Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection.
Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection.
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DOI：
10.1017/ice.2015.130
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发表时间：
2015-09
影响因子：
4.5
通讯作者：
Polgreen PM
中科院分区：
文献类型：
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作者：
Simmering JE;Polgreen LA;Campbell DR;Cavanaugh JE;Polgreen PM
目的探讨转运对院内难辨梭菌感染(CDI)发生率的影响。回溯性队列使用2005-2011年医疗保健成本和利用项目加利福尼亚州住院患者数据库的数据，我们确定了2,752,639次转移。然后，我们构建了一系列网络，详细描述了医院之间的联系。我们计算了两个连接性度量，Indegree和加权Indegree，分别衡量进入医院的转接到达的医院数量和传入转接的总数。我们使用对数转换的网络测量以及医院固定效应、对数中位住院时间、65岁或65岁以上患者的对数比例、季度和年度指标作为预测因子，估计了CDI病例的多变量模型。我们发现，对数指数每增加1，CDI发生率增加4.8%(95%可信区间：2.3-7.4)，对数加权指数每增加1，CDI发生率增加3.3%(95%可信区间：1.5-5.2)。此外，在模型中加入了连通性的衡量标准，大大改善了它们的适合性。我们的结果表明，感染控制并不是由某家医院独家控制的，还受到医院与其他医院之间的联系和联系数量的影响。
To determine the effect on inter-hospital patient sharing via transfers on the rate of Clostridium difficile infections (CDI) in a hospital. Retrospective cohort Using data from the Healthcare Cost and Utilization Project California State Inpatient Database, 2005–2011, we identified 2,752,639 transfers. We then constructed a series of networks detailing the connections formed by hospitals. We computed two measures of connectivity, indegree and weighted indegree, measuring the number of hospitals from which transfers into a hospital arrive, and the total number of incoming transfers, respectively. We estimated a multivariate model of CDI cases using the log-transformed network measures as well as covariates for hospital fixed effects, log median length of stay, log fraction of patients aged 65 or older, quarter and year indicators as predictors. We found an increase of one in the log indegree was associated with a 4.8% increase in incidence of CDI (95% CI: 2.3–7.4) and an increase of one in log weighted indegree was associated with a 3.3% increase in CDI incidence (95% CI: 1.5–5.2). Moreover, including measures of connectivity in the models greatly improved their fit. Our results suggest infection control is not under the exclusive control of a given hospital but is also influenced by the connections and number of connections that hospitals have with other hospitals.