track the spread of resistance genes between ecological niches.
●
Collect and report subtyping data e.g. PFGE, genomic sequence for serotypes with important resistance patterns.
●
Periodically evaluate the surveillance methods used and the data collected to ensure that they are the most useful for public health purposes; make adjustments to address emerging
hazards such as other pathogens and commodities.
●
Improve methods, but ensure that improvements do not compromise comparisons with historical data.
●
Collaborate with colleagues in other countries to ensure that new methods are adopted in a way that enables and encourages comparison of data among countries.
●
Report temporal and spatial data on resistance together with data on antimicrobial use in humans and animals, to help increase understanding of practices that may contribute
to resistance.
Programmes of integrated surveillance of antimicrobial resistance in foodborne bacteria exist in several countries. These programmes can be used as models for new programmes of
integrated sur veillance of antimicrobial resistance in foodborne bacteria. E xamples of programmes already in place around the world include:
●
Danish Integrated Monitoring Programme DANMAP
●
US National Antimicrobial Resistance Monitoring System NARMS
●
Canadian Integrated Programme for Antimicrobial Resistance Surveillance CIPARS
●
Dutch Monitoring of Antimicrobial Resistance and Antibiotic Usage in Animals MARAN
●
Netherlands’ Human Antimicrobial Resistance Surveillance NethMap
●
Norway’s NORM-VET Programme
●
Swedish Veterinary Antimicrobial Resistance Monitoring SVARM Programme
●
National Antimicrobial Resistance Monitoring Programme NARMP in the Republic of Korea These and other programmes of integrated surveillance of antimicrobial resistance in foodborne
bacteria listed in the report of the first meeting of AGISAR 21. Whole genome sequencing WGS combined with bioinformatic tools are now being used to
monitor antimicrobial resistance. In recent years, whole genome sequencing WGS has become increasingly more affordable. In some countries, using WGS costs less than using conventional
microbiology, including isolation, detection and molecular typing. There are free bioinformatics tools available online which have been developed for detection and typing of all microorganisms
4
. Several online tools created for the detection of antimicrobial resistance genes have been used
for genotypic monitoring of antimicrobial resistance. The results of these monitoring efforts have been in approximately 99 concordance with the phenotypic data. For programmes of
integrated surveillance of antimicrobial resistance in foodborne bacteria, WGS will most likely replace conventional laboratory methodologies in the future see Appendix 4.
1.9. References
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