infect Control Hosp Epidemiol 1999;20:526.Google Scholar
14.Humble, C. Infect Control Hosp Epidemio 1999;20:527.Google Scholar
15.Brown, SM, Benneyan, JC, Theobald, DA, et al. Binary cumulative sums and moving average in nosocomial infection cluster detection. Emerg Infect Dis 2002;8:1426–1432.Google Scholar
16.Gustafson, TL. Practical risk-adjusted quality control charts for infection control. Am J Infect Control 2000;28:406–414.Google Scholar
17.Quesenberry, CP. Statistical process control geometric Q-chart for nosocomial infection surveillance. Am J Infect Control 2000;28:314–318.Google Scholar
18.Benneyan, JC. Number-between g-type statistical quality control charts for monitoring adverse events. Health Care Manag Sci 2001;4:305–318.Google Scholar
19.Benneyan, JC. Performance of number-between g-type statistical control charts for monitoring adverse events. Health Care Manag Sci 2001;4:319–336.Google Scholar
20.Curran, ET, Benneyan, JC, Hood, J. Controlling methicillin-resistant Staphylococcus aureus: a feedback approach using annotated statistical process control charts. Infect Control Hosp Epidemiol 2002;23:13–18.Google Scholar
21.Harrington, G, Watson, K, Bailey, M, et al. Reduction in hospitalwide incidence of infection or colonization with methicillin-resistant Staphylococcus aureus with use of antimicrobial hand-hygiene gel and statistical process control charts. Infect Control Hosp Epidemiol 2007;28:837–844.Google Scholar
22.Iversen, BG, Jacobsen, T, Eriksen, HM, et al. An outbreak of Pseudomonas aeruginosa infection caused by contaminated mouth swabs. Clin Infect Dis 2007;44:794–801.Google Scholar
23.Johnson, NL, Kotz, S, Balakrishnan, N. Discrete Multivariate Distributions. New York: Wiley; 1997.Google Scholar
24.Johnson, RA, Wichern, DW. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River, NJ: Pearsson Prentice Hall; 2007.Google Scholar
25.Gras-Le Guen, G, Lepelletier, D, Debillon, T, Gournay, V, Espaze, E, Roe, JC. Contamination of a milk bank pasteuriser causing a Pseudomonas aeruginosa outbreak in a neonatal intensive care unit. Arch Dis Child Fetal Neonatal 2003;88:434–435.Google Scholar
26.Bukholm, G, Tannaes, T, Bye Kjeldsberg, AB, Smith-Erichsen, N. An outbreak of multidrug resistant Pseudomonas aeruginosa associated with increased risk of patient death in an intensive care unit. Infect Control Hosp Epidemiol 2002;23:441–446.Google Scholar
27.Nasjonalt Folkehelseinstitutt. Pseudomonas-utbrudd i sykehus? MSIS-rapport nr. 10,2002. Available at: http://www.fhi.no/eway/default.aspx?pid=233&trg=MainArea_5661&MainArea_5661=5619:0:15,1344:1:0:0:::0:0. Accessed March 14, 2002.Google Scholar
28.Nasjonalt Folkehelseinstitutt. Pseudomonas-utbrudd i sykehus. MSIS-rapport nr. 12,2002. Available at http://www.fhi.no/eway/default.aspx?pid=2338ctrg=MainArea_5661&MainArea_5661=5619:0:15,1344:1:0:0:::0:0. Accessed March 28, 2002.Google Scholar
29.Nasjonalt Folkehelseinstitutt. Utbrudd av Pseudomonasinfeksjoner i sykehus skyldes antagelig “Dent-o-sept” munnpensel. MSIS-rapport nr. 14, 2002. Available at http://www.fhi.no/eway/default.aspx?pid=2338rtrg=MainArea_56618&MainArea_5661=5619:0:15,1344:l:0:0:::0:0. Accessed April 11, 2002.Google Scholar
30.van Belle, G. Statistical Rules of Thumb. New York: Wiley-Interscience; 2002.Google Scholar