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A Computerized Nationwide Network for Nosocomial Infection Surveillance in Belgium

Published online by Cambridge University Press:  02 January 2015

Raf Mertens ,
Beatrice Jans  and
Xavier Kurz
Raf Mertens*
Affiliation:
Institute of Hygiene and Epidemiology, Epidemiology Section, Brussels, Belgium
Beatrice Jans
Affiliation:
Institute of Hygiene and Epidemiology, Epidemiology Section, Brussels, Belgium
Xavier Kurz
Affiliation:
Institute of Hygiene and Epidemiology, Epidemiology Section, Brussels, Belgium
*
Institute of Hygiene and Epidemiology, Epidemiology Section, 14 Rue J. Wytsman, 1050 Brussels, Belgium
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Abstract

Objective:

To assess the feasibility of computerized nationwide surveillance of nosocomial infections in Belgium, and to obtain preliminary national and hospital-specific incidence data.

Design:

Prospective multicenter cohort study of surgical wound infections (SWI).

Setting:

All 218 acute care hospitals in Belgium in the period October 14 to December 14, 1991.

Results:

Eighty-five of 218 acute care hospitals (39%) succeeded in collecting the required information and in completing this pilot study, although 50% of the participating hospitals had no previous experience in nosocomial infection surveillance. Seventy percent of the small-size hospitals (<200 beds) did not participate, mainly because of shortages of manpower. A lack of collaboration from clinicians was a problem in most participating hospitals.

SWI postdischarge surveillance was most successful when based on information collected by the surgeons at the surgical outpatient clinic; by this method, postdischarge information was obtained on 43.9% of all surgical procedures.

A total of 201 infections were observed among 10,537 operations, with a crude incidence rate of 1.91 per 100 operations or 1.51 per 1,000 person-days of observation. Infection rates by operation type and risk indicators are congruent with those of the literature. Survival analysis showed that the overall cumulative infection risk at 21 days postprocedure attained 8 1.6% of the 30-days risk.

Conclusion:

This nationwide network for nosocomial infection surveillance has introduced the practice of computerized surveillance of performance in a large number of hospitals. Still, several aspects of the surveillance demand to be improved: the collaboration of the clinicians, the quality of the data, and the postdischarge surveillance methodology.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 15 , Issue 3 , March 1994 , pp. 171 - 179
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1994

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References

1. Fabry, J, Meynet, R, Joron, MT, et al. Cost of nosocomial infections: analysis of 512 digestive surjrerv patients. World I Surg 1982;6:362–365.CrossRefGoogle ScholarPubMed
2. Wakefield, DS. Understanding the costs of nosocomial infections. In: Wenzel, RP ed. Prevention and Control of Nosocomial Infections. 2nd ed. Baltimore, MD: Williams & Wilkins; 1993:21–41.Google Scholar
3. Haley, RW. Measuring the costs of nosocomial infections: methods for estimating economic burden on the hospital. Am J Med 1991;91(3B):32S–38S.Google Scholar
4. Cruse, PJ, Foord, R. A five-year prospective study of 23,649 surgical wounds. Arch Surg 1973;107:206–210.Google Scholar
5. Halev, RW, Culver, DH, White, JW, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in U.S. hospitals. Am J Epidemiol 1985;121:182–205.Google Scholar
6. Mertens, R, Kegels, G, Stroobant, A, et al. The national prevalence survey of nosocomial infections in Belgium, 1984. J Hosp Infect 1987;9:219–229.CrossRefGoogle ScholarPubMed
7. Mertens, R, Verbist, L, Gordts, B, et al. National study on the utilization of prophylactic antibiotics in surgery, Belgium, 1986. Epidemiol Infect 1989;103:311–322.CrossRefGoogle Scholar
8. Emori, TG, Culver, DH, Horan, TC, et al. National nosocomial infections surveillance system (NNIS): description of surveillance methods. Am J Infect Control 1991;19:19–35.CrossRefGoogle ScholarPubMed
9. Garner, JS, Jarvis, WR Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections. 1988. Am J Infect Control 1988;16:128–140.Google Scholar
10. Centers for Disease Control. NNIS Manual. Atlanta, GA U.S. Dept. of Health and Human Services, Public Health Services; 1988.Google Scholar
11. Editorial. The ASA classification of physical status--a recapitulation. J Anes 1978;49:233–236.Google Scholar
12. Owens, WD, Felts, JA, Spitznagel, EL. ASA physical status classifications: a study of consistency of ratings. J Anes 1978;49:239–243.Google Scholar
13. Altemeier, WA, Burke, JF, Pruitt, BA, Sandusky, WR, eds. Manual on Control of Infection in Surgical Patients. 2nd ed. Philadelphia, PA: JB Lippincott; 1984:29 Google Scholar
14. Kjaeldgaard, P, Cordtz, T, Sejberg, D, et al. The DANOP-DATA system: a low-cost personal computer-based program for monitoring of wound infections in surgical ward. J Hosp Infect 1989;13:273–279.Google Scholar
15. Worning, AM, Mertens, R. Developing quality of care through information systems. Journal of Healthcare Materiel Management 1991;74–76.Google Scholar
16. Haley, RW, Culver, DH, Morgan, WM, White, JW, Emori, TG, Hooton, TM. Identifying patients at high risk of surgical wound infection. A simple multivariate index of patients susceptibility and wound contamination. Am J Epidemiol 1985;121:206–215.CrossRefGoogle ScholarPubMed
17. Haley, RW. Nosocomial infections in surgical patients: developing valid measures of intrinsic patient risk. Am J Med 1991;91(3B):145S–151S.CrossRefGoogle ScholarPubMed
18. Culver, DH, Horan, TC, Gaynes, RP, et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. Am J Med 1991;91(3B):152S–157S.CrossRefGoogle ScholarPubMed
19. Kleinbaum, DG, Kupper, LK, Morgenstern, H. Epidemiologie Research: Principles and Quantitative Methods. New York, NY: Van Nostrand Reinhold; 1982.Google Scholar
20. Friedman, LM, Furberg, CD, DeMets, DL. Fundamentals of clinical trials. Survival Analysis. Littleton, CO: PSG Publishing Co; 1985.Google Scholar
21. Armitage, P, Berry, G. Statistical Methods in Medical Research. 2nd ed. Oxford: Blackwell Scientific Publications; 1987.Google Scholar
22. Goodman, LA. Kruskal, WH. Measures of Association for Cross-Classifications. New York, NY: Springer-V&lag; 1979.CrossRefGoogle Scholar
23. Haxhe, JJ. Nouvelle legislation (AR 07.11.88) relative au Comité d'Hygiène Hospitalière, au Médecin en Hygiene Hospitalière et à l'Infirmière en Hygiene Hospitaliere. Bulletin d'Information Hygiène Hospitaliere 1989;XI:9–11.Google Scholar
24. Van den Berg, JMJ, Mertens, R, Jans, B, Veerman-Brenzikofer, MLV, Kurz, X, Klazinga, N. International comparison of results of infection surveillance. The Netherlands versus Belgium. Presented at the Third Annual Meeting of The Society for Hospital Epidemiology of America; April 18-21, 1993; Chicago, Illinois.Google Scholar
25. The Society for Hospital Epidemiology of America, the Association for Practitioners in -Infection Control, the Centers for Disease Control and Prevention, the Sureical Infection Society, Consensus paper on the surveillance of surgical wound infections. Infect Control Hosp Epidemiol 1992;13:599–605.Google Scholar
26. Weigelt, JA, Dryer, D, Haley, RW. The necessity and efficiency of wound surveillance after discharge. Arch Surg 1992;127:77–82.CrossRefGoogle ScholarPubMed
27. Wax, Y Calai, N, Carey, V, Simchen, E. Cox regression models for intermediate events, with discharge from hospital as an example. Epidemiology 1993;4:120–127.Google Scholar
28. Garibaldi, RA, Cushing, D, Lerer, T. Risk factors for postoperative infection. Am J Med 1991;91(3B):158S–162S.Google Scholar
29. Simchen, E, Wax, Y, Pevsner, B, et al. The Israeli study of surgical infections (ISSI), I: methods for developing a standardized surveillance system for a multicenter study of surgical infections. Infect Control Hosp Epidemiol 1988;9:232–240.Google Scholar
30. Haley, RW. The vicissitudes of prospective multihospital surveillance studies: the Israeli study of surgical infections. Infect Control Hosp Epidemiol 1988;9:228–231.Google Scholar
31. Hierholzer, WJ. Health care data, the epidemiologist's sand: comments on the quantity and quality of data. Am J Med 1991;91(suppl 3B):21S–26S Google Scholar