Bacterial biofilms are structured communities of bacterial cells enclosed in a self-produced polymer matrix that is attached to a surface. Biofilms protect and allow bacteria to survive and thrive in hostile environments. Bacteria within biofilms can withstand host immune responses, and are much less susceptible to antibiotics and disinfectants when compared to their planktonic counterparts. The ability to form biofilms is now considered an attribute of many microorganisms. Diseases associated with biofilms require novel methods for their prevention, diagnosis and treatment; this is largely due to the properties of biofilms. Furthermore, the presence of biofilms on surfaces found at farms, slaughterhouses or food processing plants will have an impact on the efficacy of disinfection protocols. Surprisingly, biofilm formation by bacterial pathogens of veterinary or zoonotic importance has received relatively little attention. The objective of this brief Review article is to bring awareness about the importance of biofilms to animal health stakeholders.

Susceptibility results for Pasteurella multocida and Streptococcus suis isolated from swine clinical samples were obtained from January 1998 to October 2010 from the Animal Health Laboratory at the University of Guelph, Guelph, Ontario, and used to describe variation in antimicrobial resistance (AMR) to 4 drugs of importance in the Ontario swine industry: ampicillin, tetracycline, tiamulin, and trimethoprim–sulfamethoxazole. Four temporal data-analysis options were used: visualization of trends in 12-month rolling averages, logistic-regression modeling, temporal-scan statistics, and a scan with the “What’s strange about recent events?” (WSARE) algorithm. The AMR trends varied among the antimicrobial drugs for a single pathogen and between pathogens for a single antimicrobial, suggesting that pathogen-specific AMR surveillance may be preferable to indicator data. The 4 methods provided complementary and, at times, redundant results. The most appropriate combination of analysis methods for surveillance using these data included temporal-scan statistics with a visualization method (rolling-average or predicted-probability plots following logistic-regression models). The WSARE algorithm provided interesting results for quality control and has the potential to detect new resistance patterns; however, missing data created problems for displaying the results in a way that would be meaningful to all surveillance stakeholders.