Microbiome and Host Health, South Australia Health and Medical Research Institute, Adelaide, SA, 5001, Australia.
SAHMRI Microbiome Research Laboratory, Flinders University College of Medicine and Public Health, Adelaide, SA, Australia.
Antimicrob Resist Infect Control. 2021 Mar 18;10(1):56. doi: 10.1186/s13756-021-00915-w.
Antimicrobial resistance (AMR) represents a profound global health threat. Reducing AMR spread requires the identification of transmission pathways. The extent to which hospital wards represent a venue for substantial AMR transmission in low- and middle-income countries settings is poorly understood.
Rectal swabs were obtained from adult male inpatients in a "Nightingale" model general medicine ward in Yangon, Myanmar. Resistome characteristics were characterised by metagenomic sequencing. AMR gene carriage was related to inter-patient distance (representing inter-patient interaction) using distance-based linear models. Clinical predictors of AMR patterns were identified through univariate and multivariate regression.
Resistome similarity showed a weak but significant positive correlation with inter-patient distance (r = 0.12, p = 0.04). Nineteen AMR determinants contributed significantly to this relationship, including those encoding β-lactamase activity (OXA-1, NDM-7; adjusted p < 0.003), trimethoprim resistance (dfrA14, adjusted p = 0.0495), and chloramphenicol resistance (catB3, adjusted p = 0.002). Clinical traits of co-located patients carrying specific AMR genes were not random. Specifically, AMR genes that contributed to distance-resistome relationships (OXA-1, catB3, dfrA14) mapped to tuberculosis patients, who were placed together according to ward policy. In contrast, patients with sepsis were not placed together, and carried AMR genes that were not spatially significant or consistent with shared antibiotic exposure.
AMR dispersion patterns primarily reflect the placement of particular patients by their condition, rather than AMR transmission. The proportion of AMR determinants that varied with inter-patient distance was limited, suggesting that nosocomial transmission is a relatively minor contributor to population-level carriage.
抗菌药物耐药性(AMR)是一个严重的全球健康威胁。减少 AMR 的传播需要确定传播途径。在中低收入国家,医院病房在多大程度上成为 AMR 传播的重要场所,这一点了解甚少。
从缅甸仰光的“南丁格尔”模式综合医学病房的成年男性住院患者中采集直肠拭子。通过宏基因组测序来描述耐药组特征。使用基于距离的线性模型,将 AMR 基因携带情况与患者间距离(代表患者间的相互作用)相关联。通过单变量和多变量回归来确定 AMR 模式的临床预测因子。
耐药组相似性与患者间距离呈弱但显著正相关(r=0.12,p=0.04)。19 个 AMR 决定因素对这种关系有显著贡献,包括编码β-内酰胺酶活性的基因(OXA-1、NDM-7;调整后的 p<0.003)、甲氧苄啶耐药基因(dfrA14,调整后的 p=0.0495)和氯霉素耐药基因(catB3,调整后的 p=0.002)。携带特定 AMR 基因的同病房患者的临床特征并非随机。具体而言,对距离耐药组关系有贡献的 AMR 基因(OXA-1、catB3、dfrA14)与结核病患者有关,他们根据病房政策被安置在一起。相比之下,败血症患者未被安置在一起,且携带的 AMR 基因在空间上不显著或与共用抗生素暴露无关。
AMR 分布模式主要反映了根据病情对特定患者的安置,而不是 AMR 传播。与患者间距离变化的 AMR 决定因素比例有限,表明医院内传播对人群水平携带的贡献相对较小。