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美国住院医疗机构患者转运网络对医院感染发生率的影响。

Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections.

机构信息

Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.

Institute for Cross-Disciplinary Physics and Complex Systems, Campus Universitat de les Illes Balears, Carretera de Valldemossa, km 7,5 Edificio Científico-Técnico, 07122, Palma de Mallorca, Islas Baleares, Spain.

出版信息

Sci Rep. 2017 Jun 7;7(1):2930. doi: 10.1038/s41598-017-02245-7.

Abstract

Antibiotic-resistant bacterial infections are a substantial source of morbidity and mortality and have a common reservoir in inpatient settings. Transferring patients between facilities could be a mechanism for the spread of these infections. We wanted to assess whether a network of hospitals, linked by inpatient transfers, contributes to the spread of nosocomial infections and investigate how network structure may be leveraged to design efficient surveillance systems. We construct a network defined by the transfer of Medicare patients across US inpatient facilities using a 100% sample of inpatient discharge claims from 2006-2007. We show the association between network structure and C. difficile incidence, with a 1% increase in a facility's C. difficile incidence being associated with a 0.53% increase in C. difficile incidence of neighboring facilities. Finally, we used network science methods to determine the facilities to monitor to maximize surveillance efficiency. An optimal surveillance strategy for selecting "sensor" hospitals, based on their network position, detects 80% of the C. difficile infections using only 2% of hospitals as sensors. Selecting a small fraction of facilities as "sensors" could be a cost-effective mechanism to monitor emerging nosocomial infections.

摘要

耐抗生素细菌感染是发病率和死亡率的一个主要来源,并且在住院环境中有共同的储层。在医疗机构之间转移患者可能是这些感染传播的一种机制。我们想评估由住院病人转移联系起来的医院网络是否会导致医院感染的传播,并研究如何利用网络结构来设计有效的监测系统。我们构建了一个使用 2006-2007 年 100%的住院病人出院索赔样本定义的网络,该网络定义了美国住院设施之间的转移。我们展示了网络结构与艰难梭菌发病率之间的关联,一个设施的艰难梭菌发病率增加 1%,相邻设施的艰难梭菌发病率就会增加 0.53%。最后,我们使用网络科学方法来确定要监测的设施,以最大限度地提高监测效率。一种基于网络位置选择“传感器”医院的最优监测策略,仅使用 2%的医院作为传感器,就能检测到 80%的艰难梭菌感染。选择一小部分设施作为“传感器”可能是一种经济有效的监测新出现的医院感染的机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93da/5462812/a431724b206e/41598_2017_2245_Fig1_HTML.jpg

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