Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
Prev Vet Med. 2010 Apr 1;94(1-2):77-83. doi: 10.1016/j.prevetmed.2009.11.014. Epub 2009 Dec 22.
Statistical methods employed to analyze antimicrobial resistance (AR) phenotypic data have largely focused on multiple individual antimicrobial resistance outcomes without considering the pharmacologic and biological dependence among these data. In our 3-year longitudinal study, the relationship between AR phenotype of E. coli isolates from integrated multi-site group cohorts of humans and swine and the following risk factors: host-species (human versus swine) and human vocation (swine-workers versus non-workers) was assessed; first, by using cluster analysis techniques and then multivariate generalized estimating equation (GEE) models. Human sewage wastewater draining from occupation-specific housing and swine fecal E. coli isolates (n=3,113 and 3,428, respectively) were tested for antimicrobial susceptibility to 15 agents on the 2003 NARMS panel using the Sensititre system (Trek Diagnostics, Cleveland, OH). The MIC values for each isolate were interpreted according to standardized breakpoints into resistant or susceptible. The phenotypic data (n=6,541) were cluster-analyzed using Ward's minimum variance with Jaccard's distance measure. The multivariate relationships of E. coli cluster membership with the risk factors in the study were assessed using a multivariate GEE model in a SAS((R)) macro to adjust for the multiple cluster dependencies as well as adjusting for response dependencies within each unit location. The cluster solution that best described our entire dataset and where the multivariate GEE model converged was 14. In general, the adjusted odds-ratios of the multiple clusters (i.e., 14 clusters) for human isolates were significantly (P<0.05) at a higher odds of being in the pansusceptible cluster (OR=12.8), and also in clusters that contained high levels of resistance to amoxicillin/clavulanic acid, ampicillin, cefoxitin, nalidixic acid, sulfisoxazole, and/or trimethoprim/sulfamethoxazole, when compared to swine isolates. The adjusted odds-ratios of the multiple clusters for non-swine worker isolates were at significantly (P<0.05) higher risk of being in the pansusceptible cluster (OR=13.6) compared to swine-worker isolates (OR=12.1) (swine isolates were the referent group). In general, the adjusted odds-ratios of the multiple clusters for swine-worker E. coli isolates were significantly (P<0.05) at higher odds of being in multi-resistant clusters (defined as resistant to >or=3 antimicrobial agents) as compared to non-swine worker isolates. Considering vocation, swine-worker E. coli isolates exhibited increased odds of falling in multi-drug resistance clusters compared to those isolates arising from non-swine-workers.
用于分析抗生素耐药性（AR）表型数据的统计方法主要集中在多个个体抗生素耐药性结果上，而没有考虑这些数据之间的药理和生物学依赖性。在我们为期 3 年的纵向研究中，评估了来自人类和猪综合多地点群组队列的大肠杆菌分离株的 AR 表型与以下风险因素之间的关系：宿主物种（人类与猪）和人类职业（猪工与非猪工）；首先使用聚类分析技术，然后使用多变量广义估计方程（GEE）模型。使用 Sensititre 系统（Trek Diagnostics，克利夫兰，俄亥俄州）对来自特定职业住房的人类污水废水和猪粪便大肠杆菌分离株（分别为 3113 和 3428）进行了 15 种抗生素药敏性测试，该系统基于 2003 年 NARMS 小组的药敏试验。根据标准化断点，将每个分离株的 MIC 值解释为耐药或敏感。使用 Ward 的最小方差和 Jaccard 的距离度量对表型数据（n=6541）进行聚类分析。使用 SAS((R))宏中的多变量 GEE 模型评估大肠杆菌聚类成员与研究中风险因素的多变量关系，以调整多个聚类的依赖性以及每个单位位置内的响应依赖性。聚类解决方案最好地描述了我们的整个数据集，并且多变量 GEE 模型在此解决方案上收敛。一般来说，人类分离株的多个聚类（即 14 个聚类）的调整后优势比（OR）显著（P<0.05），更有可能处于泛敏感聚类（OR=12.8）中，并且还存在高水平的耐阿莫西林/克拉维酸、氨苄西林、头孢西丁、萘啶酸、磺胺异恶唑和/或甲氧苄啶/磺胺甲恶唑的聚类。与猪分离株相比，非猪工分离株的多个聚类的调整后优势比（OR）显著（P<0.05），更有可能处于泛敏感聚类（OR=13.6）中。一般来说，与非猪工分离株相比，猪工分离株的多个聚类的调整后优势比（OR）显著（P<0.05），更有可能处于多耐药聚类（定义为对>或=3 种抗生素耐药）中。考虑到职业，与非猪工分离株相比，猪工分离株的大肠杆菌更容易出现多药耐药性聚类。
