Infect Chemother.  2008 Apr;40(2):93-101. 10.3947/ic.2008.40.2.93.

Surveillance of Antimicrobial Use and Antimicrobial Resistance

• Yoon YK
• Kim MJ
• Sohn JW
• Park DW
• Kim JY
• Chun BC

• KMID: 2230011
• DOI: http://doi.org/10.3947/ic.2008.40.2.93

Abstract

BACKGROUND: The purpose of this study was to investigate the relationship between antimicrobial consumption and antimicrobial resistance and to predict for the level of antimicrobial resistance by a time series analysis.
MATERIALS AND METHODS
In a 750-bed medical center, antimicrobial consumption data of 12 classes of antimicrobials and surveillance of resistant profiles from all microbial isolates were collected from 1/2004 through 3/2007 by database from the hospital's computerized order system. World Health Organization 2004 definition of defined daily doses per 1,000 patient days were used to express the antimicrobial use density (AUD). The monthly proportion of resistant isolates (PR) of selected pathogens and monthly AUD were analyzed by time series analysis with transfer function model by using the SAS/ETS software.
RESULTS
The microbial surveillance data covered 15,522 isolates. PR of ciprofloxacin-resistant E.coli (EC-CFX), imipenem-resistant P. aureginosa, and methicillin-resistant S. aureus (MRSA) were 32.5%+/-14.0, 11.4%+/-8.1, and 78.6%+/-6.9. The two highest monthly AUD of 12 class antimicrobials were 156.2+/-6.5 AUD for aminoglycosides and 145.7+/-6.0 AUD for 3rd-generation cephalosporins. By using time series analysis, we verified a significant correlation between the monthly CFX use and the PR of EC-CFX, and between the monthly penicillin use and the PR of MRSA.
CONCLUSION
Antibiotic consumption and PR of antimicrobial resistant pathogens remained stable over the period of study. Furthermore, we could confirm the usefulness of a time series analysis to demonstrate a temporal relationship between antimicrobial use and resistance, to predict the effect of antibiotics use on antimicrobial resistance.

Keyword

Antimicrobial utilization; Antibiotic resistance; Surveillance

MeSH Terms

Aminoglycosides
Anti-Bacterial Agents
Cephalosporins
Drug Resistance, Microbial
Humans
Methicillin Resistance
Methicillin-Resistant Staphylococcus aureus
Penicillins
World Health Organization
Aminoglycosides
Anti-Bacterial Agents
Cephalosporins
Penicillins

Figure

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  Fig. 1 Trend of Antimicrobial Consumption and Resistance Proportion. (A) Imipenem and P. aeruginosa, (B) 3rd cephalosporin and K. pneumonia, (C) Oxacillin and MRSA, (D) Ciprofloxacin and E. coli.

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  Fig. 2 Scatter Diagram between Antimicrobial Consumption and Resistance Proportion. (A) 3rd cephalosporin and K. pneumonia, (B) Imipenem and P. aeruginosa.

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  Fig. 3 Parameters and parameter significance for ARIMA and transfer function models between hospital antibiotic resistance and antibiotic uss. (A) Monthly observed and predicted percentage of ciprofloxacin (CFX)-resistant E. Coil (EC) isolates and hospital CFX use, (B) Monthly observed and predicted percentage of methicillin-resistant S. aureus (MRSA) isolates and hospital oxacillin use. Numbers of lag (month) and P vaule in the tables show a significant correlation between the amount of CFX use and the proportion of CFX-resistant EC per month, and between the amount of oxacillin use and the proportion of MRSA per month.

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