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安大略省猪源多杀性巴氏杆菌和猪链球菌临床分离株的抗菌药物耐药性监测

Surveillance of antimicrobial resistance in clinical isolates of Pasteurella multocida and Streptococcus suis from Ontario swine.

作者信息

Glass-Kaastra Shiona K, Pearl David L, Reid-Smith Richard J, McEwen Beverly, Slavic Durda, Fairles Jim, McEwen Scott A

机构信息

Department of Population Medicine (Glass-Kaastra, Pearl, Reid-Smith, McEwen) and Department of Pathobiology (Reid-Smith), University of Guelph, 50 Stone Road East, Guelph, Ontario N1G 2W1; Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, 160 Research Lane, Guelph, Ontario N1G B52 (Reid-Smith); Animal Health Laboratory, University of Guelph, 419 Gordon Street, Guelph, Ontario N1G 2W1 (McEwen, Slavic, Fairles).

出版信息

Can J Vet Res. 2014 Oct;78(4):241-9.

Abstract

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.

摘要

1998年1月至2010年10月期间,从安大略省圭尔夫大学动物健康实验室获取了从猪临床样本中分离出的多杀性巴氏杆菌和猪链球菌的药敏结果,并用于描述对安大略省养猪业中4种重要药物的耐药性变化:氨苄西林、四环素、替米考星和甲氧苄啶-磺胺甲恶唑。使用了四种时间数据分析方法:12个月滚动平均值趋势可视化、逻辑回归建模、时间扫描统计以及使用“近期事件有何异常?”(WSARE)算法进行扫描。单一病原体的抗菌药物之间以及单一抗菌药物的病原体之间的耐药趋势各不相同,这表明针对病原体的耐药监测可能比指标数据更可取。这四种方法提供了互补的结果,有时还有冗余。使用这些数据进行监测的最合适分析方法组合包括时间扫描统计与可视化方法(逻辑回归模型后的滚动平均值或预测概率图)。WSARE算法为质量控制提供了有趣的结果,并且有可能检测到新的耐药模式;然而,缺失数据给以对所有监测利益相关者有意义的方式展示结果带来了问题。

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