Nekkab Narimane, Astagneau Pascal, Temime Laura, Crépey Pascal
Laboratoire MESuRS, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, Paris, France.
Institut Pasteur, Cnam, Unité PACRI, 25-28, rue du Docteur Roux, Paris, France.
PLoS Comput Biol. 2017 Aug 24;13(8):e1005666. doi: 10.1371/journal.pcbi.1005666. eCollection 2017 Aug.
Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.
医院获得性感染（HAIs），包括新出现的多重耐药菌，对全球医疗系统构成威胁。有效的医院获得性感染控制措施必须调动整个医疗网络。因此，为了更好地理解如何降低医院获得性感染疫情传播的潜在规模，我们探索了法国医疗系统中的患者转诊模式。利用2014年法国所有医院出院小结的详尽数据库，我们基于以下内容构建并分析了三个患者网络：医院获得性感染患者的转诊（特定医院获得性感染网络）；疑似医院获得性感染患者（疑似医院获得性感染网络）；以及所有患者（一般网络）。所有这三个网络都有不同的患者流动情况，并呈现出小世界和无标度特征。构成这些网络的患者群体在其流动模式上也各不相同。通过中心性度量对医院进行排名，并使用社区检测算法比较社区聚类，结果表明，尽管患者群体存在差异，但特定医院获得性感染网络和疑似医院获得性感染网络依赖于与一般网络相同的底层结构。因此，在研究医院获得性感染的潜在传播方面，一般网络可能更可靠。最后，我们确定了法国地区和部门（县）层面的转诊模式，这些模式对于识别关键医院中心、患者流动轨迹以及可能作为新型大规模感染控制策略基础的区域集群非常重要。
